Fenofibrate Toxicity and Impact on Clinical Biomarkers in Patients with Dyslipidaemia

Home , Fenofibrate, Pitavastatin, Torcetrapib, UPCR

Fenofibrate toxicity and impact on clinical biomarkers in patients with dyslipidaemia

Ivo Sama Fombon

Department of Applied Sciences

University of the West of England, Bristol

May 2020

A thesis submitted in partial fulfilment of the requirements of the University of the West of England, Bristol for the degree of Professional Doctorate in Biomedical Sciences Fombon, I.S. (2020)

Copyright Disclaimer This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without appropriate acknowledgement.

Fombon, I.S. (2020)

ACKNOWLEDGEMENTS

This thesis and the entire project could not have been achieved without the support of my supervisors, colleagues, family and friends. I would like to thank them immensely for their support.

Firstly, I would like to thank my director of studies, Prof Myra Conway, for her guidance, support and encouragement. I am extremely grateful for her valuable time, expertise and advice on the design and conduct of the studies described in this thesis. I have learnt an incredible amount from her teachings, patience and hard work.

I would like to thank Dr David James for making it possible for this project to be carried out at the laboratories of the Southwest Pathology Services, Taunton. Thank you too, Prof. Tim Reynolds for accepting to take part in this project and for helping to obtain the ethics approval for this project. Prof Tim Reynolds and Dr David James also recruited the participants for this project.

To the staff of Southwest Pathology Services, Taunton, and Queen’s Hospital, Burton-on-Trent, I say thank you for helping with the collection and storage of patient samples. Your continuous encouragement and enquiry on the progress of the project also helped to keep me going. I am also thankful to the staff and students at the CRIB laboratory for their support during my lab sessions at UWE, particularly to Team Conway – Jon, Matt, Tom, Chris, Grace, Fred, Marcela and Mai. It was a pleasure working with you all.

I would also like to say thank you to my wife, Irine, for having to listen about fenofibrate toxicity for the last six years, and to the entire Fombon family. I am thankful to the families of Chief and Rev. Mrs Anayo, Rudolf Dobdinga, Akin Ojo, Eric Ndifor and other friends who have been there with me on my journey.

I am also thankful to all the participants of this project without whom this work would not have been realised. And to all, whose names have not been mentioned, I am not guilty of any omission, your names are engraved deep in my heart and I will forever remember you.

Fombon, I.S. (2020)

ABSTRACT

Introduction and aims: Fenofibrate (FF) is the most widely used fibric acid derivative for managing mixed dyslipidaemia, characterised by elevated levels of cholesterol, triglycerides and low-density lipoprotein cholesterol and low levels of high-density lipoprotein cholesterol. It is linked with various adverse effects, notably FF-associated creatininaemia (or inferred nephrotoxicity), but also injury to liver and muscle cells. However, the mechanism and clinical significance of FF toxicity in these tissues remain unclear. Monitoring of kidney function is recommended in patients receiving FF therapy. However, due to limitations of measuring serum creatinine (a routinely used marker of kidney function), cystatin C, a low molecular weight protein has been proposed as a better marker of kidney function. The impact of FF on cystatin C clearance and its clinical utility for monitoring kidney function in patients receiving FF therapy have not been fully elucidated, a main aim of this study. The overall focus was to investigate the impact of FF on routine clinical biomarkers in patients with dyslipidaemia, evaluate the utility of cystatin C for monitoring kidney function in patients receiving FF therapy and investigate underlying mechanisms of FF toxicity to kidney and muscle cells.

Methods: The impact of FF therapy on routine clinical biomarkers was examined in 19 adult (14 male, 5 female) patients with mixed dyslipidaemia, aged 34 – 70 years. All the particpants were administered FF at 160 mg/day and clinical biomarkers including kidney function test, liver function tests, lipid profile, markers of inflammation and muscle damage, and thyroid function tests were measured at baseline and after 3, 6 and 12 months after starting treatment. The impact of FF therapy on serum cystatin C level and cystatin C-based estimates of glomerular filtration rate (eGFR) was compared with serum creatinine level and creatinine- based eGFR. Furthermore, FF cytotoxicity was assessed in kidney (NRK52E and HEK293) and skeletal muscle (L6) cell lines using the MTS assay and the potential mechanisms investigated using the reactive oxygen species (ROS) assay and western blot analysis to determine changes in related apoptotic protein markers.

Results: FF therapy reduced serum triglyceride concentrations and increased HDL cholesterol levels in patients with dyslipidaemia. A moderate increase in

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Fombon, I.S. (2020) serum creatinine, urea and cystatin C levels were observed after 3 months of treatment. These were accompanied by a decrease in eGFR using creatinine- based and cystatin C-based equations. Interestingly, this effect was reversed even when treatment was continued (after 12 months). Fenofibrate therapy also reduced serum uric acid concentration via increased urinary excretion. Serum liver enzymes were also reduced, with a much greater impact on serum ALP and GGT. The impact on liver enzymes was greater in individuals with higher pre-treatment values. Cystatin C-based eGFRs were higher than creatinine-based eGFRs suggesting underestimation of kidney function by the creatinine-based equations. Data from in vitro studies showed a dose- and cell line-dependent anti-proliferation impact of fenofibrate on the kidney and skeletal muscle cell lines, associated with oxidative stress due to intracellular ROS accumulation. Results from the western blot analysis of apoptosis-related proteins (Bax and Bcl-2) and the NFκB and MAPK signalling pathways did not indicate apoptosis as a potential mechanism of FF toxicity.

Conclusion: This study confirms the association of fenofibrate therapy with increased serum creatinine levels, which is shown to be partially reversible even with long-term fenofibrate administration. Fenofibrate therapy is also shown to reduce serum uric acid and liver function tests. The clinical significance of the impact of FF therapy on serum uric acid and liver function tests is unclear. This study also suggests that fenofibrate is safe when administered within the therapeutic range. However, increased doses may be toxic to kidney and muscle cells via a mechanism that involves increased intracellular oxidative stress.

Fombon, I.S. (2020)

PRESENTATIONS AND PUBLISHED WORKS

Poster presentation

Fenofibrate induces apoptosis of normal muscle cells via accumulation of intracellular reactive oxygen species, Postgraduate Research Forum 2017, University of the West of England.

Fombon, I.S. (2020)

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... ii ABSTRACT ...... iii PRESENTATIONS AND PUBLISHED WORKS ...... v Poster presentation ...... v TABLE OF CONTENTS ...... vi LIST OF FIGURES AND TABLES ...... xi List of Figures ...... xi List of Tables ...... xiv ABBREVIATIONS ...... xviii Chapter 1...... 1 Introduction and main aims ...... 1 1.1 DYSLIPIDAEMIA ...... 1 1.2 CLASSIFICATION OF DYSLIPIDAEMIA...... 2 1.2.1 Familial (or primary) hyperlipidaemia ...... 2 1.2.2 Acquired (or secondary) hyperlipidaemia ...... 3 1.3 MANAGEMENT OF DYSLIPIDAEMIA ...... 4 1.3.1 Guidelines for managing dyslipidaemia ...... 5 1.3.2 Therapeutic lifestyle changes ...... 7 1.3.3 Pharmacological management of dyslipidaemia ...... 9 1.3.4 Role of Fenofibrate in managing dyslipidaemia ...... 25 1.3.5 Pleiotropic effects of FF therapy ...... 27 1.3.6 Limitations of FF in the management of dyslipidaemia ...... 28 1.4 IMPACT OF FF ON KIDNEY FUNCTION ...... 30 1.5 MONITORING KIDNEY FUNCTION IN FF THERAPY...... 32 1.6 ESTIMATING GFR USING EXOGENOUS MARKERS ...... 33 1.7 ESTIMATING GFR USING ENDOGENOUS MARKERS ...... 34 1.7.1 Urea as a marker for estimating GFR ...... 34 1.7.2 CREA as a marker for estimating GFR ...... 37 1.7.3 Use of low-molecular weight proteins as markers of GFR ...... 39 1.7.4 Use of equations to estimate GFR ...... 42 1.8 AIMS AND OBJECTIVES ...... 45 Chapter 2 ...... 47

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Materials and Methods ...... 47 2.1 - MATERIALS ...... 47 2.1.1 Materials used in clinical biomarkers studies ...... 47 2.1.2 Materials used in cell line studies...... 49 2.1.3 Antibodies ...... 50 2.1.4 Cell lines ...... 50 2.2 – METHODS ...... 51 2.2.1 Ethical considerations ...... 52 2.2.2 Study participants ...... 52 2.2.3 Sample collection, storage and analyses ...... 54 2.2.4 Determination of electrolyte by the indirect ISE method ...... 58 2.2.5 Measurement of CREA by the Jaffé (compensated) method ...... 59 2.2.6 Measurement of urea by the urease/GLDH method ...... 59 2.2.7 Determination of uric acid by the uricase/POD method ...... 60 2.2.8 Alanine aminotransferase activity assay ...... 61 2.2.9 Alkaline phosphatase activity assay ...... 61 2.2.10 Serum Albumin estimation by the Bromocresol green method ...... 62 2.2.11 Determination of serum total bilirubin concentration ...... 62 2.2.12 Aspartate aminotransferase activity assay ...... 63 2.2.13 Determination of serum total cholesterol concentration ...... 63 2.2.14 Determination of serum triglyceride concentration ...... 64 2.2.15 Determination of HDL-cholesterol concentration ...... 65 2.2.16 Creatine kinase activity assay ...... 66 2.2.17 Lactate dehydrogenase activity assay ...... 67 2.2.18 Measurement of C reactive protein concentration ...... 67 2.2.19 Urine total protein by the Pyrogallol red-molybdate method ...... 67 2.2.20 Urine albumin concentration by the turbidimetric method ...... 68 2.2.21 Determination of other urine parameters ...... 68 2.2.22 Immunoassay parameters (Thyroid function tests) ...... 69 2.2.23 Determination of serum CYSC by PETIA method ...... 73 2.2.24 Analytical performance of the Gentian CYSC method ...... 74 2.2.25 Estimated glomerular filtration rate (eGFR) ...... 77 2.2.26 Mammalian cell culture and cell line maintenance ...... 77 2.2.27 Examination of changes in cell morphology ...... 78

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2.2.28 Cell viability assay ...... 79 2.2.29 Detection of intracellular reactive oxygen species (ROS) activity ...... 79 2.2.30 Detection of cell apoptosis by flow cytometry ...... 80 2.2.31 Extraction of cell lysate (protein extraction) ...... 81 2.2.32 Measurement of protein concentration ...... 81 2.2.33 SDS-PAGE Electrophoresis ...... 83 2.2.34 Protein transfer (wet) and western blot analysis ...... 83 2.2.35 Statistical analysis ...... 85 Chapter 3 ...... 86 Effect of FF therapy on routine clinical biomarkers in patients with dyslipidaemia ...... 86 3.1 INTRODUCTION ...... 86 3.2 SPECIFIC AIMS ...... 89 3.3 RESULTS ...... 90 3.3.1 Impact of FF therapy on markers of kidney function ...... 90 3.3.2 Impact of FF therapy on markers of liver function ...... 119 3.3.3 The impact of FF therapy on lipid profile ...... 131 3.3.4 Impact of FF therapy on markers of inflammation and muscle damage ...... 142 3.3.5 Effect of FF therapy on thyroid function tests ...... 148 3.4 DISCUSSION ...... 154 3.4.1 FF increased serum CREA, reduced UA and impacted kidney function ...... 155 3.4.2 FF therapy associated with a reduction in liver enzymes ...... 157 3.4.3 FF reduced serum TRIG and increased HDL cholesterol levels ...... 159 3.4.4 Impact of FF therapy on markers of inflammation and muscle damage ...... 160 3.4.5 Thyroid function tests unchanged by FF therapy ...... 162 3.5 SUMMARY ...... 162 Chapter 4 ...... 164 Utility of CYSC as biomarker of kidney function in patients receiving FF treatment for dyslipidaemia ...... 164 4.1 INTRODUCTION ...... 164 4.2 SPECIFIC AIMS ...... 166

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4.3 RESULTS ...... 167 4.3.1 Analytical performance of Gentian CYSC on the Beckman Coulter AU640 analyser ...... 167 4.3.2 Impact of FF therapy on serum CREA, CYSC and eGFR calculated using CREA- and CYSC-based equations ...... 172 4.3.3 Comparison between serum CREA- and serum CYSC-based equations for calculating the GFR in patients administered FF therapy ...... 185 4.3.4 Concordance analysis of serum CREA vs serum CYSC as biomarkers for monitoring kidney function ...... 190 4.4 DISCUSSION ...... 213 4.4.1 Analytical performance of Gentian CYSC exceeds manufacturer’s specifications on Beckman Coulter AU640 analyser ...... 214 4.4.2 Impact of FF therapy on serum CYSC, serum CREA and eGFR calculated using CYSC- and CREA-based equations ...... 215 4.4.3 Comparison of CYSC- and CREA-based eGFRs in patients administered FF therapy ...... 217 4.4.4 Agreement between serum CREA- and CYSC-based equations for determining eGFR in patients administered FF ...... 221 4.4.5 Summary ...... 223 Chapter 5 ...... 225 Mechanisms of FF cytotoxicity in kidney and muscle cell lines ...... 225 5.1 - INTRODUCTION ...... 225 5.2 – SPECIFIC AIMS ...... 227 5.3 - RESULTS ...... 228 5.3.1 FF induces morphological changes in NRK52E, L6 and HEK293 cell lines ...... 228 5.3.2 FF reduces cell viability of NRK52E, L6 and HEK293 cells ...... 230 5.3.3 FF induces the accumulation of ROS in NRK52E, L6 and HEK293 cells ...... 234 5.3.4 Effect of FF on Bax and Bcl-2 expression in NRK52E, L6 and HEK293 cells...... 236 5.3.5 Impact of FF on the expression of NFκB in NRK52E, L6 and HEK293 cell lines ...... 240

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5.3.6 Impact of FF on MAPK signalling proteins in NRK52E, L6 and HEK293 cell lines ...... 242 5.4 DISCUSSION ...... 245 5.4.1 FF induces ROS accumulation in NRK52E, L6 and HEK293 cell lines 245 5.4.2 FF induces cytotoxicity of kidney and muscle cells via activation of NFκB and MAPK signalling pathway ...... 246 5.5 SUMMARY ...... 249 Chapter 6 ...... 250 Discussion, conclusion and future work...... 250 6.0 – DISCUSSION AND CONCLUSION ...... 250 6.1 Limitations and recommendations for future work ...... 258 REFERENCES ...... 259 APPENDIX ...... 300

Fombon, I.S. (2020)

LIST OF FIGURES AND TABLES

List of Figures

Chapter 1

Figure 1.1 Mechanism of action of statins...... 12 Figure 1.2 Mechanism of action of fibrates...... 16 Figure 1.3 Mechanism of action of bile acid sequestrants...... 20 Figure 1.4 Mechanism of action of niacin...... 22 Figure 1.5 Lipid lowering mechanism of FF...... 27

Chapter 2 Figure 2.1 Schematic representation of the TSH assay using two-site (sandwich) immunoenzymatic assay by chemiluminescence...... 71 Figure 2.2 Gentian CYSC assay by particle enhanced turbidimetric immunoassay (PETIA) method...... 74

Chapter 3 Figure 3.1 Effect of 3 months of FF therapy on serum markers of kidney function in Group A Participants...... 93 Figure 3.2 Effect of 3 months of FF therapy on urine markers of kidney function. 96 Figure 3.3 Effect of 6 months of FF therapy on serum markers of kidney function...... 99 Figure 3.4 Effect of 6 months of FF therapy on urine markers of kidney function...... 102 Figure 3.5 Effect of 12 months of FF therapy on serum markers of kidney function in Group C participants...... 105 Figure 3.6 Effect of 12 months of FF therapy on urine markers of kidney function in Group C participants...... 107 Figure 3.7 Changes in serum markers of kidney function during 6 months of FF therapy...... 111 Figure 3.8 Changes in urine markers of kidney function during 6 months of FF therapy...... 113

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Figure 3.9 Changes in markers of kidney function during 12 months of FF therapy...... 116 Figure 3.10 Effect of 3 months of FF therapy on the liver function tests ...... 122 Figure 3.11 Effect of 6 months of FF therapy on the liver function tests ...... 123 Figure 3.12 Effect of 12 months of FF therapy on the liver function tests ...... 124 Figure 3.13 Changes in liver function tests during 6 months of FF therapy ...... 128 Figure 3.14 Changes in serum liver function tests during 12 months of FF therapy in Group E participants...... 130 Figure 3.15 Effect of 3 months of FF therapy on the lipid profile ...... 134 Figure 3.16 Effect of 6 months of FF therapy on the lipid profile in Group B participants...... 135 Figure 3.17 Effect of 12 months of FF therapy on the lipid profile ...... 136 Figure 3.18 Changes in lipid profile during 6 months of FF therapy ...... 139 Figure 3.19 Changes in lipid profile in during 12 months of FF therapy ...... 141 Figure 3.20 Impact of FF therapy on serum CRP concentration...... 145 Figure 3.21 Impact of FF therapy on markers of muscle damage...... 146 Figure 3.22 Changes in markers of muscle damage during 6 and 12 months of FF therapy...... 147 Figure 3.23 Effect of 3 months of FF therapy on thyroid function tests...... 150 Figure 3.24 Effect of 6 months of FF therapy on thyroid function tests...... 151 Figure 3.25 Effect of 12 months of FF therapy on thyroid function tests...... 152 Figure 3.26 Changes in the thyroid function tests during 6 and 12 months of FF therapy...... 153

Chapter 4 Figure 4.1 Representative plot of the linearity of the Gentian CYSC assay...... 170 Figure 4.2 Correlation between Gentian CYSC and CREA by the Jaffe (compensated) method on the AU640 analyser...... 171 Figure 4.3 Effect of 3 months of FF on serum CREA, CYSC and eGFR calculated by the different equations after 3 months of FF therapy...... 176 Figure 4.4 Effect of 6 months of FF on serum CREA, CYSC and eGFR calculated using the CREA- and CYSC-based equations...... 177 Figure 4.5 Effect of 12 months of FF on serum CREA, CYSC and eGFR calculated using CREA- and CYSC-based equations...... 178

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Figure 4.6 Changes in serum CREA, serum CYSC and eGFR calculated using serum CREA- and CYSC-based equations during 6 months of FF therapy...... 181 Figure 4.7 Changes in serum CREA, serum CYSC and eGFR calculated by different equations during 12 months of FF therapy...... 184 Figure 4.8 Comparison of eGFR calculated using serum CREA- and CYSC-based equations after 12 months of FF therapy...... 188 Figure 4.9 Bland-Altman plots between the MDRD and the other equations at baseline ...... 200 Figure 4.10 Bland-Altman plots between the Gentian, CKD-EPI CREA, CKD-EPI CYSC, and the CKD-EPI CREA-CYSC equations at baseline ...... 201 Figure 4.11 Bland-Altman plots between the MDRD and the other equations after 3 months of FF therapy...... 203 Figure 4.12 Bland-Altman analysis between the Gentian, the CKD-EPI CREA, CKD-EPI CYSC, and the CKD-EPI CREA-CYSC equations after 3 months of FF therapy ...... 204 Figure 4.13 Bland-Altman plots between the MDRD and the other equations after 6 months of FF therapy...... 207 Figure 4.14 Bland-Altman analyses between the different eGFR equations after 6 months of FF therapy...... 208 Figure 4.15 Bland-Altman plots between the MDRD and the other equations after 12 months of FF therapy ...... 210 Figure 4.16 Bland-Altman plots between the Gentian, the CKD-EPI CREA, CKD- EPI CYSC, and the CKD-EPI CREA-CYSC equations after 12 months of FF therapy ...... 211

Chapter 5 Figure 5.1 Representative photomicrograph showing morphological changes in NRK52E, L6 and HEK293 cells exposed to FF...... 229 Figure 5.2 Dose-response curves of the effect of FF and 5-FU on viability of (A) NRK52E, (B) HEK293 and (C) L6 cells...... 231 Figure 5.3 Dose-response curves of the effect of FF and ethanol on cell viability of NRK52E, HEK293 and L6 cells...... 233

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Figure 5.4 Increase in intracellular ROS in (A) NRK52E, (B) L6 and (C) HEK293 cells in response to FF toxicity...... 235 Figure 5.5 Expression of Bax and Bcl-2 in NRK52E cells exposed to FF...... 237 Figure 5.6 Expression of Bax and Bcl-2 in L6 cells exposed to FF...... 238 Figure 5.7 Expression of Bax and Bcl-2 in HEK293 cells exposed to FF...... 239 Figure 5.8 Effect of FF on NFκB p65 expression in NRK52E, L6 and HEK293 cell lines...... 241 Figure 5.9 Expression of JNK protein in NRK52E, L6 and HEK293 cell lines treated with FF...... 243 Figure 5.10 Effect of FF on MAPK p38 expression in NRK52E, L6, and HEK293 cell lines...... 244 Chapter 6 Figure 6.1 Impact of FF therapy on routine clinical biomarkers...... 254 Figure 6.2 Molecular mechanism of FF-induced toxicity in muscle and kidney cells...... 257

List of Tables

Chapter 1

Table 1.1 Fredrickson classification of dyslipidaemia ...... 4

Table 1.2 Choice of lipid-lowering drugs by treatment goal based on the recommendations of the American Diabetes Association ...... 10

Table 1.3 Major genes involved in the PPARα-mediated action of fibrates on lipid metabolism and inflammation ...... 18

Table 1.4 Factors affecting conventional markers of kidney function (Bagshaw, 2008) ...... 36

Table 1.5 GFR estimating equations in widespread use ...... 44

Chapter 2

Table 2.1 Materials used in studies involving clinical biomarkers ...... 47

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Table 2.2 Materials used in the cell line studies ...... 49

Table 2.3 List of antibodies...... 50

Table 2.4 Groups of participants based on sample provision ...... 54

Table 2.5 List of serum biochemistry and immunoassay parameters, methods and traceability ...... 57

Table 2.6 Table 2.6 Amido Black Standard Solutions ...... 82

Table 2.7 Antibody dilutions used in this study ...... 84

Chapter 3

Table 3.1 Criteria for defining the various conditions of kidney function ...... 90

Table 3.2 Changes in serum markers of kidney function after 3 months of FF therapy in Group A participants ...... 92

Table 3.3 Effect of 3 months of FF treatment on urine markers of kidney function...... 95

Table 3.4 Effect of 6 months of FF therapy on serum markers of kidney function. 98

Table 3.5 Effect of 6 months of FF therapy on urine markers of kidney function in Group B participants ...... 101

Table 3.6 Effect of 12 months of FF therapy on serum markers of kidney function in Group C participants ...... 104

Table 3.7 Effect of 12 months of FF treatment on urine markers of kidney function in Group C participants ...... 106

Table 3.8 Changes in serum markers of kidney function during 6 months of FF therapy in Group D participants ...... 110

Table 3.9 Changes in urine markers of kidney function during six months of FF treatment in Group D participants ...... 112

Fombon, I.S. (2020)

Table 3.10 Changes in serum markers of kidney function during 12 months of FF treatment in Group E participants ...... 115

Table 3.11 Changes in urine markers of kidney function after 12 months of FF therapy ...... 117

Table 3.12 Impact of FF therapy on liver function tests after 3, 6 and 12 months compared with baseline values...... 121

Table 3.13 Changes in serum liver function tests during 6 months of FF therapy ...... 127

Table 3.14 Changes in liver function tests during 12 months of FF therapy in Group E participants ...... 129

Table 3.15 Impact of 3, 6, and 12 months of FF therapy on the lipid profile in patients with dyslipidaemia ...... 133

Chapter 4

Table 4.1 Summary of imprecision data of the Gentian CYSC on Olympus AU640 analyser ...... 169

Table 4.2 Analytical recovery of the Gentian CYSC* assay on Olympus AU640 analyser ...... 169

Table 4.3 Effect of FF therapy on serum CYSC, serum CREA and eGFR calculated using CYSC-based and CREA-based equations ...... 175

Table 4.4 Changes in serum CREA, CYSC and eGFR calculated using serum CREA- and CYSC-based equations during 6 months of FF therapy . 180

Table 4.5 Changes in serum CREA, CYSC and eGFR, calculated using serum CREA- and CYSC-based equations in during 12 months of FF treatment...... 183

Table 4.6 Frequency of CKD stages in the various groups according to the equations used in this study ...... 189

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Table 4.7 Spearman’s correlation coefficient between serum CREA, serum CYSC and the eGFR calculated using serum CREA- and CYSC-based equations at baseline ...... 191

Table 4.8 Spearman’s correlation coefficient between serum CREA, serum CYSC and eGFR calculated using serum CREA- and CYSC-based equations after 3 months of FF therapy ...... 193

Table 4.9 Spearman’s correlation coefficient between serum CREA, serum CYSC and eGFR calculated using serum CREA- and CYSC-based equations after 6 months of FF therapy ...... 195

Table 4.10 Spearman’s correlation coefficient between serum CREA, serum CYSC and eGFR calculated using serum CREA- and CYSC-based equations after 12 months of FF therapy ...... 197

Table 4.11 Bland-Altman analysis showing the absolute bias (mean difference) (bold) and precision (SD around the bias) (italic) between the eGFR equations at baseline ...... 199

Table 4.12 Bland-Altman analysis showing the absolute bias (mean difference) (bold) and precision (SD around the bias) (italic) between the eGFR equations after 3 months of FF therapy ...... 202

Table 4.13 Bland-Altman analysis showing absolute bias (mean difference) (bold) and precision (SD around the bias) (italic) between the eGFR equations after 6 months of FF therapy ...... 206

Table 4.14 Bland-Altman analysis showing the absolute bias (mean difference) (bold) and precision (SD around the bias) (italic) between the equations after 12 months of FF therapy ...... 209

Chapter 5

Table 5.1 Half-maximal inhibitory concentration (IC50)* of FF compared with 5-FU in NRK52E, HEK293 and L6 cell lines after 72 hours of treatment .... 232

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ABBREVIATIONS 4-AAP 4-aminophenazone 4-NP 4-nitrophenol 4-NPP 4-nitro-phenylphosphate 51Cr-EDTA 51Chromium ethylene-diamine tetra-acetic acid 5-FU 5-fluorouracil 99mTc-DTPA 99mtechnetium labelled diethylene-triamine-pentacetate ABCA1 ATP binding cassette transporter 1 ACCORD Action to Control Cardiovascular Risk in Diabetes ADP adeninine diphosphate ALB albumin ALP alkaline phosphatase ALT alanine aminotransaminase AP2 adaptor protein 2 Apo A-I apolipoprotein A-I Apo A-II apolipoprotein A-I Apo B apolipoprotein B Apo C2 apolipoprotein C2 Apo C-III apolipoprotein C-III Apo E2 apolipoprotein E2 APS ammonium persulphate AST aspartate aminotransferase ATP Adult Treatment Panel B2M beta 2 microglobulin BCA Bicinchoninic acid BCG bromocresol green Bcl-2 B-cell lymphoma 2 BHF British Heart Foundation BHT Burton Hospital’s NHS Foundation Trust BSA Bovine serum albumin BTP beta trace protein BUN blood urea nitrogen C&G Cockcroft and Gault

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CAD coronary artery disease CDC Centre for Disease Control CERP cholesterol efflux regulatory protein CHD coronary heart disease CHE cholesterol esterase CHO cholesterol oxidase CHOL total cholesterol CHRA total cholesterol to high-density lipoprotein ratio CK creatine kinase CKD EPI Chronic Kidney Disease-Epidemiology Collaboration CKD chronic kidney disease CKD-EPI CREA Chronic Kidney Disease Epidemiology Collaboration creatinine equation (2009) CKD-EPI CREA-CYSC Chronic Kidney Disease Epidemiology Collaboration creatinine-cystatin C equation (2012) CKD-EPI CYSC Chronic Kidney Disease Epidemiology Collaboration cystatin C equation (2012) CK-NAC creatine kinase (N-acetyl-cystein activated) COX-2 cyclooxygenase 2 CREA creatinine CRF chronic renal failure CRIB Centre for Research in Biosciences CRP C reactive protein CSF cerebrospinal fluid CTT Cholesterol Treatment Trial CV coefficient of variation CVD cardiovascular disease CYSC cystatin C DCF 2', 7'-dichlorofluorescin DCFDA 2’, 7’-dichlorofluorescein diacetate DGAT2 diacylglycerol acyltransferase-2 DMEM Dulbecco’s modified Eagle medium DMSO dimethylsulfoxide DPD 3, 5-dichlorophenyldiazonium tetrafluoroborate

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EDTA ethylene diaminetetraacetic acid eGFR estimated glomerular filtration rate FA fibric acid FAC fenofibrate-associated creatininaemia FATP fatty acid transporter protein FBS foetal bovine serum F-DAOS N-ethyl-N-(2-hydroxy-3-sulfopropyl-3.5-dimethoxy-4- flouranaline FENA fractional excretion of sodium FEUA fractional excretion of uric acid FEUR fractional excretion of urea FF fenofibrate FIELD Fenofibrate Intervention and Event Lowering in Diabetes FITC fluorescein isothiocyanate FT3 free T3 (triiodothyronine) FT4 free T4 (thyroxine) GFR glomerular filtration rate GK glycerol kinase GLDH glutamate-dehydrogenase GPO glycerol phosphate oxidase H hour HDL high density lipoprotein cholesterol HEDTA N-hydroxyethylethylenediaminetriacetic acid HMG-CoA 3-hydroxy-3-methylglutaryl coenzyme A HPLC high performance liquid chromatography HRP horseradish peroxidise hTSH human thyroid stimulatory hormone HUNT Nord-Trondelag Health Study IDL intermediate-density lipoprotein IFCC International Federation of Clinical Chemistry IgG immunoglobulin G IMPROVE-IT Improved Reduction of Outcomes Vytorin Efficacy International Trial

Fombon, I.S. (2020)

IQR interquartile range ISE indirect ion selective electrode JNK c-Jun N-terminal kinases LCAT lecithin: cholesteryl acyl transferase LDH lactate dehydrogenase LDL low density lipoprotein cholesterol LIMS Laboratory Information Management System LoB limit of blank LoD limit of detection LoQ limit of quantitation Lpa lipoprotein a L-PGDS lipocalin prostaglandin D2 synthase LPL lipoprotein lipase MADB N, N-bis (4-sulfobutyl)-3, 5-dimethylalanine, disodium salt MAPK mitogen-activated protein kinase MDRD Modification of Diet in Renal Disease MHC major histocompatibility complex MTS 3-[4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide solution NADH nicotinamide adenine dinucleotide NCEP National Cholesterol Education Program NFκB nuclear factor kappa-light-chain-enhancer of activated B-cells NHDL non-high density lipoprotein cholesterol NHS National Health Service NICE National Institute for Health and Care Excellence NKDEP National Kidney Disease Education Program NPC1-L1 Nieman Pick C1 like protein NRES National Research Service PAGE polyacrylamide gel electrophoresis PAP p-aminophenol PBS phosphate buffered saline PC phosphatidylcholine

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PCSK9 proprotein convertase subtilisin/kexin type 9 PETIA particle-enhanced turbidimetric immunoassay POD peroxidase PPAR peroxisome proliferator-activated receptors PPRE peroxisome proliferator response elements PVDF polyvinylidene fluoride R&D Research and Development RA retinoic acid RCT reverse cholesterol transport REC research ethics committee RLU Relative Light Units RXR retinoic acid X receptor SCORE systematic coronary risk evaluation SDS Sodium Dodecyl Sulphate SEAS Simvastatin and Ezetimibe in Aortic Stenosis SHARP Study of Heart and Renal Protection SOD Superoxide dismutase SR-B1 scavenger receptor, class B type 1 SSI Site Specific Information SST Serum separator tube TBIL total bilirubin TCA trichloroacetic acid TEMED Tetramethylethylenediamine TLC therapeutic lifestyle changes TRIG triglyceride TRL triglyceride-rich lipoproteins TSH thyroid stimulatory hormone TST Taunton and Somerset NHS Foundation Trust UA uric acid UACR urine albumin creatinine ratio UAPR urine albumin total protein ratio UCRE urine creatinine UK United Kingdom UPCR urine total protein creatinine ratio

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UPRO urine total protein URAT1 urate transporter 1 US United States VCAM-1 vascular cell adhesion molecule-1 VLDL very low density lipoprotein cholesterol WHO World Health Organisation

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Chapter 1

Introduction and main aims

1.1 DYSLIPIDAEMIA Dyslipidaemia is a major risk factor for cardiovascular disease (CVD), which represents an important cause of morbidity and mortality worldwide (Jameson et al., 2013). In 2012, an estimated 17.5 million people died from CVD, representing 31% of all global deaths (Al-Mawali, 2015; WHO, 2015). In the UK, there are about seven million people living with CVD and over 160,000 deaths (more than a quarter of all deaths) each year (British Heart Foundation, 2015). Dyslipidaemia refers to a spectrum of lipid disorders which usually include excessive levels of cholesterol, triglyceride (TRIG) and low-density lipoprotein (LDL) cholesterol, and low levels of high-density lipoprotein (HDL) cholesterol (Williams, 2013). Increased level of LDL cholesterol is reported as primarily responsible for the development of atherosclerosis, as oxidised LDL cholesterol and monocytes penetrate the endothelium and combine to form cholesterol-rich macrophages, which drive the development of atherosclerotic plaques (Hansson, 2005). Hypertriglyceridaemia is often associated with low HDL cholesterol levels, diabetes, hypertension, obesity and excess alcohol consumption. Excessively high levels of TRIG have also been linked with the risk of CVD and pancreatitis (Williams, 2013). High levels of TRIG together with low levels of HDL cholesterol are considered as risk factors for coronary heart disease (CHD) (Di Angelantonio et al., 2009), and ischaemic stroke (O’Donnell et al., 2010). However, it is unclear whether hypertriglyceridaemia is an independent risk factor for CVD when adjusted for other cholesterol disorders such as low HDL cholesterol and comorbidities (Williams, 2013).

Recent prospective studies have suggested that a high TRIG level in combination with a low HDL cholesterol level (also referred to as atherogenic dyslipidaemia) may be a clinical marker to identify adults at risk of atherosclerotic CVD (Rana et al., 2010; Abdel-Maksoud et al., 2012; Anderson et al., 2014; Verges, 2015). Management of dyslipidaemia using lifestyle changes and hypolipidaemic agents (e.g. statins, fibrates, ezetimibe and niacin) is a vital strategy for reducing the risks of CVD (Hendrani et al., 2016). While statins are the first choice hypolipidaemic

Fombon, I.S. (2020) drugs for managing dyslipidaemia, statin therapy may be limited by the failure to reach non-HDL cholesterol goals and sometimes poses the problem of intolerance or poor response to statin monotherapy in certain patients, thereby indicating the use of another class of lipid-lowering drug (NICE, 2014). Management guidelines are based on risk assessments for CVD and treatment targets, which in turn rely on the type or classification of dyslipidaemia (Thompson, 2004).

1.2 CLASSIFICATION OF DYSLIPIDAEMIA Classification of dyslipidaemia is aimed at providing the foundation for the clinical management of the prevalent group of risk factors for CVD and pancreatitis, although it is sometimes overlooked in the mistaken belief that causes, consequences and management of disturbances in the plasma lipid profile are similar in the vast majority of patients (Sullivan and Lewis, 2011). According to Sullivan and Lewis (2011), the correct identification of the pattern of lipoprotein disorders and their primary or secondary origin provides important information concerning the natural history and treatment options that will optimise patient outcome. Dyslipidaemia can be classified on the basis of lipid phenotype as hypercholesterolaemia (i.e. increased level of cholesterol), hypertriglyceridaemia (i.e. increased level of TRIG), or mixed hyperlipidaemia (i.e. increased levels of both cholesterol and TRIG), or on the basis of causative factors such as familial (primary) hyperlipidaemia or acquired (secondary) hyperlipidaemia (Thompson, 2004; Verma, 2016). Each one of these phenotypes can result from dysfunctional mutations of dominantly expressed genes encoding receptors, enzymes or transfer proteins involved in lipoprotein metabolism, usually indicated by a familial pattern of inheritance (Thompson, 2004).

1.2.1 Familial (or primary) hyperlipidaemia Familial or primary hyperlipidaemia is usually caused by a genetic defect, which may be monogenic (i.e. a single gene defect) or polygenic (multiple gene defects). The traditional framework for the classification of dyslipidaemia is dominated by the Fredrickson classification (Table 1.1) in which familial dyslipidaemia is

Fombon, I.S. (2020) classified into five types on the basis of electrophoresis or ultracentrifugation pattern of lipoprotein abnormalities (Fredrickson and Lees, 1965; Nirosha et al., 2014). This system of classification was an early development which recognised that the variation in plasma lipid levels were due to disturbances in lipoprotein metabolism and provided a nomenclature that abbreviated the description of the various patterns of lipoprotein accumulation that were relevant to clinical practice (Sullivan and Lewis, 2011). It was later adopted by the World Health Organisation (WHO) as a useful tool for the management of hyperlipidaemia.

A major limitation of the Fredrickson classification is that it provides only limited prognostic information when used in isolation. Most Fredrickson types are heterogeneous because the patterns of the most commonly associated CVD are usually the product of gene-environment interactions in which the quantitative severity of LDL cholesterol and HDL cholesterol abnormalities is of major importance (Sullivan and Lewis, 2011). The Fredrickson classification also overlooked altered levels of HDL cholesterol, elevation of lipoprotein a (Lpa) and adverse changes in the composition of LDL cholesterol in hypertriglyceridaemia, commonly seen in practice when both cholesterol and TRIG levels are elevated due to an increase in LDL and VLDL concentration (Nirosha et al., 2014). It remains a popular system of classification, but is considered outdated by many (Jain et al., 2007).

1.2.2 Acquired (or secondary) hyperlipidaemia Acquired hyperlipidaemia (or secondary dyslipoproteinaemias) is caused by underlying disorders and leads to alterations in plasma lipid and lipoprotein metabolism (Verma, 2016). Acquired hyperlipidaemias may mimic primary forms of the disease and can have similar consequences. They may result in an increased risk of premature atherosclerosis, pancreatitis and other complications of the chylomicronaemia syndrome. The most common causes of acquired hyperlipidaemia include diabetes mellitus, use of drugs e.g. diuretics, ꞵ-blockers and oestrogens, alcohol consumption, hypothyroidism, renal failure, nephrotic syndrome and some rare disorders and metabolic disorders (Nirosha et al., 2014).

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Table 1.1 Fredrickson classification of dyslipidaemia

Type Disorder Cause Elevated lipoprotein Type I Familial hyperchylomicronaemia LPL deficiency or Chylomicrons or primary hyperlipoproteinaemia altered ApoC2 Type IIa Familial hypercholesterolaemia or LDL receptor LDL Polygenic hypercholesterolaemia deficiency Type IIb Familial combined hyperlipidaemia Decreased LDL LDL and receptors VLDL Type III Familial dysbetalipoproteinaemia Defect in ApoE2 IDL synthesis and increased Apo B Type IV Familial hypertriglyceridaemia Increased VLDL VLDL production and decreased excretion Type V Endogenous hypertriglyceridaemia Increased VLDL Chylomicrons production and and VLDL decreased LPL Abbreviations: LDL, low density lipoprotein cholesterol; VLDL, very low density lipoprotein cholesterol; IDL; intermediate density lipoprotein cholesterol; LPL, lipoprotein lipase.

1.3 MANAGEMENT OF DYSLIPIDAEMIA Management of dyslipidaemia is at the centre of strategies of preventing CVD (Thompson, 2004). In 1987 the National Institute of Health (NIH) established the National Cholesterol Education Program (NCEP) which was directed by the Adult Treatment Panel (ATP) with the aim of issuing information for health professionals and the general public concerning testing, evaluating, monitoring and treating dyslipidaemia. An important criterion of the ATP guidelines is the development of treatment goals for dyslipidaemia based on a patient’s risk of coronary CHD (NCEP, 2002; Verma, 2016).

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1.3.1 Guidelines for managing dyslipidaemia Various organisations and agencies have issued recommendations for the management of dyslipidaemia with various commonalities and differences among them (Thompson, 2004; Jacobson et al., 2015). Most of the current guidelines reflect the results of five major statin trials that were published between 1994 and 1998, which demonstrated a reduction in the risk of CHD and total mortality in men and women below and above the age of 65, respectively (LaRosa et al., 1999; Thompson, 2004).

Guidelines for the management of dyslipidaemia in the United States are based on the reports of the NCEP. The third report of the NCEP (NCEP, 2002) reiterated the use of the LDL cholesterol value as the criterion for when to initiate treatment and as a therapeutic goal; the greater the risk of CHD, the lower the concentration of LDL cholesterol at which treatment is initiated and the lower the target value to be achieved (NCEP 2001). By these guidelines, patients with CHD, diabetes or multiple risk factors, as in metabolic syndrome, that confer a 10 year risk of CHD of > 20% over 10 years are considered as high risk (Thompson, 2004). The treatment strategies include the use of lipid lowering drugs, which is required by the majority of patients to achieve the LDL cholesterol goal of < 2.6 mmol/L and therapeutic lifestyle changes to achieve less stringent target values of < 4.1 mmol/L and < 3.4 mmol/L recommended for those at low and moderate risk, respectively.

The European guidelines rely on the executive summary of the Third Joint Task Force of the European and other society on cardiovascular disease prevention in clinical practice (constituted by representative of eight societies and by invited experts) (De Backer, 2003). The European guidelines differ fundamentally from the US and UK guidelines as it is focused on the prevention of fatal CVD rather than CHD events (De Backer, 2003). CVD risk assessment is based on the systematic coronary risk evaluation (SCORE) system, which defines high risk as ≥ 5% chance of fatal CVD within 10 years (Reiner et al., 2011). The treatment goal is to reduce total cholesterol and LDL cholesterol in high risk subjects to values below 5 mmol/L and 3 mmol/L, respectively, unless they have clinical CVD, diabetes or serum total and LDL cholesterol concentrations, which are already

Fombon, I.S. (2020) below these values, when the goals of treatment become < 4.5 mmol/L and 2.5 mmol/L, respectively. Similar to the US guidelines, lifestyle changes is the initial step in the management of dyslipidaemia and drugs are added when treatment goals are not achieved within 3 months. There are no specific goals set for TRIG and HDL cholesterol but a TRIG value of > 1.7 mmol/L and HDL cholesterol < 1.0 mmol/L (men) and 1.2 mmol/L (women) are regarded as markers of increased risk (Catapano et al., 2016).

In the UK, guidelines for the management of dyslipidaemia are based on the joint recommendations of the British Cardiac and Hypertension Societies and the British Hyperlipidaemia and Diabetic Associations published in 1998. Unlike the European guidelines, which use the SCORE system for risk assessment, the absolute risk of CHD is estimated using the Framingham based criteria, which include HDL cholesterol as a variable (Thompson, 2004). The Framingham risk score is a gender-specific algorithm used to estimate the 10-year cardiovascular risk of an individual. It is based on data from the Framingham heart study carried out between 1971 – 1974 involving 2489 men and 2856 women 30 to 74 years old, which demonstrated that blood pressure, total cholesterol and LDL cholesterol effectively predict CHD risk, leading to the development of a coronary disease prediction algorithm using categorical variables, which allows physicians to predict multivariate CHD risk in patients without overt CHD (Wilson, et al., 1998; D’Agostino et al., 2008). Based on the Framingham criteria, the UK guidelines, prioritised treatment for persons with existing CHD or with an estimated risk of ≥ 15% over the following 10 years, either at current age or if extrapolated to age 60. Therapeutic lifestyle changes are recommended to persons with serum total cholesterol ≥ 5 mmol/L or LDL cholesterol ≥ 3 mmol/L with the aim of reduction to values < 5mmol/L and < 3 mmol/L, respectively (Hermansson and Kahan, 2017). More recent guidelines applicable in England and Wales as set out in the National Service Framework for CHD, recommend that patients with CHD or at high risk (defined as ≥ 30% per 10 years) should be treated with diet and statins, with the aim of reducing serum total cholesterol below 5 mmol/L or by 20 - 25%, whichever would result in the lowest level; equivalent figures for LDL cholesterol are below 3 mmol/L or by 30% (Lloyd-Mostyn, 2000).

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Although management guidelines tend to differ in different parts of the world in terms of methods of risk assessment and treatment targets, all the guidelines have a common goal of curbing risks of CVD, by reducing high levels of total cholesterol, LDL cholesterol and TRIG, whilst increasing low levels of HDL cholesterol in blood. All the guidelines also recognise the importance of therapeutic lifestyle changes as the initial step in the management of dyslipidaemia, especially in those with low to moderate risk of CVD, and the use of therapeutic drugs is considered only when treatment goals are not achieved (Varady and Jones, 2005; Catapano et al., 2016). The most commonly used options for the pharmacologic treatment of dyslipidaemia are statins, resins, fibrate, niacin, and their combinations. However, other possibilities in selected cases are available, such as the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and the newly approved selective inhibitor of microsomal TRIG transfer protein and antisense oligonucleotide drugs (Zodda et al., 2018).

1.3.2 Therapeutic lifestyle changes Therapeutic lifestyle changes (TLC) are non-pharmacologic measures that are often tried as initial treatment for dyslipidaemia, especially in mild cases of hyperlipidaemia and in persons with mild to moderate risk of CHD. Lifestyle management includes diet modification, regular physical activity, smoking cessation and weight reduction (Thompson, 2004). The Third Report of the NCEP (ATP III) recommends TLC in place of drug therapy for patients who fall into an intermediate range of CHD risk (NCEP, 2001). This approach was based on the panel’s review of the evidence in 1999, which concluded that diet and exercise can have a beneficial effect on serum levels of total cholesterol, LDL cholesterol, HDL cholesterol and triglycerides (Kelly, 2010). Some reports have also suggested that many patients prefer non-drug therapies for many reasons including adverse effects of antilipid drugs, contraindications or allergic reactions to drugs, perceptions of adverse effects of drugs, or personal preference for natural or alternative therapy (Varady and Jones, 2005).

TLC has shown effectiveness in lowering serum cholesterol levels, with the most notable benefits obtained from diet modifications and weight loss (Dalen and

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Devries, 2014; Catapano et al., 2016). However, it is important to keep in mind that when dieting, cholesterol intake is reduced but at the same time, production of cholesterol (especially by the liver) increases (Cohen, 2008). The NCEP ATP III recommended that daily energy intake of total fat should be restricted to 25 – 35%, saturated fatty acids be reduced to less than 7% of daily energy intake and cholesterol intake should also be less than 200 mg daily (NCEP, 2001; Verma, 2016). The intake of plant sterol esters and soluble fibre is advisable. It is estimated that a healthy diet can result in 10 – 15% reduction of cholesterol blood level. Evidence from several reports has shown that lifestyle interventions also influence the metabolism of HDL cholesterol and TRL remnants (Maeda et al., 2003; Nicholls et al., 2006; Kodama et al., 2007; Wang et al., 2009).

The effects of exercise on serum lipid levels have been studied extensively. Published data have shown that regular aerobic exercise increases HDL cholesterol levels by 1.9 to 2.5 mg/dL (0.05 - 0.06 mmol/L) (Kodama et al., 2007). Other effects include decreases in total cholesterol, LDL cholesterol, and triglyceride levels by an average of 3.9, 3.9, and 7.1 mg/dL (0.10, 0.10, and 0.08 mmol/L), respectively (Kelly, 2010). HDL cholesterol level has also been shown to increase by an average of 9% (3.7 mg/dL or 0.10 mmol/L) in patients with CVD who exercise aerobically and therefore suggesting greater benefits in this high-risk group (Kelly et al., 2006; Wang and Xu, 2017). Although, the improvement in HDL cholesterol levels seem to be related more to the amount of activity than to the intensity of exercise or improvement of fitness (Kraus et al., 2002, Khoury, Evans and Ratchford, 2019), physical inactivity is shown to have profound negative effects on lipid metabolism, including increase in LDL cholesterol levels (Slentz et al., 2007; Lavie et al., 2019).

Also, the combination of exercise and changes in diet (i.e. with a low-saturated fat diet or plus nutritional supplements) have shown additive, or at least synergistic effects in reducing LDL cholesterol and TRIG levels, while increasing HDL cholesterol levels (Kelly, 2010, Wang and Xu, 2017). In subjects with type 2 diabetes mellitus, intensive lifestyle intervention (weight loss, diet, and increased physical activity) had beneficial effects on glycaemic control and cardiometabolic risk factors, including HDL cholesterol and TRIG (Ilanne-Parikka et al., 2008; Wing

Fombon, I.S. (2020) et al., 2010), and in the longer term was associated with reduction in CVD risk (Uusitupa et al., 2009).

Moderate alcohol consumption is reported to increase HDL cholesterol and apolipoprotein A-I and has been linked with a reduction in all cause mortality, however, over-consumption has been associated with a higher mortality rate (Criqui, 1996; Catapano et al., 2016). Restriction of alcohol is advised in patients with dyslipidaemia as it increases blood pressure and plasma TRIG levels and can precipitate acute pancreatitis in individuals with high TRIG levels (Sasakabe et al., 2018). Smoking increases TRL remnants and decreases HDL cholesterol levels (Chelland et al., 2008), secondary to insulin resistance and hyperinsulinaemia (Facchini et al., 1992; Chapman et al., 2011); these effects are rapidly reversed on cessation (Maeda et al., 2003). Although lifestyle changes have been shown to be important in reducing risks of CVD, long-term adherence to this approach to treatment has been a major challenge, and therefore many of these patients require additional pharmacological intervention (Middleton et al., 2013).

1.3.3 Pharmacological management of dyslipidaemia Although TLC is the method of choice for managing dyslipidaemia, lipid-lowering drugs are widely used to reduce high levels of cholesterol, LDL cholesterol and TRIG and to increase low levels of HDL cholesterol in patients with dyslipidaemia at risk of CVD. Pharmacological treatment of dyslipidaemia is usually tailored to correct as much of the total dyslipidaemia as possible without inducing drug- related side effects. Recent studies have shown the use of lipid-lowering drugs as an effective way of controlling cardiovascular risk (Reklou et al., 2019). Drug therapy is often considered for people with high risk of CVD and those with known atherosclerosis. A collaborative approach to the use of medical therapies has been shown to improve patient compliance and quality of life (Fletcher et al., 2005). The primary target of lipid therapy is to reduce LDL cholesterol, which is the major atherogenic lipoprotein. It is expected that reduction of LDL cholesterol will reduce atherosclerosis and in turn reduce cardiovascular adverse events (Shattat, 2014).

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Five major classes of hypolipidaemic drugs are commonly used in the management of dyslipidaemia, namely statins (3-Hydroxy-3-methylglutaryl coenzyme A reductase inhibitors), fibrates (fibric acid derivatives), bile acid sequestrants (anionic exchange resins), niacin (nicotinic acid derivatives) and Ezetimibe (selective cholesterol absorption inhibitor) (Pahan, 2006, Drexel, 2009). The choice of hypolipidaemic agent often depends on the type of dyslipidaemia; while statins are the first choice treatment for reducing high serum levels of cholesterol, fibrates are known to effectively reduce elevated levels of TRIG and LDL cholesterol and improve HDL cholesterol levels in patients with dyslipidaemia (Drexel, 2009). Other drugs such as Ezetimibe, colesevelam, torcetrapib, avasimibe, implitapide, and niacin are also being considered for the management of dyslipidaemia (Table 1.2). These drugs have been shown to reduce fatal and non-fatal cardiovascular abnormalities in the general population (Pahan, 2006, Drexel, 2009).

Table 1.2 Choice of lipid-lowering drugs by treatment goal based on the recommendations of the American Diabetes Association

Goal Drug of Choice

Lower LDL Statin, second choice: Bile acid binding resin or fenofibrate

Increase HDL Fibrate (or nicotinic acid, with careful monitoring)

Lower triglycerides Fibrate (fenofibrate, gemfibrozil), high-dose statin (in hypertriglyceridaemic patients with elevated LDL cholesterol levels).

Treat combined High dose statin, second choice: statin + fibrate (fenofibrate, hyperlipidaemia gemfibrozil), third choice: bile acid binding resin + fibrate (fenofibrate or gemfibrozil) or statin + nicotinic acid (with careful monitoring of glycaemic control)

LDL, low-density lipoprotein; HDL, high-density lipoprotein

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1.3.3.1 Statins (or HMG-CoA reductase inhibitors) Statins (3-Hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors) are the most widely used evidence-based drugs for managing dyslipidaemia, with LDL cholesterol as their main target (NICE, 2014). Members of this class of hypolipidaemic drugs include Lovastatin, Simvastatin, Pravastatin, Fluvastatin, Atorvastatin, Rosuvastatin and Pitavastatin (Asztalos et al., 2002, Ward et al., 2019). These drugs are structural analogues of HMG-CoA reductase substrates and act through mechanisms that inhibit cholesterol biosynthesis (Raghow, 2017). The primary mechanism of action of statins is the selective and competitive inhibition of HMG-CoA reductase. HMG-CoA reductase is an enzyme that catalyses the conversion of HMG-CoA to mevalonic acid, a precursor of sterols, including cholesterol (Figure 1.1). By inhibiting HMG-CoA reductase, statins initially cause a reduction in liver cholesterol, but compensatory mechanisms induce greater expression of both HMG-CoA reductase and LDL receptors (Zodda et al., 2018). Hence, through an indirect mechanism, statins also reduce LDL cholesterol by increasing receptor–mediated absorption of LDL cholesterol. The increase in the number of these receptors also causes a decrease in very low density lipoprotein (VLDL) cholesterol and intermediate density lipoprotein (IDL) cholesterol, which are LDL cholesterol precursors, and further contribute to lowering LDL cholesterol (Raghow, 2017). In particular, atorvastatin and rosuvastatin produce a marked decrease in plasma TRIG, as they remove larger amounts of VLDL particles rich in TRIG (Stender et al., 2005).

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Figure 1.1 Mechanism of action of statins. Statins are competitive inhibitors of the enzyme HMG-CoA reductase. This is a critical enzyme in the production of mevalonate and inhibition consequently reduces its derivatives, cholesterol, steroids and isoprenoids. The diagram shows the pathway of cholesterol synthesis and the point at which statins interfere with the metabolism of HMG- CoA (Mora et al., 2009).

Overall, the effect of statins on the lipid profile is consistent between statins, with reductions in total cholesterol, LDL cholesterol and triglycerides and an increase in HDL cholesterol. Despite having the same mechanism of action and comparative effects on lipid profiles, statins can still be subdivided into two categories: Type I, fungal-derived statins (lovastatin, pravastatin, simvastatin) or type II, synthetically- derived (fluvastatin, cerivastatin, atorvastatin, rosuvastatin and pitavastatin) (Istvan, 2003). Type I statins maintain close structural homology to mevastatin, the first statin to be developed, maintaining the lactone/open acid moiety in addition to the substituted decalin ring skeleton, while Type II are fully synthetic inhibitors of HMG-CoA reductase, developed to maintain the HMG-CoA-like lactone moiety for binding. The Type II statins are reported to exhibit highly varied pharmacokinetic

Fombon, I.S. (2020) properties, including differences in metabolism, excretion, half-lives, bioavailability, dosing times and lipophilicity (Istvan, 2003; McFarland et al., 2014).

The LDL cholesterol reduction by statins is dose dependent and varies between the different statins (Weng et al., 2010). Statins are among the most studied hypolipidaemic drugs used in CVD prevention and its effect in larger populations and subgroups have been examined in various meta-analyses (Mills et al., 2008; Baigent et al., 2010; Fulcher et al., 2015). Evidence from a meta-analysis involving 170,000 participants in 26 randomised controlled trials with statins, known as the cholesterol treatment trial (CTT), showed that high-dose statin treatment was associated with a highly significant reduction in major vascular events of 28% per 1.0 mmol/L LDL-cholesterol reduction achieved, compared with usual dose statin therapy (Baigent et al., 2010). They reported a 10% proportional reduction in all- cause mortality and 20% proportional reduction in coronary artery disease (CAD) death per 1.0 mmol/L (or 40 mg/dL) LDL cholesterol reduction were reported. Risk of major coronary event and risk of stroke were also reduced by 23% and 17% per 1.0 mmol/L LDL cholesterol reduction, respectively (Baigent et al., 2010). Reports by Fulcher and Keech (2012) also showed a directly proportional relationship between the reduction in absolute LDL-cholesterol concentration and the reduction in major vascular events with statin therapy.

Evidence from experimental and clinical studies further suggests that apart from their cholesterol-lowering effects, statins may also have some other beneficial effects (pleiotropic effects of statins), such as anti-inflammatory and antioxidant effects, which are potentially relevant for the prevention of CVD (Davignon, 2004; Zhou and Liao, 2010). The effect of statins in other clinical conditions including dementia (McGuinness et al., 2010), hepatic steatosis (Eslami et al., 2013), cancer (Emberson et al., 2012; Nielsen et al., 2012), venous thromboembolism (Li et al., 2011), polycystic ovary syndrome (Raval et al., 2011) and Alzheimer’s disease (Gauthier and Massicotte, 2015) has also been evaluated, although the results remain controversial (Pederson, 2010; Schultz, Patten and Berlau, 2018).

Statins are generally well tolerated with the most common adverse effects being transient gastrointestinal symptoms, headache and dizziness. These symptoms are common with higher doses of the medication. However, treatment with statins

Fombon, I.S. (2020) has also been associated with liver and muscle toxicity, which may be mild or severe ranging from myalgia to rhabdomyolysis which affects 5 – 10% of patients administered statins (Pinal-Fernandez et al., 2018). Recent evidence indicates that statin therapy can cause either self-limited myotoxicity, presumably due to the direct effect of statins in the muscle, or autoimmune myopathy associated with autoantibodies targeting HMG-CoA reductase (Pinal-Fernandez et al., 2018; Ward et al., 2019). Direct myotoxicity is dose-dependent and is often complicated by concomitant treatment with drugs and substances such as cyclosporine, azole antifungins, grapefruit juice, erythromycin, and clarithromycin, which inhibit the cytochrome p450 3A4 pathway via which most statins are metabolised (Thompson et al., 2016). Direct myotoxicity is usually resolved after discontinuing statin treatment (Pinal-Fernandez et al., 2018). Statin-induced autoimmune myopathy has been reported in a very small number of patients (approximately 2 - 3 per 100,000 patients treated each year) usually characterised by proximal muscle weakness, muscle-cell necrosis and presence of autoantibodies against HMG- CoA reductase. This type of myopathy does not revert after stopping statin treatment (Mammen, 2016; Pinal-Fernandez et al., 2018).

Other complications of statins include the risk of developing diabetes mellitus which is estimated to occur in about 10 – 20 per 10,000 patients treated per year, although the pathogenesis is unknown (Shattat et al., 2014; Pinal-Fernandez et al., 2018). Statin therapy is also associated with an increase in serum liver enzymes, although serious liver injury due to statin treatment is a rare occurrence, and is thought to increase when statin is administered in combination with other drugs such as fibrates.

1.3.3.2 Fibrates (or Fibric acid derivatives) Fibrates or fibric acid derivatives are lipid-lowering drugs that have been used in clinical practice for many decades (Katsiki et al., 2013). Members of this class of drugs include clofibrate, ciprofibrate, bezafibrate, fenofibrate and gemfibrozil. They are recommended for lowering TRIG and raising HDL cholesterol levels. Therefore, patients with diabetes and/or metabolic syndrome that are frequently characterised by increased TRIG and decreased HDL cholesterol levels, are those

Fombon, I.S. (2020) more likely to benefit from fibrate treatment (Farnier, 2008; Katsiki et al., 2013). The lipid-modulating action of fibrates is mediated by their capacity to mimic the structure and biological functions of free fatty acids; in this way fibrates bind to the nuclear transcription factor, peroxisome proliferator-activated receptors (PPARs), members of the subfamily of nuclear hormone receptors (Chapman, 2003; Goldenberg et al., 2008). The PPARs become activated by ligand binding and migrate from the cytoplasm of the cell to the nucleus, where they form heterodimers with retinoic acid X receptor, RXR (Figure 1.2). The PPAR-RXR heterodimer subsequently recognises and binds to specific response elements for PPARs in the regulatory regions of a spectrum of target genes termed PPAR response elements (PPREs) leading to activation or repression of the expression of such genes (Staels, et al., 1998, Chapman, 2003; Goldenberg et al., 2008).

The PPARs represent a family of three nuclear receptor isoforms, namely PPARα, PPARγ and PPARδ/β, which are encoded by different genes (Boitier et al., 2003). These receptors also play important roles in the regulation of glucose and lipid homeostasis as well as in cell proliferation, differentiation and inflammatory responses (Lamers et al., 2012). Fibrates bind to PPARα, which is predominantly expressed in the liver, kidney, heart and skeletal muscle; all of these tissues depend in large part on free fatty acids as a source of energy (Chapman, 2003; Fazio and Linton, 2004). The major genes implicated in the PPARα-mediated action of fibrates are listed in Table 1.3. The mechanism of action of fibrates on lipid metabolism can be divided into five groups, including induction of lipoprotein lipolysis, induction of hepatic fatty acid uptake which causes a reduction in hepatic triglyceride production, increased removal of LDL particles, reduction in neutral lipid and increase in HDL cholesterol production and stimulation of reverse cholesterol transport (RCT) (Staels et al., 1998).

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Figure 1.2 Mechanism of action of fibrates. Fibrates are rapidly converted by tissue and plasma esterases into their active metabolite (fibric acid) before they enter the cell. Fibric acid binds to the PPARα and forms a heterodimer complex with retinoid X receptor (RXR). This complex then binds to specific peroxisome proliferator response elements (PPREs) to activate target gene transcription (Koltai, 2015).

Fibrate treatment induces an increase in lipoprotein lipase (LPL) activity and inhibition of apolipoprotein C3 (apo C-III) expression (a strong inhibitor of LPL), leading to increased lipolysis of plasma TRL as well as increased accessibility of TRL for the action of LPL (Katsiki et al., 2013). Reduced apo C-III synthesis results in enhanced LPL-mediated catabolism of VLDL particles. This represents the most likely hypolipidaemic mechanism of fibrates (Staels et al., 1998; Noonan et al., 2013). Studies have also shown that fibrates increase fatty acid uptake by stimulation of fatty acid transporter protein (FATP) and conversion to acyl-CoA by induction of acyl-CoA synthase activity. By enhancing β-oxidation and decreasing fatty acid synthesis, fibrates reduce fatty acid availability for triglyceride synthesis.

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This process is further amplified by fibrate-induced inhibition of hormone-sensitive lipase in adipose tissue (Katsiki et al., 2013). Fibrate treatment also results in the formation of LDL cholesterol with a higher affinity for the LDL receptor, which is thus catabolised rapidly. Furthermore, the cholesteryl ester content of LDL cholesterol increases, resulting in larger, less-dense LDL particles. These changes also increase interactions of LDL particles with the LDL receptor, thereby improving LDL clearance (Goldenberg et al., 2008). It is also suggested that fibrate therapy induces a decrease in the pool of TRL leading to a reduction of net cholesteryl ester transfer from HDL to TRLs, which in association with unchanged lecithin:cholesterol acyl transferase (LCAT) activity, ultimately leads to an increase in cholesteryl ester and a decrease in TRIG content of HDL cholesterol (Staels et al., 1998). The LPL-mediated lipolysis of TRL and increase in HDL cholesterol precursor (apo A-I and apo A-II) synthesis also contributes to the rise in HDL cholesterol as well as a more efficient RCT (Staels et al., 1998).

Evidence from recent studies suggest that fibrates are able to reduce total cholesterol by 8%, LDL cholesterol by 8% and TRIG by 15 – 50%, and increase HDL cholesterol by 9% (Loomba and Arora, 2010; McCullough et al., 2011). Fibrates are administered as monotherapy or in combination with other lipid- lowering drugs, mainly statins, but also Ezetimibe, Colesevelam and niacin (Farnier, 2008). Fibrate-statin combinations have been shown to attain lipid targets to a greater extent than monotherapy in patients with mixed dyslipidaemia, and these changes were maintained long-term (up to 64 weeks) (Farnier et al., 2010; Farnier et al., 2011). Common side effects fibrate therapy include gastrointestinal symptoms, increased liver function tests (hepatotoxicity), reversible increase in serum creatinine (nephrotoxicity) and myopathy (myalgia, myositis and rhadomyolysis) (Remick et al., 2008).

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Table 1.3 Major genes involved in the PPARα-mediated action of fibrates on lipid metabolism and inflammation

Target gene Function of gene product Gene expression Apo C-III Inhibitor of VLDL clearance ↓

Lipoprotein lipase Lipolysis, TRL ↑

Apo AI HDL formation, RCT ↑

Apo AII HDL formation, RCT ↑

SR-BI/CLA-1 receptor Cellular cholesterol efflux (macrophage) ↑

ABCA 1 transporter Cellular fatty acid uptake ↑ Fatty acid binding protein Acyl CoA synthetase Fatty acid activation; acyl CoA esters ↑

β-oxidation pathway Fatty acid oxidation ↑

Acetyl CoA carboxylase Fatty acid synthesis ↓

Fibrinogen Blood clotting ↓

C reactive protein Acute phase reactant ↓

Cyclooxygenase-2 Fatty acid metabolism ↓

VCAM-1 Adhesion molecule ↓

Abbreviations: Apo, apolipoprotein; VLDL, very low-density lipoprotein; RCT, reverse cholesterol transport; TRL, triglyceride-rich lipoprotein; HDL, high-density lipoprotein; SR- BI, the scavenger receptor, class B type 1; ABCA, ATP binding cassette transporter A1; VCAM-1, vascular cell adhesion molecule -1; ↑ indicates increase; ↓ indicates decrease (Staels et al., 1998; Bougarne et al., 2018).

1.3.3.3 Bile acid sequestrants (or anionic exchange resins) Bile acid-binding resins are a class of lipid-lowering drugs used in the clinic to reduce elevated serum levels of total cholesterol and LDL cholesterol, whilst increasing low level of HDL cholesterol. Bile acid sequestrants include: Choletyramine, Colesevelam, Colestipol, and Colestimide (Zodda et al., 2018). Cholestyramine and colestipol are the two bile acids currently available (Shattat et al., 2014). Cholestyramine is a quaternary amine composed of styrene and divinylbenzene polymers, while Colestipolis is a copolymer of diethylenetriamine

Fombon, I.S. (2020) and 1-chloro-2, 3-epoxypropane (Jain et al., 2007). Bile acid synthesis is the main pathway of cholesterol catabolism in the liver; it is estimated that about 500 mg of cholesterol is converted daily into bile acids in the adult human liver. When secreted into the intestine, bile acids play an important role in facilitating the absorption of fats from food (Russell, 2003).

Bile acid sequestrants are positively charged resins that are neither absorbed systemically nor metabolised by digestive enzymes. They are administered orally. At the level of the stomach, they bind to the two main biliary acids (glycocholic acid and taurocholic acid) to form an insoluble complex that is excreted in the faeces (Shattat et al., 2014; Chang and Robidaux, 2017). This leads to a continuous, though partial, removal of bile acids from the enterohepatic circulation. Consequently, the decreased concentration of bile acids in the liver causes an obstruction of the 7α-hydroxylase inhibition feedback mechanism and leads to an increase in hepatic conversion of cholesterol to bile acids (Zodda et al., 2018). Also, the decrease in the concentration of hepatic cholesterol causes a number of compensatory effects, such as increased plasma LDL cholesterol and IDL cholesterol uptake by increasing the number of high affinity receptors for LDL cholesterol present on cell membranes, especially in the liver and induction of the HMG-CoA reductase enzyme (Figure 1.3). Despite the fact that bile acid sequestrants cause increased hepatic cholesterol synthesis, there is a lowering of cholesterol level in the plasma. Bile acid sequestrants have also been shown to reduce TRIG and induce a 5% increase in HDL cholesterol levels (Steinmetz and Schonder, 2005).

Cholestyramine and Colestipol are two hygroscopic molecules with a high molecular weight (>1,000,000), effective in lowering total cholesterol and LDL cholesterol and reduces mortality and cardiovascular events by 7% and 14%, respectively (Steinmetz and Schonder, 2005). They are often prescribed to patients who do not respond adequately to dietary adjustments for the treatment of primary hypercholesterolaemia (Scaldaferri et al., 2013). However, both drugs are not indicated when the primary alteration is hypertriglyceridaemia and are not effective in patients with homozygous familial hypercholesterolaemia who do not have functional receptors, as it does not alter the removal of plasma LDL

Fombon, I.S. (2020) cholesterol via non-receptor mechanisms, although they may be useful in heterozygous patients for receptor alterations (Zodda et al., 2018).

Figure 1.3 Mechanism of action of bile acid sequestrants. Bile acid sequestrants bind to bile acids and reduce the bile acid pool. Decrease in bile acid pool leads to increased bile acid synthesis from cholesterol in the liver, thereby lowering the intrahepatic cholesterol pool, which in turn triggers the LDL receptors resulting in increased LDL cholesterol clearance (Zodda et al., 2018)

A major limitation to the use of bile acid sequestrants as initial therapy is their poor tolerance. However, the most common side effects of bile acid sequestrants are gastrointestinal disturbances. These include constipation, nausea, indigestion, bloating and flatulence (Shattat et al., 2014). Chronic use of resins can interfere with digestion, fat absorption and liposoluble vitamins (vitamin A, D, K, K1), with the latter causing an increase in bleeding tendency due to hypoprothrombinaemia from vitamin K deficiency (Scaldaferri et al., 2013). Long term treatment with bile 20

Fombon, I.S. (2020) acid sequestrants has been associated with a reduction in serum or erythrocyte folate (Zodda et al., 2018), osteoporosis due to calcium loss and aggravation of hypertriglyceridaemia by an unknown mechanism (Shattat et al., 2014). They may also delay or reduce the absorption of certain drugs such as Phenylbutazone, Warfarin, Chlorothiazide, Tetracycline, Penicillin G and Phenobarbital, preparations of thyroid hormone and thyroxine (Zodda et al., 2018). In addition, it may interfere with drugs (e.g. oestrogens) that, like bile acids, are subject to enterohepatic circulation (Scaldaferri et al., 2013).

1.3.3.4 Niacin (or nicotinic acid derivatives)

Niacin, also known as the water-soluble vitamin B3, vitamin PP, or nicotinic acid, is the oldest lipid lowering agent used to treat hyperlipidaemia and proved to decrease cardiovascular morbidity and total mortality (Shattat et al., 2014). It is reported as the most effective therapy available for the treatment of low HDL cholesterol levels (Carlson, 2005). Niacin is approved for the treatment of hypercholesterolaemia and hypertriglyceridaemia and is indicated for familial combined hyperlipidaemia in patients who do not respond to non-pharmacological approaches such as diet and exercise (Chapman et al., 2011). It is also indicated for the treatment of heterozygous familial hypercholesterolaemia (Zodda et al., 2018). The mechanism of action of niacin is not fully elucidated (Chapman et al., 2011). Niacin decreases high levels of VLDL cholesterol and LDL cholesterol, through mechanisms that do not involve cholesterol biosynthesis or catabolism. As a powerful inhibitor of the intracellular lipase system, it prevents lipolysis in adipose tissue and generates multiple effects that eventually lead to the reduction of plasma cholesterol and triglycerides (Gille et al., 2008, Kei and Elisaf, 2012). Reduction in lipolysis decreases free fatty acid mobilisation, reducing their levels in the liver, resulting in a decrease in the hepatic synthesis of triglycerides with consequent lower production of VLDL cholesterol. Nicotinic acid also increases the clearance of VLDL cholesterol via its ability to stimulate the activity of lipoprotein lipase; the decrease in VLDL cholesterol leads to reduced levels of LDL (Figure 1.4) (Gille et al., 2008, Zodda et al., 2018).

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Figure 1.4 Mechanism of action of niacin. Niacin directly inhibits diacylglycerol acyltransferase-2 (DGAT2) activity leading to a reduction in triglyceride (TRIG) synthesis and activation of Apo B degradation and effectively inhibiting the synthesis of Apo B containing lipoproteins, VLDL, LDL and Lp(a) (Cooper et al., 2015; Image adapted with permission from Elvisier).

Niacin treatment has been plagued by low compliance rates due to some adverse effects of the drug. The most common side effect of niacin is skin vasodilation (flushing and itching), an annoying but harmless sensation felt by about 70% of patients receiving niacin and which causes about 20% of niacin-treated subjects to drop out of clinical trials (Birjmohun et al., 2005; Dunbar and Gelfand, 2010; Song and FitzGerald, 2013). This side effect can be reduced by taking aspirin, ibuprofen, or indomethacin before the drug (Gille et al., 2008). Gastrointestinal intolerance manifested by symptoms such as heartburn, indigestion, nausea, and diarrhoea or stomach pain is reported after niacin administration. However, these effects can be minimised by administering the drug with meals (Boden et al.,

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2011). Niacin is contraindicated in patients with liver disease, as it often causes a reversible increase in plasma levels of liver enzymes (e.g. alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase and lactate dehydrogenase). It is used with caution in patients with diabetes mellitus and gout, given its ability to raise glucose and uric acid levels (Safeer and Lacivita, 2000; Bodor and Offermanns, 2008). Niacin has also been known to reduce insulin sensitivity and, in combination with statins, may interact with other factors to reveal a predisposition to diabetes (Heemskerk et al., 2014). The combination of niacin and statin has also been associated with an increased risk of myopathy and rhabdomyolysis (Gille et al., 2008).

1.3.3.5 Ezetimibe (or selective cholesterol absorption inhibitor)

Ezetimibe, or 1-(4-fluorophenyl)-(3R)-[3-{4-fluorophenyl}-{3S}-hydroxyprophyl]- (4S)-(4-hydroxyphenyl)-(2-azetidinone), is the first member of a group of drugs that selectively inhibit the intestinal absorption of phytosterols and dietary cholesterol (Lipka, 2003; Shattat et al., 2014; Zodda et al., 2018). Its mechanism of action involves targeting gastrointestinal cholesterol absorption within the brush border of the small intestine, where it selectively inhibits the cholesterol transport protein Nieman Pick C1 like protein (NPC1L1), thereby preventing uptake of intestinal luminal miscelles, which contain cholesterol, into enterocytes (Phan et al., 2012). Current evidence indicates that NPC1L1 protein works in conjunction with the adaptor protein 2 (AP2) complex and clathrin to facilitate internalisation of free cholesterol into the enterocyte. AP2 is a classical AP that facilitates the internalisation of molecules into cells, such as cholesterol entering clathrin-coated pits (Phan et al., 2012). The exact mechanism by which Ezetimibe reduces the entry of cholesterol into the enterocytes is not completely understood. Ge et al., (2008) suggest that Ezetimibe prevents NPC1L1/sterol complex from interacting with AP2 in clathrin coated vesicles. It is also thought that ezetimibe may change the shape of NPC1L1 and make it incapable of binding to sterols or may interfere with the binding of free cholesterol to the cell membrane (Pearse, Smith and Owen, 2000; Phan et al., 2012). Through reduced cholesterol uptake, ezetimibe therapy also causes a depletion of hepatic LDL cholesterol stores, resulting in

Fombon, I.S. (2020) upregulation of hepatic LDL receptors, which in turn causes LDL cholesterol uptake by the liver from the circulation. In addition to reducing gastrointestinal cholesterol absorption, it is also thought that ezetimibe inhibits hepatic NPC1L1, thereby reducing hepatic cholesterol absorption (Phan et al., 2012). However, ezetimibe does not appear to have an effect on the absorption of dietary lipid- soluble vitamins and drugs (Hammersley and Signy, 2017).

Ezetimibe is indicated both in association with statins, to reduce elevated levels of total cholesterol, LDL cholesterol, and Apolipoprotein B in patients with primary hypercholesterolaemia (familial or non-familial heterozygosity) that are not adequately controlled with statins alone, as well as in monotherapy in this same group of patients for whom statins are considered inappropriate or not tolerated (Davidson et al., 2002; Hammersley and Signy, 2017). It is also indicated for homozygous familial hypercholesterolaemia in combination with statins (atorvastatin or simvastatin) and homozygous familial sitosterolaemia, when the patients have not responded to diet, physical activity and other lifestyle intervention measures (Bove et al., 2017). Early trials demonstrated an additional reduction in LDL cholesterol levels of 12 – 19% when ezetimibe was taken in conjunction with statins (Ballantyne et al., 2003). However, whether ezetimibe therapy confers additional reduction in cardiovascular risk is still a controversy; little evidence of this has been reported (Hammersley and Signy, 2017). The safety of ezetimibe as monotherapy or in combination with other lipid modifying agents such as statins has been well documented (Robinson et al., 2009; Pandor et al., 2009).

Ezetimibe is generally well tolerated; the most common side effects include headache, abdominal pain and diarrhoea. Ezetimibe is also associated with an increase in liver enzymes (alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase and lactate dehydrogenase); however, life threatening liver failure with Ezetimibe as monotherapy or in combination with statins is extremely rare, with only a handful of reported cases (Stolk et al., 2006; Tuteja et al., 2008; Castellote et al., 2008). Recent publication of the Simvastatin and Ezetimibe in Aortic Stenosis (SEAS) study raised concerns for cancer with treatment with Ezetimibe plus statin (Rossebo et al., 2008). However, a combined analysis of two

Fombon, I.S. (2020) larger Ezetimibe-plus-statin trials, namely, the Study of Heart and Renal Protection (SHARP) and Improved Reduction of Outcomes: Vytorin Efficacy International Trial (IMPROVE-IT) studies that were carried out at the same time as the SEAS analysis did not support such a hypothesis (Peto et al., 2008; Phan et al., 2012).

Ezetimibe is a safe alternative option for dyslipidaemic patients intolerant to other lipid lowering drugs, as well as a beneficial supplementary agent for patients who do not reach the recommended serum cholesterol level with their current hypolipidaemic treatment. However, as is the case with all new medications, physicians should be alert to recognise adverse effects associated with Ezetimibe and report them to the regulatory authorities (Florentin, Liberopoulos and Elisaf, 2007).

1.3.4 Role of Fenofibrate in managing dyslipidaemia Fenofibrate (FF) or 2-4[(4-chlorobenzoyl) phenoxy]-2-methylpropanoic acid, 1- methylethyl ester is the most commonly prescribed fibric acid derivative for the management of dyslipidaemia (Lian et al., 2018). It is a third generation fibric acid derivative, widely used as a monotherapy or in combination with other agents, mainly statins, but also Ezetimibe and Colesevelam, to reduce blood levels of cholesterol (LDL and VLDL) and TRIG and increase HDL cholesterol levels in patients at risk of CVD and for treatment of atherosclerosis (Farnier, 2008; Abbas et al., 2012). It is recommended for patients with hypertriglyceridaemia (Fredrickson types IV and V hyperlipidaemia) and hypercholesterolaemia (Frederickson types IIa and IIb) (Yang and Keating, 2009). Following oral administration, FF is rapidly hydrolysed by esterases in the liver and intestinal wall to its active metabolite, fenofibric acid (Lian et al., 2018). Like other fibric acid derivatives, the lipid lowering activity of FF is mediated through its interactions with PPARα, which regulates the gene expression of various enzymes involved in fatty acid oxidation and lipoprotein metabolism as illustrated in Figure 1.5 (Lian et al., 2018). Important molecular features of FF compared with other fibric acid derivatives include the conversion of the phenoxy-2-methyl-2-propanoic acid chain in clofibrate and introduction of a chlorobenzoyl-ketone group for the chlorine atom in clofibrate (Najib, 2002).

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FF stimulates the oxidation of free fatty acids in the liver, which results in the reduction of fatty acids for VLDL cholesterol synthesis and secretion. FF also increases the gene expression of lipoprotein lipase and decreases Apo C-III expression in the liver. Thus, FF lowers the concentration of TRIG, both by reducing the rate of synthesis and increasing the rate of hydrolysis of TRL (Staels et al., 1998; Keating and Croom, 2007; Farnier, 2008). Moreover, FF treatment reduces the proportion of small, dense LDL cholesterol, with the formation of larger, less dense LDL particles with a higher affinity for the LDL receptor and which are thus catabolised more rapidly (Chapman, 2006; Noonan et al., 2013). PPARα activation by FF also causes an increase in gene expression for both apolipoprotein A-I (Apo A-I) and apolipoprotein A-II (Apo A-II), decreases cholesteryl ester transfer protein-mediated transfer of cholesterol from HDL to VLDL, enhances cell cholesterol efflux by induction of cell ATP-binding cassette transporter (ABCA1) expression and decreases the scavenger receptor, class B type 1 (SR-B1) in the liver. ABCA1, Apo A-I and Apo A-II play an essential role in the nascent HDL cholesterol formation. ABCA1 exports excess cellular cholesterol and phosphatidylcholine (PC) to lipid free Apo A-I in serum, thereby generating nascent HDL cholesterol (Nagao et al., 2011; Ishigami et al., 2018). SR-B1 is an HDL receptor whose primary role is to mediate selective uptake or influx of HDL derived cholesteryl esters into cells and tissues. SR-B1 also facilitates the efflux of cholesterol from peripheral tissues, including macrophages, back to the liver (Shen et al., 2018). All these effects contribute to the increase in plasma HDL cholesterol concentrations.

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Figure 1.5 Lipid lowering mechanism of FF. FF is hydrolysed by tissue and plasma esterases into fenofibric acid, which binds to and activates PPARα, and forms a heterodimer complex with RXR. The PPARα-RXR complex binds to specific PPREs to activate target gene transcription resulting in decreased TRIG, LDL particles and inflammation and increased HDL synthesis and RCT. Abbreviations: HDL, high-density lipoprotein; LDL, low-density lipoprotein; RCT, reverse cholesterol transport; PPARα, peroxisome proliferator-activated receptor-α; PPRE, peroxisome proliferator response elements; RXR, retinoid X receptor; ↑ indicates increase; ↓ indicates decrease (Ramasamy, 2016; Image adapted with permission from Elvisier).

1.3.5 Pleiotropic effects of FF therapy Apart from its lipid-lowering effects, FF also has many pleiotropic effects which are believed to be mediated by the activation of PPARα (Paumelle and Staels, 2008). Evidence from various studies suggests that FF therapy improves endothelial function (Playford et al., 2002; Capell et al., 2003; Koh et al., 2005). Koh et al., (2005) reported that FF therapy significantly improved percent flow-mediated dilator response to hyperaemia, reduced inflammation marker levels, increased adiponectin levels, and improved insulin sensitivity in patients with

Fombon, I.S. (2020) hypertriglyceridaemia or metabolic syndrome. In a recent study involving twenty- four healthy normolipidaemic men and women (aged 50 - 70 years), Walker et al., (2012) demonstrated that treatment with FF improved vascular endothelial function by reducing oxidative stress and inducing an increase in endothelial nitric oxide synthase. FF has also been reported to exert anti-inflammatory activities via reduction in C reactive protein and a number of cytokines (e.g. interleukin 6, tumour necrosis factor-alpha). In a study involving patients with hypertriglyceridaemia and metabolic syndrome, Rosenson et al., (2008) reported that FF therapy reduced whole blood production of inflammatory cytokines and hepatic synthesised inflammatory proteins, via VLDL- and LDL-mediated pathways.

Other effects of FF therapy include decreasing procoagulant factors such as fibrinogen and plasminogen activator inhibitor-1 and reduction of monocyte chemoattractant protein-1 (Maison et al., 2002; Paumelle and Staels, 2008). Among the fibrate class of anti-lipidaemic drugs, FF significantly reduces uric acid levels and has been suggested for the treatment of hyperuricaemia and gout (Feher et al., 2003; Jung et al., 2018). Increasing evidence from recent studies has demonstrated the potential of FF as an anti-cancer drug, although these were all based on in vitro studies. Reports suggest that FF exerts anti-cancer effects in several human cancer cell lines, such as breast, liver, glioma, prostate, pancreas, and lung cancer (Lian et al., 2018).

1.3.6 Limitations of FF in the management of dyslipidaemia Although FF is generally well-tolerated, it has been associated with some adverse events which may be causally related: hepatitis, cholelithiasis, cholecystitis, hepatomegaly, myalgias, myasthenia, rhabdomyolysis, photosensitivity, eczema, and allergic pulmonary alveolitis. However, these events all occur in less than 10% of fenofibrate recipients (Guay, 2002). Just as with all other fibrates, FF enhances the formation of cholesterol gallstones by altering biliary composition and increasing the lithogenic index of bile (Liang et al., 2011; Kassim et al., 2018). Patients with pre-existing gallbladder disease are advised against its use (Guay, 1999; Ghonem et al., 2015). In the Fenofibrate Intervention and Event Lowering in

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Diabetes (FIELD) trial, patients allocated to FF at 200 mg/day had statistically significant, though only marginally greater, overall risk of pancreatitis and pulmonary embolism, than those allocated to placebo (Ting et al., 2011). The risk seems to increase when FF is combined with statins, most particularly simvastatin (McDonald et al., 2002).

FF-associated rhabdomyolysis is a rare phenomenon although cases of rhabdomyolysis associated with fenofibrate alone or in combination with statins have been reported in patients with dyslipidaemia (Wu et al., 2009). Rhabdomyolysis is a syndrome characterized by muscle necrosis and the release of intracellular muscle contents into the systemic circulation. The risk for serious muscle toxicity appears to be increased in elderly patients and in those with diabetes (Barker et al., 2003; Jacob et al., 2005), kidney failure and hypothyroidism (Satarasinghe et al., 2007). Despite its low prevalence, patient monitoring is strongly recommended because rhabdomyolysis may be very severe with fatal consequences (Kato et al., 2011).

Cases of FF toxicity to the liver have also been reported and this appears to be dose related (Kobayashi et al., 2009; Gandhi et al., 2014). Mild, transient increases in serum transaminases have been reported in up to 20% of patients receiving FF, but values above three times the upper limit of normal have been reported in only 3 – 5% of cases. These abnormalities are usually asymptomatic and transient, resolving even with continuation of FF, but occasionally may require drug discontinuation. The mechanism of FF-induced hepatotoxicity is unclear. However, patients with metabolic syndrome and uncontrolled dyslipidaemia of long duration are considered to be at greater risk of developing hepatic steatosis (Macchesini et al., 2001). Monitoring of liver function is recommended for patients receiving FF and discontinuation if liver enzymes persist above the upper limit of normal (Gandhi et al., 2014).

FF-associated creatininaemia is the most commonly reported adverse effect of FF therapy (Lipscombe et al., 2001; Ansquer et al., 2008; Ting and Keech, 2013; Kim et al., 2017). FF-associated creatininaemia is common in patients with chronic diseases such as diabetes, hypertension and chronic kidney failure. However, recent studies have shown that FF also affects kidney function in relatively healthy

Fombon, I.S. (2020) adults without CVD (Kim et al., 2017). The significance of this increase in serum creatinine with FF has been debated (Ting and Keech, 2013). The mechanism of FF-induced creatininaemia is not fully understood and kidney function monitoring is recommended for patients receiving FF therapy (Keech et al., 2005; Ncube et al., 2012).

1.4 IMPACT OF FF ON KIDNEY FUNCTION The implication of dyslipidaemia as a risk factor for progressive kidney disease is extensively reported (Fox et al., 2004) and the acute increase in serum creatinine (CREA) level (or inferred nephrotoxicity) following treatment with FF has raised concerns about the safety of its use in patients with chronic kidney disease (Ansquer et al., 2008). Some studies and case reports suggest that FF can potentially cause further kidney damage in patients with underlying kidney disease (Broeders et al., 2000, Lipscombe et al., 2001). In addition to these case reports of patients with pre-existing kidney disease, increased serum CREA concentrations have been reported in patients with normal baseline kidney function taking FF (Ansquer et al., 2008). However, the effect of FF on kidney function remains unclear and is an important unmet clinical need in this field. Evidence from long- term studies such as the FIELD study and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study, suggest that FF-associated increase in serum CREA is reversible (Davis et al., 2011; Mychaleckyj et al., 2012). In the FIELD study, after 5 years of treatment, the increase in serum CREA in patients allocated FF, subsided fully within six weeks of withdrawing treatment (to a lower level than in the placebo group) (Davis et al., 2011). A similar pattern of change was observed in the ACCORD Lipid trial (Mychaleckyj et al., 2012). These reports indicate that whatever the mechanisms of the increase in serum CREA during FF treatment, no permanent injury results. However, it is important to note that this FF-associated nephrotoxicity is interpreted on the basis of increases in serum CREA concentrations, with the presumption that serum CREA is a surrogate for the underlying kidney function (Ting and Keech, 2013).

Suggestions that have been made to explain the mechanism of the increase in serum CREA level following FF therapy include; analytical interference with serum

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CREA assay, increased metabolic production or reduced renal clearance of CREA (Abbas et al., 2012). In a retrospective study involving 10 adult male patients with hyperlipidaemia, Lipscombe et al., (2001) reported a concomitant increase in serum CREA and Urea concentrations following fibrate administration. Both parameters were decreased after discontinuation of the drug. They suggested that the most plausible cause of the deterioration of kidney function is one based on kidney haemodynamics, given the rapid and complete reversibility that was noted and the finding that most of the patients had risk factors for vascular disease.

In a similar (retrospective) study, Tsimihodimos et al., (2001) reviewed the reports of patients treated with fibrates and reported significant increases in both serum CREA and Urea concentrations in patients administered clofibrate and FF, but no significant change in those treated with gemfibrozil. They proposed that with the exception of gemfibrozil, fibrates including FF, ciprofibrate and bezafibrate impair the generation of vasodilatory prostaglandins in the kidney, probably by activating PPARs which can down-regulate the expression of the inducible cyclooxygenase (COX-2) enzyme. COX-2 is an enzyme that catalyses the formation of prostaglandins. Gemfibrozil, in contrast to the other fibrates, fails to bind and activate PPARs which may account for the absence of nephrotoxicity observed (Tsimihodimos et al., 2001). Hence, they believed that the increase in serum CREA is due to FF-induced kidney impairment.

With regards to analytical interference as the cause of the FF-associated increase in serum CREA, Hottelart et al., (2002) compared the Jaffe reaction with high- performance liquid chromatography (HPLC) and suggested that the increase in serum CREA is most likely due to increased metabolic production, as urinary levels of CREA were not reduced and the glomerular filtration rate (GFR) measured by inulin (an independent marker for renal function) was not reduced. Ansquer et al., (2008) also compared the Jaffe colorimetric method and mass spectrometry and suggested that the increase in serum CREA is not method dependent. Recent studies have reported concomitant increases in serum CREA and serum cystatin C concentrations in patients administered FF therapy (Forsblom et al., 2010; Mychalekyj et al., 2012; Ncube et al., (2012).

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Cystatin C is considered as an independent predictive marker of kidney function (Murty et al., 2013). The increase in serum cystatin C concentration following FF administration suggests that the impact of FF is not just on serum CREA alone. The clinical relevance of the increase in both markers needs to be assessed in a long-term outcome study of kidney function. There is much debate in the literature, but the mechanism of the FF-induced creatininaemia is still not fully understood and kidney function monitoring is recommended for patients receiving FF therapy, particularly in those with pre-existing kidney disease (Sica, 2009; Kim et al., 2017).

1.5 MONITORING KIDNEY FUNCTION IN FF THERAPY The kidneys play a fundamental role in the metabolism of FF (Mckeage and Keating, 2011). Following oral administration, the FF is extensively absorbed and transported through the blood stream by albumin and is taken up mainly by the liver and kidney (Chapman, 1987). FF is excreted (60%) in the urine as fenofibric acid and its glucoronide conjugate (Mckeage and Keating, 2011). Coupled with its non dialyzable nature, a reduction in kidney function would therefore result in slower elimination of FF causing accumulation in blood and an increased risk of adverse effect as observed in patients with reduced kidney function administered FF for dyslipidaemia (Ting and Keech, 2013). Routine kidney function monitoring is therefore recommended in patients receiving FF therapy, most particularly in those with baseline chronic kidney disease (Davidson et al., 2007). However, accurate measurement of kidney function is methodically difficult because the kidney has several different interlinked functions, including excretion of waste products, regulation of water and electrolytes, acid-base homeostasis and hormone secretion (Levey and Inker, 2017). Kidney function is assessed in the clinic by methods that measure the GFR of the kidneys.

The GFR is considered the best overall index for measurement of kidney function and correlates well with renal function impairment (Stevens and Levey, 2009; Levey and Inker, 2017). Glomerular filtration is the physiologic process of creating an ultrafiltrate of blood as it flows through the glomerular capillaries. In principle, the GFR is the product of the number of nephrons times the average single- nephron glomerular filtration rate. Its determination also includes haemodynamic

Fombon, I.S. (2020) factors within the glomerular capillary network and the properties of the capillary wall (Levey et al., 2014; Levey and Inker, 2017). The GFR cannot be measured directly in the human body. In practice, it is assessed by measuring the clearance or serum levels of filtration markers, exogenous or endogenous solutes that are mainly eliminated by glomerular filtration (Levey et al., 2014).

An ideal marker of GFR is one that is only eliminated through the kidney via glomerular filtration, is freely filtered by the glomerulus (e.g. not subject to protein binding), not eliminated via tubular secretion, and is not reabsorbed after being filtered by the glomerulus (Seegmiller et al., 2018). The GFR is calculated by the kidney clearance or plasma disappearance of endogenous or exogenous markers. For a kidney clearance study, the blood concentration is monitored during a timed urine collection and the amount of the filtration marker excreted in the urine is measured. The GFR is calculated based upon the kidney elimination of the marker concentration in the blood during the collection. For a plasma disappearance study, the exogenous marker is administered as a single bolus. Blood is sampled multiple times after that to reconstruct a curve based on its elimination from the blood stream. The rate of elimination will then equal GFR (Schwartz et al., 2006; Seegmiller et al., 2018).

1.6 ESTIMATING GFR USING EXOGENOUS MARKERS The gold standard for measuring GFR is the urinary clearance of inulin described by Homer Smith, against which other clearance methods and filtration markers are evaluated (Inker et al., 2015). Other exogenous markers which have been shown to produce measures of GFR that correlate well with inulin clearance include, isotopic markers e.g. 51Chromium ethylene-diamine tetra-acetic acid (51Cr-EDTA), 99mTechnetium labelled diethylene-triamine- pentacetate (99mTc-DTPA) and 125I- iothalamate, as well as non-radioisotopically labelled markers such as iohexol (Stevens and Levey, 2009). Although inulin clearance is considered the gold- standard method for measuring GFR, the need for continuous infusion, multiple blood samples and urine collection, make it cumbersome and expensive for use in routine clinical practice (Lopez-Giacoman and Madero, 2015). In a recent report, Soveri et al., (2014) suggested that the kidney excretion of 51Cr-EDTA or

Fombon, I.S. (2020) iothalamate, and plasma clearance of 51Cr-EDTA or iohexol, using inulin clearance as reference, were sufficiently accurate methods to measure GFR. However, all these methods (based on exogenous substances) still require the need of continuous infusion or bolus administration (subcutaneous or intravenous) and like inulin, their complexity limits their application in routine clinical practice and epidemiological studies, mostly for the length of time that the procedure entails (Lopez-Giacoman and Madero, 2015). They are costly and cumbersome and are generally used only in selected secondary situations. Surrogate markers, such as serum values of endogenous markers are used in routine practice (Ferguson and Waiker, 2012).

1.7 ESTIMATING GFR USING ENDOGENOUS MARKERS Currently identified endogenous filtration markers include metabolites (e.g. CREA and urea) and low-molecular weight proteins (e.g. cystatin C, β-trace protein) (Levey and Inker, 2014). Filtered metabolites are often excreted in the urine, but may undergo renal tubular reabsorption or secretion, which may be assessed by comparing their urinary clearances to urinary clearance of exogenous filtration markers. Extrarenal elimination of endogenous markers can also be assessed by comparing their urinary excretion rate to their generation rate (Levey and Inker, 2017). By contrast, filtered plasma proteins are reabsorbed and degraded within the tubules with minimal appearance in the urine, making it more difficult to evaluate the renal tubular reabsorption, secretion and extrarenal elimination of low-molecular weight plasma proteins than metabolites (Inker et al., 2015). Factors affecting conventional endogenous markers of kidney function vary with the different markers and are summarised on Table 1.4.

1.7.1 Urea as a marker for estimating GFR Historically, Urea was the first marker used to formally assess kidney function (Ferguson and Waikar, 2012). It is a major nitrogenous breakdown product of protein and amino acid catabolism, produced by the liver and distributed throughout intracellular and extracellular fluid. It is eliminated almost entirely

Fombon, I.S. (2020) through urinary excretion, making it a suitable candidate for estimating GFR. In 1827, Richard Bright associated the accumulation of Urea in blood with decrease in urine among individuals with kidney disease (Smith, 1951; Ferguson and Waikar, 2012). This led to the introduction of blood Urea nitrogen (BUN) into clinical medicine as a diagnostic test in the 1900s (Smith, 1951). The most frequently determined clinical indices for estimating kidney function depend on the concentration of BUN. It is useful in the differential diagnosis of acute kidney failure and pre-renal conditions where the BUN/CREA ratio is increased (Rosner and Bolton, 2006). Recent reports suggest that BUN is now generally considered as a suboptimal marker for estimating GFR (Gowda et al., 2010). Several limitations have been highlighted against its use in assessing GFR. Urea clearance is a poor indicator of GFR as its overproduction rate depends on several non-kidney related factors including diet and Urea cycles.

Urea is readily absorbed by the tubules, particularly in conditions of volume depletion, resulting in increased concentrations while GFR is preserved. Apart from kidney disease (e.g. kidney failure and urinary track obstruction by kidney stones), increase in BUN concentrations is also observed in increased dietary protein intake, hypercatabolism, congestive heart failure, dehydration, corticosteroid use, fever, shock, gastrointestinal bleeding or during late pregnancy (Gowda et al., 2010; Ferguson and Waikar, 2012). Low levels are also seen in fluid excess, trauma, surgery, opioid use, malnutrition and anabolic steroid use (Gowda et al., 2010). Therefore, interpretation of BUN concentration has to be carefully considered in the clinical context. Although some studies have reported concomitant increases in BUN and serum CREA concentrations following FF administration, the impact of FF therapy on BUN have not been fully elucidated (Broeders et al., 2000)

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Table 1.4 Factors affecting conventional markers of kidney function (Bagshaw, 2008)

Factors Serum marker Increased Decreased

Urea Decreased effective circulating Aggressive volume expansion volume Increased dietary protein Pregnancy

Critical illness (fever, trauma, burns, SIADH‡ sepsis) Gastrointestinal bleeding Dietary protein restriction

Drugs (corticosteroids, tetracyclines) Liver disease

Creatinine Younger age Older age

Male sex Female sex

Large muscle mass Muscle wasting (neuromuscular diseases, malnutrition) Ingestion of cooked meat Protein restriction (renal disease, liver disease) Jaffe reaction (ketotic states, Vegetarian diet hyperglycaemia) Drugs (cimetidine, trimethoprim) Jaffe reaction (hyperbilirubinaemia) Vigorous exercise Amputation

Cystatin C Older age

Male sex Female sex

Greater body mass Lower body mass

Smoker Immunosuppressive therapy (corticosteroids) States of inflammation Hypothyroidism

Hyperthyroidism

‡SIADH, syndrome of inappropriate antidiuretic hormone.

Fombon, I.S. (2020)

1.7.2 CREA as a marker for estimating GFR First identified in 1847 and later proposed as a filtration marker in 1926, CREA supplanted urea as a marker of GFR in the mid 1900s and remains the most widely used laboratory marker for estimation of GFR today (Ferguson and Waikar, 2012). CREA is a small molecule (molecular weight 113-Dalton) derived from the breakdown of creatine phosphate in muscle as well as from dietary meat intake or creatine supplements (Lopez-Giacoman and Madero, 2015; Levey and Inker, 2017). CREA generation is proportional to muscle mass, which can be estimated from age, sex, race and body size, but many other factors can affect CREA generation (Ferguson and Waikar, 2012). CREA is distributed in total body water, not protein bound, freely filtered across the glomerulus and is not reabsorbed by the kidney tubules and its concentration is inversely related to GFR (Ferguson and Waikar, 2012; Levey and Inker, 2017). It is also convenient and cheap to measure. In clinical practice, measured CREA clearance is also often assessed from the CREA excretion in a 24-hour urine collection and a single measurement of serum CREA in the steady state (Levey and Inker, 2017).

Although CREA is the most commonly used kidney function biomarker in the clinic, the limitations of serum CREA and CREA clearance for the estimation of GFR are well documented (Sica, 2009). Serum CREA levels are known to be affected by various factors that are independent of changes in GFR, such as, muscle mass, age, sex, race, catabolic state, medication use, tubular secretion and extrarenal elimination in severe kidney impairment (Levey et al., 2014). CREA concentrations may alter as its generation may not be simply a product of muscle mass but influenced by muscle function, muscle composition, activity, diet and health status (Banfi and Del Fabbro, 2006; Gowda et al., 2010). Several medications, such as cimetidine, trimethoprim, and possibly FF, competitively inhibit CREA secretion, leading to a rise in the serum CREA concentration without any effect on the GFR (Levey and Inker, 2017).

Furthermore, CREA is contained in intestinal secretions and can be degraded by bacteria; gastrointestinal elimination of CREA is increased at higher levels of serum CREA but can be reduced by changes in gut flora due to antibiotic use (Levey and Inker, 2017). Clinically, it can be difficult to distinguish a rise in serum CREA concentration caused by inhibition of CREA secretion or extrarenal 37

Fombon, I.S. (2020) elimination from a decline in GFR, especially in patients with low GFR (Inker et al., 2015). Tubular secretion of CREA may increase proportionally relative to its glomerular filtration as kidney function declines, resulting in a significant overestimation of true GFR. As a result, an increase in serum CREA may not be observed until a substantial decrease in GFR has occurred (Ferguson and Waikar, 2012).

Historically, considerable variability has been known to exist with respect to serum CREA measurement, generally resulting in less accurate estimation of GFR when serum concentrations are within or slightly above the reference interval (Myers et al., 2006). This led to the launching of the Creatinine Standardisation Program in 2008 by the National Kidney Disease Education Program (NKDEP) in collaboration with the International Federation of Clinical Chemistry (IFCC) and the European Communities Confederation of Clinical Chemistry to reduce interlaboratory variability in CREA assay calibration (Levey and Inker, 2017). Following on from this, most laboratories now use a CREA assay that has calibration traceable to an isotope dilution mass spectroscopy method, enabling interlaboratory comparison. However, the effect of standardisation still varies among clinical laboratories.

Overall, standardisation has led to lower values for serum CREA and higher values for estimated GFR using CREA as a filtration marker than before standardisation (Levey and Inker, 2017). Also, measured CREA clearance, which is usually assessed from the CREA excretion in a 24-hour urine collection, and a single measurement of serum CREA in a steady state, is reported to be unreliable due to the variations in CREA excretion rates. Deviations from estimated CREA excretion can indicate errors in timing or completeness of urine collection, but cannot be relied upon due to wide variability in CREA generation (Inker, 2014). Collectively, the choice of CREA as a marker for assessing kidney function in patients receiving FF therapy is debatable as the underlying mechanism and clinical significance of the reversible increase in serum CREA witnessed during treatment are not fully understood.

Fombon, I.S. (2020)

1.7.3 Use of low-molecular weight proteins as markers of GFR Recent reports suggest that low molecular weight proteins such as cystatin C, beta-2 microglobulin and beta-trace protein are potential markers of GFR (Argyropoulos et al., 2017; Beker et al., 2018). These proteins are freely filtered by the glomerulus, reabsorbed and catabolised but not secreted by the kidney tubules. As a result, increases in plasma concentration of these proteins are associated with a reduction in GFR. The impact of FF on these novel markers of kidney function is not fully elucidated. Although recent studies have shown concomitant increase in serum CREA and cystatin C following FF therapy, the impact on B2M and BTP have not been reported (Ncube et al., 2012; Mychalekyj et al., 2012)

1.7.3.1 Cystatin C (CYSC) as a marker of kidney function CYSC has been described as a promising endogenous marker and has generated considerable interest in recent years as a marker of GFR. It is a 122 amino acid low molecular weight protein (13 kDa) that is a cysteine protease inhibitor (Levey et al., 2014). It is produced at constant rate by all nucleated cells and distributed in extracellular fluid. The function of CYSC seems to be the protection of connective tissues from destruction by intracellular enzymes. It may also have some antibacterial or antiviral function (de Vries and Rabelink, 2013). CYSC is freely filtered by the glomerulus and approximately 99% is reabsorbed by the proximal tubular cells, where it is also almost completely catabolised, with the remainder eliminated largely intact in the urine (Levey and Inker, 2017). Earlier reports indicate that CYSC is not secreted by the renal tubules (Larteza et al., 2002), but recent evidence suggests the existence of tubular secretion as well as extrarenal elimination, the latter estimated at 15 – 21% of urinary clearance (Levey and Inker, 2017).

Unlike serum CREA, serum CYSC concentration appears to be independent of age, sex, and muscle mass (Filler et al., 2005). It is suggested that CYSC may be more reliable than serum CREA-based methods in estimating GFR, particularly in those individuals with a mild reduction in GFR, in whom changes in serum CREA are typically not observed (the so-called creatinine blind range of GFR) (Herget-

Fombon, I.S. (2020)

Rosenthal, Bökenkamp and Hofmann, 2007). Serum CYSC may also be superior to serum CREA in estimating mortality and cardiovascular outcomes (Herget- Rosenthal et al., 2007; Ferguson and Waikar, 2012). Reports suggest that serum CYSC rises faster than serum CREA after a fall in GFR, suggesting that serum CYSC may be a an ideal marker for early detection of kidney impairment, as is often required for monitoring the effect of drugs on the kidney function (Herget- Rosenthal et al., 2007).

Several GFR-prediction equations based on serum CYSC have been proposed, including equations that incorporate both serum CREA and CYSC (Inker et al., 2012; Grubb et al., 2014). The serum CYSC-based equations appear simpler and more accurate than serum CREA-based equations (Hojs et al., 2008). Although, these equations have shown only modest improvement in the accuracy of GFR estimation compared with serum CREA-based equations, there is increasing evidence that the use of serum CYSC outperforms serum CREA in the stratification of risk (Shiplak et al., 2013). Recent studies have shown that GFR equations that incorporate both serum CREA and CYSC perform better than those using either of these two markers (Inker et al., 2012). However, concerns have been raised about the lack of standardisation in serum CYSC measurement. A recent study by White et al., (2011) found that there were significant differences in serum CYSC measurement between laboratories even when the same assay was used from the same manufacturer.

In response to these concerns, the IFCC in collaboration with the Institute for Reference Materials and Measurements has produced and characterised a serum CYSC reference material (ERM-DA471/IFCC). However, international standardisation of cystatin C assay is still in process and important differences remain (Eckfeldt et al., 2015; Bargnoux et al., 2017). Further limitation to the use of serum CYSC as a biomarker of kidney function includes the fact that circulating CYSC concentrations may be affected by other factors such corticosteroid administration, thyroid dysfunction, smoking, inflammation, adiposity and malignant neoplasms (e.g. breast, lung, colorectal and ovarian cancer) due to increased cell turnover (Levey and Inker, 2017; Jones et al., 2017).

Fombon, I.S. (2020)

1.7.3.2 Beta 2-microglobulin (B2M) B2M is an 11.8kDa protein associated with both classical and non-classical major histocompatibility complex (MHC) class 1 molecules expressed on the surface of all nucleated cells and is critical for antigen presentation (Argyropoulos et al., 2017). It dissociates from the heavy chain in the setting of cellular turnover and enters the circulation as a monomer. B2M is freely filtered at the glomerulus and almost entirely (>99.9%) reabsorbed and metabolised in the proximal tubule (Argyropoulos et al., 2017). Although, serum concentrations of B2M appear to be largely independent of age and muscle mass (Filler et al., 2002), there does not appear to be a clear advantage of B2M over serum creatinine in detecting small changes in GFR (Ferguson and Waikar, 2012). First suggested as a biomarker for glomerular filtration in the 1980s, the potential of B2M as a candidate for a single- marker equation has been limited because of its lack of specificity. Serum levels of B2M are impacted by the amount generated and shed by nucleated cells, body distribution kinetics and the amount eliminated through glomerular filtration (Argyropoulos et al., 2017). As an acute phase reactant, its serum level tends to increase in a variety of inflammatory and infectious disorders. Also, due to its ubitiquous presence on the surface of all cells, increase in B2M concentration is seen with diseases associated with high cell turnover, such as several malignancies (Filler et al., 2002; Beker et al., 2018).

1.7.3.3 Beta-trace protein (BTP) BTP is another low molecular weight protein (23 -29 kDa) that has been investigated as a biomarker of GFR (Beker et al., 2018). Also known as lipocalin prostaglandin D2 synthase (L-PGDS), BTP is generated at a constant rate by glial cells in the central nervous system. The variation in size depends on the degree of post-translational glycosylation, with the larger isoforms of BTP in serum and urine, and the smaller isoforms with truncated side chains in cerebrospinal fluid (Beker et al., 2018). It is freely filtered by the glomerulus and reabsorbed by the proximal tubule with minimal nonrenal elimination (Ferguson and Waikar, 2012). BTP was first noted to be raised in patients with chronic kidney disease in 1987, but its specific potential as a filtration marker was only suggested 10 years later, in

Fombon, I.S. (2020) a study that observed very high concentrations of BTP in patients on haemodialysis (Hoffmann, Nimtz and Conradt, 1997; Beker et al., 2018).

Recent studies suggest that serum BTP concentrations perform at a similar level to creatinine and CYSC, not only in the estimation of GFR, but also in the prediction of progressive kidney dysfunction (Spanaus et al., 2010). GFR equations based on BTP have been derived, but these have largely involved kidney transplant patients. Further validation of the equations is required in diverse populations (Beker et al., 2018). Like CYSC, corticosteroid administration appears to impact serum concentrations of BTP (Abbink et al., 2008), but unlike CREA, CYSC and B2M, there are currently no certified reference materials available for BTP. Hence, there is no standardisation amongst currently available BTP assays. Furthermore, given the known variation in post-translational modification which creates a variety of glycoprotein epitopes, immunoassays utilising different antibodies would be expected to give disparate BTP results (Beker et al., 2018). These suggest that much is still to be done to confirm the utility of BTP in the routine assessment of GFR and to establish reference laboratory standards to ensure inter- and intra-laboratory consistency in measurement.

1.7.4 Use of equations to estimate GFR Given the limitations of CREA as a marker of kidney function, implementation of prediction equations has been used to estimate GFR from endogenous filtration markers. Most of these equations incorporate demographic and clinical variables (Earley et al., 2012). The most commonly used equations in clinical practice are shown in Table 1.5 and include the Cockroft and Gault (C&G) (1976) equation, the several iterations of the Modification of Diet in Renal Disease (MDRD) Study equations (MDRD-4 and MDRD-6), the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI, 2009) equation, and more recently the CKD-EPI CYSC equation and the equation that combines CREA and CYSC (Inker et al., 2012; Levey et al., 2014).

The C&G formula was published as a bedside equation for estimated CREA. Until recently, it was solely used for drug dosing but has been widely adapted into

Fombon, I.S. (2020) clinical practice due to its convenience and perceived accuracy (Hudson and Nolin, 2018). However, the C&G formula is an imprecise estimate of true GFR mainly due to its failure to adequately compensate for several non-GFR determinants of serum CREA. It is also based on a small and non-diverse study population (a selection of mostly men and all white patients) (Delanaye and Cohen, 2008; Casal et al., 2019). Since the standardisation of CREA, the C&G equation is barely used in clinical practice (Earley et al., 2012).

The MDRD equation was developed from a large population of mostly white and non diabetic patients with CKD. The original equation included 6-variables but was subsequently simplified to a four variable equation that included age, sex, serum CREA and race differentiation as white or black (Lopez-Giacoman and Madero, 2015). Unlike the C&G equation the MDRD equation does not require body weight or height. Although the MDRD equation has demonstrated high accuracy in individuals with CKD, it tends to underestimate GFR in healthy individuals resulting in false positive diagnosis of CKD in this population (Rule et al., 2004).

The main objective of the CKD-EPI equation was to develop an equation that was superior to the MDRD, especially amongst those with GFR > 60 mL/min/1.73 m2 (Lopez-Giacoman and Madero, 2015). Compared to the MDRD cohort, the CKD- EPI had higher GFR (68 mL/min/1.73 m2) than MDRD (40 mL/min/1.73 m2), younger age, included diabetics, black individuals and kidney transplant recipients (Levey et al., 2009; Earley et al., 2012). However, reports have shown that the CKD-EPI equations produce lower estimates of CKD prevalence than the MDRD equation (Levey et al., 2009). In order to overcome the imprecision of CREA estimating equations, eGFR equations for CYSC (using CYSC alone, CYSC with demographic factors and CYSC with serum CREA and demographic factors were developed and compared with measured GFR using iothalamate and 51Cr-EDTA. Reports have shown that CYSC-based equations perform better than CKD-EPI equations (Lopez-Giacoman and Madero, 2015). Inker et al., (2012) have also shown that the combined equation including CYSC and CREA performs better than equations based on either CYSC or serum CREA and should be used in subjects where CKD needs to be confirmed. However, the impact of FF therapy on the GFR estimated using the different equations have not been fully elucidated.

Fombon, I.S. (2020)

Table 1.5 GFR estimating equations in widespread use

Equations Expression

C&G¥ CrCl (mL/min) = [(140 – Age) x Wt] / (0.813 x CREA) (x 0.85 if female)

Where CREA is serum creatinine (µmol/L)

MDRD§† GFR (mL/min/1.73 m2) = 175 x (CREA x 0.011312)-1.154 x (age)-0.203 x (1.212 if black) x (x 0.742 if female)

Where CREA is serum creatinine (µmol/L)

CKD-EPI‡ GFR (mL/min/1.73 m2) = 141 x min ([CREA x 0.011312]/k, 1)α x Creatinine max ([CREA x 0.011312]/k, 1)-1.209 x 0.993Age x 1.018 (if female) x 1.159 (if black)

Where CREA is serum creatinine (µmol/L), min indicates the minimum of CREA/k or 1, max indicates the maximum of CREA/k or 1, k = 0.7 (female) or 0.9 (male) and α = -0.329 (female) or -0.411 (male).

CKD-EPI‡ GFR (mL/min/1.73 m2) = 133  min (CYSC/0.8, 1)-0.499  max Cystatin C (CYSC/0.8, 1)-1.328  0.996Age  0.932 (if female)

Where CYSC is serum cystatin C (mg/L), min indicates the minimum of CYSC/k or 1, and max indicates the maximum of CYSC/k or 1.

CKD-EPI‡ GFR (mL/min/1.73 m2) = 135 x min([CREA x 0.011312]/k, 1)α x Creatinine & max([CREA x 0.011312]/K, 1)-0.601 x min(CYSC/0.8, 1)-0.375 x Cystatin C max(CYSC/0.8, 1)-0.711 x 0.995Age x 0.969 (if female) x 1.08 (if black) combined Where CREA is serum creatinine (µmol/L), CYSC is serum cystatin C (mg/L), k is 0.7 (if female) and 0.9 (if male), α is -0.248 (if female) and -0.207 (if male), min indicates the minimum of CREA/k or 1, and max indicates the maximum of CREA/k or 1.

¥C&G, Cockcroft & Gault; §MDRD, Modification of Diet in Renal Disease; ‡ CKD-EPI, Chronic Kidney Disease-Epidemiology Collaboration. †The so-called ‘simplified’ (four- variable) ID-MS traceable form of the MDRD equation is shown, for use when serum creatinine is measured with a reference (ID-MS) procedure

Fombon, I.S. (2020)

1.8 AIMS AND OBJECTIVES Overall aim: Investigate the impact of FF, a widely used lipid-lowering drug, on clinical biomarkers of kidney function in adult patients with dyspilidaemia

Part 1 - Investigate the impact of FF on routine biochemical parameters:

1. Kidney function tests [creatinine, Urea, uric acid and electrolytes (sodium and potassium)]. 2. Liver function tests (albumin, total bilirubin, alanine aminotransferase, alkaline phosphatase, gamma glutamyl transferase and aspartate aminotransferase). 3. Lipid profile (total cholesterol, triglycerides, high-density lipoprotein cholesterol, non-HDL cholesterol and cholesterol to HDL ratio) following FF therapy. 4. Inflammatory marker (C reactive protein) and markers of muscle damage (creatine kinase and lactate dehydrogenase). 5. Thyroid function tests (thyroid stimulating hormone, thyroxine and triiodothyronine).

Part 2 – Investigate the utility of cystatin C as a marker of kidney function in patients receiving FF therapy with the following specific aims:

1. To determine the analytical performance of Gentian Cystatin C using the Beckman Coulter AU640 analyser

2. To investigate the impact of FF (160 mg/day) therapy on serum cystatin C and serum creatinine concentrations in patients with dyslipidaemia

3. To compare changes in serum cystatin C-based eGFRs with serum creatinine-based eGFRs after FF therapy in patients with dyslipidaemia

4. To examine the concordance between serum creatinine-based, and serum cystatin C-based equations for estimating GFR in patients receiving FF therapy for dyslipidaemia.

Part 3 – Investigated the mechanism of cytotoxicity of FF in the kidney and muscle using rat renal proximal tubular cells (NRK52E), rat skeletal muscle cells (L6) and

Fombon, I.S. (2020) human embryonic kidney cells (HEK293), and characterise the intracellular events involved with the following specific aims:

1. To determine and compare the cytotoxicity of FF with 5-fluorouracil (5- FU) in NRK52E, L6 and HEK293 cell lines using the MTS assay.

2. To evaluate the role of oxidative stress in FF-induced cell death in NRK52E, L6 and HEK293 cell lines.

3. To investigate the molecular mechanism of FF-induced cell death by western blot analysis for markers of apoptosis.

Fombon, I.S. (2020) Chapter 2

Materials and Methods

2.1 - MATERIALS

2.1.1 Materials used in clinical biomarkers studies The materials used in investigating changes in clinical biomarkers are summarised in Table 2.1

Table 2.1 Materials used in studies involving clinical biomarkers

Material (Reagents) Reference Manufacturer Location

-glutamyltransferase OSR6120 Beckman Coulter High Wycombe, UK Access 2 Reaction Vessels 81901 Beckman Coulter High Wycombe, UK Access 2 Sample Cup 0.5 mL 651412 Beckman Coulter High Wycombe, UK Access 2 Sample Cup 2 mL 81902 Beckman Coulter High Wycombe, UK Access 2 Substrate 61906 Beckman Coulter High Wycombe, UK Access 2 Wash Buffer II A16792 Beckman Coulter High Wycombe, UK Access Free T3 A13422 Beckman Coulter High Wycombe, UK Access Free T3 Calibrator A13430 Beckman Coulter High Wycombe, UK Access Free T4 Calibrator 33885 Beckman Coulter High Wycombe, UK Access Free T4 Reagent 33880 Beckman Coulter High Wycombe, UK Access TSH (3rd IS) B63284 Beckman Coulter High Wycombe, UK Access TSH (3rd IS) Calibrator B63285 Beckman Coulter High Wycombe, UK Alanine aminotransferase OSR6107 Beckman Coulter High Wycombe, UK Albumin OSR6202 Beckman Coulter High Wycombe, UK Alkaline phosphatase OSR6204 Beckman Coulter High Wycombe, UK Aspartate aminotransferase OSR6209 Beckman Coulter High Wycombe, UK BD Vacutainer® SST™ tubes 367956 Becton Dickinson Swindon, UK C reactive protein (Latex) OSR6299 Beckman Coulter High Wycombe, UK Citranox® 81912 Beckman Coulter High Wycombe, UK Cl – Electrode MU9196 Beckman Coulter High Wycombe, UK Cleaning Solution 66039 Beckman Coulter High Wycombe, UK Contrad 70* 81911 Beckman Coulter High Wycombe, UK Creatine Kinase (CK-NAC) OSR6279 Beckman Coulter High Wycombe, UK Creatinine OSR6678 Beckman Coulter High Wycombe, UK CRP Latex Calibrator Normal ODC0026 Beckman Coulter High Wycombe, UK (N Set)

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Gentian Cystatin C Calibrator A52763 Gentian AS Moss, Norway Gentian Cystatin C Control A57265 Gentian AS Moss, Norway Gentian Cystatin C A57261 Gentian AS Moss, Norway HDL Cholesterol OSR6287 Beckman Coulter High Wycombe, UK HDL Cholesterol Calibrator ODC0011 Beckman Coulter High Wycombe, UK ISE Buffer 66320 Beckman Coulter High Wycombe, UK ISE High Serum Standard 66316 Beckman Coulter High Wycombe, UK ISE Internal Reference 66314 Beckman Coulter High Wycombe, UK ISE Low Serum Standard 66317 Beckman Coulter High Wycombe, UK ISE Low/High Urine Standard 66315 Beckman Coulter High Wycombe, UK ISE Mid Standard 66319 Beckman Coulter High Wycombe, UK ISE Reference 66318 Beckman Coulter High Wycombe, UK K – Electrode MU9195 Beckman Coulter High Wycombe, UK Lactate Dehydrogenase OSR6227 Beckman Coulter High Wycombe, UK LIH OSR62166 Beckman Coulter High Wycombe, UK Liquichek Microalbumin 378/379 Bio-Rad Irvine, USA Control Level 1 and 2 Liquichek Urine Controls Level 397/398 Bio-Rad Irvine, USA 1 and 2 Multichem P SP40PX Technopath Tipperary, Ireland Multichem-IA Plus IA310X Technopath Tipperary, Ireland Multichem™ S Plus CH101CRP Technopath Tipperary, Ireland CH102CRP CH103CRP Na – Electrode MU9194 Beckman Coulter High Wycombe, UK Serum Protein Multi-calibrator ODR3021 Beckman Coulter High Wycombe, UK System Calibrator 66300 Beckman Coulter High Wycombe, UK Total Bilirubin OSR6112 Beckman Coulter High Wycombe, UK Total Cholesterol OSR6516 Beckman Coulter High Wycombe, UK Triglyceride OSR60118 Beckman Coulter High Wycombe, UK Urea Nitrogen (BUN) STAT OSR6141 Beckman Coulter High Wycombe, UK Uric acid OSR6298 Beckman Coulter High Wycombe, UK Urine/CSF Albumin B46435 Beckman Coulter High Wycombe, UK Urine/CSF Albumin Calibrator B358859 Beckman Coulter High Wycombe, UK Urine/CSF Protein Calibrator OSR6170 Beckman Coulter High Wycombe, UK Urine/CSF Protein Reagent OSR6170 Beckman Coulter High Wycombe, UK Wash Solution OSR0001 Beckman Coulter High Wycombe, UK

Abbreviations: HDL, high-density lipoprotein; LIH, lipaemia, icterus, haemlysis indices; ISE, ion-selective electrode; CSF, cerebrospinal fluid; CRP, C reactive protein.

Fombon, I.S. (2020)

2.1.2 Materials used in cell line studies The materials used in the cell line studies are listed in Table 2.2

Table 2.2 Materials used in the cell line studies

Material Manufacturer Location

Beta-mercaptoethanol Sigma-Aldrich Poole, UK Acrylamide/Bis-acrylamide 37, 5:1 Sigma-Aldrich Poole, UK solution Amido Black 10B sodium salt Sigma-Aldrich Poole, UK Ammonium persulphate (APS) Sigma-Aldrich Poole, UK Bovine serum albumin (BSA) (standard Seralabs Hull, UK grade) CellTiter96® Aqueous One Solution Promega Southampton, UK Dulbecco’s Modified Eagle medium Fisher Scientific Loughborough, UK (DMEM) Ethanol (molecular biology grade) Sigma-Aldrich Poole, UK Ethylenediaminetetraacetic acid (EDTA) Fisher Scientific Loughborough, UK Hydrogen peroxide Sigma-Aldrich Poole, UK Isopropanol (molecular biology grade) Sigma-Aldrich Poole, UK Marvel milk powder Tesco Welwyn Garden City, UK Methanol (technical grade) Fisher Scientific Loughborough, UK Nitrocellulose membrane (marked) Sigma-Aldrich Poole, UK Polyvinylidene difluoride (PVDF) Fisher Scientific Loughborough, UK membrane Sodium Dodecyl Sulphate (SDS) Fisher Scientific Loughborough, UK Sodium hydroxide Fisher Scientific Loughborough, UK Tetramethylethylenediamine (TEMED) Sigma-Aldrich Poole, UK Trypsin-EDTA Fisher Scientific Loughborough, UK Trypan blue dye solution Fisher Scientific Loughborough, UK Tween-20 Sigma-Aldrich Poole, UK FF ≥ 99%, powder Sigma-Aldrich Poole, UK 5-fluorouracil ≥ 99%, powder Sigma-Aldrich Poole, UK Annexin V-FITC apoptosis kit BioVision Mountain View, USA Foetal bovine serum (FBS) Fisher Scientific Loughborough, UK 2’, 7’-dichlorofluorescein diacetate Sigma-Aldrich Poole, UK (DCFDA) Triton X-100 Sigma-Aldrich Poole, UK Phosphate buffered saline (PBS) tablets Oxoid Hampshire, UK Bromophenol blue dye Sigma-Aldrich Poole, UK

Fombon, I.S. (2020)

2.1.3 Antibodies The antibodies used in this study are listed in Table 2.3

Table 2.3 List of antibodies

Antibody Antibody type Manufacturer Location

Superoxide dismutase Rabbit polyclonal Abcam Cambridge, UK (SOD1)

c-Jun N-terminal kinase Rabbit polyclonal Abcam Cambridge, UK (JNK) p38 (MAPK) Mouse monoclonal Abcam Cambridge, UK

Bax Rabbit monoclonal Abcam Cambridge, UK

Bcl-2 Mouse monoclonal Abcam Cambridge, UK

NFkB-p65 Rabbit polyclonal Abcam Cambridge, UK

β-actin Mouse monoclonal Abcam Cambridge, UK

Goat anti-rabbit IgG-HRP Rabbit polyclonal Vector Labs Peterborough, linked UK Horse anti-mouse IgG-HRP Mouse polyclonal Vector Labs Peterborough, linked UK Abbreviations: MAPK, mitogen-activated protein kinase; NFκB, nuclear factor kappa- light-chain-enhancer of activated B-cells; HRP, horseradish peroxidase; IgG, immunoglobulin G.

2.1.4 Cell lines Rat renal proximal tubular (NRK-52E) cell-line was a donation from Dr Prabal Chatterjee (School of Pharmacy and Biomolecular Sciences, University of Brighton, UK), rat skeletal muscle (L6) cell-line and the human embryonic kidney (HEK293) cell-lines were purchased from the American Tissue Culture Collection (Teddington, UK).

Fombon, I.S. (2020)

2.2 – METHODS This study was carried out in three parts. The first part of the study was patient- based and involved adult patients with dyslipidaemia who required treatment with FF. Changes in clinical biomarkers such as creatinine, Urea and uric acid following FF therapy have been reported in isolated cases, but its impact on routine clinical markers in patients with dyslipidaemia has not been determined. In this study, the impact of FF (160 mg/day) therapy on routine clinical biomarkers during 12 months of therapy was investigated. The biomarkers examined included markers of kidney function, liver function, lipid profile, thyroid function, inflammatory markers and markers of muscle damage. This part of the study was carried out at the laboratories of Southwest Pathology Services, Musgrove Park Hospital, Taunton, Somerset, UK.

In the second part of the study, the impact of FF therapy on serum concentrations of CYSC, an endogenous marker of kidney function/damage was examined. The utility of serum CYSC as a marker of kidney function in patients receiving FF treatment for dyslipidaemia was also examined. The study compared changes in GFR estimated using serum CYSC-based equations to those estimated using serum CREA-based equations. This part of the study was carried out at the laboratories of Southwest Pathology Services, Musgrove Park Hospital, Taunton, Somerset, UK. Finally, the cytotoxicity of FF in kidney and muscle cells, and the mechanism of FF-associated toxicity were examined using cell line models. FF toxicity was tested in 3 cell lines: rat renal proximal tubular cells (NRK52E), rat skeletal muscle cells (L6) and the human embryonic kidney cells (HEK293). The cytotoxic effect of FF in these cell lines was compared with the toxicity of 5- fluorouracil (5-FU), an anticancer drug used in the clinic and known to be toxic to kidney and muscle cells. 5-FU was used as a positive control for the experiments. In order to determine the mechanism of FF cytotoxicity in these cell lines, FF- induced intracellular reactive oxygen species accumulation was determined in cells exposed to various concentrations of FF. Cells exposed to various doses of FF were examined for changes in proteins in apoptotic signalling. The cell-based study was carried out at the laboratories of the Centre for Research in Biosciences (CRIB), Faculty of Health and Applied Sciences, University of the West of England, Bristol, UK.

Fombon, I.S. (2020)

2.2.1 Ethical considerations

The research protocol was approved by the National Research Ethics Service (NRES) Committee South West Exeter (REC reference: 14/SW/1104) and Site Specific Information (SSI) approval was obtained from the Research and Development (R&D) committees of both BHT and TST. Written informed consent was obtained from all participants. This study was also approved by the Faculty Research Degrees Committee of the University of the West of England, Bristol (see letters in appendix).

2.2.2 Study participants Participants in this study included adult (male and female) patients diagnosed with dyslipidaemia who required treatment with FF. The participants were recruited from the lipid clinics at Burton Hospitals NHS Foundation Trust (BHT) and Taunton & Somerset NHS Foundation Trust (TST). Exclusion criteria included:

 patients who had thyroid disease or whose medication for thyroid disease had not been stable for the past six months;

 patients who had started or changed their oral contraceptive regimen in the past six months;

 patients who had critical illness, severe malnutrition or known kidney impairment;

 patients who lacked capacity to give informed consent.

All the participants were treated with FF at 160 mg/day. Nineteen participants (14 male and 5 female) were enrolled in this study: eleven (8 male, 3 female) were enrolled by the Burton Hospitals NHS Foundation Trust, while eight (6 male, 2 female) were enrolled by the Taunton & Somerset NHS Foundation Trust. The median age of the participants was 50 years (range 34 - 70). Four participants (3 male and one female) were withdrawn from the study due to failure to provide any post treatment samples. Fifteen participants (11 male, 4 female) provided the baseline sample and at least one post-treatment sample. Participants were placed

Fombon, I.S. (2020) into various groups (A - E) based on sample provision (Table 2.4). Group A consisted of 13 participants (9 male and 4 female), median age 51 years (range 37 – 65), and included all the participants who provided samples (serum and urine) at baseline and after 3 months of treatment with FF. In Group B, there were 10 participants (7 male and 3 female), median age 51 years (range 37 – 63) and included all the participants who provided samples at baseline and after 6 months of treatment. Group C was made up of eight participants (5 male and 3 female), median age 51 years (range 37 – 63) and included all the participants who provided samples at baseline and after 12 months of FF therapy. In Group D, we included all the participants who gave samples at baseline, 3 and 6 months after starting FF. This group was made up of nine participants (6 male and 3 female) with median age 50 years (range 42 – 55). Participants who had given samples at all visits (baseline, 3, 6 and 12 months) were put in Group E. This group consisted of four participants (2 male and 2 female), with median age 51 years (range 43 – 57).

Fombon, I.S. (2020)

Table 2.4 Groups of participants based on sample provision

Group characteristics

Group A 4 Female, 9 Male participants, aged 37 – 65 (median 51 years) Provided samples at baseline and 3 months (n=13) Participant B01, B02, B03, B04, B05, B06, B08, code: B10, B11, T01, T01, T02, T04, T05

Group B 3 Female, 7 Male participants, aged 37 – 63 (median 51 years) Provided samples at baseline and 6 months (n=10) Participant B01, B02, B03, B04, B05, B06, B10, code: T02, T03, T04

Group C 3 Female, 5 Male participants, aged 37 – 63 (median 51 years) Provided samples at baseline and 12 months (n=8) Participant B04, B05, B06, T01, T02, T03, T05, code: T08

Group D 3 Female, 6 Male participants, aged 42 – 55 (median 50 years) Provided samples at baseline, 3 and 6 months Participant B01, B02, B03, B04, B05, B06, B10, (n=9) code: T02, T04

Group E 2 Female, 2 Male participants, aged 43 – 57 (median 51 years) Provided samples at baseline, 3, 6 and 12 months Participant B04, B05, B06, T02 (n=4) code:

2.2.3 Sample collection, storage and analyses Fasting venous blood was collected from the participants during each visit to the clinics into plain vacutainer serum/plasma separator tubes (SST) and allowed to clot. The blood tubes were centrifuged at 3200 rpm (1843 g) for 10 minutes and the serum obtained was stored frozen at -80C until they were analysed. To avoid errors from inadequate collection of 24-hour urine samples, spot midstream urine samples were collected from each participant into plain universal containers at the time of blood collection and stored frozen at -80C. Baseline samples were

Fombon, I.S. (2020) collected before FF intervention and follow-up samples were collected at 3, 6, and 12 months after commencing treatment. All the patient samples were analysed as a batch to minimise interassay variations.

Clinical biomarkers in patient samples were determined using the Beckman Coulter Olympus AU640 analyser system. The clinical biomarkers including methods and traceability are listed on Table 2.5. Unless otherwise stated, all serum biochemistry test parameters were calibrated using the Beckman Coulter System Calibrator (66300). The System Calibrator is a lyophilised human serum based product used to provide calibration for Beckman Coulter AU reagents for the quantitative determination of the relevant analytes on Beckman Coulter AU analysers. The System Calibrator was reconstituted in 5 mL deionised water and aliquots were stored frozen at -20 C for no longer than 7 days. The HDL- cholesterol assay was calibrated using Beckman Coulter HDL calibrator (ODC0011). The HDL cholesterol calibrator was supplied in lyophilised form and was reconstituted in 3 mL deionised water. Reconstituted HDL calibrator aliquots were stored frozen at -20 ºC for no longer than 7 days. The CRP assay was calibrated using Beckman Coulter serum protein multi-calibrator (ODR3021), which is traceable to International Federation of Clinical Chemistry (IFCC) standard CRM 470 (RPPHS). The CRP calibrators were supplied ready to use.

All serum enzyme tests (i.e. alanine aminotransferase, alkaline phosphatase, aspartate aminotransferase, creatine kinase, gamma-glutamyl transferase, and lactate dehydrogenase) were calibrated using the System Calibrator, with calibration values traceable to the Beckman Coulter Master Calibrator. Serum electrolytes (Na and K) were calibrated using the Beckman Coulter HIGH (Reference. number 66316) and LOW (Reference number. 66317) external serum standards. During calibration of ISE, both ISE MID Standard solution and the external standard (HIGH and LOW) solutions, which have known concentrations, were measured. Urine electrolytes were calibrated using the Beckman Coulter ISE High/Low Urine Standards (Reference. number. 66315), while urine total protein was calibrated using Beckman Coulter Urine/CSF Protein Calibrator (OSR6170) and urine albumin, using Beckman Coulter Urine/CSF Albumin Calibration kit (Reference number. B358859). All other urine tests parameters were calibrated using Beckman Coulter Urine Calibrator (B64606).

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Quality control of serum biochemistry assays (unless otherwise stated) was performed using Multichem Plus S Controls (Technopath Clinical Diagnostics, Ballina, Co. Tipperary, Ireland) with values pre-determined using the Beckman Coulter AU640 analyser. The Multichem S Plus Controls is a multi-analyte quality control material used to monitor the precision of laboratory testing procedures for chemistry assays. The quality control materials were supplied ready to use and kept frozen. The quality control materials were defrosted away from light and treated the same as patient specimens and run in accordance with the manufacturers’ instructions, and following local laboratory policies for assay quality control. Urine albumin quality control was performed using Bio-Rad Liquichek Microalbumin Control Level 1 and Level 2 (Reference number. 378/379), while for all other urine tests, precision was performed using the Bio-Rad Liquichek Urine Chemistry Controls Level 1 and Level 2 (Reference number. 397/398).

All tests procedures followed routine laboratory procedures for analysis. All the samples, reagents, calibrators and quality control materials were loaded on the analyser and were analysed by the automation system. Samples were tested after a satisfactory assay calibration and acceptable quality control performance. Test results were automatically calculated by the Beckman Coulter AU analyser system and the Labcentre Laboratory Information Management System (LIMS).

Fombon, I.S. (2020)

Table 2.5 List of serum biochemistry and immunoassay parameters, methods and traceability

Assay Method Traceability Reference Unit range Creatinine Jaffe IDMS NIST SRM 967 L1 & 50 - 120 µmol/L (Method A) L2 Urea Urease/GLDH NIST SRM 909 L1 <75y: 2.5 – 6.6 mmol/L >75y: 3.9 – 9.9

Sodium Indirect ISE 136 - 145 mmol/L

Potassium Indirect ISE 3.5 – 5.0 mmol/L

Uric acid Uricase/POD IDMS Reference F: 154.7 – 357.0 µmol/L Method M: 208.3 – 428.4 Albumin Bromocresol Certified Reference 35 - 50 g/L Green Material (CRM) 470 Total Bilirubin DPD NIST SRM 916a 5 – 17 µmol/L

Alanine IFCC Modified Beckman Coulter 0 – 40 U/L aminotransferase without P5P Master Calibrator Alkaline IFCC Method Beckman Coulter 25 - 110 U/L phosphatase Master Calibrator Aspartate IFCC Modified Beckman Coulter F<35 U/L aminotransferase without P5P Master Calibrator M<50 Gamma- Szasz Beckman Coulter 9 – 64 U/L glutamyltransferase (modified) Master Calibrator

Total Cholesterol CHO-POD CDC Reference 3.5 – 6.5 mmol/L Method (Abell- Kendall) HDL cholesterol CHO-POD US CDC Cholesterol F: 1.3 – 1.5 mmol/L reference method M: 1.0 – 1.3 Triglyceride GPO-POD IDMS Reference 0.6 – 2.0 mmol/L Method Creatine kinase IFCC IFCC Reference 30 - 223 U/L Method Lactate SCE 1982 Beckman Coulter 140 - 271 U/L dehydrogenase 37C Master Calibrator C reactive protein Turbidimetry IFCC Standard 0 – 10 mg/L CRM 470 (RPPHS) TSH Immunoassay Two-site (sandwich) 0.34 – 5.6 mU/L assay Free T4 immunoassay Two step assay 7.9 - 20 pmol/L

Free T3 Immunoassay Competitive binding 4.0 – 6.6 pmol/L assay

Abbreviations: IDMS, Isotope Dilution Mass Spectrometry; NIST, National Institute of Standards and Technology; SRM, Standard Reference Material; GLDH, glutamate dehydrogenase; ISE, ion selective electrode; POD, GPO, glycerol phosphate oxidase; peroxidise; PETIA, particle-enhanced turbidimetric immunoassay; IFCC, International Federation of Clinical Chemistry; DPD, 3,5-dichlorophenyldiazonium tetrafluoroborate; P5P, pyridoxal-5-phosphate; CHO, choline oxidase; HDL, high-density lipoprotein; CDC, Centre for Disease Control; TSH, thyroid stimulating hormone; F, female; M, male

Fombon, I.S. (2020)

2.2.4 Determination of electrolyte by the indirect ISE method Serum and urine electrolytes (sodium and potassium) were measured by the indirect ion selective electrode (ISE) method using crown ether membrane electrodes. This method measures the electrical potential that develops between the inner and outer surfaces of an ion-selective electrode according to the Nernst Equation for the specific ion. The electrode (membrane) is made of a material that is selectively permeable to the ion being measured. The potential is measured by comparing it to the potential of a reference electrode. The electrical potential is translated into voltage. Since the potential of the reference electrode is held constant, the difference in voltage between the two electrodes is attributed to the concentration of the ion (Na or K) in the sample.

First, ISE Reference Solution was pumped through the REF electrode and a reference measurement was taken. Next, Serum (20 µL) or urine (25 µL) was mixed with 10 µL of ISE Buffer solution in the dilution pot of the ISE unit. The diluted sample was aspirated and passed through the sodium and potassium electrodes (Flowcell), and the potential generated at the electrodes was measured. Excess diluted sample in the sample pot was pumped through the Bypass tubing to the waste. After a urine sample was processed, ISE Buffer Solution was delivered to the sample pot and pumped through the Flowcell to rinse the Flowcell. After each sample was processed, the ISE MID Standard was pumped through the Flowcell to condition the electrodes and take measurements and the solution was sent to the waste. When compared to an internal reference, the electrical potential was translated into voltage and then into the ion concentration of the sample.

The assay range was 50 – 200 mmol/L for serum sodium, 10 – 400 mmol/L for urine sodium, 1.0 – 10 mmol/L for serum potassium and 2.0 – 200 mmol/L for urine potassium, while the reference range for serum sodium was 136 - 145 mmol/L and serum potassium 3.5 – 5.0 mmol/L. There were no reference ranges for random urine electrolytes.

Fombon, I.S. (2020)

2.2.5 Measurement of CREA by the Jaffé (compensated) method Serum/urine CREA concentration was measured by a kinetic modification of the Jaffé procedure in which CREA in the sample reacts with picric acid at alkaline pH to form a yellow complex (Janovsky complex) (Debus et al., 2015). Although, this reaction is not completely specific for CREA, since other reducing substances such as glucose, pyruvate, ascorbic acid and acetoacetates will react with picrate to form a similar colour (Delanghe and Speekaert, 2011); Fabiny and Ertingshauesn (1971) had shown that alkaline CREA picrate reaches maximum colour development at a different rate than pseudo-creatinine material. Cook (1971) also utilized different reaction rates of alkaline picrate positive substances to obtain greater specificity with the Jaffé reaction. In this study, the protocol outlined in the Beckman Coulter Setting Sheet was followed.

Briefly, 20 µL of serum was added to 120 µL of Reagent 1 (containing sodium hydroxide) and 120 µL of Reagent 2 (picric acid), while 1.2 µL of urine sample was added to 35 µL of Reagent 1 and 35 µL of Reagent 2. The mixture was incubated at 37 ºC. The rate of change in absorbance at 520/800 nm was measured and was proportional to the concentration of creatinine in the sample. Interference from protein in the serum creatinine measurement was mathematically corrected by subtracting 18 µmol/L from each test result (Wyuts et al., 2003). The dynamic range for serum and urine creatinine on the AU640 analyser was 23 – 2,218 µmol/L and 0.088 – 35.36 mmol/L, respectively. The reportable range for serum CREA was 23 – 2,218 µmol/L, extending to 22,180 µmol/L with automatic on board dilution, while urine CREA was 0.088 – 35 mmol/L, extending to 141 mmol/L with automatic on board dilution on the AU640 analyser. Adult reference range for serum CREA was 60 – 120 µmol/L and urine CREA was 0 - 24 mmol/L.

2.2.6 Measurement of urea by the urease/GLDH method Serum/urine urea levels were measured by an enzymatic method based on an adaptation of the method of Talke and Schubert (1965) using Beckman Coulter Urea STAT reagent. By this method, urea is hydrolysed in the presence of water and Urease to produce NH3 and CO2. The NH3 produced combines with 2- oxoglutarate and nicotinamide adenine dinucleotide (NADH) in the presence of 59

Fombon, I.S. (2020) glutamate-dehydrogenase (GLDH) in the reagent to yield glutamate and NAD+. Simultaneously, a molar equivalent of the reduced form of NADH was also oxidised. Two molecules of NADH were oxidised for each molecule of urea hydrolysed. The rate of change in absorbance at 340 nm, due to the disappearance of NADH, was directly proportional to the concentration of urea in the sample. In this study, serum (2.5 µL) or urine (2.0 µL) was incubated with 100 µL of the Urea STAT reagent at 37ºC. The lowest detectable limit of serum and urine urea was 0.23 mmol/L and 2.92 mmol/L, respectively. However, the serum assay was linear within the range 0.8 – 50 mmol/L, while the urine assay was linear within the range 10 – 750 mmol/L. The adult reference range for serum urea concentration was 2.8 – 7.2 mmol/L.

2.2.7 Determination of uric acid by the uricase/POD method Uric acid (UA) concentration in serum and urine samples was measured by an enzymatic method which involves measurement of oxygen consumed or hydrogen peroxide produced by uricase reaction. The procedure is a modification of the Fossati method (1980). By this method, UA is converted by uricase to allantoin and hydrogen peroxidase. The hydrogen peroxidase formed reacts with N, N- bis(4-sulfobutyl)-3,5-dimethylaniline, disodium salt (MADB) and 4- aminophenazone (p-aminophenol, PAP) in the presence of peroxidase to yield a chromophore, which is read bichromatically at 660/800 nm. The amount of dye formed is proportional to the concentration of uric acid in the sample.

In this study, 7.0 µL of serum or 1.5 µL of urine sample was added to 60 µL of Reagent 1 in a cuvette and mixed. Reagent 2 (25 µL) was added and the mixture was incubated at 37ºC. The absorbance was measured at 660/800 nm. The assay was linear within a concentration range of 89 – 1785 µmol/L for serum and 119 – 23,800 µmol/L for urine. According to Beckman Coulter the lowest level of detection in serum and urine was 2 µmol/L and 10 µmol/L, respectively. These represent the lowest measurable level of UA that can be distinguished from zero. The adult reference range for serum UA was 154.7 – 357.0 µmol/L for females and 208.3 – 428.4 µmol/L for males.

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2.2.8 Alanine aminotransferase activity assay Serum alanine aminotransferase (ALT) was determined using a procedure based on the principles outlined by Ladue and Wroblewski (1956), and a modification of the methodology recommended by the International Federation of Clinical Chemistry (IFCC) (Schumann et al., 2002). By this method, 10 µL of serum sample was added to 100 µL of Reagent 1 and mixed in a reaction cuvette. Reagent 2 (50 µL) was added to the mixture and then incubated at 37ºC. ALT in the sample transferred amino groups from alanine to 2-oxoglutarate to form pyruvate and glutamate. The pyruvate entered a lactate dehydrogenase (LDH) catalysed reaction with NADH to produce lactate and NAD+. The decrease in absorbance due to the consumption of NADH was directly proportional to the ALT activity in the sample and was measured at 340 nm. The lowest detectable level in serum was estimated at 2 U/L, while the assay was linear within an enzyme activity range of 3 – 500 U/L. The reference range of ALT in adults was 0 – 40 U/L.

2.2.9 Alkaline phosphatase activity assay Alkaline phosphatase (ALP) activity was estimated in serum samples using a procedure based on the method developed by Bowers and McComb (1966) and formulated following the recommendation of IFCC (Schumann et al., 2002). Briefly, a serum sample (3.0 µL) was mixed with equal volumes (60 µL) of ALP Reagent 1 and Reagent 2 in a cuvette and incubated at 37ºC. ALP in the sample catalysed the hydrolysis of 4-nitro-phenylphosphate (4-NPP) in the reagent to form phosphate and free 4-nitrophenol (4-NP), which in dilute acid solutions was colourless. Under alkaline conditions, 4-NP was converted to the 4-nitrophenoxide ion, a yellow quinonoid compound, in the presence of magnesium and zinc ions as cofactors. The rate of change in absorbance due to the formation of 4-NP was measured bichromatically at 410/480 nm and was directly proportional to the ALP activity in the sample. N-hydroxyethylethylenediaminetriacetic acid (HEDTA) was used as a chelating agent, and 2-amino-2-methyl-1-propanol (AMP) as phosphate acceptor at pH 10.4.

In this method although Zn2+ ions are usually present in a total concentration of 1 mmol/L, most are bound to HEDTA, leaving only a small, experimentally

Fombon, I.S. (2020) determined optional concentration of free ions. A similar situation exists for Mg2+ ions. Thus HEDTA acts as a metal ion buffer, maintaining optimal concentrations of both ions (Tietz et al., 1983). The assay was linear within an enzyme activity range of 5 – 1500 U/L and the lowest detectable level according Beckman Coulter was estimated at 1 U/L. The reference range for serum ALP in adults older than 17 years was 30 – 120 U/L.

2.2.10 Serum Albumin estimation by the Bromocresol green method Serum Albumin (ALB) was measured by photometric test method based on the specific binding of bromocresol green (BCG), an anionic dye, to ALB in the sample at acid pH 4.2 to form ALB-BCG complex. Serum sample (2.0 µL) was added to 58 µL of the reagent in a reaction vessel and the mixture was incubated at 37ºC. The reaction led to a colour change of the BCG dye from yellow-green to green-blue and a shift in the absorption wavelength which was measured bichromatically (600/800 nm). The intensity of the colour formed was proportional to the concentration of ALB in the sample. The lowest detectable level in serum was estimated at 0.15 g/L, although the assay was linear within a concentration range 15 – 60 g/L. Adult reference range was estimated at 35 – 52 g/L.

2.2.11 Determination of serum total bilirubin concentration Serum total bilirubin (TBIL) was determined by a photometric colour test, using Beckman Coulter Total Bilirubin kit (OSR6112). This TBIL reagent is a variation of the classical method. By this method, a stabilised diazonium salt, 3, 5- dichlorophenyldiazonium tetrafluoroborate (DPD) reacts with conjugated bilirubin directly and with unconjugated bilirubin in the presence of an accelerator to form azobilirubin. The absorbance at 540 nm is proportional to the TBIL concentration. Serum sample (6.0 µL) was added to 30 µL of the reagent in a reaction cuvette and incubated at 37ºC. Caffeine and a surfactant were used as reaction accelerators. A separate sample blank was performed to reduce endogenous serum interference. The test was linear within a concentration range of 0 – 513

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µmol/L and the lowest detectable level in serum was estimated at 0.20 µmol/L. Adult reference range was 5 – 17 µmol/L.

2.2.12 Aspartate aminotransferase activity assay Serum aspartate aminotransferase (AST) activity was measured by a kinetic UV test method using a procedure that followed the modification of the methodology recommended by the IFCC (Schumann et al., 2002). When serum sample (10 µL) was mixed with equal volumes (50 µL) of AST Reagent 1 and Reagent 2, AST in the sample catalysed the transamination of aspartate and α-oxoglutarate, to form L-glutamate and oxaloacetate, the oxaloacetate produced was reduced to L- malate by malate dehydrogenase, while NADH was simultaneously converted to NAD+. The decrease in absorbance due to the consumption of NADH was measured at 340 nm and was proportional to the AST activity in the serum sample. The test was linear within an enzyme activity range of 3 – 1000 U/L and the lowest detectable level was 1 U/L. The reference range for serum AST was less than 35 U/L in female (adult) and less than 50 U/L in male (adult).

2.2.13 Determination of serum total cholesterol concentration Serum total Cholesterol (CHOL) was measured by an enzymatic colour test method. Briefly, 30 µL of serum sample was added to 45 µL of Total Cholesterol reagent in a cuvette and incubated at 37ºC. Cholesterol esters in the sample were hydrolysed by cholesterol esterase (CHE) to produce free cholesterol. The free cholesterol was, in turn, oxidised by cholesterol oxidase (CHO) to cholestene-3- one with the simultaneous production of hydrogen peroxide (H2O2), which oxidatively coupled with 4-aminoantipyrine and phenol in the presence of peroxidase (POD) to yield a chromophore (quinoneimine). The change in absorbance due to the formation of the red quinoneimine dye was directly proportional to the concentration of cholesterol in the sample and was measured at 540/600 nm.

Fombon, I.S. (2020)

The lowest detectable level in serum was estimated at 0.02 mmol/L and the assay was linear within a concentration range of 0.5 – 18.0 mmol/L. The reference ranges based on the NCEP-ATP III recommendations (2001) were <5.2 mmol/L (desirable), 5.2 – 6.2 mmol/L (borderline high) and ≥6.0 mmol/L (high).

2.2.14 Determination of serum triglyceride concentration Serum triglyceride (TRIG) level was measured by an enzymatic colour test method using a procedure that is based on a series of coupled enzymatic reactions. Serum sample (2.0 µL) was added to 100 µL of Reagent 1 and mixed in a cuvette. Reagent 2 (25 µL) was then added to the mixture and incubated at 37ºC. TRIG in the sample was hydrolysed by a combination of microbial lipases to give glycerol and fatty acids. The glycerol was phosphorylated by adenosine triphosphate in the presence of glycerol kinase (GK) to produce glycerol-3-phoaphate. The glycerol-3- phosphate produced was oxidised to molecular oxygen in the presence glycerol phosphate oxidase (GPO) to yield H2O2 and dihydroxyacetone phosphate. The

H2O2 formed reacted with 4-aminophenazone (4-AAP) and N, N-bis (4-sulfobutyl)- 3, 5-dimethylalanine, disodium salt (MADB) in the presence of peroxidase to produce a chromophore, which was read at 660/800 nm. The increase in absorbance was proportional to the concentration of TRIG in the sample. The lowest detectable level of TRIG in serum was estimated at 0.01 mmol/L and the test was linear within a concentration range of 0.1 – 11.3 mmol/L. Adult reference range was 0.6 – 2.0 mmol/L.

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2.2.15 Determination of HDL-cholesterol concentration Serum HDL was determined by enzymatic colour test method. This method directly measures HDL-cholesterol without the need for any pre-treatment or centrifugation of the serum sample. Serum sample (1.2 µL) was added to 108 µL of reagent 1 (R1) containing anti-human-β-lipoprotein antibody that binds to lipoproteins other than HDL (LDL, VLDL, and chylomicrons). The antigen-antibody complexes formed block enzyme reactions with these lipoprotein. Reagent 2 (36 µL), containing the enzymes cholesterol esterase (CHE) and cholesterol oxidase (CHO) was added to the reaction vessel. These enzymes reacted only with HDL to form H2O2 which was measured in a peroxidise condensation reaction in the presence of an enzyme chromogen system (N-ethyl-N-(2-hydroxy-3-sulfopropyl- 3.5-dimethoxy-4-flouranaline or F-DAOS), to yield a blue colour complex. The increase in absorbance of the blue dye was measured by colorimetric method at wavelength 600 nm and is directly proportional to the concentration of HDL in the sample.

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The lowest detectable level of HDL in serum sample was calculated as 0.002 mmol/L and the test was linear within a concentration range of 0.05 – 4.65 mmol/L. Samples with values above the measuring range were automatically diluted 1 in 5 by the analyser using normal saline (diluents). This onboard dilution extended the measuring range to 0.05 - 23.25 mmol/L. The concentration of HDL in the sample was reported in mmol/L. Adult reference range was 0.7 – 2.2 mmol/L.

2.2.16 Creatine kinase activity assay Serum creatine kinase (CK) activity was measured by a kinetic UV test method. Serum sample (3.0 µL) was added to 120 µL of Reagent 1 and mixed. Reagent 2 (30 µL) was then added and the mixture was incubated at 37ºC. CK present in the sample catalysed the transfer of a phosphate group from creatine phosphate to adeninine diphosphate (ADP) to yield creatine and adenosine triphosphate (ATP). The ATP produced was used to phosphorylate glucose to produce glucose-6- phosphate and ADP. This reaction was catalysed by hexokinase which required magnesium ions for maximum activity. The glucose-6-phosphate was oxidised by glucose-6-phoaphate dehydrogenase (G6P-DH) with simultaneous reduction of the coenzyme nicotinamide adenine dinucleotide (NADP) to give NADPH and 6- phosphogluconate. The rate of increase in absorbance at 340/660 nm due to the formation of NADPH was directly proportional to the activity of CK in the sample. These reactions occurred in the presence of N-acetyl-L-cysteine (NAC) which was present as an enzyme reactivator.

The lowest detectable level was estimated at 3 U/L, but the test was linear within an enzyme activity range of 10 – 2000 U/L. The reference range of CK in female was ≤ 145 U/L and ≤ 171 U/L in male (Schumann and Klauke, 2003).

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2.2.17 Lactate dehydrogenase activity assay Serum lactate dehydrogenase (LDH) level was measured by a kinetic UV test based on the method recommended by the Scandinavian Committee on Enzymes (Horder and Gerhardt, 1985). Following Beckman Coulter setting up data, 3.0 µL of serum sample was added to 100 µL of Reagent 1 in a cuvette and then mixed. Next, 50 µL of Reagent 2 was added and the mixture was incubated at 37ºC. Lactate dehydrogenase in the sample catalysed the reduction of pyruvate to lactate at a neutral pH. This reaction was coupled with the oxidation of NADH to NAD+. The decrease in NADH is directly proportional to the enzyme activity in the sample and was measured at 340 nm. The lowest detectable level of LDH in serum was estimated at 15 U/L and the assay was linear within an enzymatic activity range of 50 – 3000 U/L. The reference range of serum LDH in adults was 208 – 378 U/L.

2.2.18 Measurement of C reactive protein concentration Serum C reactive protein (CRP) level was measured by a turbidimetric method using the Beckman Coulter CRP Latex reagent. By this method immune complex formed in solution scattered light in proportion to their size, shape and concentration, and the turbidimetric method measures the reduction of incidence light due to reflection, absorption or scatter. In this study, 2.0 µL of serum sample was mixed with 150 µL of CRP Latex Reagent 1 and 150 µL of Reagent 2 and the mixture was incubated at 37C. The rate of decrease in light intensity transmitted (increase in absorbance) through particles suspended in solution was the result of complexes formed during the antigen-antibody reaction. The lowest detectable level of serum CRP was 0.09 mg/L but the test was linear within a concentration range of 0.2 – 480 mg/L. The reference range was < 5 mg/L.

2.2.19 Urine total protein by the Pyrogallol red-molybdate method Urine total protein (UPRO) was measured by a colorimetric method, based on the change in absorption which occurs when pyrogallol red-molybdate dye (reagent) binds basic amino acid groups of protein molecules in the urine sample to form a

Fombon, I.S. (2020) purple-blue complex with maximum absorbance at 600 nm. Urine sample (2 µL) was added to 190 µL of Urine/CSF Protein reagent and incubated at 37C. Protein in the urine sample bound to pyrogallol red-molybdate dye and the change in absorbance due to the formation of the purple-blue complex was directly proportional to the concentration of protein in the sample and was measured at 600 nm. The lowest detectable level of UPRO was estimated at 0.007 g/L, representing the lowest measurable level of total protein that can be distinguished from zero. The test was linear within a concentration range of 0.01 – 2.00 g/L and the reference range of UPRO was 0 – 0.1 g/L.

2.2.20 Urine albumin concentration by the turbidimetric method Urine albumin or microalbumin (MALB) concentration was measured by turbidimetric method using the Beckman Coulter Urine/CSF Albumin reagent. Following the manufacturer’s information for use the analyser was set to mix 4 µL of urine sample with 138 µL of Urine/CSF Albumin Reagent 1 and 16 µL of Reagent 2. The mixture was incubated at 37C. Anti-human serum albumin antibodies in the reagent combined with albumin from the sample to form immune complexes that scattered light in proportion to their size, shape and concentration. The absorbance of these aggregates was proportional to the concentration of albumin in the urine sample. Change in absorbance was measured at 380 nm with subtraction of a reference wavelength at 800 nm. The assay was calibrated using Beckman coulter Urine/CSF Albumin Calibration kit (B358859) and quality control was performed using Liquichek Microalbumin Control Levels 1 and 2 (Ref. No. 378/379) (Bio-Rad Laboratories, Hertfordshire, UK). The analytical range was 7 – 450 mg/L.

2.2.21 Determination of other urine parameters The following urine parameters were calculated using standard formulas.

 Urine albumin creatinine ratio (UACR):

Fombon, I.S. (2020)

UACR (mg/mmol) = MALB (mg/L) / UCRE (mmol/L), where MALB is urine albumin concentration and UCRE is urine creatinine concentration.

 Urine protein creatinine ratio (UPCR): UPCR (mg/mmol) = UPRO (mg/L) / UCRE (mmol/L), where UPRO is urine total protein concentration and UCRE is urine creatinine concentration.  Urine albumin protein ratio (UAPR): UAPR = UACR (mg/mmol) / UPCR (mg/mmol)  Fractional excretion of sodium (FENA): FENA (%) = [UNA (mmol/L)  CREA (mmol/L)] / [NA (mmol/L)  UCRE (mmol/L)]  100, where UNA is urine sodium concentration, CREA is serum creatinine concentration, NA is serum sodium concentration and UCRE is urine creatinine concentration.  Fractional excretion of Urea (FEUR): FEUR (%) = [UURE (mmol/L)  CREA (mmol/L)] / [UREA (mmol/L)  UCRE (mmol/L)]  100, where UURE is urine Urea concentration, CREA is serum creatinine concentration, UREA is serum Urea concentration and UCRE is urine creatinine concentration.  Fractional excretion of uric acid (FEUA): FEUA (%) = [UUA (mmol/L)  CREA (mmol/L)] / [UA (mmol/L)  UCRE (mmol/L)]  100, where UUA is urine uric acid concentration, CREA is serum creatinine concentration, UA is serum uric acid concentration and UCRE is urine creatinine concentration.

2.2.22 Immunoassay parameters (Thyroid function tests)

The immunoassays were performed on a Beckman Coulter Access 2 Immunoassay analyser system using reagent kits supplied by Beckman Coulter (High, Wycombe, UK). The serum samples were assayed for thyroid function markers including, thyroid stimulating hormone (TSH), free T3 (FT3) and free T4 (FT4), using enzyme-mediated chemiluminescence methods. The automated analysis employed various steps to determine the concentration of the analytes. Sample and reagent components were dispensed into a reaction vessel and

Fombon, I.S. (2020) mixed. The reaction vessel was incubated for the required time for each assay and then washed three times in a magnetic field to remove unbound material. A substrate (Lumi-Phos⃰ 530) was added and the reaction vessel was incubated to allow the bound analyte complexes to react with the substrate. The substrate reacted with alkaline phosphatase bound to the paramagnetic particles to produce light. The light generated was measured by a luminometer in Relative Light Units (RLU). The RLU reading was converted to analyte concentration using a pre- determined calibration curve. All the samples were processed after a successful calibration of the assay and acceptable quality control performance. Quality control was performed using Technopath Multichem IA Tri-level controls. Test results were automatically calculated by the Beckman Coulter Access 2 analyser system.

2.2.22.1 Measurement of serum TSH concentration

Serum TSH was determined using the Beckman Coulter Access TSH (3rd IS) assay in an automated two-site immunoenzymatic (“sandwich”) assay (Figure 2.1). Serum sample (50 µL) was added to a reaction vessel containing 50 µL of mouse anti-human TSH-alkaline phosphatase conjugate. Buffered protein solution (50 µL) and 25 µL paramagnetic particles coated with immobilised mouse monoclonal anti-hTSH antibody was added and the mixture was incubated at 37C for 10 minutes. The human TSH (hTSH) in the sample bound to the immobilised monoclonal anti-hTSH antibody on the solid phase while the mouse anti-hTSH- alkaline phosphatase conjugate reacted with a different antigenic site on the hTSH. After incubation, the reaction vessel was washed three times with Wash Buffer II. Materials bound to the solid phase were held in a magnetic field while unbound materials were washed away in the Wash Buffer II. Chemiluminescent substrate Lumi-Phos⃰ 530 was added to the reaction vessel and light generated by the reaction was measured with a luminometer and the light produced was directly proportional to the concentration of hTSH in the serum sample. The amount of analyte in the sample was determined from a stored, multi-point calibration curve. The assay was calibrated using the Beckman Coulter Access TSH (3rd IS) calibrators. Reference range was 0.34 – 5.6 mIU/L.

Fombon, I.S. (2020)

Paramagnetic Monoclonal Sample particles coated with anti-hTSH alkaline containing mouse monoclonal phosphatase TSH anti-hTSH antibody conjugate

Incubate at Sandwich formation Wash ≈36.5C for 3 times ≈10 minutes

Add Incubate Read substrate 5 minutes light

The signal produced is directly proportional to the concentration of TSH in the sample.

Figure 2.1 Schematic representation of the TSH assay using two-site (sandwich) immunoenzymatic assay by chemiluminescence. TSH in the sample bound to monoclonal anti-TSH antibody on the solid phase, while the mouse anti-hTSH-alkaline phosphatase conjugate reacted with a different antigenic site on the hTSH. After incubation at 37C for 10 minutes, unbound materials were washed away and materials bound to the solid phase were held in a magnetic field. Chemiluminescent substrate Lumi-Phos⃰ 530 was added and light generated by the reaction was measured by a luminometer (Source: Beckman Coulter, UK).

Fombon, I.S. (2020)

2.2.22.2 Measurement of serum FT4 (thyroxine) concentration The serum FT4 was measured using the Beckman Coulter Access Free T4 assay kit in a two-step enzyme immunoassay technique. Serum sample (30 µL) was added to a reaction vessel containing 50 µL monoclonal anti-Thyroxine (T4) antibody coupled to biotin, buffered protein solution and streptavidin-coated solid phase and the mixture was incubated at 36.5 C for 7 minutes. During this first incubation phase, the anti-T4 antibody coupled to biotin bound to the solid phase and the free T4 in the sample. After incubation, the reaction vessel was washed three times in Wash Buffer II. Materials bound to the solid phase were held in a magnetic field while unbound materials were washed away.

Next, buffered protein solution and triiodothyronine (T3)-alkaline phosphate conjugate were added to the reaction vessel and further incubated at 36.5 C for 5 minutes. The T3-alkaline phosphatase conjugate bound to the vacant anti-T4 antibody binding sites. After incubation, the reaction vessel was again washed three times with Wash Buffer II and materials bound to the solid phase were held in a magnetic field while unbound materials were washed away. Chemiluminescent substrate Lumi-Phos⃰ 530 was added to the vessel and light generated by the reaction was measured with a luminometer. The light production was inversely proportional to the concentration of free T4 in the sample. The concentration of FT4 in the serum sample was determined from a multi-point calibration curve. The free T4 assay was calibrated using the Beckman Coulter Access Free T4 calibrators (Category Number 33885).

Test results were determined automatically by the Beckman Coulter Access 2 analyser system software. The assay range was 3.2 – 77.2 pmol/L (this is defined as the lowest detection limit and the maximum level of the master calibration curve). The reference range for serum FT4 was 7.9 – 20 pmol/L.

2.2.22.3 Measurement of serum FT3 concentration The serum level of FT3 was measured using the Beckman Coulter Access Free T3 assay – a paramagnetic particle, chemiluminescent immunoassay for the quantitative determination of free triiodothyronine levels in human serum and

Fombon, I.S. (2020) plasma. The Access Free T3 assay is a competitive binding immunoenzymatic assay. Serum sample (50 µL) was added to a reaction vessel containing 25 µL of anti-T3 monoclonal antibody conjugated to alkaline phosphatase and incubated at 36.5C for 29 minutes. FT3 in the sample reacted with the anti-T3 antibody. Next, 50 µL of particles coated with streptavidin and biotinylated T3 analog were added to the mixture. Unoccupied binding sites on the anti-T3 antibody were bridged to the particle through the T3 analog. After incubation in the reaction vessel was washed three times with Access Wash Buffer II. Materials bound to the solid phase were held in the magnetic field while unbound materials were washed away. The chemiluminescent substrate Lumi-Phos⃰ 530 was added to the vessel and light generated by the reaction was measured with a luminometer. The light production was inversely proportional to the concentration of FT3 in the serum sample. The concentration of FT3 in the sample was determined from the measured light production by means of the stored calibration data. The assay was calibrated using the Beckman Coulter Access Free T3 calibrators (Cat No. A13430). Test results were determined automatically by the Access 2 analyser software system. The assay analytical range was 1.4 – 46 pmol/L, while the reference range for FT3 was 4.0 – 6.6 pmol/L.

2.2.23 Determination of serum CYSC by PETIA method Serum CYSC was determined by particle-enhanced turbidimetric immunoassay (PETIA) method, using the Gentian Cystatin C (Gentian AS) kit. This method is traceable to the certified cystatin C reference material (ERM®-DA471/IFCC). The assay was deployed on the Beckman Coulter AU640 analyser according to the manufacturers’ instructions. Serum sample (2 µL) was mixed with 150 µL of Reagent 1 (buffer) and the mixture incubated for five minutes. The diluted sample was reacted with a suspension of 30 µL of Reagent 2 containing latex enhanced particles coated with anti-CYSC antibodies (rabbit). The immunoparticles agglutinated with the human CYSC in the sample to form antigen-antibody complex (Figure 2.2). The degree of turbidity caused by the aggregate was determined by turbidimetry at 540 nm and was proportional to the amount of CYSC in the sample.

Fombon, I.S. (2020)

Serum CYSC level was obtained via interpolation on an established standard calibration curve. The assay was calibrated using Gentian Cystatin C calibrator kit (Ref #: A52763) which is standardised against the international calibrator standard ERM-DA471/IFCC and quality control was performed using Gentian Cystatin C Quality Control kit (Ref #: A52765), which is a two level (low and high) control. The assay was calibrated and quality controls run before the project samples were tested. The test results were automatically calculated in mg/L by the AU640 analyser. The reference range was 0.53 – 1.01 mg/L.

Figure 2.2 Gentian CYSC assay by particle enhanced turbidimetric immunoassay (PETIA) method. CYSC agglutinates with latex enhanced particles coated with anti-cystatin C antibodies to form antigen-antibody complex. The change in turbidity was measured at 540 nm and is directly proportional to the concentration of cystatin C (Source: Cobas, Roche Diagnostics Ltd, Rotkruez, Switzerland).

2.2.24 Analytical performance of the Gentian CYSC method

2.2.24.1 Gentian CYSC assay sensitivity The assay limit of blank (LoB), limit of detection (LoD) and limit of quantitation were determined using standard methods by the Clinical and Laboratory Standards Institute (CLSI) as published in the guideline EP17, Protocols for Determination of Limits of Detection and Limits of Quantitation (CLSI, 2004). To determine the LoB, aliquots of the assay diluent (blank sample) were tested to

Fombon, I.S. (2020) determine the lowest concentration that can be measured and then calculated the mean and corresponding standard deviation (SD) for 20 samples. Assuming a Gaussian distribution of the raw analytical signals from blank samples, the LoB represents 95% of the observed values. The LoB, LoD and LoQ were then evaluated based on the standard deviation of the blank using the following equations (Little, 2015):

LoB = Mean blank + 1.645 SD blank (one sided 95%)

LoD = Mean blank + 3.3  SD blank (one sided 95%  2)

LoQ = Mean blank + 10  SD blank (One sided 95%  6)

2.2.24.2 Gentian CYSC assay precision Assay precision was assessed using the Gentian Cystatin C quality control materials (Low and High) and pooled waste patient samples not more than 5 days old obtained from the SPS Blood Sciences Laboratory, Lisieuw Way, Taunton. The samples were collected into 3 sterile universal containers based on the serum CREA concentrations (pre-determined) as follows:

Tube A: Serum creatinine concentration < 50 µmol/L (low level of CYSC)

Tube B: Serum creatinine concentration 100 – 200 µmol/L (medium level CYSC)

Tube C: Serum creatinine concentration > 500 µmol/L (high level CYSC)

For intra-assay precision, the pooled serum from each tube was aliquoted into 10 test tubes and analysed for CYSC. The quality control material (Low and High Levels) was also aliquoted into 10 tubes and run as a batch. The inter-assay imprecision was determined by measuring the three levels of pooled serum samples (Low, medium and high) twice a day, over a period of 5 days, and the two levels of quality control material once a day over 10 days. The mean and standard deviation (SD) were calculated and the percentage coefficient of variation [CV (%)] was determined by dividing the standard deviation (SD) of each level by the mean and then multiplied by 100.

Fombon, I.S. (2020)

2.2.24.3 Gentian CYSC assay Linearity and recovery The method accuracy was assessed by evaluation of the linearity after dilution. A high-concentration serum pool (7.90 mg/L) was prepared from waste patient samples. The pool was then diluted serially in isotonic saline solution (0.9%, w/v) to give the following calculated (expected) concentrations of the high pool: 7.90, 3.95, 1.98, 0.99, 0.49, and 0.25 mg/L. Each sample was measured in triplicate and the mean of the measured (detected) CYSC concentrations were plotted against the calculated (expected) values. Linear regression analysis was performed to calculate the corresponding correlation coefficient. The percentage recovery was calculated from the results of the diluted samples using the formula:

Concentration Detected (mg/L) Recovery (%) =  100

Concentration Expected (mg/L)

2.2.24.4 Stability of CYSC in frozen serum samples The stability of CYSC in serum samples was assessed to determine whether it was affected by the storage condition. The concentration of CYSC (baseline) in some 40 waste patient samples was determined. The samples were less than 5 days collected the from storage (4ºC) at the Sothwest Pathology Services Laboratory, tested and then stored frozen at -80ºC for 30 months. The concentration of serum CYSC was again determined after storage and the results obtained were compared with the baseline values.

2.2.24.5 Comparison of CYSC assay and the Jaffe CREA method The relation between serum CYSC and serum CREA was examined in some 20 patient serum samples across a wide range. Serum CYSC concentrations ranged from 0.7 – 6.93 mg/L and serum CREA concentrations ranged from 47 – 1101 µmol/L. The linear regression analysis between the two methods was performed to establish the correlation coefficient.

Fombon, I.S. (2020)

2.2.25 Estimated glomerular filtration rate (eGFR) The GFR was estimated using five equations. First, eGFR from serum CREA concentrations was calculated according to the IDMS-traceable MDRD study equation (Levey et al., 2006) and by the CKD-EPI equation (Levey et al., 2009). Both equations take account of sex, age and ethnicity (black versus non-black). However, the ethnicity was not relevant in this study as the participants only included white British. Next, the eGFR was calculated using serum CYSC-based equations, namely the Gentian and CKD-EPI CYSC equations (Inker et al., 2012). Finally, the eGFR was calculated using the combined serum CREA and serum CYSC equation as proposed by Inker et al., (2012).

2.2.26 Mammalian cell culture and cell line maintenance All the cell lines were maintained in 75 cm2 tissue culture flasks in Dulbecco’s Modified Eagle medium (DMEM) supplemented with 10% (v/v) FBS and grown at

37°C in a humidified atmosphere of 5% CO2 and 95% air, as recommended by the manufacturers. When the cells were not being used for experiments, they were collected by trypsinisation, suspended in freezing medium [10% (v/v) DMSO in foetal bovine serum (FBS)] and stored overnight at -80ºC. The next day, the frozen aliquots where transferred to liquid nitrogen (-180ºC) for long term storage. The cells were stored in aliquots (1 mL) in cryovials.

When required for the experiments, the cells were recovered from liquid nitrogen storage by rapidly defrosting in a water bath at 37C and the content of the cryovial (1 mL cell suspension) was transferred into a 75 cm2 tissue culture flask containing 9 mL of fresh culture medium. The cells were incubated overnight at

37°C in a humidified environment of 5% CO2 and 95% air, and allowed to attach. Following attachment, medium was withdrawn from the flask and the cells washed briefly with phosphate buffered saline (PBS) to get rid of any unattached or dead cells. Fresh culture medium was added into the culture flask and the cells were incubated at 37ºC. The growth medium was changed every three days and cells were grown until 80% confluent, before subculture.

Fombon, I.S. (2020)

For subculture, spent culture medium was removed from the tissue flask and the flask was rinsed with 5 mL of sterile PBS. Next, 3 mL of pre-warmed (37C) trypsin-EDTA solution (0.25% trypsin, 0.1 mM EDTA) was added into the flask and spread to cover the adherent cells. The flask was incubated for about 5 minutes at 37C and then checked under the microscope for complete detachment. Periodically, the flask was gently tapped from side to side to aid detachment of cells from the flask. When the cells were completely detached 3 mL of fresh serum-containing culture medium was added to neutralise the effect of trypsin, and the cells were collected into a 15 mL test tube and spun at 1000 revolutions per minute for 5 minutes. The supernatant was discarded and the pellet was re- suspended in 3-5 mL fresh medium (depending on the size of the pellet) and a cell count was performed. A 50 µL aliquot of the suspension was transferred into a 500 µL Eppendorf tube and mixed with 50 µL of 0.4% trypan blue dye solution and the cell density was counted using a glass haemocytometer (Improved Neubauer Counting Chamber) and a light microscope. Cells were seeded in the 96-well plates at a density of 5.0  103 cells/well for the viability assay and 2.5  104 cells/well for ROS activity assay. In the 25 cm2 flasks the cells were seeded with 2.0  105 cells, and in 75 cm2 flasks with 2.0  106 cells.

2.2.27 Examination of changes in cell morphology

The NRK52E, L6 and HEK293 Cells were seeded (2.5  106 cells) in 75 cm2 flasks and incubated overnight to attach. The medium was withdrawn from the flasks and replaced with fresh medium containing various concentrations of FF [0 (untreated), 250, 500, and 1000 µM] and further incubated for 24 h at 37ºC. Change in cell morphology was examined by phase contrast microscopy. Images of the FF- treated cells were taken using a Nikon Eclipse TE300 inverted microscope with phase-contrast objective (magnification 200) and the Nikon’s NIS-Elements Microscope Imaging software (Kingston, UK).

Fombon, I.S. (2020)

2.2.28 Cell viability assay Cell viability was determined using the CellTiter96 AQueous One Solution Cell Proliferation Assay kit; (MTS Promega, Madison, WI). The MTS assay is a colorimetric method for determining the number of viable cells in proliferation, based on the reduction of the MTS tetrazolium compound by dehydrogenase activities of viable cells to generate a coloured formazan product that is soluble in cell culture media. The amount of formazan produced is directly proportional to the number of living cells. NRK52E, L6 and HEK293 cells (5  103 cells/well) in 200 µL of complete medium were seeded in 96-well flat-bottomed culture plates and incubated overnight at 37 °C to attach. The next day, medium was withdrawn from the wells and replaced with fresh complete medium (100 µL/well) containing the various concentrations of FF (0, 7.8, 15.6, 31.5, 62.5, 125, 250, 500, and 1000 µM) and 5-FU (0, 7.8, 15.6, 31.5, 62.5, 125, 250 and 500 µM). The cells were exposed to the drugs for 72 h at 37°C, and 20 µL of CellTiter 96 Aqueous One Solution was added to each of the wells containing medium and the culture plates incubated for one 1 hour in the dark. Absorbance was measured at 490 nm using a FLUOstar OPTIMA microplate reader (Aylesbury, UK). All experiments were repeated for at least 3 times with triplicate samples in each experiment.

The data was transferred to Microsoft Excel spreadsheet for analysis. The effects on viability of the cells following exposure to FF and 5-FU were analysed and concentration-effect curves were generated as a plot of the fraction of viable cells versus drug concentration. Viability was expressed as a percentage of the untreated controls that were processed simultaneously. The IC50 was defined as the concentration that inhibited cell viability by 50% (50% reduction of absorbance) compared with untreated controls. The IC50 of the individual drugs (FF and 5-FU) in the different cell lines were established. The effect of FF on cell viability was compared with 5-FU, which is known to be toxic to kidney and muscle cells.

2.2.29 Detection of intracellular reactive oxygen species (ROS) activity ROS activity in cells exposed to FF was determined using DCFDA Cellular ROS Detection Assay Kit (Abcam®). The assay uses the cell permeant reagent 2', 7'- dichlorofluorescin diacetate (DCFDA), a fluorogenic dye that measures hydroxyl, 79

Fombon, I.S. (2020) peroxyl and other ROS activity within the cell. After diffusion into the cell, DCFDA is deacetylated by cellular esterases to a non-fluorescent compound, which is later oxidised by ROS into 2', 7'-dichlorofluorescin (DCF). DCF is a highly fluorescent compound which can be detected by fluorescence spectroscopy with maximum excitation and emission spectra of 485 nm and 535 nm respectively.

NRK52E, L6 and HEK293 cells (2.5  104 cells/well)/100 µL were seeded in a dark, clear-bottom 96-well plate overnight to attach. Medium was withdrawn from the wells and replaced with fresh phenol red-free medium (100 µL/well) containing various concentrations of FF (0, 250, 500 and 1000 µmol/L) and 30 µmol/L H2O2 (positive control) was added into the wells and incubated for 6 h at 37C. After drug exposure, 30 µM of DCFDA solution in a total volume of 200 µL phenol red- free medium was added into each well and the plates were further incubated at 37ºC for 45 minutes, wrapped in aluminium foil to protect from light. The fluorescence intensity was measured at excitation/emission wavelengths of 485/535 nm using a Fluostar OPTIMA microplate reader (Aylesbury, UK). The results were expressed as percentage change in fluorescence intensity compared with control (untreated) cells.

2.2.30 Detection of cell apoptosis by flow cytometry

NRK52E, L6 and HEK293 cells (2.0  105 cells) were seeded in 25 cm2 flasks overnight to attach. The next day, medium was withdrawn from the flasks and replaced with fresh medium containing various concentrations of FF (250, 500, and 1000 µM). Untreated cells and cells treated with H2O2 (25 µM) were used as controls in the experiment. The cells were exposed to the treatments for 4 hours. After drug exposure, the cells were collected by trypsinisation, washed twice in PBS and stained using Annexin V-FITC Apoptosis Detection kit (BioVision, Mountain View, CA, USA), according to the manufacturers’ instructions. The cells were stained with Annexin V-FITC and propidium iodide at 1 µg/mL for 5 minutes, protected from light. Apoptosis was detected by flow cytometry on the BD AccuriTM C6 flow cytometer (Accuri Cytometers Ltd., St, Ives, UK). A minimum of 10,000 cells were collected for each sample, and the data were analysed using the BD AccuriTM C6 software. The flow cytometry software was used to directly calculate

Fombon, I.S. (2020) the percentage of apoptotic cells. The average percentage of apoptotic cells was calculated from at least two biological replicates.

2.2.31 Extraction of cell lysate (protein extraction) NRK52E, L6 and HEK293 cells exposed to various concentrations of FF (0, 250, 500 and 1000 µmol/L) for 72 hours were collected and whole protein extracted for Western blot analysis. Culture medium was withdrawn from the culture flasks and the cells were washed with PBS. The cells were collected by trypsinisation and the cell pellets were collected in Eppendorf tubes. The cell pellets were re-suspended in protein extraction (RIPA) buffer consisting of 50 mM Tris-HCl pH 8.0, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1 % Triton X-100, 1 complete protease inhibitor, and 0.1% (w/v) sodium dodecyl sulphate (SDS). The cell suspension were incubated for 1 hour on ice-water bath and then sonicated for about 1 minute with a 10 second pause for every 10 seconds of sonication. The lysed cells were then centrifuged at 15000rpm for 10 minutes at 4C and the supernatant (whole protein extract) was collected into fresh Eppendorf tubes and protein concentration was determined using Amido Black assay or Bicinchoninic Acid assay (Noble and Bailey, 2009). The lysates were stored in the freezer at -80C until analysed by western blotting.

2.2.32 Measurement of protein concentration We employed two methods to measure the concentration of protein in cell lysates. The choice of the method depended amongst other things on convenience and availability of test materials. The methods used in this study included Amido Black assay and Bicinchoninic acid (BCA) assay.

2.2.32.1 Amido Black assay method Extracted protein sample (2.5 µL) was diluted in 267.5 µL of ultrapure water. The standard protein (1 mg/mL BSA) was also diluted (1 in 10) in ultrapure water and the working protein standard solutions were then prepared as shown in Table 2.6 below. Tris-HCL buffer (30 µL) and 60 µL of 60% trichloroacetic acid (TCA) were

Fombon, I.S. (2020) added to each sample including the standards and the preparations were mixed and incubated at room temperature for 5 minutes. A Buchner flask was assembled to create a vacuum and nitrocellulose membrane was added to the filter and washed 3 times with 6% TCA. The standards and samples were spotted on the nitrocellulose membrane and the membrane was washed with 6% TCA and warm water and allowed to dry. The membrane was stained in Amido Black stain for 2 minutes, and then destained in several transfers of destains until the background was clear.

Table 2.6 Table 2.6 Amido Black Standard Solutions

BSA (µg/µL) BSA (µL) Ultrapure Water (µL)

0 0 270

1 10 260

2.5 25 245

5 50 220

7.5 75 195

10 100 170

Abbreviation: BSA, bovine serum albumin

Each spot was carefully cut out and dissolved in 500 µL of eluent solution and the stain was allowed to dissolve completely. 200 µL of each sample was added to a 96-well plate and the absorbance was read at 520 nm. A BSA standard curve was plotted and the concentration of each sample was obtained by interpolation on the standard curve.

2.2.32.2 Bicinchoninic Acid (BCA) assay method The BCA working reagent was prepared by mixing 50 parts of BCA solution A with one part of BCA reagent B (50:1, Reagent A: B). Standard protein (BSA) and protein samples (25 µL) were added into each well of a 96-well plate and 200 µL

Fombon, I.S. (2020) of working reagent was added into each well. Each standard and protein sample was run in triplicate. The 96-well plate was incubated at 37C for 30 minutes and protected from light. The absorbance was read at 562 nm on a microplate reader. The protein standard curve was plotted and used to calculate the concentration of the protein samples.

2.2.33 SDS-PAGE Electrophoresis Pre-prepared 4-12% polyacrylamide gel (30% acrylamide, 1.5 M Tris HCl pH 8.8, 0.5 MTris-HCl pH 6.8, 10% SDS, 10% APS, TEMED) was assembled into an electrophoresis tank using the cassette adapter and the tank was filled with 1  running buffer (25 mM Tris-HCl pH 6.8, 192 mM glycine, 0.1% SDS). The protein samples were prepared by mixing 6.25 µL of loading buffer, 1 µL of β- mercaptoethanol, 10 µg of protein extract and distilled water to make up a final volume of 25 µL. Each sample was incubated at 95 C for 10 minutes on a heating block and 20 µL was loaded into the gel. Protein ladder (Spectra ladder) was loaded into a separate well. The gel was electrophoresed at 100V for 20 minutes and then 160V for 80 minutes for the protein markers to clearly separate.

2.2.34 Protein transfer (wet) and western blot analysis

Whatman® gel blotting papers were soaked in 1 transfer buffer (25mM Tris-HCl pH8.3, 192 mM glycine, 20% methanol), while the PVDF membrane was first activated by incubation in absolute methanol for 5 minutes and then soaked in the transfer buffer (1). A transfer unit was setup by placing the SDS-PAGE gel on top of the PVDF membrane, and sandwiched by two layers of the blotting papers and sponges soaked in transfer buffer. Protein was transferred at 60V and 500 mA overnight at 4ºC.

When protein was transferred completely from the gel onto the PVDF membrane, the membrane was removed and washed 3 times in 10 mL of 1 TBS-T (10 minutes per wash cycle) with gentle agitation. The membrane was blocked using 1 TBS-T working solution supplemented with 5 % fat-free milk (Marvel) for at

Fombon, I.S. (2020) least one hour at room temperature with gentle agitation, to prevent non-specific binding of primary and secondary antibodies to the protein-free areas of the PVDF membrane. The blocking buffer was withdrawn and primary antibody diluted in fresh blocking buffer (Table 2.6) was added into the staining tray containing the membrane. The membrane was incubated in primary antibody solution overnight at 4ºC with gentle agitation. After an overnight incubation, the primary antibody solution was withdrawn and the membrane was washed 3 times (10 minutes per wash cycle) in 10 mL of 1 TBS-T before secondary antibody diluted in blocking buffer (Table 2.7) was added and the membrane was incubated for 1 hour at room temperature, with gentle agitation. The PVDF membrane was washed three times (10 minutes per wash cycle) in 10 mL of 1 TBS-T and protein bands detected and evaluated. Visualisation and analysis of the protein bands was performed using the Li-cor Odyssey imaging systems and Image Studio Lite version 5.2 (Li-cor Biosciences).

Table 2.7 Antibody dilutions used in this study

Primary Primary Secondary Antibody Secondary Antibody Antibody Antibody Dilution Dilution

SOD1 1:500 Goat anti-rabbit IgG-HRP linked 1:500

JNK 1:5000 Goat anti-rabbit IgG-HRP linked 1:1000

p38 1:1000 Horse anti-mouse IgG-HRP linked 1:5000

Bax 1:2000 Goat anti-rabbit IgG-HRP linked 1:5000

Bcl-2 1:500 Horse anti-mouse IgG-HRP linked 1:2000

p65 1:1000 Goat anti-rabbit IgG-HRP linked 1:4000

β-actin 1:10000 Horse anti-mouse IgG-HRP linked 1:5000

Abbreviations: JNK, c-Jun N-terminal kinases; SOD, superoxide dismutase

Fombon, I.S. (2020)

2.2.35 Statistical analysis The Graphpad Prism software version 7.0 was used for all statistical analysis. All participant test results were automatically calculated by the Beckman coulter AU analyser system and the Labcentre Laboratory Information Management System (LIMS). The data were first collected and entered into Microsoft Excel spreadsheet before being exported to Graphpad Prism software for analysis. All participant data were anonymously analysed. The data were presented as median and interquartile range (IQR) and significant differences were examined using the Wilcoxon signed rank tests for paired data and Mann-Whitney U test for unpaired data. Correlations between variables were determined using Spearman’s correlation coefficient. For changes in clinical biomarkers at different time points during 6 and 12 months of FF therapy in Group D and E participants, respectively, the Friedman test (a non-parametric alternative to repeated measures analysis of variance) with Dunn’s multiple comparison tests was used to analyse the data.

Two-way ANOVA with Tukey’s multiple comparisons test was used to compare the impact of the different concentrations of FF and 5-FU on cell viability and to compare the IC50 of FF and 5-FU in the different cell lines, while the two-way ANOVA with Holm-Sidak’s multiple comparisons was used to compare the cytotoxicity of FF versus 5-FU in the different cell lines. Ordinary one-way ANOVA with Dunnett’s multiple comparisons test was used to compare the impact of the various concentrations of FF and 5-FF on cell viability versus control (untreated) cells, the impact of various concentrations of FF on intracellular ROS accumulation and the impact of FF on proteins involved in the various signalling pathways. Densitometry data was analysed using data obtained from the Li-Cor Odyssey Imaging Systems and the Image Studio Lite Version 5.2. The data were estimated as fold increase relative to loading control (ꞵ-actin).

CKD was defined as eGFR < 60 mL/min/1.73 m2. The prevalence of CKD by stages calculated using the various eGFR equations was compared using the Chi- squared test. Bias between eGFR equations was determined by the Bland-Altman analysis and presented as the absolute mean of the differences and SD around the mean. In all statistical data, p<0.05 was considered to be an indicator of statistical significance.

Fombon, I.S. (2020) Chapter 3

Effect of FF therapy on routine clinical biomarkers in patients with dyslipidaemia

3.1 INTRODUCTION FF-associated creatininaemia (or inferred nephropathy) is a major side effect of FF (Attridge et al., 2013; Kim et al., 2017). Recent studies have demonstrated an increase in serum CREA concentration in patients receiving FF therapy. In a retrospective study involving patients without established CKD treated with fibrates (including FF), Tsimihodimos et al., (2001) showed that FF was associated with a significantly raised serum CREA and serum urea levels by 12% and 8% respectively. A recent report by Ncube et al., (2012) showed a significant increase in serum CREA (15.1%) and serum CYSC (9.9%) after patients with hyperlipidaemia were treated with FF at 267 mg/day for 3 months. The mechanism of the reversible increase in serum CREA is not fully understood, but kidney function monitoring has been recommended in patients receiving FF therapy.

The effect of FF on liver function is not fully understood and reports from various studies have been contradictory. In a study with male F344/DuCrlCrlj (Fischer) rats administered a single dose of 400 mg/kg of FF, Kobayashi et al., (2009) reported slight increase in plasma transaminase activities directly related to the pharmacological action of the drug. They suggested that the increases were in parallel with increases in hepatic transaminase activities associated with increases in the transaminase genes in the liver and were not considered to be a consequence of hepatotoxicity from the drug. However, Fernandez-Miranda et al., (2008) reported improvement in liver function and metabolic syndrome; with minimal changes in liver histology after 16 patients with non-alcoholic fatty liver disease were administered 200 mg/day of FF for 48 weeks. It is recommended that regular monitoring of liver function be carried out during fibrate therapy and the drug discontinued if levels persisted above three times the upper limit of the normal reference range (Gandhi et al., 2014).

Fombon, I.S. (2020)

Reports show that inflammatory processes play a vital role in the pathogenesis of CVD, acute atherothrombotic events and atherosclerosis and occurrence of acute coronary syndromes (Belfort et al., 2010). High levels of CRP, which is a prototypic inflammatory marker, are associated with an increased risk for CVD (Belfort et al., 2010; Ye et al., 2011). Although the exact mechanism is unknown, many studies indicate that reducing CRP concentration leads to significant benefits with regards to future cardiovascular events (Ye et al., 2011). Studies have shown that FF individually or in combination with other drugs has an impact on serum CRP concentrations (Wi et al., 2010). However, results from FF trials have been inconsistent with some studies indicating a reduction (Muhlestein et al., 2006; Kim, 2006; Wi et al., 2010), while others indicate no change or an increase in CRP (Fegan et al., 2005; Hogue et al., 2008). Ye et al., (2011) reported a significant decrease in CRP concentration in a meta-analysis that examined short- term effects of FF.

A similar finding was reported by Min et al., (2012) who showed a significant reduction in serum CRP concentration after FF therapy in hypertriglyceridaemic patients with high risks for CVD. However, Kim (2006) reported that FF administered at 200 mg/day, failed to reduce serum CRP levels in patients with hyperlipidaemia. Some explanations that have been made to explain this inconsistency in outcome include underpowered studies (with less than 50 patients per study), differences in dose and formulation of FF, variable lengths of follow up and the use of diverse participation pools (Ye et al., 2011). Therefore, the impact of FF therapy on serum concentration of CRP remains unclear.

Several case studies have also linked FF therapy with rhabdomyolysis, a clinical and biochemical syndrome that results from skeletal muscle injury and subsequent release of muscle fibre cellular components, including myoglobin, creatine kinase, LDH, aldolase and AST, into the blood stream (Wu et al., 2009; Kato et al., 2011; Erdur et al., 2012; Wang and Wang, 2018). Risk factors of FF-associated rhabdomyolysis are not fully understood (Wu et al., 2009). Although only a few cases of rhabdomyolysis associated with FF alone or in combination with statins have been reported in patients with dyslipidaemia, patient monitoring is strongly recommended because rhabdomyolysis may be very severe with fatal consequences (Kato et al., 2011).

Fombon, I.S. (2020)

Reports on the effect of FF therapy on thyroid function are rare. Studies conducted to date have concentrated almost exclusively on cases of fibrate-induced rhabdomyolysis in patients with thyroid hypofunction (Satarasinghe et al., 2007; Krysiak et al., 2014). Dyslipidaemia is reported as a common metabolic abnormality in patients with hypothyroidism, either in its overt or subclinical form, and it appears that co-existing hypothyroidism predisposes individuals to the potentiation of muscle damage (Satarasinghe et al., 2007). The use of FF therapy for the treatment of dyslipidaemia in hypothyroidism has been linked with muscle damage and rhabdomyolysis, but it is not known whether FF treatment would cause hypothyroidism (Wang and Wang, 2018). Some case studies have recommended that all patients receiving FF and other hypolipidaemic drugs be screened for occult hypothyroidism which seems to aggravate the muscle damage due to the drugs, with or without other risk factors (Satarasinghe et al., 2007).

The impact of FF therapy on routine clinical biomarkers in patients with dyslipidaemia has not been fully investigated. In this study, we examined the impact of FF therapy during a 12-month period on markers of kidney function, liver function, thyroid function, inflammatory marker (CRP) and markers of muscle damage (CK and LDH).

Fombon, I.S. (2020)

3.2 SPECIFIC AIMS The main aim of this chapter was to investigate the impact of FF administered at 160 mg/day on routine clinical biomarkers in patients with dyslipidaemia. The specific aims were to examine the impact of FF on the following biomarkers:

1. Markers of kidney function [CREA, urea, UA and electrolytes (Na and K)].

2. Markers of liver function (ALB, TBIL, ALT, ALP, GGT and AST).

3. Lipid profile (CHOL, TRIG, HDL cholesterol, NHDL and CHRA)

4. Inflammatory marker (CRP) and markers of muscle damage (CK and LDH).

5. Thyroid function tests (TSH, FT4 and FT3).

Fombon, I.S. (2020)

3.3 RESULTS

3.3.1 Impact of FF therapy on markers of kidney function In order to investigate the impact of FF on kidney function, changes in serum and urine markers of kidney function in the various groups of participants were examined. The concentrations of the biochemical parameters at various stages of treatment (e.g. 3, 6 and 12 months after commencing treatment) were compared with the baseline results. The criteria for defining the various conditions such as FF-associated creatininaemia (FAC), chronic renal failure (CRF), proteinuria, tubular pathology, glomerular proteinuria, prerenal disease and intrinsic renal disease or acute tubular necrosis are shown in Table 3.1.

Table 3.1 Criteria for defining the various conditions of kidney function

Description Criteria

FAC ≥ 20% increase in serum creatinine from baseline

CRF eGFR < 60mL/min/1.73 m2

Proteinuria UACR > 3 mg/mol PCR > 15 mg/mol Tubular pathology UAPR < 0.4 together with proteinuria

Glomerular proteinuria UAPR > 0.4

FENA < 1% Prerenal disease FEUR < 35% FEUA < 10%

FENA > 2% Intrinsic renal disease or ATN FEUR > 50% FEUA > 10%

Prerenal or intrinsic renal disease FENA > 1% and < 2%, FEUR > 35% and < 50% Abbreviations: FENA, fractional excretion of sodium; FEUR, fractional excretion of Urea; FEUA, fractional excretion of uric acid; UAPR, urine albumin-protein ratio; PCR, protein- creatinine ratio; UACR, urine albumin-creatinine ratio; eGFR, estimated glomerular filtration rate; ATN, acute tubular necrosis.

Fombon, I.S. (2020)

3.3.1.1 Impact of 3 months of FF therapy on kidney function tests The impact of 3 months of FF (160 mg/day) therapy on serum markers of kidney function in Group A participants is summarised in Table 3.2. The median serum CREA concentration increased significantly by 11.5% from baseline (p=0.0016, n=13). Individual participant results (Figure 3.1A) also showed an increase in serum CREA concentration in 11 participants (84.6%, n=13). Three of the participants in this group (23.1%) had FAC (serum CREA ≥ 20 µmol/L), although the change in their serum CREA concentration was not considered clinically significant. A 26% increase in median serum urea concentration was observed, although this was not statistically significant (p=0.3359, n=13). Individual participant results showed that serum urea concentration was increased in seven participants (53.8%, n=13). However, in five participants (38.5%, n=13), a decrease in serum urea level was also observed (Figure 3.1B). A small but statistically significant reduction in median serum Na concentration was observed (p=0.0010, n=13), although the change in median serum K concentration was not statistically significant (p=0.8441, n=13). Individual participant results showed a decrease in serum Na level in 11 participants (84.6%, n=13) (Figure 3.1C). Also, the median serum UA concentration was decreased significantly after 3 months of FF therapy (p=0.0002, n=13). Individual participant results showed a decrease in serum UA concentration in all the participants in this group (Figure 3.1D).

The results also showed a significant reduction in median eGFR estimated using both the MDRD (p=0.0039, n=13) and CKD-EPI CREA equations (p=0.0229, n=13). Individual results showed a decrease in eGFR level in 10 participants (77%, n=13) with both equations (Figure 3.1E and Figure 3.1F). With the MDRD equation, two of the participants (15.4%, n=13) had baseline CRF (i.e. GFR<60 mL/min/1.73 m2), while four of the participants (30.8%, n=13) had CRF after 3 months of FF therapy. However, with the CKD-EPI CREA equation, only one of the participants (7.7%, n=13) had CRF at baseline while four participants (30.8%, n=13) had CRF after 3 months of FF therapy. The change in eGFR in these participants was not considered to be clinically significant as none of them required any change in treatment plan.

Fombon, I.S. (2020)

Table 3.2 Changes in serum markers of kidney function after 3 months of FF therapy in Group A participants

Parameters Baseline 3 Months p value

Number of participants 13 13

Age [Years; median (range)] 51 (37 – 65) 51 (37 – 65)

Serum CREA (µmol/L) 87 (70 – 101) 97 (72 – 121) 0.0015***

Serum Urea (mmol/L) 5.0 (3.9 – 6.1) 6.3 (3.9 – 6.9) 0.2358

Serum Na (mmol/L) 141 (139 – 143) 139 (137 – 140) 0.0010***

Serum K (mmol/L) 4.4 (4.0 – 4.6) 4.5 (4.3 – 4.5) 0.8441

Serum UA (µmol/L) 363 (333 – 486) 305 (240 – 348) 0.0002***

MDRD (mL/min/1.73 m2) 78 (67 – 87) 73 (55 – 77) 0.0039**

CKD-EPI CREA (2009) 84 (68 – 98) 81 (59 – 87) 0.0229* (mL/min/1.73 m2)

Data are presented as median (IQR). IQR: interquartile range; CREA; creatinine, K, potassium; Na, sodium; UA, uric acid; MDRD: Modification of Diet in Renal Disease; CKD- EPI CREA: Chronic Kidney Disease Epidemiology Collaboration creatinine equation (2009). Significant difference between the median concentrations at baseline and after 3 months of FF therapy was determined using the Wilcoxon signed-rank test. *P < 0.05, **P < 0.01 and ***P < 0.001 vs baseline value.

Fombon, I.S. (2020)

Figure 3.1 Effect of 3 months of FF therapy on serum markers of kidney function in Group A Participants. Scatter plots showing serum concentrations of (A) serum CREA, (B) serum urea, (C) serum Na, (D) serum UA, (E) MDRD, (F) CKD-EPI CREA at baseline and after 3 months of FF therapy in individual participants in Group A; CREA, creatinine; Na, sodium, UA, uric acid; MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration creatinine (2009) equation. Significant difference between the median concentrations at baseline and after 3 months of FF therapy was determined using the Wilcoxon signed-rank test; p<0.05 was considered statistically significant (n=13).

Fombon, I.S. (2020)

The impact of 3 months of FF therapy on urine markers of kidney function is summarised in Table 3.3. The results showed significant reduction in median urine CREA concentration from 14 mmol/L (IQR 12 – 23 mmol/L) at baseline to 12 mmol/L (IQR 9.5 – 20 mmol/L) after 3 months of FF therapy (p=0.0325, n=13). Figure 3.2A showed that urine CREA concentration was reduced in 12 participants (92.3%, n=13) after 3 months of FF treatment. The median urine K concentration was also reduced significantly from baseline values (p=0.0105, n=13). Individual participant results showed a decrease in urine K concentration in 11 participants (84.6%, n=13) (Figure 3.2B). The median urine urea and urine Na did not change significantly. Also, there was no significant change in urine ALB, urine protein and urine UA, hence no significant changes in UACR, UPCR and UAPR as shown in Table 3.3. In all the participants urine protein was normal (i.e. UACR < 3 mg/mmol and/or UPCR < 15 mg/mmol).

The median UUACR was reduced significantly by 30.4% from the baseline level after 3 months of FF treatment (p<0.0001, n=13). Results from individual participants showed that UUACR was reduced in all the participants (Figure 3.2C). A significant increase in FENA (Figure 3.2D, p=0.0459, n=13), FEUR (Figure 3.2E, p=0.0118, n=13) and FEUA (Figure 3.2F, p=0.0061, n=13) was also observed, suggesting increased excretion of Na, urea and UA. However, all the participants had FENA < 1% and FEUR < 50%, which suggests that any change in the kidney function was most likely due to prerenal causes rather than from an intrinsic kidney disease. One of the participants in this group had FEUA > 10% after 3 months of FF, which may be attributed to the increase in renal UA clearance associated with the uricosuric effect of FF therapy, a phenomenon which has been widely reported (Uetake et al., 2010).

The results from Group A participants suggest that administration of FF at 160 mg/day for 3 months would cause a moderate increase in serum CREA and urea and moderate reduction in kidney function. The results also suggest that treatment with FF at 160 mg/day for 3 months would significantly decrease serum UA level through increased excretion. The reduction in UA may be beneficial in patients with gout (hyperuricaemia).

Fombon, I.S. (2020)

Table 3.3 Effect of 3 months of FF treatment on urine markers of kidney function.

Parameters Baseline 3 Months p value

Number of participants 13 13

Age [Years; median (range)] 51 (37 – 65) 51 (37 – 65)

Urine CREA (mmol/L) 14 (12 – 23) 12 (10 – 20) 0.0105*

Urine Urea (mmol/L) 313 (236 – 377) 298 (218 – 391) 0.3757

Urine UA (mmol/L) 2.7 (1.7 – 3.5) 2.2 (1.3 – 3.0) 0.3396

Urine Na (mmol/L) 113 (87 – 126) 117 (80 – 147) 0.7737

Urine K (mmol/L) 105 (70 – 133) 73 (46 – 94) 0.0105*

Urine ALB (mmol/L) 2.3 (1.4 – 8.9) 3.2 (1.0 – 7.2) 0.4746

Urine Protein (g/L) 0.10 (0.07 – 0.23) 0.07 (0.07 – 0.16) 0.0718

UACR (mg/mmol) 0.12 (0.09 – 0.51) 0.19 (0.07 – 0.62) 0.5293

UPCR (mg/mmol) 8.0 (4.5 – 9.0) 7.0 (6.0 – 9.0) 0.8662

UAPR 0.03 (0.01 – 0.06) 0.03 (0.01 – 0.06) 0.7971

UUACR (mmol/mmol) 4.6 (4.0 – 5.9) 3.2 (2.7 – 3.8) 0.0002***

FENA (%) 0.39 (0.25 – 0.48) 0.60 (0.37 – 0.72) 0.0459*

FEUR (%) 33 (24 – 38) 36 (33 – 46) 0.0103*

FEUA (%) 2.7 (2.3 – 6.0) 6.5 (4.2 – 7.7) 0.0061**

Data are presented as median (IQR). IQR: interquartile range; CREA, creatinine; Na, sodium; K, potassium; ALB, albumin; UACR: urine albumin-creatinine ratio; UPCR: urine protein-creatinine ratio; UAPR: urine albumin -protein ratio; UUACR: urine uric acid- creatinine ratio; FENA: fractional excretion of sodium; FEUR: fractional excretion of Urea, FEUA: fractional excretion of uric acid. Statistical significant difference between the median concentrations at baseline and after 3 months of FF therapy was determined using the Wilcoxon signed-rank test. *P<0.05, **P<0.01 and ***p<0.001 vs baseline value.

Fombon, I.S. (2020)

Figure 3.2 Effect of 3 months of FF therapy on urine markers of kidney function. Scatter plots showing levels of (A) urine CREA, (B) urine Potassium, (C) UUACR, (D) FENA, (E) FEUR and (F) FEUA at baseline and after 3 months of FF therapy in individual participants in Group A. CREA, creatinine; UUACR, urine uric acid creatinine ratio; FENA, fractional excretion of sodium; FEUR, fractional excretion of urea; FEUA, fractional excretion of uric acid. Statistical significant difference between the median concentrations at baseline and after 3 months of FF therapy was determined using the Wilcoxon signed- rank test. P<0.05 was considered statistically significant (n=13).

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3.3.1.2 Impact of 6 months of FF therapy on kidney function tests in Group B participants Changes in the markers of kidney function after 6 months of FF therapy were examined in Group B participants. This Group consisted of 10 patients (7 male and 3 female) between 37 and 63 years of age, who had provided study samples before and after 6 months of FF (160 mg/day therapy). Changes in serum markers of kidney function in Group B participants are summarised in Table 3.4. The median serum CREA concentration increased by 9.1% from baseline, although this was not statistically significant (p=0.1934, n=10). Individual participant results showed that serum CREA concentration was increased in eight participants (80%, n=10) (Figure 3.3A). None of the participants had FAC after 6 months of FF therapy. A significant increase in median serum urea concentration from 5.4 mmol/L (IQR 4.1 – 6.0 mmol/L) at baseline to 6.3 mmol/L (IQR 4.5 -7.0 mmol/L) was observed (p=0.0316, n=10). Individual participant results also showed that serum urea level was increased in eight participants (80%, n=10) (Figure 3.3B). A concomitant increase in serum CREA and serum urea concentrations was observed in six participants (60%, n=10) after 6 months of FF therapy.

The changes in median serum Na and serum K were not statistically significant. Median serum UA concentration was reduced significantly by 12.9% (p=0.0059, n=10), compared with the baseline values. Figure 3.3C showed that serum UA level was reduced in nine participants (90%, n=10). There was no significant change in median MDRD and CKD-EPI CREA (p=0.2031 and p=0.5332, respectively), although both equations showed a slight reduction in eGFR after 6 months of FF. Individual participant results showed that the eGFR was reduced in seven participants (70%, n=10), when eGFR was calculated using both formulas (Figure 3.3D-E). It was also observed that in one of the participants who had CRF at baseline, their kidney function was improved (i.e. eGFR≥60 mL/min/1.73 m2) after 6 months of treatment with FF, while one other patient developed a CRF after 6 months of therapy. However, the change in their kidney function was not considered to be clinically significant as they did not require any change in treatment plan.

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Table 3.4 Effect of 6 months of FF therapy on serum markers of kidney function.

Parameters Baseline 6 Months p value

Number of participants 10 10

Age [Years; median 51 (37 – 63) 51 (37 – 63) (range)] Serum CREA (µmol/L) 88 (71 – 98) 96 (76 – 108) 0.1934

Serum Urea (mmol/L) 5.4 (4.1 – 6.0) 6.3 (4.5 – 7.0) 0.0273*

Serum Na (mmol/L) 141 (139 – 143) 140 (138 – 141) 0.0508

Serum K (mmol/L) 4.6 (4.2 – 4.8) 4.4 (4.3 – 4.8) 0.7785

Serum UA (µmol/L) 373 (335 – 463) 325 (273 – 386) 0.0059**

MDRD (mL/min/1.73 m2) 75 (68 – 85) 70 (61 – 80) 0.2031

CKD-EPI CREA (2009) 81 (71 - 97) 79 (66 – 91) 0.5332 (mL/min/1.73 m2)

Data are presented as median (IQR). IQR: interquartile range; CREA; creatinine, K, potassium; Na, sodium; UA, uric acid; MDRD: Modification of Diet in Renal Disease; CKD -EPI CREA: Chronic Kidney Disease Epidemiology Collaboration creatinine (2009) equation. Statistical significant difference between the median concentrations at baseline and after 6 months of FF therapy was determined using the Wilcoxon signed- r ank test. *p<0.05, **p<0.01 and ***p<0.001 vs baseline value (n=10).

Fombon, I.S. (2020)

Figure 3.3 Effect of 6 months of FF therapy on serum markers of kidney function. Scatter plots showing serum concentrations of (A) serum CREA, (B) serum urea, (C) serum UA (D) MDRD, (E) CKD-EPI CREA at baseline and after 6 months of FF therapy in individual study participants in Group B; CREA: creatinine; UA: uric acid; MDRD: Modification of Diet in Renal Disease; CKD-EPI CREA: Chronic Kidney Disease Epidemiology Collaboration creatinine (2009) equation. Statistical significant difference between the median concentrations at baseline and after 6 months of FF therapy were analysed using the Wilcoxon signed-rank test. P< 0.05 was considered as statistically significant (n=10).

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The impact of 6 months of FF therapy on urine markers of kidney function is summarised in Table 3.5. Nine participants in this group provided urine sample at baseline and after 6 months of FF therapy. The median UPCR and UUACR were significantly reduced (p=0.0469 and p=0.0078, respectively), while the median FEUA was significantly increased (p=0.0195, n=9) after 6 months of FF therapy. Changes in all the other urine markers of kidney function were not statistically significant. Although the change in median urine K and UACR were not statistically significant, the median urine K and UACR were reduced by 34.2% and 42.9%, respectively. Individual participant results are shown in Figure 3.4. Urine protein and UACR levels were each reduced in six participants (66.7%, n=9), while urine K and UPCR levels were reduced in seven participants (77.8%, n=9). The greatest impact in terms of number of participants affected was on the level UUACR, which was reduced in eight of the nine participants (88.9%). The FEUA was seen to increase in seven participants (77.8%, n=9). The greatest change in the levels of UPCR and FEUA was observed in participant with the higher baseline values. It was also observed that FEUA was not increased in participants with baseline values less than 2%. All the participants also had a FENA < 1%, and FEUR < 50%, which suggest that any changes in the kidney function were due to pre-renal conditions.

These results suggest that administering FF at 160 mg/day for 6 months would cause moderate concomitant increase in serum CREA and urea and a significant reduction in serum UA concentrations. However, the impact on eGFR estimated using the MDRD and CKD-EPI CREA equations was not significant. The results also suggest that the decrease in serum UA was as a result of increased excretion as shown by the increase in FEUA. Furthermore, the decrease in serum UA level induced by FF therapy was not related to changes in the kidney function as serum UA levels in this group of participants were significantly reduced even though the change in serum CREA was not significant.

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Table 3.5 Effect of 6 months of FF therapy on urine markers of kidney function in Group B participants

Parameters Baseline 6 Months p value

Number of participants 9 9

Age [Years; median 51 (37 – 63) 51 (37 – 63) (range)] Urine CREA (mmol/L) 14 (12 – 25) 15 (11 – 20) 0.4258

Urine Urea (mmol/L) 311 (236 – 400) 330 (288 – 371) 0.8203

Urine UA (mmol/L) 2.5 (1.5 – 3.1) 2.0 (1.1 – 3.1) 0.4961

Urine Na (mmol/L) 118 (74 – 126) 110 (63 – 130) 0.8203

Urine K (mmol/L) 114 (73 – 159) 75 (61 – 106) 0.0547

Urine ALB (mmol/L) 2.3 (1.3 – 7.6) 2.3 (0.1 - 8.8) 0.2734

Urine Protein (g/L) 0.10 (0.06 – 0.27) 0.08 (0.05 – 0.15) 0.1328

UACR (mg/mmol) 0.28 (0.06 – 0.49) 0.16 (0.01- 0.52) 0.3906

UPCR (mg/mmol) 8.0 (5.5 – 9.0) 6.0 (4.5 – 8.0) 0.0469*

UAPR 0.03 (0.01 – 0.06) 0.03(0.00 – 0.06) 0.8711

UUACR (mmol/mmol) 4.6 (4.0 – 5.9) 2.9 (2.6 – 4.9) 0.0078**

FENA (%) 0.40 (0.24 – 0.51) 0.43 (0.32 – 0.70) 0.1719

FEUR (%) 33 (24 – 40) 38 (27 – 48) 0.3555

FEUA (%) 2.5 (1.8 – 4.8) 5.1 (2.0 – 8.8) 0.0195*

Data are presented as median (IQR). IQR: interquartile range; UACR: urine albumin- creatinine ratio; UPCR: urine protein-creatinine ratio; UAPR: urine albumin-protein ratio; UUACR: urine uric acid-creatinine ratio; FENA: fractional excretion of sodium; FEUR: fractional excretion of Urea, FEUA: fractional excretion of uric acid. Statistical significant difference between the median concentrations at baseline and after 6 months of FF therapy were analysed using the Wilcoxon signed -rank test. *P < 0.05 and **P < 0.01 vs baseline value.

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Figure 3.4 Effect of 6 months of FF therapy on urine markers of kidney function. Scatter plots showing (A) Urine Protein (g/L), UACR (mg/mmol), (C) urine K (mmol/L), (D) UPCR (mg/mmol), (E) UUACR (mmol/mmol) and (F) FENA (%) at baseline and after 6 months of FF therapy in individual participants in Group B; UACR, urine albumin- creatinine ratio; UPCR, urine protein-creatinine ratio; UUACR, urine uric acid-creatinine ratio; FENA, fractional excretion of sodium. Statistical significant difference between median concentrations at baseline and after 6 months of FF therapy was analysed using the Wilcoxon signed-rank test, p values <0.05 were considered significant (n=9).

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3.3.1.3 Impact of 12 months of FF therapy on kidney function tests

Changes in serum markers of kidney function after 12 months of FF therapy in Group C participants are presented in Table 3.6. The results showed that after 12 months of FF therapy, there was no significant change in all the serum markers of kidney function, except in the eGFR calculated using the CKD-EPI CREA equation which indicated that kidney function was significantly reduced after 12 months of FF therapy (p=0.0391, n=8). However, both the median serum CREA and urea concentrations were increased by 11.8% from baseline (p=0.0703 and p=0.2500, respectively), while median serum UA was reduced by 9.7% (p=0.0781).

Results from individual participants showed that serum CREA concentration was increased in six participants (75%, n=8) (Figure 3.5A). Two of the participants (25%, n=8) developed FAC after 12 months of treatment. In both cases, baseline serum CREA concentration was relatively higher than in all other participants. Serum urea concentration was also increased in six participants (75%, n=8) (Figure 3.5B), while in seven participants (87.5%, n=8), the serum UA level was reduced Figure 3.5C). The eGFR estimated using the MDRD and CKD-EPI equations showed that kidney function was reduced in six of the participants (75%, n=8) (Figure 3.5D and Figure 3.5E). Results from both equations showed that two participants (25%, n=8) developed CRF after 12 months of FF therapy. All the changes in serum markers of kidney function were not clinically significant. Changes in the urine markers of kidney function following 12 months of FF therapy are shown in Table 3.7 and Figure 3.6. The results showed a significant increase in FENA (Figure 3.6A; p=0.0156, n=8) and FEUA (Figure 3.6B; p=0.0234, n=8), indicative of an increase in the excretion of Na and UA, respectively. FEUR was also increased in six of the participants (75%, n=8) (Figure 3.6C, p=0.0625, n=8). Changes in all other urine markers were not statistically significant.

These results suggest that 12 months of FF therapy administered at 160 mg/day would cause a moderate but non-significant increase in serum CREA and urea and a mild reduction in kidney function, estimated by the MDRD and CKD-EPI CREA equations. Twelve months of FF would also cause a moderate reduction in serum UA concentration due to increased excretion as indicated by the increase in FEUA.

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Table 3.6 Effect of 12 months of FF therapy on serum markers of kidney function in Group C participants

Parameters Baseline 12 Months p value

Number of participants 8 8

Age [Years; median (range) 53 (40 – 65) 53 (40 – 65)

Serum CREA (µmol/L) 85 (61 – 98) 95 (59 – 119) 0.0703

Serum Urea (mmol/L) 5.1 (3.8 – 7.2) 5.7 (3.7 – 7.7) 0.2500

Serum Na (mmol/L) 139 (139 – 141) 138 (137 – 140) 0.0703

Serum K (mmol/L) 4.1 (4.0 – 4.6) 4.3 (4.1 – 4.6) 0.7653

Serum UA (µmol/L) 351 (326 – 425) 317 (286 – 365) 0.0781

MDRD (mL/min/1.73 m2) 82 (68 – 91) 73 (55 – 94) 0.1484

CKD-EPI CREA (2009) 91 (72 – 100) 80 (59 – 100) 0.0391* (mL/min/1.73 m2)

Data are presented as median (IQR); CREA, creatinine; UA, uric acid; IQR, interquartile range; MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration creatinine equation. Statistical significant difference between median concentrations at baseline and after 6 months was analysed using the Wilcoxon signed-rank test. *p<0.05

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Figure 3.5 Effect of 12 months of FF therapy on serum markers of kidney function in Group C participants. Scatter plots showing (A) Serum CREA, (B) Serum Urea and (C) Serum UA concentrations at baseline and after 12 months of FF therapy in individual study participants in Group C; CREA, serum creatinine; UA, serum uric acid; FF, fenofibrate. MDRD: Modification of Diet in Renal Disease; CKD-EPI CREA: Chronic Kidney Disease Epidemiology Collaboration creatinine (2009) equation. Statistical significant difference between the median concentrations at baseline and after 12 months of FF therapy was analysed using the Wilcoxon signed-rank test. P<0.05 was considered statistically significant (n=8). 105

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Table 3.7 Effect of 12 months of FF treatment on urine markers of kidney function in Group C participants

Parameters Baseline 12 Months p value

Number of participants 8 8

Age [Years; median (range)] 53 (40 – 65) 53 (40 – 65)

Urine CREA (mmol/L) 13 (12 – 24) 10 (8.2 – 13) 0.0547

Urine Urea (mmol/L) 325 (220 – 385) 264 (194 – 383) 0.4609

Urine UA (mmol/L) 2.3 (1.9 – 3.3) 2.1 (1.1 – 3.2) 0.6474

Urine Na (mmol/L) 88 (60 – 121) 105 (59 – 139) 0.4609

Urine K (mmol/L) 70 (63 – 125) 59 (41 – 73) 0.1094

Urine ALB (mmol/L) 5.3 (2.2 – 17) 2.4 (0.3 – 3.5) 0.1484

Urine Protein (g/L) 0.10 (0.05 – 0.24) 0.08 (0.03 – 0.10) 0.0781

UACR (mg/mmol) 0.44 (0.15 – 0.59) 0.21 (0.05 – 0.36) 0.3438

UPCR (mg/mmol) 8.0 (4.8 – 9.0) 8.0 (6.0 – 8.8) 0.5938

UAPR 0.06 (0.03 – 0.11) 0.03 (0.01 – 0.06) 0.2183

UUACR (mmol/mmol) 4.5 (3.9 – 5.4) 3.5 (2.6 -5.2) 0.3828

FENA (%) 0.34 (0.24 – 0.43) 0.59 (0.42 – 0.85) 0.0156*

FEUR (%) 36 (25 – 40) 45 (40 – 49) 0.0625

FEUA (%) 2.8 (2.4 – 6.0) 6.1 (5.6 – 8.5) 0.0234*

Data are presented as median (IQR). IQR: interquartile range; UACR: urine albumin- creatinine ratio; UPCR: urine protein-creatinine ratio; UAPR: urine albumin-protein ratio;

UUACR: urine uric acid-creatinine ratio; FENA: fractional excretion of sodium; FEUR: fra ctional excretion of Urea, FEUA: fractional excretion of uric acid. Statistical significant difference between the median concentrations at baseline and 6 months was analysed using the Wilcoxon signed-rank test. *P<0.05 vs baseline value.

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Figure 3.6 Effect of 12 months of FF therapy on urine markers of kidney function in Group C participants. Scatter plots showing (A) FENA (%), (B) FEUA (%) (C) FEUR (%) levels at baseline (0) and after 12 months in individual participants in Group C; FENA, fractional excretion of sodium; FEUR, fractional excretion of urea; FEUA, fractional excretion of uric acid. Statistical significant difference between the median concentrations was analysed using the Wilcoxon signed-rank test; p<0.05 were considered significant (n=8).

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3.3.1.4 Changes in markers of kidney function during 6 months of FF therapy Changes in kidney function parameters during a period of six months of FF therapy were examined in nine participants (6 male and 3 female) who had provided samples at three visits (i.e. baseline, 3 months and 6 months). Results of the changes in serum markers of kidney function are presented in Table 3.8, while changes in individual participants are shown in Figure 3.7. A significant increase in median serum CREA concentration was observed after 3 months (p=0.0423, n=9) but after 6 months of FF treatment, the median serum CREA level was not significantly different from the baseline value (p=0.5846, n=9). The Friedman’s test showed that the difference between the median serum CREA concentrations at the different time points was statistically significant (p=0.0266). The difference between the median serum Na and serum UA concentration at different time points was also statistically significant (p=0.066 and p=0.0029, respectively). Post hoc analysis using the Dunn’s test showed a small but statistically significant reduction in median serum Na concentration after 3 months of FF therapy (p=0.0096), but after 6 months of treatment, the median serum Na level was not significantly different from the baseline values.

The median serum UA was also reduced significantly after 3 months of FF treatment (p=0.0065). However, after 6 months of treatment, the median UA had increased by 12.5% from the 3-month value, but remained significantly lower than the baseline concentration (p=0.0286). The changes in the median serum urea, K and eGFR calculated using the MDRD and CKD-EPI CREA equations were not statistically significant. However, a moderate reduction in the median eGFR estimated using both MDRD and CKD-EPI CREA equations was observed after 3 months, but the levels did not change significantly after six months of FF treatment as the values were similar to those obtained after 3 months of FF therapy.

Individual participant results showed that in eight participants (88.9%, n=9), serum CREA concentration was increased after the first 3 months of FF therapy and then slightly reduced after 6 months of the treatment, but remained higher than the baseline values (Figure 3.7A). On the other hand, the serum Na concentration was reduced in all the participants after 3 months of treatment and then increased slightly after the following 3 months (Figure 3.7B). Also, serum UA was reduced in eight participants (88.9%, n=9) during the first 3 months of treatment, but was 108

Fombon, I.S. (2020) slightly increased in five participants (55.6%, n=9) and continued to decrease in four participants (44.4%, n=9) after 6 months of FF therapy (Figure 3.7C). It was observed that eGFR calculated by the MDRD and CKD-EPI CREA equations, was reduced during the first 3 months and then slightly increased after 6 months in the majority (66.7%, n=9) of the participants, although in most cases the 6-month value was lower than baseline values (Figure 3.7D&E).

Changes in urine markers of kidney function at the different points during 6 months of FF therapy are shown in Table 3.8 and Figure 3.8. All the urine parameters were within acceptable reference ranges. The Friedman test revealed that the changes in median urine CREA concentration (p=0.0303), UAPR (p=0.0388), UUACR (p=0.0048) and FENA (p=0.0303) were statistically significant. Post Hoc analysis showed that urine CREA concentration and FENA were significantly increased after 3 months of FF therapy (Figure 3.8A, P=0.0373 and Figure 3.8D, p=0.0373, respectively). However, after 6 months of treatment, both parameters were slightly reduced although the levels after 6 months of treatment were higher than the baseline values. Similarly, the median UAPR was increased during the first 3 months of FF therapy but after 6 months, a small decrease (20%, p=0.2404) was observed and the value after 6 months of treatment was also higher than the baseline value (Figure 3.8B). On the other hand, the median UUACR was significantly decreased after the initial 3 months of treatment (p=0.0081), but a small increase (12.9%), compared with 3-month value, was observed after 6 months of continuous FF therapy (Figure 3.8C). The median UUACR after 6 months was lower than the baseline values. The changes in all the other urine markers were not statistically significant.

Collectively, these results suggest that the impact of FF on kidney function is reversible. The impact appears to be greatest during the first 3 months of treatment than during the later stages of treatment. The results also showed that urine CREA was reduced as serum CREA was increased, which suggests that FAC maybe associated to decreased excretion of CREA. Furthermore, the results from this study showed that FF therapy is associated with increased excretion of Na and UA as evidenced by the increase in FENA and FEUA.

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Table 3.8 Changes in serum markers of kidney function during 6 months of FF therapy in Group D participants

Parameters Baseline 3 Months 6 Months P value

Number of participants 9 9 9

Age [years; median (range)] 50 (42 – 55) 50 (42 – 55) 50 (42 – 55)

Serum CREA (µmol/L) 87 (70 – 96) 97 (71 – 121)¹ 94 (73 – 107) 0.0266*

Serum Urea (mmol/L) 5.8 (4.2 – 6.1) 5.9 (3.9 – 6.9) 6.2 (4.4 – 7.2) 0.0542

Serum Na (mmol/L) 141 (140 – 143) 139 (139 – 140)² 140 (139 – 142) 0.0066**

Serum K (mmol/L) 4.6 (4.1 – 4.7) 4.4 (4.3 – 4.7) 4.3 (4.3 – 5.0) 0.9414

Serum UA (µmol/L) 360 (333 – 471) 305 (221 – 336)² 343 (258 – 386)¹ 0.0029**

MDRD (mL/min/1.73 m2) 78 (69 – 85) 73 (55 – 78) 70 (63 – 81) 0.1966

CKD-EPI CREA (2009) (mL/min/1.73 m2) 84 (70 – 97) 81 (59 – 89) 81 (68 – 92) 0.1966

Data are presented as median (IQR); CREA, creatinine; Na, sodium; K, potassium; UA, uric acid; MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration creatinine (2009) equation; significant difference between median concentration at the different time points was analysed using the Friedman test with Dunn’s multiple comparison test; ¹significantly different from baseline (p<0.05), ²significantly different from baseline (p<0.01), *statistically significant difference between group medians at the different time points *p<0.05, **p<0.01.

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Figure 3.7 Changes in serum markers of kidney function during 6 months of FF therapy. Scatter plots showing (A) serum CREA, (B) serum Na and (C) serum UA in individual participants at baseline (0), 3 and 6 months after commencing FF treatment in individual participants in Group D. CREA, creatinine; Na, sodium; UA, uric acid; MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration creatinine equation (2009); statistical significant difference between median concentrations at different time points was analysed using the Friedman test with Dunn’s multiple comparison test; *p<0.05; **p<0.01 (n=9).

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Table 3.9 Changes in urine markers of kidney function during six months of FF treatment in Group D participants

Parameter Baseline 3 months 6 months P value

Number of participants 8 8 8 Age [Years; median (range)] 51 (37 – 63) 51 (37 – 63) 51 (37 – 63) Urine CREA (mmol/L) 16 (13 – 26) 12 (11 – 18)¹ 16 (11 – 20) 0.0303* Urine Urea (mmol/L) 329 (283 – 403) 295 (188 – 337) 342 (315 – 373) 0.1197 Urine Na (mmol/L) 121 (87 – 126) 139 (83 – 152) 112 (73 – 133) 0.2851 Urine K (mmol/L) 122 (85 – 171) 74 (70 – 110) 84 (63 – 107) 0.1197 Urine UA (mmol/L) 2.5 (1.3 – 3.3) 1.8 (1.3 – 2.3) 2.2 (1.0 – 3.2) 0.5306 Urine ALB (mg/L) 3.5 (0.9 – 8.4) 3.6 (1.3 – 7.9) 4.3 (0.1 – 9.7) 0.4861 Urine Protein (g/L) 0.14 (0.06 – 0.27) 0.07 (0.06 – 0.13)¹ 0.10 (0.07 – 0.16) 0.1083 UACR (mg/mmol) 0.25 (0.04 – 0.50) 0.30 (0.09 -0.66) 0.27 (0.01 – 0.52) 0.0515 UPCR (mg/mmol) 8 (5 -9) 6 (6 – 9) 7 (4 – 8) 0.0791 UAPR 0.02(0.01 – 0.06) 0.05 (0.02 – 0.07) 0.04 (0.00 – 0.06) 0.0388* UUACR (mmol/mmol) 4.7 (4.1 – 5.9) 3.1 (2.7 – 4.0)³ 3.5 (2.6 – 5.2)² 0.0048** FENA (%) 0.34 (0.23 – 0.49) 0.64 (0.37 – 0.80) 0.43 (0.30 – 0.59) 0.0303* FEUR (%) 32 (23 – 39) 36 (30 – 43) 36 (27 – 45) 0.3553 FEUA (%) 2.5 (1.5 – 3.3) 4.3 (3.4 – 6.5) 4.3 (1.4 – 5.9) 0.1495

Data are presented as median (IQR); CREA, creatinine; Na, sodium; K, potassium; UA, uric acid; ALB, albumin; UACR, urine albumin- creatinine ratio; UPCR, urine protein-creatinine ratio; UAPR, urine albumin-protein ratio; UUACR, urine uric acid-creatinine ratio; FENA, fractional excretion of sodium; FEUR, fractional excretion of urea; FEUA, fractional excretion of uric acid. Significant difference between median concentration at the different time points was analysed using the Friedman test with Dunn’s multiple comparison test; ¹significantly different from baseline (p<0.05); ²significantly different from baseline (p<0.01); ³significantly different from baseline (p<0.001); *significant difference between group medians; *p<0.05, **p<0.01.

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Figure 3.8 Changes in urine markers of kidney function during 6 months of FF therapy. Scatter plots showing levels of (A) urine CREA, (B) UAPR (C) UUACR and (D) FENA at baseline (0), 3 and 6 months after commencing treatment in individual participants in Group D. FF, fenofibrate; CREA, creatinine; UAPR, urine albumin-protein ratio; UUACR, urine uric acid-creatinine ratio; FENA, fractional excretion of sodium; significant difference between median concentrations at the different time points was analysed using the Friedman test with Dunn’s multiple comparison test; *p<0.05, **p<0.01, (n=8).

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3.3.1.5 Changes in markers of kidney function during 12 months of FF therapy In this section, changes in serum and urine markers of kidney function were examined in four study participants at 3, 6 and 12 months after commencing FF therapy. The changes in serum and urine markers of kidney function are presented in Table 3.10 and Table 3.11, respectively. The Friedman test showed that the difference between the median UA concentration at the various time points was statistically significant (p=0.0190). Post hoc analysis using the Dunn’s test showed that the median serum UA concentration was significantly reduced after 6 months of FF treatment, compared with the baseline value (p=0.0370, n=4). The changes in all the other parameters were not statistically significant. However, median serum CREA and urea concentrations were increased after 3 months of treatment by 24.7% and 10.2%, respectively. It was observed that after 3 months, median serum CREA and urea decreased steadily throughout the following nine months, although the serum CREA level remained slightly higher than the baseline value after 12 months of treatment. These results suggest that FAC is partially reversible when FF treatment is administered in a long-term.

Individual participant results (Figure 3.9) showed that participants with higher serum CREA concentration or mildly reduced kidney function (eGFR 60 – 89 mL/min/1.73 m2) at baseline had a greater increase in their serum CREA concentration after starting FF treatment compared to those with lower serum CREA concentration or eGFR > 90 mL/min/1.73 m2. The eGFR calculated using MDRD and CKD-EPI CREA equations were reduced during the first 3 months of treatment but partially recovered after 12 months of continuous FF treatment. Changes in the urine markers were not statistically significant. These results also showed the reversibility of kidney impairment after 12 months of FF therapy. However, the changes in kidney function were not clinically significant as none of the participants in this group required any change in treatment plan during this period of therapy.

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Table 3.10 Changes in serum markers of kidney function during 12 months of FF treatment in Group E participants

Parameters Baseline 3 Months 6 Months 12 Months P value

Number of participants 4 4 4 4

Age [years; median (range)] 51 (43 – 57) 51 (43 – 57) 51 (43 – 57) 51 (43 – 57)

Serum CREA (µmol/L) 77 (60 – 99) 96 (68 – 122) 90 (67 – 117) 85 (59 – 118) 0.0933

Serum Urea (mmol/L) 4.9 (2.9 – 7.1) 5.4 (3.8 – 7.1) 5.3 (4.1 – 7.5) 4.2 (3.4 – 6.8) 0.8885

Serum Na (mmol/L) 141 (140 – 143) 139 (137 – 139) 139 (137 – 142) 139 (138 – 140) 0.0828

Serum K (mmol/L) 4.4 (4.1 – 4.6) 4.3 (4.2 – 4.8) 4.8 (4.2 – 5.4) 4.4 (3.9 - 4.8) 0.8577

Serum UA (µmol/L) 338 (318 – 425) 310 (223 – 327) 293 (218 – 332)1 307 (233 – 320) 0.0190*

MDRD (mL/min/1.73 m2) 82 (69 – 93) 66 (54 – 81) 68 (57 – 78) 74 (56 – 96) 0.0819

CKD-EPI CREA (2009) 91 (75 – 102) 74 (58 – 92) 76 (61 – 89) 83 (60 – 103) 0.0819 (mL/min/1.73 m2)

Data are presented as median (IQR); CREA, creatinine; Na, sodium; K, potassium; UA, uric acid; MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration creatinine equation (2009); significant difference between median concentration at the different time points was analysed using the Friedman test with Dunn’s multiple comparison test; ¹significantly different from baseline (p<0.05), *statistically significant difference between the median concentrations at the different time points, *p<0.05.

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Figure 3.9 Changes in markers of kidney function during 12 months of FF therapy. Scatter plots showing (A) serum CREA, (B) serum Na and (C) serum UA in individual participants at baseline (0), 3, 6 and 12 months after commencing FF treatment in individual participants in Group E. CREA, creatinine; UA, uric acid; MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration creatinine equation (2009); statistical significant difference between medians at different time points was analysed using the Friedman test with Dunn’s multiple comparison test, *p<0.05; (n=4).

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Table 3.11 Changes in urine markers of kidney function after 12 months of FF therapy

Parameter Baseline 3 months 6 months 12 months P value Number of participants 4 4 4 4 Age [years; median (range)] 51 (43 – 57) 51 (43 – 57) 51 (43 – 57) 51 (43 – 57) Urine CREA (mmol/L) 20 (12 – 31) 15 (6 – 19) 14 (11 – 19) 11 (10 – 15) 0.4321 Urine Urea (mmol/L) 346 (220 – 515) 307 (159 – 364) 361 (284 – 463) 264 (195 – 370) 0.5076 Urine Na (mmol/L) 105 (67 – 126) 139 (93 – 157) 112 (78 – 147) 136 (131 – 149) 0.1411 Urine K (mmol/L) 121 (80 – 182) 73 (48 – 104) 69 (61 – 98) 67 (58 – 84) 0.1585 Urine UA (mmol/L) 3.0 (2.0 – 3.6) 1.8 (1.4 – 2.2) 2.4 (1.8 – 3.9) 2.6 (1.9 – 4.2) 0.7539 Urine ALB (mg/L) 5.3 (2.8 – 16.3) 4.6 (3.5 – 11.2) 4.3 (0.7 – 9.6) 3.2 (1.4 – 13.6) 0.6366 Urine Protein (g/L) 0.18 (0.09 – 0.29) 0.09 (0.07 – 0.16) 0.10 (0.07 – 0.15) 0.10 (0.09 – 0.13) 0.2443 UACR (mg/mmol) 0.44 (0.16 – 0.59) 0.50 (0.22 – 1.09) 0.33 (0.05 – 0.52) 0.26 (0.10 – 1.37) 0.4657 UPCR (mg/mmol) 9 (8 – 9) 8 (6 – 14) 7 (5 – 10) 9 (7 – 13) 0.4724 UAPR 0.06 (0.02 – 0.06) 0.06 (0.04 – 0.08) 0.04 (0.01 – 0.06) 0.03 (0.01 – 0.10) 0.4736 UUCR (mmol/mmol) 4.5 (4.0 – 5.5) 2.7 (2.7 – 4.1) 2.8 (2.6 – 4.1) 4.8 (3.2 – 6.5) 0.1851 FENA (%) 0.26 (0.21 – 0.40) 0.64 (0.44 – 1.41) 0.52 (0.33 – 0.74) 0.75 (0.45 – 1.04) 0.2377 FEUR (%) 32 (22 – 40) 40 (31 – 46) 43 (38 – 48) 44 (34 – 48) 0.1871 FEUA (%) 2.58 (2.39 – 5.43) 4.31 (4.16 – 6.52) 5.38 (3.94 – 10.10) 6.51 (5.58 – 8.47) 0.1824 Data are presented as median (IQR); CREA, creatinine; Na, sodium; K, potassium; UA, uric acid; ALB, albumin; UACR, urine albumin- creatinine ratio; UPCR, urine protein-creatinine ratio; UAPR, urine albumin-protein ratio; UUACR, urine uric acid-creatinine ratio; FENA, fractional excretion of sodium; FEUR, fractional excretion of urea; FEUA, fractional excretion of uric acid. Statistical significant difference between median concentrations at the different time points was analysed using the Friedman test with Dunn’s multiple comparison test.

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3.3.1.6 Summary of the effect of FF therapy on kidney function tests Results from all the groups showed moderate increase in serum CREA concentration after 3 months of commencing FF therapy, with median levels that were modestly higher than the baseline values. It was observed that the serum CREA levels decreased as treatment was continued after 6 months and 12 months of FF treatment. The eGFR calculated using the MDRD and CKD-EPI CREA equations were decreased as serum CREA concentration was increased during the first three months of FF therapy and were reversed as serum CREA levels decreased after treatment was continued for 6 months and 12 months. The increase in serum CREA was also accompanied by a moderate increase in serum Urea (an independent biomarker of kidney function). The concomitant increase in serum CREA and serum Urea, together with the decrease in eGFR by MDRD and CKD-EPI equations, is indicative of a genuine impact of FF therapy on the kidney function. The data also confirms that the FAC is partially reversible when FF is administered in the long-term. Serum Na and serum UA concentrations were also decreased with FF therapy.

Data from urine analysis suggest that the decrease in serum Na and AU was due to increased excretion as demonstrated by increased FENA and FEUA, respectively. This study did not show any significant impact of FF therapy on urine proteins as urine ALB, urine protein and all related urine ALB and protein parameters, namely UACR, UPCR and UAPR were within the normal reference ranges.

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3.3.2 Impact of FF therapy on markers of liver function First, changes in liver function tests after 3, 6 and 12 months FF (160 mg/day) therapy was examined. Serum concentrations of the various markers of liver function (TBIL, ALB, ALP, ALT, AST and GGT) after 3, 6 and 12 months of FF treatment compared with baseline values are shown in Table 3.12. In Group A participants (n=13), changes in the liver enzymes after 3 months of treatment were compared with baseline values. The results showed a significant decrease in median serum ALP (p=0.0002, n=13) and serum GGT (p=0.0005, n=13) concentrations after 3 months of FF therapy. There was no significant change in the median serum ALB, TBIL, ALT and AST concentrations (p=0.1807, p=0.8555, p=0.6694, and p=0.6426, respectively).

Results from individual study participants did not show any significant changes in serum ALB and TBIL concentrations after 3 months of FF therapy (Figure 3.10A & 3.10B, respectively). It was observed that the serum ALT concentration was increased in eight participants (61.5% n=13), but reduced in the other five participants (38.5%, n=13) in this group (Figure 3.10C). Serum ALP and GGT levels were also reduced in all the participants, while serum AST concentration was reduced in eleven participants (84.6% n=13), as shown in Figure 3.10D-F. Together, these results showed that FF therapy administered for 3 months would not impact serum ALB and TBIL levels, but would cause a moderate reduction in serum AST and a significant decrease in serum ALP and GGT levels in patients with dyslipidaemia. The greatest changes in the liver enzymes were observed in participants with higher baseline values.

In Group B participants, it was also observed that the median serum ALP (p=0.0039, n=10) and GGT (p=0.0020, n=10) concentrations were significant reduced after 6 months of FF therapy (Table 3.12). The median AST concentration was also moderately reduced from 28 U/L (IQR 24 – 42 U/L) at baseline to 23 U/L (IQR 20 – 30 U/L), although this was not statistically significant (p=0.1875, n=10). No significant change in median serum TBIL, ALB and ALT concentrations after 6 months of FF therapy. The results from individual study participants are presented in Figure 3.11.

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Serum ALB and TBIL concentrations at baseline and after 6 months of FF therapy were similar and the changes in both parameters were within the normal reference ranges (Figure 3.11 A-B). Serum ALT concentration was reduced (by 3.6 – 55.1%) in seven participants (70%, n=10) (Figure 3.16C), while the serum ALP concentration was also reduced in nine participants (90%, n=10). Serum GGT and AST concentrations were also reduced in seven participants (70%, n=10) as shown in Figure 3.11D-F. These results indicate that administration of FF for 6 months would cause a moderate decrease in serum AST level, but would significantly reduce serum levels of ALP and GGT in the patients. These results are similar to those obtained after 3 months of FF therapy in Group A participants. Again, the greatest impact in liver enzymes was observed in participants with higher baseline values.

In Group C participants, a significant reduction in median serum ALP (p=0.0078, n=8) and AST (p=0.0469, n=8) were observed after 12 months of FF therapy and although the changes in median serum ALT and GGT concentrations were not statistically significant, the median concentration of both markers were decreased by 20.8% (p=0.1016, n=8) and 48.1% (p=0.0781, n=8), respectively (Table 3.12). Changes in median serum ALB and TBIL levels were not statistically significant (p=0.2031, n=8 and p=0.6875, n=8, respectively). Individual participant results also showed no notable changes in serum ALB and TBIL concentrations after 12 months of FF therapy (Figure 3.12A-B). However, the serum ALT concentration was reduced in 62.5% (5/8) of the participants, serum ALP concentration was reduced in all the participants, and serum GGT and AST were reduced in 75% (7/8) of the participants as shown in Figure 3.12C-F.

Collectively, the results from Group A - C participants suggest that administration of FF at 160 mg/day would lead to a mild reduction in serum ALT and AST concentrations and a significant reduction in ALP and GGT in most of the patients, with a greater impact in participants with higher baseline values.

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Table 3.12 Impact of FF therapy on liver function tests after 3, 6 and 12 months compared with baseline values

Parameters Baseline 3 Months P value Number of participants 13 13 Age [Years; median (range)] 51 (37 – 65) 51 (37 – 65) TBIL (mmol/L) 8 (6 – 12) 9 (7 – 12) 0.1807 ALB (mmol/L) 42 (40 - 46) 42 (41 – 45) 0.8555 ALT (U/L) 21 (15 – 31) 20 (17 - 28) 0.6694 ALP (U/L) 74 (65 – 82) 54 (49 – 60) 0.0002*** AST (U/L) 28 (24 – 45) 27 (22 – 43) 0.6426 GGT (U/L) 57 (42 – 122) 41 (23 – 90) 0.0005*** Parameters Baseline 6 Months P value Number of participants 10 10 Age [Years; median (range)] 51 (37 – 63) 51 (37 – 63) TBIL (mmol/L) 8 (6 - 13) 8 (6 – 11) 0.7500 ALB (mmol/L) 44 (41 – 46) 44 (41 – 45) >0.9999 ALT (U/L) 20 (16 – 47) 21 (14 – 25) 0.7815 ALP (U/L) 74 (63 – 82) 55 (48 - 65) 0.0039** AST (U/L) 28 (24 – 42) 23 (20 – 30) 0.1875 GGT (U/L) 56 (43 – 79) 35 (22 – 57) 0.0020** Parameters Baseline 12 Months P value Number of participants 8 8 Age [Years; median (range)] 53 (40 – 65) 53 (40 – 65) TBIL (mmol/L) 11 (6 – 13) 7 (6 – 12) 0.6875 ALB (mmol/L) 43 (41 – 47) 42 (40 – 43) 0.2031 ALT (U/L) 24 (18 – 35) 19 (14 – 24) 0.1016 ALP (U/L) 79 (69 – 93) 59 (46 – 74) 0.0078** AST (U/L) 29 (25 – 39) 22 (20 – 34) 0.0469* GGT (U/L) 54 (22 – 67) 28 (21 – 49) 0.0781 Data are presented as median (IQR). IQR, interquartile range; ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; GGT, gamma glutamyl transferase; statistical significant difference between the median concentrations were determined using the Wilcoxon signed-rank test; *p<0.05, **p<0.01, ***p<0.001.

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Figure 3.10 Effect of 3 months of FF therapy on the liver function tests Scatter plots showing the concentrations of (A) serum ALB, (B) Serum TBIL, (C) serum ALT, (D) serum ALP, (E) serum GGT and (F) serum AST at baseline (0), and after 3 months of FF therapy in individual participants. ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, gamma glutamyl transferase; AST, aspartate aminotransferase. Statistical significant difference between the median concentrations at baseline and after 3 months of FF therapy was analysed using the Wilcoxon signed-rank test; p<0.05 was statistically significant (n=13).

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Figure 3.11 Effect of 6 months of FF therapy on the liver function tests Scatter plots showing the concentrations of (A) serum ALB, (B) Serum TBIL, (C) serum ALT, (D) serum ALP, (E) serum GGT and (F) serum AST at baseline (0), and after 6 months of FF therapy in individual participants. ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, gamma glutamyl transferase; AST, aspartate aminotransferase. Statistical significant difference between the median concentrations at baseline and after 6 months of FF therapy was analysed using the Wilcoxon signed-rank test; p<0.05 was statistically significant (n=10).

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Figure 3.12 Effect of 12 months of FF therapy on the liver function tests Scatter plots showing the concentrations of (A) serum ALB, (B) Serum TBIL, (C) serum ALT, (D) serum ALP, (E) serum GGT and (F) serum AST at baseline (0), and after 12 months of FF therapy in individual participants. ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, gamma glutamyl transferase; AST, aspartate aminotransferase. Statistical significant difference between the median concentrations at baseline and after 12 months of FF therapy was analysed using the Wilcoxon signed-rank test. P<0.05 was considered to be statistically significant (n=8).

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Next, changes in the liver function tests were examined at different time points during 6 months and 12 months of FF (160 mg/day) therapy in Group D and E participants, respectively. In Group D participants, the liver function test results at 3 and 6 months of FF therapy were compared with baseline values. The median serum TBIL, ALB and ALT concentrations did not change significantly at either 3 or 6 months of FF therapy compared with the baseline values (Table 3.13, Figure 3.13A-C). The results showed a significant decrease in median ALP (p=0.0035, p=9) and GGT (p=0.0005, n=9) during the 6 months of FF therapy. During the first 3 months of treatment, median serum ALP concentration fell from 73 U/L (IQR 63 – 77 U/L) at baseline to 53 U/L (IQR 47 – 59 U/L) (p=0.0140, n=9) and remained significantly lower than the baseline value after 6 months of FF therapy (p=0.0140, n=9) (Figure 3.13D). Also, median serum GGT concentration was decreased from 54 U/L (IQR 42 – 91 U/L) at baseline to 30 U/L (23 – 65 U/L) after 3 months of treatment (p=0.0140, n=9) and remained significantly lower than the baseline value after 6 months (p=0.0044, n=9) (Figure 3.13F). A small but non-statistically significant reduction in median serum AST concentration was observed i.e. from 27 U/L (IQR 24 – 43 U/L) at baseline to 24 U/L (IQR 22 – 38 U/L) after 3 months and 22 U/L (IQR 21 – 28 U/L) after 6 months of FF therapy (p=0.2863, n=9) as shown in Figure 3.13E.

Changes in liver function tests during 12 months of FF treatment are summarised in Table 3.14. The results showed that median serum ALB, and TBIL concentrations did not change significantly at either 3, 6, or 12 months of FF therapy compared with baseline values. Individual participant results did not show any noticeable changes in serum ALB concentrations, while changes in serum TBIL concentration varied among the participants as shown in Figure 3.14A and Figure 3.14B, respectively. A small but non-statistically significant decrease in median serum ALT concentration (by 15%) was observed after 6 months of treatment. After 12 months, the median ALT level was slightly increased although at this time point the value remained lower than the baseline value (p=0.1719, n=4). Individual participant results also showed a gradual decrease in serum ALT concentration during the first 6 months (Figure 3.14C).

The Friedman test showed significant changes in median serum ALP and GGT concentrations during the 12 months FF treatment (P=0.0190 and p=0.0477,

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Generally, the results showed that all the liver enzymes (ALT, ALP, GGT and AST) were reduced following the administration of FF (160 mg/day). Also, results from individual participants indicated that the greatest changes in liver enzymes occurred in patients with higher baseline values. In one of the study participants (Group E), baseline serum GGT was four times higher than the upper reference limit (i.e. > 64 U/L), but all the other liver function tests were within clinical reference ranges. However, the AST/ALT ratio was greater than 2, suggestive of an alcohol liver disease. In the course of FF therapy, it was observed that the serum GGT level decreased from 266 U/L (AST/ALT = 3) at baseline to 206 U/L (AST/ALT=2.5) after 3 months and 145 U/L (AST/ALT=1.7) after 6 months of treatment. However, after 12 months of FF treatment serum GGT concentration had increased to 286 U/L (i.e. higher than baseline value) and AST/ALT = 2.3. These results indicated a relapse of alcoholic liver disease during the 12 months of FF therapy. The reason for the relapse is unclear.

The results from this study suggest that FF therapy does not impact on serum ALB and TBIL but would cause a moderate reduction in liver enzymes, with a greater impact on serum ALP and GGT levels. The results also showed that the greatest changes in liver enzymes occur in participants with higher baseline values.

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Table 3.13 Changes in serum liver function tests during 6 months of FF therapy

Parameters Baseline 3 Months 6 Months P value

Number of participants 9 9 9

Age [years; median 50 (42 – 55) 50 (42 – 55) 50 (42 – 55) (range)]

TBIL (mmol/L) 7 (6 – 12) 7 (7 – 12) 7 (6 – 12) 0.5324

ALB (mmol/L) 43 (40 – 46) 42 (41 – 45) 43 (41 – 46) 0.6279

ALT (U/L) 19 (15 – 38) 19 (17 – 28) 19 (14 – 25) 0.2781

ALP (U/L) 73 (63 – 77) 53 (47 – 59)1 54 (47 – 59)1 0.0035**

AST (U/L) 27 (24 - 43) 24 (22 – 38) 22 (21 – 28) 0.2863

GGT (U/L) 54 (42 – 91) 30 (23 – 65)1 30 (21 – 67)2 0.0005***

Data are presented as median (IQR); ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, gamma glutamyl transferase; AST, aspartate aminotransferase; statistical significant difference between median concentration at the different time points was analysed using the Friedman test with Dunn’s multiple comparison test; ¹significantly different from baseline (p<0.05), ²significantly different from baseline (p<0.01), **p<0.01, ***p<0.001.

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Figure 3.13 Changes in liver function tests during 6 months of FF therapy Scatter plots showing individual participant levels of (A) serum ALB, (B) Serum TBIL, (C) serum ALT, (D) serum ALP, (E) serum GGT and (F) serum AST concentrations at baseline (0), and after 6 months of FF therapy. ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, gamma glutamyl transferase; AST, aspartate aminotransferase. The Friedman test with Dunn’s multiple comparison test was used to analyse the significance difference between the median concentrations at the different time points. *p<0.05, **p<0.01, (n=9).

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Table 3.14 Changes in liver function tests during 12 months of FF therapy in Group E participants

Parameters Baseline 3 Months 6 Months 12 Months P value

Number of participants 4 4 4 4

Age [years; median (range)] 51 (43 – 57) 51 (43 – 57) 51 (43 – 57) 51 (43 – 57)

TBIL (mmol/L) 9 (6 – 12) 8 (6 – 11) 7 (5 – 8) 8 (7 – 13) 0.1875

ALB (mmol/L) 43 (40 – 45) 42 (40 – 45) 42 (40 – 45) 42 (40 – 42) 0.9080

ALT (U/L) 20 (18 – 25) 17 (13 – 19) 14 (12 – 16) 17 (11 – 20) 0.1719

ALP (U/L) 73 (64 – 91) 56 (49 – 59) 52 (47 – 55)1 51 (43 – 60) 0.0190*

AST (U/L) 26 (25 – 51) 23 (21 – 37) 21 (19 – 22) 22 (21 – 39) 0.0596

GGT (U/L) 48 (22 - 213) 27 (16 – 162) 21 (14 – 114) 21 (14 – 220) 0.0477*

Data are presented as median (IQR); ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, gamma glutamyl transferase; AST, aspartate aminotransferase; statistical significant difference between median concentrations at the different time points was analysed using the Friedman test with Dunn’s multiple comparison test; ¹significantly different from baseline (p<0.05), *p<05.

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Figure 3.14 Changes in serum liver function tests during 12 months of FF therapy in Group E participants. Scatter plots showing individual participant levels of (A) serum ALB, (B) Serum TBIL, (C) serum ALT, (D) serum ALP, (E) serum GGT and (F) serum AST concentrations at baseline (0), and after 12 months of FF therapy. ALB, albumin; TBIL, total bilirubin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, gamma glutamyl transferase; AST, aspartate aminotransferase. The Friedman test with Dunn’s multiple comparison tests was used to analyse the significance difference between the median concentrations at the different time points. *p<0.05 (n=4).

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3.3.3 The impact of FF therapy on lipid profile The main aim of FF therapy is to reduce elevated levels of triglyceride, and increase low levels of HDL cholesterol in patients with dyslipidaemia. In this study, the impact of FF (160 mg/day) therapy on the lipid profile (CHOL, HDL, TRIG, CHRA and NHDL) was examined after 3, 6, and 12 months of commencing treatment. The results (Median and IQR) at baseline and after 3, 6 and 12 months after commencing treatment are summarised on Table 3.14. In Group A participants, a significant increase in the median serum HDL cholesterol concentration from 0.87 mmol/L (0.70 – 1.20 mmol/L) at baseline to 0.93 mmol/L (0.81 – 1.20 mmol/L) after 3 months of FF therapy was observed (p=0.0200, n=13). The results also showed a reduction in the median serum TRIG concentration by 36.7% after 3 months of FF therapy, although this change was not statistically significant (p=0.1672, n=13). The changes in serum levels of CHOL, CHRA and NHDL were not statistically significant.

Individual participant results showed that after 3 months of FF therapy, CHOL was increased in eight participants (61.5%, n=13) (Figure 3.15A). As expected, serum HDL cholesterol concentration was increased in 10 participants (76.9%, n=13), while serum TRIG concentration was also decreased in 10 participants (76.9%, n=13) as shown in Figure 3.15B-C. Apart from reducing high levels of TRIG and increasing HDL levels, the aim of treatment in dyslipidaemia is also to reduce high levels of CHRA and NHDL, both of which are used to assess the risk of heart disease. After 3 months of FF therapy, CHRA was decreased in six participants (46.2%, n=13), while NHDL was decreased in five participants (38.5%, n=13) (Figure 3.15D-E). These results indicate that treatment with FF (160 mg/day) for 3 months would cause a significant increase in serum HDL concentration, a moderate decrease in serum TRIG concentration, but would not cause any significant changes in CHOL, CHRA and NHDL levels. The greatest change in serum TRIG was observed in participants with higher baseline values.

In Group B participants, changes in the lipid profile were not statistically significant (Table 3.14). Individual participant results showed that serum CHOL concentration was increased in five of the participants (50%, n=10) (Figure 3.16A). Serum HDL concentration was increased in eight of the participants (80%, n=10) and serum TRIG concentration was also reduced in eight of the participants (80%, n=10) 131

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(Figure 3.16B-C). The CHRA was decreased in six of the participants (60%, n=10), while the NHDL concentration was reduced in five of the participants (50%, n=10) (Figure 3.16D-E). These results suggest that administration of FF at 160 mg/day for 6 months would cause a moderate increase in HDL cholesterol concentration and decrease in serum TRIG concentration in patients with dyslipidaemia. The results also showed that administering FF (160 mg/day) for 6 months would not significantly impact on CHOL, CHRA and NHDL levels in patients with dyslipidaemia, although individual results showed that CHRA and NHDL levels were reduced in some participants.

In Group C participants, the changes in lipid profile were not statistically significant (Table 3.14). However, median CHOL, TRIG and NHDL concentrations were reduced by 22.2% (p=0.3828, n=8), 20.4% (p=0.8438, n=8), and 23.8% (p=0.4609, n=8), respectively. Individual participant results also showed that serum CHOL concentration was decreased in six participants (75%, n=8), while serum HDL cholesterol was increased in five participants (62.5%, n=8) (Figure 3.17A-B). Serum TRIG concentration was also reduced in four participants (50%, n=8) and the greatest changes in TRIG levels were observed in participants with relatively higher baseline levels (Figure 3.17C). The CHRA and NHDL levels were also reduced in 50% and 75% of the participants, respectively (Figure 3.17D-E).

Put together, these results suggest that FF administered at 160 mg/day would cause a moderate decrease in serum TRIG and a moderate increase in HDL levels in patients with dyslipidaemia. Although the impact of the treatment varied amongst the participants, the greatest impact on TRIG level was observed in participants with a relatively higher baseline concentration of TRIG. Aldo, the impact on the CHRA and NHDL varied amongst the participants.

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Table 3.15 Impact of 3, 6, and 12 months of FF therapy on the lipid profile in patients with dyslipidaemia

Parameters Baseline 3 Months P value

Number of participants 13 13

Age [Years; median (range)] 51 (37 – 65) 51 (37 – 65)

CHOL (mmol/L) 5.1 (3.6 – 8.6) 5.8 (4.8 – 6.8) 0.8533

HDL (mmol/L) 0.87 (0.70 – 1.20) 0.93 (0.81 – 1.20) 0.0200*

TRIG (mmol/L) 4.9 (2.9 – 6.1) 3.1 (1.8 – 4.5) 0.1672

CHRA 5.1 (4.4 – 7.1) 6.0 (4.6 – 6.3) 0.8257

NHDL (mmol/L) 4.2 (2.8 – 7.6) 4.7 (3.7 – 5.9) 0.9460

Parameters Baseline 6 Months P value

Number of participants 10 10

Age [Years; median (range)] 51 (37 – 63) 51 (37 – 63)

CHOL (mmol/L) 5.1 (3.2 – 7.7) 5.4 (4.0 -6.2) 0.9805

HDL (mmol/L) 0.87 (0.67 – 0.98) 0.98 (0.77 – 1.00) 0.1016

TRIG (mmol/L) 3.5 (3.0 – 5.5) 2.8 (2.1 – 3.3) 0.0645

CHRA 5.0 (4.1 – 8.7) 5.3 (4.2 – 7.1) 0.6426

NHDL (mmol/L) 4.1 (2.3 – 6.8) 4.4 (3.0 – 5.1) 0.9219

Parameters Baseline 12 Months P value

Number of participants 8 8

Age [Years; median (range)] 53 (40 – 65) 53 (40 – 65)

CHOL (mmol/L) 7.2 (5.3 – 8.8) 5.6 (5.1 – 7.4) 0.3828

HDL (mmol/L) 1.10 (0.87 – 1.30) 1.00 (0.86 – 1.30) 0.7422

TRIG (mmol/L) 4.9 (3.2 – 5.8) 3.9 (2.1 – 6.8) 0.8438

CHRA 5.5 (4.3 – 7.6) 5.7 (4.6 – 6.8) >0.9999

NHDL (mmol/L) 6.3 (4.1 – 7.9) 4.8 (4.0 – 6.1) 0.4609

Data are presented as median (IQR); CHOL, cholesterol; HDL, high-density lipoprotein cholesterol; TRIG, triglyceride; CHRA, cholesterol to HDL cholesterol ratio; NHDL, non- HDL cholesterol; statistical significant difference between median concentrations was determined using the Wilcoxon signed-rand test; *p<0.05.

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Figure 3.15 Effect of 3 months of FF therapy on the lipid profile Scatter plots showing concentrations of (A) serum CHOL, (B) serum HDL cholesterol, (C) serum TRIG, (D) CHRA and (D) NHDL at baseline and after 12 months of FF therapy in individual participants in Group A. CHOL, cholesterol; HDL, high-density lipoprotein cholesterol; TRIG, triglyceride; CHRA, cholesterol to HDL cholesterol ratio; NHDL, non- HDL cholesterol; FF, fenofibrate; statistical significant difference between median concentrations was determined using the Wilcoxon signed-rank test. A p<0.05 was considered as statistically significant.

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Figure 3.16 Effect of 6 months of FF therapy on the lipid profile in Group B participants. Scatter plots showing concentrations of (A) serum CHOL, (B) serum HDL cholesterol, (C) serum TRIG, (D) CHRA and (D) NHDL at baseline and after 6 months of FF therapy in individual participants in Group B. CHOL, cholesterol; HDL, high-density lipoprotein cholesterol; TRIG, triglyceride; CHRA, cholesterol to HDL cholesterol ratio; NHDL, non- HDL cholesterol; FF, fenofibrate; statistical significant difference between median concentrations was determined using the Wilcoxon signed-rank test. A p<0.05 was considered as statistically significant.

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Figure 3.17 Effect of 12 months of FF therapy on the lipid profile Scatter plots showing concentrations of (A) serum CHOL, (B) serum HDL cholesterol, (C) serum TRIG, (D) CHRA and (D) NHDL at baseline and after 12 months of FF therapy in individual participants in Group C. CHOL, cholesterol; HDL, high-density lipoprotein cholesterol; TRIG, triglyceride; CHRA, cholesterol to HDL cholesterol ratio; NHDL, non- HDL cholesterol; FF, fenofibrate; significant difference between median concentrations was determined using the Wilcoxon signed-rank test. A p<0.05 was considered as statistically significant.

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Changes in the lipid profile during 6 and 12 months of FF (160 mg/day) therapy were examined in Group D and Group E participants, respectively. Results from Group D participants are shown in Table 3.15 and Figure 3.18. In this group, the lipid profile was measured at baseline, 3 months and 6 months after commencing FF therapy. The Friedman test revealed significant differences between median serum HDL cholesterol concentrations at baseline, 3 and 6 months (p=0.0035, n=9) and Dunn’s post hoc test showed that median serum HDL cholesterol concentration after 3 months and 6 months of FF therapy was significantly higher than the baseline value (p=0.0140 and p=0.0140, respectively, Figure 3.18B). The median serum CHOL, TRIG, CHRA and NHDL levels did not change significantly at either 3 months or 6 months of FF therapy compared with baseline values (Figures 3.18). The results also showed great variation on the impact of FF therapy on the lipid profile in the individual participants at the different time points.

In Group E, changes in the lipid profile at 3, 6 and 12 months were compared with baseline values in four participants. The results are presented in Table 3.16 and Figure 3.19. The Friedman test showed that there was no significant difference between the median serum CHOL, HDL cholesterol, TRIG, CHRA and NHDL at the different time points during the 12 months of FF therapy (p=0.6615, p=0.3895, p=0.1584, 0.6968 and p=0.5239, respectively). However, it was observed that the median serum TRIG level was decreased as the duration of treatment was increased i.e. from 5.06 mmol/L (IQR 3.74 – 13.68 mmol/L) at baseline to 2.24 mmol/L (IQR 1.63 – 10.62 mmol/L) after 12 months of FF therapy. The median serum HDL cholesterol concentration was increased from 0.88 mmol/L (IQR 0.68 – 1.13 mmol/L) at baseline to 1.01 mmol/L (IQR 0.84 – 1.18 mmol/L) during the 12 months of FF therapy.

Collectively, these results suggest that FF (160 mg/day) therapy would cause a moderate but significant increase in serum HDL cholesterol and a moderate reduction in serum TRIG concentrations when administered in a long-term. Also, the greatest impact of FF (160 mg/day) therapy on the lipid profile was observed after the first 3 months of treatment. In this study, the greatest changes in the parameters were observed in participants with relatively higher baseline values. Also, FF administered at 160 mg/day did not have any significant impact on serum CHOL, CHRA and NHDL cholesterol levels.

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Table 3.16 Changes in the lipid profile during six months of FF therapy

Parameters Baseline 3 Months 6 Months P value Number of participants 9 9 9

Age [Years; 50 (42 – 55) 50 (42 – 55) 50 (42 – 55) median (range)]

CHOL (mmol/L) 5.1 (2.9 – 8.1) 5.8 (3.7 – 6.8) 5.5 (3.8 – 6.5) 0.7407

HDL (mmol/L) 0.86 (0.65 – 0.89) 0.91 (0.79 – 1.08)¹ 0.95 (0.77 – 1.01)¹ 0.0035**

TRIG (mmol/L) 3.9 (2.9 – 5.8) 3.1 (2.0 – 4.5) 3.0 (2.3 – 3.5) 0.2744

CHRA 5.1 (4.4 – 9.3) 6.0 (4.9 – 6.3) 5.8 (4.4 – 7.1) 0.9712

NHDL (mmol/L) 4.2 (2.1 – 7.3) 4.9 (2.8 – 5.9) 4.4 (2.9 – 5.5) 0.6854

Data are presented as median (IQR); CHOL, cholesterol; HDL, high-density lipoprotein cholesterol; TRIG, triglyceride; CHRA, cholesterol to HDL cholesterol ratio; NHDL, non- HDL cholesterol; statistical significant difference between median concentrations at the different time points during 6 months of FF therapy was analysed using the Friedman test with Dunn’s multiple comparison test; ¹significantly different from baseline (p<0.05), **p<0.01.

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Figure 3.18 Changes in lipid profile during 6 months of FF therapy Scatter plots showing levels of (A) serum CHOL, (B) serum HDL cholesterol, (C) serum TRIG, (D) CHRA and (E) NHDL at baseline (0), 3 and 6 months after commencing FF therapy in individual participants in Group D. FF, fenofibrate; CHOL cholesterol; TRIG, triglyceride; HDL, high-density lipoprotein cholesterol; CHRA, cholesterol to HDL cholesterol ratio; NHDL, non-HDL cholesterol. Statistical significance difference between the median concentrations at the different time points during 6 months of FF therapy was analysed using Friedman test with Dunn’s multiple comparison tests. *p<0.05, (n=9).

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Table 3.17 Changes in serum lipid profile during 12 months of FF therapy

Parameters Baseline 3 Months 6 Months 12 Months P value

Number of participants 4 4 4 4

Age [years; median (range)] 51 (43 – 57) 51 (43 – 57) 51 (43 – 57) 51 (43 – 57)

CHOL (mmol/L) 6.5 (4.0 – 8.6) 5.7 (4.8 – 7.3) 5.5 (4.7 – 5.8) 5.3 (4.6 – 6.5) 0.6696

HDL (mmol/L) 0.88 (0.68 – 1.13) 0.92 (0.90 – 1.18) 0.98 (0.82 – 1.26) 1.01 (0.84 – 1.18) 0.3857

TRIG (mmol/L) 5.06 (3.74 – 13.68) 3.08 (2.37 – 8.33) 2.65 (1.70 – 3.05) 2.24 (1.63 – 10.62) 0.4275

CHRA 6.4 (4.2 – 12.9) 6.2 (5.2 – 6.4) 5.3 (4.4 – 5.3) 5.6 (4.2 – 7.2) 0.4913

NHDL (mmol/L) 5.45 (3.06 – 7.86) 4.80 (3.85 – 6.13) 4.40 (3.75 – 4.71) 4.29 (3.50 – 5.61) 0.6346

Data are presented as median (IQR); CHOL, cholesterol; HDL, high-density lipoprotein cholesterol; TRIG, triglyceride; CHRA, cholesterol to HDL cholesterol ratio; NHDL, non-HDL cholesterol; statistical significant difference between median concentrations at the different time points during 12 months of FF therapy was analysed using the Friedman test with Dunn’s multiple comparison test. Significant difference was considered as a p value <0.05.

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Figure 3.19 Changes in lipid profile in during 12 months of FF therapy Scatter plots showing serum levels of (A) serum CHOL, (B) serum DL cholesterol (C) serum TRIG, (D) CHRA and NHDL at baseline (0), 3, 6 and 12 months after commencing FF therapy in individual participants in Group D. FF, fenofibrate; CHOL cholesterol; TRIG, triglyceride; HDL, high-density lipoprotein cholesterol; CHRA, cholesterol to HDL cholesterol ratio; NHDL, non-HDL cholesterol; n=4.

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3.3.4 Impact of FF therapy on markers of inflammation and muscle damage The impact of FF therapy on markers of inflammation (CRP) and muscle damage (CK and LDH) was examined after 3, 6, and 12 months and result compared with baseline values. The results are summarised in Table 3.17. Generally, FF (160 mg/day) therapy did not have any significant impact on the serum CRP, CK and LDH levels in all the Groups of participants. The impact of FF therapy on the serum CRP level in the individual participants is shown in Figure 3.20. Although, the median serum CRP concentration in Group A participants was reduced by 69.4% after 3 months of FF therapy compared to baseline values, the difference was not statistically significant (p=0.4219, n=13). In Group B participants, the median CRP concentration was instead increased by 31.3% after 6 months of treatment (p=0.4844, n=10) and by 16% after 12 months in Group C participants (p=0.5313, n=8).

Results from Group D participants in which the evolution of serum CRP was examined in nine participants during 6 months of FF therapy, showed that the median CRP concentration was initially reduced by 44.4% compared to baseline value i.e. from 1.8 mg/L (IQR 1.1 – 7.9 mg/L) at baseline to 1.0 mg/L (IQR 1.0 – 8.1 mg/L) after 3 months of treatment, but was again increased to 2.4 mg/L (IQR 1.0 – 8.5 mg/L) after 6 months of treatment. A similar trend was also observed in Group D during 12 months of FF therapy involving four participants. These results suggest that the change in serum CRP concentration was not necessarily due to the impact of FF therapy.

Individual participant results (Figure 3.20) showed that in two of the participants (B01 and B05) who had baseline values greater than the upper reference range (5 mg/L), the CRP concentration was further increased by 29.9% and 3.8%, respectively, after 3 months of treatment and by 105% and 11.3%, respectively, after 6 months of treatment. It was further observed that in both participants, their serum CRP level remained higher than the upper reference limit throughout the treatment period. All other participant results were within the reference range throughout the study period. These results also suggest that the impact of FF on serum CRP level does not depend on the baseline value.

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Changes in the median serum concentrations of CK and LDH after 3, 6 and 12 months were not statistically significant as shown in Table 3.17. However, it was observed that the median serum CK concentration was reduced after 3, 6 and 12 months of FF therapy by 30.4% (n=13), 18.1% (n=10) and 2.5% (n=8), respectively (Figure 3.21). On the other hand, the median serum LDH concentration was increased by 2% (n=13) and 10.9% (n=10) after 3 months in Group A and 6 months in Group B participants, respectively. In Group C participants, the median LDH concentration was reduced by 3% (n=8). These results suggest that changes in the levels of CK and LDH were not related to FF- induced muscle damage. In muscle injury, both CK and LDH levels are expected to rise.

In Group D and E participants, changes in CK and LDH levels were examined at different time points during 6 months and 12 months of treatment, respectively (Figure 3.22). In Group D participants, the median CK concentration was reduced by 44% after 3 months of treatment (p=0.4719, n=13) and then increased by 38.5% after 6 months of FF therapy (p=0.4719). On the other hand, the median serum LDH concentration was increased from 250 U/L (IQR 215 – 317) at baseline to 262 U/L (IQR 214 – 311 U/L) after 3 months of treatment and then decreased by 7.6% to 242 U/L (IQR 189 – 332 U/L) after 6 months of FF treatment. A similar trend was observed in Group E participants, where the median serum CK concentration was initially decreased following administration of FF (160 mg/day) and then gradually increased as treatment was continued in the long- term. In Group E, the median serum LDH was initially increased after the first 3 months of FF therapy and then slightly decreased as treatment was continued.

Individual participant results showed that in one of the participants marked as T01, a concomitant increase in serum CK and LDH concentrations was observed during six months of FF therapy (Figure 3.22A and Figure 3.22B). The clinical significance of these changes in both markers is unclear as none of the participants reported any symptoms associated with muscle injury. Also the changes in all the markers were not clinically significant to trigger any change in the treatment plan or withdrawal of FF therapy. Collectively, these results suggest that FF administered at 160 mg/day is not associated with any risk of muscle injury in patients with dyslipidaemia.

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Table 3.18 Effect of FF therapy on serum CRP, CK and LDH concentrations in patients with dyslipidaemia

Parameters Baseline 3 Months p value

Number of participants 13 13

Age [Years; median (range)] 51 (37 – 65) 51 (37 – 65)

Serum CRP (mg/L) 3.6 (1.1 – 5.0) 1.1 (1.0 – 4.9) 0.4219

Serum CK (U/L) 112 (64 – 142) 78 (59 – 144) 0.7607

Serum LDH (U/L) 263 (217 – 305) 268 (240 – 300) >0.9999

Group B Baseline 6 Months p value

Number of participants 10 10

Age [Years; median (range)] 51 (37 – 63) 51 (37 – 63)

Serum CRP (mg/L) 1.6 (1.2 – 6.6) 2.1 (1.0 – 6.9) 0.4844

Serum CK (U/L) 116 (64 – 144) 95 (71 – 144) 0.7891

Serum LDH (U/L) 247 (214 – 322) 274 (189 – 363) >0.9999

Group C Baseline 12 Months p value

Number of participants 8 8

Age [Years; median (range)] 53 (40 – 65) 53 (40 – 65)

Serum CRP (mg/L) 2.5 (1.2 – 4.1) 2.9 (1.0 – 4.5) 0.5313

Serum CK (U/L) 121 (92 – 141) 118 (84 – 159) 0.8438

Serum LDH (U/L) 304 (217 – 441) 295 (217 – 377) 0.7789

Data are presented as median (IQR). IQR, interquartile range; CRP, C reactive protein; CK, creatine kinase; LDH, lactate dehydrogenase; statistical significant difference between the median concentrations was analysed using the Wilcoxon signed-rank test; p<0.05 was considered to be statistically significant.

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Figure 3.20 Impact of FF therapy on serum CRP concentration. Scatter plots showing changes in serum CRP concentration in individual participants (A) after 3 months (n=13), (B) after 6 months (n=10), (C) after 12 months (n=8) and at different time points during (D) 6 months (n=9) and (E) 12 months of FF therapy. CRP, C reactive protein; FF, fenofibrate; The impact of FF therapy on serum CRP varied among individual participants, particular in participants B01 and B05 who started with a relatively higher baseline value.

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Figure 3.21 Impact of FF therapy on markers of muscle damage. Scatter plots showing individual participant level of CK and LDH after 3 months (A-B), 6 months (C-D) and 12 months (E-F) of FF therapy. CK, creatine kinase; LDH, lactate dehydrogenase; FF, fenofibrate; statistical significant difference between median concentrations at baseline and after FF therapy was analysed using the Wilcoxon signed- rank test, p<0.05 was considered as statistically significant.

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Figure 3.22 Changes in markers of muscle damage during 6 and 12 months of FF therapy. Scatter plots showing serum levels of CK and LDH in individual participants during 6 months (A-B) and 12 months (C-D) of FF therapy. The impact of FF therapy varied among the participants. In the participant marked as T04, serum CK and LDH increased from baseline to 6 months .FF, fenofibrate; CK, creatine kinase; LDH, lactate dehydrogenase.

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3.3.5 Effect of FF therapy on thyroid function tests The impact of FF therapy on thyroid function was assessed by measuring serum levels of TSH, FT4 and FT3 in study participants at baseline and 3, 6 and 12 months after commencing treatment with FF administered at 160 mg/day. Changes in thyroid function tests in Group A – C participants following FF therapy are shown in Table 3.18. In Group A, the changes in TSH, FT4 and FT3 were not statistically significant. All individual participant results were within the normal reference ranges (Figure 3.23). In Group B and C, a small but significant increase in the median FT4 concentration was observed after 6 months (p=0.0469, n=10) and 12 months (p=0.0234, n=8) of FF therapy. Changes in the median serum TSH and FT3 concentrations were not statistically significant. Also, changes in individual participant results were not clinically significant as all the results were within the normal reference ranges (Figure 3.24 and Figure 3.25).

Next, changes in markers of thyroid function at different time periods during 6 months and 12 months FF therapy were examined in Group D and E participants, respectively. In Group D participants, the Friedman test showed that the difference between the median TSH concentrations at the different time points was statistically significant (p=0.0080, n=13). Post hoc analysis using the Dunn’s test showed that the median serum TSH concentration was increased from 1.55 mU/L (IQR 1.10 – 3.02 mU/L) at baseline to 1.59 mU/L (IQR 1.33 – 3.13 mU/L) after 3 months of FF therapy (p=0.2969, n=9) and then reduced significantly during the following 3 months to 1.45 mU/L (IQR 1.10 – 2.43 mU/L) (p=0.0096, n=9). The changes in the median serum FT4 and FT3 concentrations during the 6 months treatment period were not statistically significant. In group E, changes in the median serum TSH and FT4 concentrations were not statistically significant. However, the difference between the median serum FT3 concentrations at the different time points was statistically significant (p=0.0313, n=4). Results from individual participants did not show any clinically significant changes as all the parameters were within normal reference ranges (Figure 3.26).

These results suggest that administration of FF at 160 mg/day would not impact on thyroid function in patients with dyslipidaemia.

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Table 3.19 Effect of FF therapy on thyroid function tests

Parameters Baseline 3 Months p value

Number of participants 13 13

Age [Years; median (range)] 51 (37 – 65) 51 (37 – 65)

Serum TSH (mIU/L) 1.7 (1.0 – 2.8) 2.4 (1.3 – 3.3) 0.0713

Serum FT4 (pmol/L) 10 (9.5 – 11.0) 11 (9.3 – 12.0) 0.0635

Serum FT3 (pmol/L) 4.7 (4.2 – 5.0) 4.8 (4.4 – 5.6) 0.2100

Group B Baseline 6 Months p value

Number of participants 10 10

Age [Years; median (range)] 51 (37 – 63) 51 (37 – 63)

Serum TSH (mIU/L) 1.4 (1.0 - 2.5) 1.4 (1.0 – 2.4) 0.5469

Serum FT4 (pmol/L) 10.0 (9.7 – 11.0) 11.0 (11.0 – 12.0) 0.0469*

Serum FT3 (pmol/L) 4.7 (4.2 – 5.0) 5.0 (4.2 – 5.3) 0.5020

Group C Baseline 12 Months p value

Number of participants 8 8

Age [Years; median (range)] 53 (40 – 65) 53 (40 – 65)

Serum TSH (mIU/L) 2.2 (1.0 – 3.1) 2.5 (1.1 – 4.2) 0.5469

Serum FT4 (pmol/L) 10.0 (8.9 – 11.0) 11.0 (8.7 – 12.0) 0.0234*

Serum FT3 (pmol/L) 4.8 (4.4 – 5.4) 4.8 (4.3 – 5.4) 0.8359

Data are presented as median (IQR). IQR, interquartile range; TSH, thyroid stimulating hormone; IQR, interquartile range; FT4, free T4 or free thyroxine; FT3, free T3 or free triiodothyronine; statistical significant differences between the median concentrations were analysed using the Wilcoxon signed-rank test; *p<0.05.

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Figure 3.23 Effect of 3 months of FF therapy on thyroid function tests. Scatter plots showing levels of (A) serum TSH, (B) serum FT4 and (C) serum FT3 at baseline and after 3 months of FF therapy in individual participants in Group A. TSH, thyroid stimulating hormone; FT4, free T4; FT3, free T3; statistical significant difference between the median concentration and baseline and after 3 months of FF therapy was determined using the Wilcoxon signed-rank test; p<0.05 was considered to be statistically significant (n=13).

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Figure 3.24 Effect of 6 months of FF therapy on thyroid function tests. Scatter plots showing levels of (A) serum TSH, (B) serum FT4 and (C) serum FT3 at baseline and after 6 months of FF therapy in individual participants in Group A. TSH, thyroid stimulating hormone; FT4, free T4; FT3, free T3; statistical significant difference between the median concentration and baseline and after 6 months of FF therapy was determined using the Wilcoxon signed-rank test; p<0.05 was considered to be statistically significant (n=10).

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Figure 3.25 Effect of 12 months of FF therapy on thyroid function tests. Scatter plots showing levels of (A) serum TSH, (B) serum FT4 and (C) serum FT3 at baseline and after 12 months of FF therapy in individual participants in Group A. TSH, thyroid stimulating hormone; FT4, free T4; FT3, free T3; statistical significant difference between the median concentration and baseline and after 12 months of FF therapy was determined using the Wilcoxon signed-rank test; p<0.05 was considered to be statistically significant (n=8).

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Figure 3.26 Changes in the thyroid function tests during 6 and 12 months of FF therapy. Scatter plots showing levels of serum TSH, FT4 and FT3 in individual participants during 6 months (A-C) and 12 months (D-F) of FF therapy. TSH, thyroid stimulating hormone; FT4, free T4; FT3, free T3; significant difference between the median concentration and baseline and after 12 months of FF therapy was determined using the Wilcoxon signed-rank test; p<0.05 was considered to be statistically significant (n=8).

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3.4 DISCUSSION FF is undoubtedly a very important hypolipidaemic drug and the most commonly prescribed fibric acid derivative, used primarily to reduce serum TRIG and LDL cholesterol levels, and increase HDL cholesterol levels in patients with dyslipidaemia (Farnier, 2008). Based on recently published studies, FF is a useful option for patients with primary combined dyslipidaemias or secondary dyslipidaemias, such as those associated with diabetes mellitus, metabolic syndrome or HIV infection. Additionally, in cases of refractory dyslipidaemia, the combination of FF with statins is a therapeutic option (Tsimihodimos et al., 2005). FF has also been shown to have pleitropic effects such as anti-inflammatory, anti-oxidant, and anti-thrombotic qualities (Playford et al., 2002; Koh et al., 2005; Walker et al., 2012). However, its association with severe adverse effects including kidney injury, liver damage, myalgia and rhabdomyolysis has limited its use in certain individuals (Davidson et al., 2007; Ahmad et al., 2017; Wang and Wang, 2018).

Recent studies have suggested that FF therapy may impact differentially on different biomarkers (Ncube et al., 2012; Gandhi et al., 2014; Wang and Wang, 2018). However, such studies have focused on the impact of FF on individual markers, organs or systems and reports on how FF affects routine clinical biomarkers in patients with dyslipidaemia are rare or lacking. In this study, the impact of 3 – 12 months of FF therapy was investigated in various groups of patients with dyslipidaemia. Patients were grouped based on provision of test samples from baseline (i.e. before they had started treatment with FF) to 12 months after starting treatment. All the participants were followed up in primary care settings and were administered FF at 160 mg/day.

As expected, this study showed that FF therapy reduced serum TRIG levels and improved on low levels of HDL cholesterol. It was also confirmed that FF therapy is associated with moderate increase in serum creatinine and Urea concentrations and a reduction in kidney function estimated using the routinely used MDRD formula and the CKD-EPI CREA equations for assessing kidney function. This study also shows that FF-associated kidney dysfunction is reversible when FF is administered in the long-term. The results also showed that FF therapy reduced serum UA levels by increasing urine UA clearance. In this study, FF therapy was also shown to

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Fombon, I.S. (2020) moderately reduce liver function enzymes (ALT, AST GGT and most notably the ALP). The impact of FF therapy on the liver function enzymes was greater in participants with higher baseline value. However, there was no significant impact of FF therapy on the inflammatory marker (CRP), markers of muscle damage (CK and LDH), and thyroid function tests.

3.4.1 FF increased serum CREA, reduced UA and impacted kidney function Evidence from various studies have shown that FF therapy is associated with increase in serum CREA levels (or inferred nephrotoxicity) (Tsimihodimos et al., 2005; Ansquer et al., 2008; Forsblom et al., 2010; Ncube et al., 2012). Consistent with these reports, this study showed that FF (160 mg/day) therapy caused a moderate increase in serum CREA and serum urea concentrations, and a decrease in kidney function estimated using the MDRD and CKD-EPI CREA (2009) equations. The increase in serum CREA concentration was statistically significant after the first 3 months of FF therapy (p=0.0016, n=13). In a recent study, Ncube et al., (2012) reported similar increases in serum CREA and CYSC after 3 months of FF (267 mg/day) in patients with hyperlipidaemia. This suggests that FF-associated creatininaemia occurs irrespective of the dose of the drug administered. Also, previous studies have shown that FF-associated creatininaemia was partially reversed when the drug was withdrawn (Michaleckyj et al., 2012; Park et al., 2017). The results from this study have also demonstrated that the increase in serum CREA was partially reversible even when treatment was administered in the long term (12 months).

The clinical significance of this reversible increase in serum CREA observed with FF therapy is unclear. However, several hypotheses have been proposed to explain the underlying mechanism. One of such views is that FF therapy impairs the synthesis of vasodilatory prostaglandins in the kidney, leading to decreased dilation of the afferent arteriole, thereby compromising glomerular capillary pressure and a reduced perfusion of the kidneys (Tsimihodimos et al., 2005). Other studies have indicated that FF competitively inhibits CREA secretion in the proximal tubular lumen leading to accumulation in the blood (Lipscombe et al., 2001). Some reports also suggest that

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FF therapy leads to over-production of CREA from damaged muscles (Hottelart, et al., 2002). Abbas et al., (2012) suggested analytical interference with methods used to measure CREA concentration is responsible for the increase in CREA concentration observed with FF therapy. Evidence from studies by Hottelart et al., (2002) and Ansquer et al., (2008) comparing the Jaffe colorimetric method with the high performance liquid chromatography (HPLC) and mass spectrometry, respectively, suggest that the increase in serum CREA observed during FF therapy is not method dependent. A more generally accepted view is that FF therapy leads to a decrease in GFR and consequently an increase in serum CREA (Ncube et al., 2012).

In this study, none of the participants complained of muscle pains, or demonstrated serum CK or an LDH level that was clinically significant during the treatment period. These results make over-production of creatinine less likely the cause of FF- associated creatininaemia. Although spot urine creatinine concentration decreased significantly as serum CREA was increased during the first three months of FF therapy, inhibition of CREA secretion or decrease CREA excretion cannot be excluded as possible causes of the increase in serum CREA, because urine CREA can vary a lot based on diet, exercise and hydration levels (Pimenta et al., 2016). The results from this study suggest that a reduction in GFR is the major cause of the increase of serum CREA in patients receiving FF treatment. The concomitant increase in serum CREA and serum urea concentrations (independent markers of kidney function), suggests that the decrease in GFR is a major cause of the increase in serum CREA in patients receiving FF therapy. Thus, indicating a genuine impact of FF therapy on the kidney function.

Previous studies have also shown that FF therapy reduces serum UA levels in patients with and without gout. In a double-blind, placebo-controlled trial over 6 weeks in men with hypertriglyceridaemia, Bastow et al., (1988) observed a 20% reduction in serum UA with FF but not with bezafibrate (another fibric acid derivative). In another placebo-controlled study, Feher et al., (1999) also showed that FF administered for 12 weeks reduced serum UA by 31% in patients with type 2 diabetes mellitus and hypercholesterolaemia. Noguchi et al., (2004) had also shown that FF (300 mg/day) for 12 weeks decreased serum uric acid concentration by 36% and increased FEUA by 62% in hyperlipidaemic patients. In a longer-term study in

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Fombon, I.S. (2020) patients with either mixed hyperlipidaemia or hypercholesterolaemia treated with FF, De La Serna and Cardaso (1999) demonstrated a 23% reduction in serum UA at 3 months, which was maintained for 2 years. A recent study by Waldman et al., (2018) also showed that FF reduced serum UA by 20%, and almost halved gout events over 5 years of treatment in adult patients with type 2 diabetes.

This study also confirmed that FF administered at 160 mg/day is associated with a significant reduction in serum UA levels in dyslipidaemic patients with normal baseline UA levels, via increased` renal UA clearance. The results also showed that the impact of FF on serum UA decreased as treatment was continued in the longer- term. This unique effect of FF compared with other fibric acid derivatives, on renal UA clearance has been attributed to its unique chemical structure (Desager et al., 1980). The mechanism is considered to be through the inhibition of a protein encoded by the gene SLC22A12, also known as urate transporter 1 or URAT1 by fenofibric acid, the major metabolite of FF. URAT1 is the central mediator in the transport of uric acid from the kidney into the blood (Uetake et al., 2010).

3.4.2 FF therapy associated with a reduction in liver enzymes Here, the impact of FF therapy on routine clinical biomarkers of liver function, namely ALB, TBIL, ALT, ALP, AST and GGT, was examined. Several studies have reported that serum ALT and AST levels were increased in some patients who received fibrate therapy (Chatrenet et al., 1993; Bernard et al., 1994). The increase in these liver enzymes is generally mild (less than three times the upper limit of normal), sometimes transient and not accompanied by any finding suggestive of hepatotoxicity (Kobayashi et al., 2009). However, results from this study demonstrated a decrease in liver enzymes, with a significant reduction in serum ALP and GGT, but also a moderate reduction in serum AST and ALT following FF (160 mg/day) treatment. The decrease in serum ALP and GGT enzymes was greater in participants with higher baseline values.

Liver plays a crucial role in lipid metabolism. Defective fatty acid homeostasis regulation may induce lipotoxic tissue damage, including hepatic steatosis (Browning and Horton, 2004). PPARα is the most abundant isoform of PPAR in hepatocytes

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Previous studies on the effect of FF on liver function have provided contrasting reports. While some studies have suggested that FF therapy improves liver function (Gandhi et al., 2014), cases of chronic active hepatitis of autoimmune origin and severe fibrosis have been reported after treatment with FF (Chatrenet et al., 1993; Bernard et al., 1994). Results from in vitro studies have shown that FF can modify the expression of the ALT and AST genes in animal and human hepatocytes and to increase the activities of these enzymes in a PPARα-dependent manner in human hepatocytes (Thulin et al., 2008). The gene modification and elevations of the enzyme activities of ALT and AST in the hepatocytes are considered to be related to the pharmacological action of FF, independently of any toxic phenomenon (Tomkiewiecz et al., 2004). Kobayashi et al., (2009) reported increase in plasma ALT and AST activities in rats treated with 400 mg/kg of FF after 24 and 48 hours, respectively, and suggested that plasma ALT is more sensitive than AST to FF therapy. The increases in plasma ALT and AST were slight and not accompanied by any changes in other liver function tests including LDH, ALP, and TBIL levels.

In this study, FF (160 mg/day) treatment caused a moderate reduction in serum ALT and AST and a significant reduction in serum ALP and GGT concentrations as described in earlier studies by Ganotakis et al., (2002). These findings are also consistent with those of Gandhi et al., (2014) who demonstrated a significant reduction in levels of GGT, ALT and ALP, following treatment with fibrates in patients with a metabolic syndrome. The greatest impact was observed on serum ALP levels. This study also revealed that the impact of FF therapy on the liver enzymes was greater in patients with higher baseline values. The mechanisms underlying the decrease in serum ALP by fibrates remain undefined. However, it is speculated that changes in hepatic fat deposition may be involved in the profound decrease in serum

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Fombon, I.S. (2020) cholestatic enzymes (Mikhailindis et al., 1998). In another hypothesis, Day et al., (1993) suggest that the decrease in liver ALP activity is attributed to reduction in the rate of liver ALP release, resulting from a reduction in the rate of hepatic bile acid secretion. The decrease in ALP activity has been used as a reliable marker for patients’ compliance to fibrate therapy (Mikhailindis et al., 1998).

3.4.3 FF reduced serum TRIG and increased HDL cholesterol levels FF therapy is used primarily to reduce elevated levels of TRIG, but it may also reduce LDL cholesterol levels and induce a moderate increase in HDL cholesterol in patients with mixed dyslipidaemia (Farnier, 2008). Previous studies have shown that FF monotherapy decreases serum TRIG levels by 20% - 50% and increases HDL cholesterol levels by 10% - 50% (Knopp et al., 1987; Sasaki et al., 2002; Birjmohun et al., 2005). Results of the FIELD study, a 5-year, randomised, placebo-control trial testing the safety and efficacy of FF 200 mg in type 2 diabetic patients showed that FF decreased serum TRIG and LDL cholesterol levels moderately (by 29% and 12%, respectively) and increased HDL cholesterol by 5% after 4 months of therapy (Keech et al., 2005). A recent study by Ncube et al., (2012) demonstrated that FF (267 mg/day) reduced serum TRIG and serum CHOL levels by 38.8% and 22.2%, respectively, but did not have any significant effect on serum HDL cholesterol level after 3 months of treatment.

This study showed the FF therapy reduced median serum TRIG concentration (20% – 37%) and increased median HDL cholesterol concentration (7% – 13%), but did not impact serum CHOL levels in all the groups of participants. FF reduces serum TRIG levels by inhibiting their synthesis and stimulating their clearance. A number of mechanisms have been described through which FF reduces serum TRIG levels. In one of these mechanisms, FF induces fatty acid oxidation, thereby reducing the availability of fatty acids for the synthesis and secretion of VLDL, which contains a very high amount of TRIG (Gotto, 1990; Minnich et al., 2001). FF may also augment LPL activity leading to increased hydrolysis of TRIG on several lipoproteins (Staels et al., 1998). It is also reported that FF can reduce serum TRIG by decreasing apo C-II and apo C-III expressions in the liver via PPAR-α activation, thereby enhancing

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Fombon, I.S. (2020) catabolism of TRLs. Apo C proteins are crucial for TRIG metabolism. Apo C-III delays catabolism of TRLs by inhibiting their binding to the endothelial surface and subsequent lipolysis by LPL (Malmendier et al., 1989).

This study also showed a moderate rise (7% - 13%) in serum HDL cholesterol concentration after 3 - 6 months of FF treatment. This is consistent with the findings of the FIELD study, which showed a moderate increase in HDL cholesterol after 4 months of treatment. FF is known to improve HDL cholesterol levels by promoting apo A-I and apo-II synthesis in the liver, which may contribute to the increase in serum HDL concentrations and a more efficient RCT pathway (Staels et al., 1998). FF may increase pre-ꞵ1-HDL cholesterol levels in patients with metabolic syndrome, reduce total plasma cholesteryl ester transfer protein activity, induce the activity of ABCA1, member of the human transporter subfamily, also known as the cholesterol efflux regulatory protein (CERP), and may also induce hepatic lipase activity.

Results from various studies have shown that FF decreases serum CHOL levels in patients with dyslipidaemia. It is suggested that FF and other drugs of the fibrate class reduce CHOL by decreasing CHOL synthesis via inhibition of HMG-CoA reductase and increasing CHOL excretion in the bile pool (Schneider et al., 1985; Li and Chiang, 2009). The evidence from this study did not demonstrate any improvement in serum CHOL levels and suggests that FF administered at 160 mg/day may not be effective for reducing serum CHOL levels in patients with mixed dyslipidaemia.

3.4.4 Impact of FF therapy on markers of inflammation and muscle damage Serum CRP is the most frequently tested inflammatory marker and high serum levels have been associated with increased risk of CVD (Ridker, 2003). Dyslipidaemia together with inflammatory processes play an important role not only in the pathogenesis of atherosclerosis but also in the occurrence of acute coronary syndromes (Ross, 1999; Libby, 2001). FF therapy has been shown to reduce serum CRP levels in both experimental studies and clinical trials (Hao et al., 2012; Min et al., 2012; Noureldein et al., 2015). Thus, suggesting that FF therapy may also have a role in curbing inflammation in patients with dyslipidaemia. However, the anti-

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Fombon, I.S. (2020) inflammatory effect of FF remains controversial because in other studies FF therapy failed to reduce serum levels of CRP (Kim, 2006; Mulvey et al., 2012).

This study showed that FF (160 mg/day) therapy did not reduce serum CRP levels in patients with dyslipidaemia. This study supports the reports by Kim (2006) and Mulvey et al., (2012). Kim (2006) showed that treatment with FF (200 mg/day) for 2 months failed to reduce serum CRP levels in hypertriglyceridaemic patients. In another related study, Mulvey et al., (2012) also demonstrated that FF 145 mg/day for 8 weeks did not cause a significant reduction in CRP and failed to suppress inflammatory responses in healthy volunteers. This contradicts reports by Wu et al., (2007) who demonstrated a 36% decrease in serum high-sensitivity CRP level in patients with metabolic syndrome after 12 weeks of FF therapy and those by Min et al., (2012) who also reported a reduction in serum CRP levels after 2 months of FF (200 mg/day) therapy in hyperlipidaemic patients with high CRP and/or low LDL CHOL levels and without severe overweight. The reason for such disparities has not been established.

All fibrates are associated with rhabdomyolysis either in monotherapy or combined with statins and other agents. The highest rates of fibrate-associated rhabdomyolysis have been reported with gemfibrozil, followed by bezafibrate, FF, ciprofibrate, and clofibrate (Wu et al., 2009). Evidence of FF-associated rhabdomyolysis has been described in several case studies and close supervision of patients treated with FF, and patient education about potential side effects have been advocated (Barker et al., 2003). This study did not show any significant impact of FF (160 mg/day) therapy on skeletal muscles as patient results were within acceptable reference ranges for both CK and LDH. Also, none of the patients reported any symptoms that may be associated with muscle injury. These findings suggest that FF (160 mg/day) treatment is safe in dyslipidaemic patients with normal baseline kidney function and thyroid function. However, close patient monitory is still recommended in patient receiving FF therapy, most particularly in those with increased risks of rhabdomyolysis.

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3.4.5 Thyroid function tests unchanged by FF therapy Previous studies have shown that hypothyroidism is closely associated with both dyslipidaemia and kidney dysfunction (Yuan et al., 2015). In a research study in Colorado, Canaris et al., (2000) demonstrated that lipid levels increased in a graded fashion as thyroid function declined. In the Nord-Trondelag Health Study (HUNT), Asvold et al., (2011) demonstrated that CKD was more common in subjects with hypothyroidism; even within the reference range. The prevalence of CKD was shown to gradually increase as serum thyrotropin levels increased. These results suggested that both kidney function and lipid metabolism could be modulated by thyroid function. Evidence from several case studies has shown that hypothyroidism (overt or subclinical form) is a risk factor for FF-associated rhabdomyolysis in patients with dyslipidaemia (Kato et al., 2011).

Reports by Gullu et al., (2005) showed that simvastatin administered to patients with subclinical hypothyroidism secondary to autoimmune thyroid disease led to a reduction in serum TSH levels, accompanied by an increase in free thyroid hormones. According to the authors of this study, this effect was due to proapoptotic properties of statins. Krysiak et al., (2011) reported that FF exhibited a similar effect to that of simvastatin on the secretory function of human monocytes and lymphocytes and on system inflammation in mixed dyslipidaemia with type 2 diabetes. However, no previous studies have examined the effect of FF on thyroid function. This study has shown that FF (160 mg/day) does not have any significant impact on thyroid function in dyslipidaemic patients with normal baseline thyroid function. The results suggest that the decrease in kidney function observed with FF therapy in this study was not associated with changes in thyroid function.

3.5 SUMMARY This study confirms that FF (160 mg/day) is associated with a moderate increase in serum CREA and serum urea concentrations, and a decrease in GFR estimated using the MDRD and CKD-EPI Creatinine (2009) equations. The study results also showed that treatment with FF at 160 mg/day would cause a significant reduction in serum UA via increased urinary excretion of UA. Another important finding of this

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Fombon, I.S. (2020) study is that FF (160 mg/day) would reduce liver function enzymes (ALT, AST, GGT and ALP) in patients with dyslipidaemia. The clinical significance of the decrease in liver enzymes is unclear. As expected, FF therapy caused a moderate reduction serum TRIG level and an increase in HDL cholesterol level but did not have any significant impact on serum CHOL level. FF therapy also failed to reduce serum CRP levels and had no significant impact on skeletal muscle enzymes and markers of thyroid function.

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Chapter 4

Utility of CYSC as biomarker of kidney function in patients receiving FF treatment for dyslipidaemia

4.1 INTRODUCTION FF is the preferred hypolipidaemic drug for the treatment of dyslipidaemia characterised by elevated levels of TRIG and reduced levels of HDL cholesterol, with or without elevated LDL cholesterol (Tziomalos and Athyros, 2006). However, due to its association with elevated levels of serum CREA (inferred nephrotoxicity); routine kidney function monitoring has been recommended in patients receiving FF, particularly in those with pre-existing CKD (Davidson et al., 2007, Mychaleckyj et al., 2012; Ncube et al., 2012). The GFR is considered as the best overall measure of kidney function (Stevens et al., 2006). It is a measure of the volume of fluid that is filtered from the kidney glomerular capillaries into the Bowman’s capsule per unit of time. However, reference methods for estimating the GFR such as the use of exogenous markers (inulin, 51CrEDTA, 99mTcDTPA, 125I-iothalamate and iohexol) are impractical in the clinical setting and for large research studies, because they are complex, time-consuming, labour-intensive and expensive and require administration of substances that make them incompatible with routine monitoring (Hojs et al., 2011, Stevens, et al., 2006).

Assessment of kidney function in clinical practice is largely dependent on measurement of serum CREA, CREA clearance and estimates of glomerular filtration rate (eGFR) derived from serum CREA-based equations (Stevens et al., 2006). However, serum CREA is not a very sensitive marker in diagnosing early stages of kidney disease. Several limitations of serum CREA and CREA clearance for the estimation of GFR are well documented (Sica, 2009). It is known that the serum level of CREA increases above the normal upper reference range when there is more than 50% reduction in GFR, leading to delayed diagnosis of kidney disease (Prigent, 2008). Serum levels of CREA are also affected by several factors that are independent of changes in GFR such as muscle mass, age, gender, race, medication

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Fombon, I.S. (2020) use, catabolic state, tubular secretion and extra-renal elimination in severe renal impairment (Perrone et al., 1992). Although anthropometric data have been incorporated into various serum creatinine-based formulas in an attempt to improve the estimation, GFR from serum CREA are reported to be affected by bias and imprecision, and their use in clinical diagnostics have been questionable (Hojs et al., 2011). Also, the methods used to measure serum creatinine have been shown to interfere with the accuracy of the GFR estimation formulae (Hojs et al., 2008).

Among several novel endogenous biomarkers of kidney function that have been tested, CYSC has been proposed as the best alternative marker to CREA, which can detect early kidney disease (Hojs et al., 2008, Bolignano, et al., 2010). CYSC is a non-glycosylated, 13.3-kDa protein belonging to the cystein protease inhibitor family (Shiplak et al., 2013). It is produced in the body by all nucleated cells at a relatively constant rate and is freely filtered across the glomerular membrane and almost completely reabsorbed and catabolised by the proximal tubules of the kidneys. Unlike serum CREA, serum CYSC level is independent of muscle mass, gender, race and metabolic state (Bagshaw and Bellomo, 2010). It is also shown to be a sensitive early indicator of decreased renal function. However, further studies are recommended to fully evaluate the potential advantages and limitations of CYSC before it is ready to supplant CREA as the most widely used GFR marker. Previous studies by Forsblom et al., (2010), Ncube et al., (2012) and Mychaleckyj et al., (2012) demonstrated concomitant increase in serum CREA and serum CYSC following short-term and long-term administration of FF treatment, but whether CYSC is a better biomarker than CREA in monitoring kidney function in patients receiving FF therapy is not fully elucidated.

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4.2 SPECIFIC AIMS CYSC has been proposed as a better marker of assessing the kidney function. In this study, we investigated whether serum CYSC is a better marker than serum CREA for monitoring kidney function in patients receiving FF therapy, with the following objectives:

1. To determine the analytical performance of Gentian CYSC assay using the Beckman Coulter AU640 analyser.

2. To investigate the impact of FF (160 mg/day) therapy on serum CYSC and CREA concentrations in patients with dyslipidaemia.

3. To compare changes in serum CYSC- and CREA-based eGFRs in patients administered FF therapy for dyslipidaemia.

4. To examine the concordance between serum CREA- and CYSC-based equations for estimating GFR in patients administered FF therapy for dyslipidaemia.

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4.3 RESULTS

4.3.1 Analytical performance of Gentian CYSC on the Beckman Coulter AU640 analyser

Gentian CYSC Assay precision The intra-assay (within-run) precision of the Gentian CYSC assay on the Beckman Coulter Olympus AU640 analyser was determined by measuring 10 aliquots of each of three pools of serum samples prepared in the laboratory to give a low, normal and high concentration of serum CYSC, respectively. All the aliquots were analysed in a single batch. The inter-assay (between-run) precision was determined by measuring aliquots of the three serum pools and two levels of control materials supplied by the manufacturer, across ten working days. The intra-assay and inter-assay coefficients of variation (CV) for the Gentian CYSC on the AU640 analyser are summarised on Table 4.1. All the CV for the serum pools were lower than the values estimated by the Gentian CYSC kit manufacturers’ i.e. 1.76 and 3.88% for quality controls level 1 (target value: 0.95 mg/L) and level 2 (target value: 3.52 mg/L), respectively. This suggests a better precision for determining serum CYSC on the AU640 analyser using the Gentian CYSC kit than specified by the manufacturers.

Gentian CYSC Assay Sensitivity The limit of blank (LoB), limit of detection (LoD) and limit of quantification (LoQ) for the Gentian CYSC assay on the Olympus AU640 analyser were determined as 0.07 mg/L, 0.13 mg/L, and 0.36 mg/L, respectively. These results support the manufacturer’s claim of an LoQ of less than 0.5 mg/L for the AU680 systems, which are modified versions of the AU640 analysers.

Gentian CYSC Assay Linearity and analytical recovery Assay linearity was confirmed by making measurements using solutions containing known concentrations of CYSC. The linear regression analysis showed a line with a good correlation coefficient (y = 1.005x + 0.5179; R2 = 0.9988) over the entire range tested (from 0.21 – 7.9 mg/L). The maximum deviation between estimated and measured CYSC was 3.8%, while the rest of the samples showed deviations of less than 2.0% (Figure 4.1). To determine the analytical recovery of the assay, a pool of

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Fombon, I.S. (2020) serum sample was diluted in normal saline (0.9%, w/v) to give six separate sera and then analysed. Analytical recovery ranged from 84% to 107%, with the lowest recovery seen in the most diluted sample (Table 4.2). The results suggest that the assay tends to under-recover below the LoQ (0.36 mg/L) and slightly over-recovers within the linear limit of the assay (0.45 – 8.0 mg/L).

Comparison of Gentian CYSC Assay using the Olympus AU640 analyser and the Jaffe CREA method A comparison of the Gentian CYSC assay on the Olympus AU640 analyser and the Jaffe (compensated) CREA method was made using 20 serum samples and following the CLSI EP9-A3 guideline. The results showed a strong positive correlation (r = 0.9830, p<0.0001) between the two methods within the range 0.70 – 4.09 mg/L, with intercept at 0.45 mg/L (95% CI 0.29 – 0.64) and a slope of 0.008 mg/L (95% CI 0.007 – 0.009) (Figure 4.2).

Stability of CYSC in serum sample

The stability of CYSC was examined in 40 serum samples stored at -80C for 30 months. The samples were subjected to a single freeze thaw cycle. The difference between the median CYSC concentration before (0.79 mg/L; IQR 0.69 – 7.60 mg/L) and after storage (0.72 mg/L; IQR 0.60 – 7.8 mg/L) was not statistically significant (p=0.1863, n=40). These results suggest that storage of serum samples for 30 months at -80C did not have any significant impact on the level of CYSC in the samples.

Collectively, these results confirmed the manufacturers’ specifications of the various parameters tested and suggest that Gentian CYSC assay performs well and often exceed the manufacturers’’ specifications on the Beckman Coulter AU640 analyser and can be used to analyse and/or compare serum CYSC and serum CREA as biomarkers of kidney function in patient samples within the range of specifications.

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Table 4.1 Summary of imprecision data of the Gentian CYSC on Olympus AU640 analyser

Pool 1 Pool 2 Pool 3 QC1* QC2* Intra-assay Mean (mg/L) 0.87 2.48 6.10

SD¥ 0.01 0.03 0.06 CV§ (%) 0.91 1.26 0.99 Inter-assay Mean (mg/L) 0.88 2.53 6.19 0.89 3.38 SD¥ 0.01 0.04 0.10 0.02 0.13

CV§ (%) 1.36 1.42 1.54 1.78 3.88

*QC, quality control; ¥SD, standard deviation; §CV, coefficient of variation (n=10).

Table 4.2 Analytical recovery of the Gentian CYSC* assay on Olympus AU640 analyser

Pool Estimated (mg/L) Measured (mg/L) Recovery (%) (dilution) Neat 7.90 7.90 100

1:2 3.95 4.20 106.3

1:4 1.98 2.07 104.5

1:8 0.99 1.05 106.1

1:16 0.49 0.52 106.1

1:32 0.25 0.21 84

*CYSC, serum cystatin C

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Figure 4.1 Representative plot of the linearity of the Gentian CYSC assay. A patient sample was diluted with increasing amounts of normal saline (0.9% NaCl) and CYSC concentration was measured in the diluted samples. Data shows measured values of CYSC (y-axis) plotted against the estimated values of CYSC (x-axis). The solid line represents the linear regression line; the dotted line represents the upper and lower 95% confidence intervals. Each sample was run in triplicate (n=6).

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Figure 4.2 Correlation between Gentian CYSC and CREA by the Jaffe (compensated) method on the AU640 analyser. Serum samples not more than 5 days old kept at 4 - 8°C were analysed on the Beckman Coulter AU640 analyser for serum CYSC and CREA and correlation between the two assays was determined using the Spearman’s correlation coefficient (r=0.9786, p<0.001, n=15); CYSC, cystatin C; CREA, creatinine.

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4.3.2 Impact of FF therapy on serum CREA, CYSC and eGFR calculated using CREA- and CYSC-based equations The impact of FF therapy on serum CYSC and serum CREA levels was examined during 12 months of FF therapy in patients with dyslipidaemia. Changes in GFR estimated using CREA-based equations were compared with those calculated by CYSC-based formulas. The participants were placed into five groups (A – E) based on samples provided as described earlier (Chapter 2.2.2 – Study participants). The serum CREA-based formulas used in this study included the MDRD and CKD-EPI CREA equations, while the serum CYSC-based formulas were the Gentian and CKD- EPI CYSC equations. The performance of the combined CKD-EPI CREA-CYSC- based equation derived by Inker et al., (2012) was also examined. First, the impact of FF therapy administered for 3, 6 and 12 months on the concentration of serum CREA, serum CYSC and the eGFR calculated using the various equations was examined. Results of the various parameters measured before starting treatment and after 3, 6 and 12 months of FF therapy are summarised in Table 4.3.

In Group A, the impact of 3 months of FF therapy on serum CYSC, CREA and the eGFR calculated using the various equations was examined in 13 participants (9 male and 4 female) with median age 51 years (range 37 – 65 years). The results showed a significant increase in the median serum CREA concentration (p=0.0015, n=13), but the change in the median serum CYSC concentration was not statistically significant (p=0.1619, n=13). A significant reduction in the median eGFR was observed when it was calculated using MDRD and CKD-EPI CREA equations (p=0.0039 and p=0.0229, respectively). The changes in the median eGFR calculated using the Gentian and CKD-EPI CYSC equations were not statistically significant (p=0.3140 and p=0.1853, respectively). However, a statistically significant reduction in the eGFR was observed with the CKD-EPI CREA-CYSC equation (p=0.0422, n=13).

Results from individual participants at baseline and after 3 months of FF therapy are presented in Figure 4.3. After 3 months of FF therapy, serum CREA concentration was increased in 11 participants (84.6%, n=13), while serum CYSC was increased in 10 participants (76.9%, n=13). A concomitant increase in serum CREA and CYSC concentrations was observed in nine participants (69.2%, (n=13), while in two

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Fombon, I.S. (2020) subjects an increase in serum CREA was accompanied by a decrease in serum CYSC. A decrease in both parameters was also observed in one subject, where a 4.2% decrease in serum CREA was accompanied by a 27.9% decrease in serum CYSC. In one subject, the serum CREA level was not changed, but serum CYSC was increased by 18.8%. The change in eGFR in the individual study participants reflected the change in their serum CREA and serum CYSC concentrations; i.e. the eGFR was reduced in the participants in whom serum CREA and serum CYSC levels were increased and the eGFR was increased in those whose serum CREA and serum CYSC levels were reduced. These results suggest that administration of FF at 160 mg/day for 3 months would cause a moderate increase in serum CREA and CYSC concentrations, and a mild reduction in kidney function in some patients. The impact of FF was greater on serum CREA than CYSC and also varied among the participants.

The impact of 6 months of FF therapy on serum CYSC serum CREA and the eGFR calculated using the different equations was examined in 10 participants.(7 male and 3 female) in Group B, median age 51 years (range 37 – 63 years). As shown in Table 4.3, the changes in the median serum CREA and serum CYSC concentrations after 6 months of FF therapy were not statistically significant. However, a 14.5% increase in the median serum CREA was observed. Also, the changes in the median eGFR calculated using both the serum CREA-based and serum CYSC-based equations were not statistically significant. Individual study participant results showed that serum CREA concentration was increased in eight participants (80%, n=10), while serum CYSC concentration was increased in five participants (50%, n=10). Both serum CREA and serum CYSC were increased in four participants (40%, n=10), while in four others, an increase in serum CREA was accompanied by a decrease in serum CYSC. In one subject, a decrease in serum CREA was accompanied by a decrease in serum CYSC, and in another, serum CREA was decreased by 8.9% while serum CYSC was increased by 10.3% (Figure 4.4).

Results from individual participants also showed that the eGFR was reduced in seven participants (70%, n=10) when it was calculated using the MDRD and CKD- EPI CREA equations. However, with the Gentian, CKD-EPI CYSC and CKD-EPI CREA-CYSC equations, the eGFR was reduced in five participants (50%, n=10).

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These results suggest that FF administered at 160 mg/day for 6 months would cause a moderate increase in the serum CREA and CYSC concentrations and a mild reduction in kidney function estimated using both serum CREA- and CYSC-based equations. These results also showed a greater impact on serum CREA than on serum CYSC and that the CREA-based equations tend to underestimate the GFR, compared with the CYSC-based equations. The impact of FF on serum CREA and CYSC also varied amongst the participants.

In Group C, the impact of 12 months of FF therapy on serum CREA, CYSC and the eGFR calculated using the different equations was examined in eight participants.(5 male and 3 female) with median age 53 years (range 40 – 65 years). The median serum CREA and serum CYSC did not change significantly from baseline values. However, the median serum CREA was increased by 11.8% (p=0.0703, n=8) and serum CYSC by 6.6% (p>0.9999, n=8). The median eGFR was significantly reduced when it was calculated using the CKD-EPI CREA equation (p=0.0391, n=8). Changes in the eGFR estimated using all the other equations were not statistically significant. Individual participant results showed that the serum CREA concentration was increased in six participants (75%, n=8), while serum CYSC was increased in four participants (50%, n=8) (Figure 4.5). A concomitant increase in both markers was observed in three participants (37.5%, n=8). In three others an increase in serum CREA was accompanied by a decrease in serum CYSC concentration. However, in one subject, a 5.2% increase in serum CREA was accompanied by a 28.6% increase in serum CYSC (Figure 4.5).

Again, these results suggest that FF therapy administered for 12 months had greater impact on serum CREA than on serum CYSC. The CREA-based eGFR equations underestimated the eGFR compared with CYSC-based equations. The results also suggest that the impact of FF therapy on serum CREA, CYSC and the eGFR calculated using the CREA- and CYSC-based equations decreases as treatment time is increased.

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Table 4.3 Effect of FF therapy on serum CYSC, serum CREA and eGFR calculated using CYSC-based and CREA-based equations

Parameters Baseline 3 Months P value Number of patients (n) 13 13 Age [Years; median (range)] 51 (37 – 65) 51 (37 – 65) Serum CREA (µmol/L) 87 (70 – 101) 97 (72 – 121) 0.0015** Serum CYSC (mg/L) 1.0 (0.73 – 1.20) 0.99 (0.74 – 1.40) 0.1619 MDRD¥ (mL/min/1.73 m2) 78 (67 – 87) 73 (55 – 77) 0.0039** Gentian CYSC‡ (mL/min/1.73 m2) 76 (65 – 127) 81 (52 – 125) 0.3140 CKD-EPI CREA§ (mL/min/1.73 m2) 84 (68 – 98) 81 (59 – 87) 0.0229* CKD-EPI CYSC# (mL/min/1.73 m2) 77 (66 – 116) 77 (57 – 103) 0.1853 CKD-EPI CREA-CYSC† (mL/min/1.73 m2) 85 (70 – 106) 72 (67 – 94) 0.0422* Parameters Baseline 6 Months P value Number of patients (n) 10 10 Age [Years; median (range)] 51 (37 – 63) 51 (37 – 63) Serum CREA (µmol/L) 88 (71 – 98) 96 (76 108) 0.1934 Serum CYSC (mg/L) 1.1 (0.67 – 1.20) 1.0 (0.65 – 1.30) >0.9999 MDRD¥ (mL/min/1.73 m2) 75 (68 – 85) 70 (61 – 80) 0.2031 Gentian CYSC‡ (mL/min/1.73 m2) 74 (64 – 144) 80 (56 – 156) 0.4766 CKD-EPI CREA§ (mL/min/1.73 m2) 81 (71 – 97) 79 (66 – 91) 0.5332 CKD-EPI CYSC# (mL/min/1.73 m2) 73 (66 – 108) 77 (60 – 118) 0.6406 CKD-EPI CREA-CYSC† (mL/min/1.73 m2) 82 (71 – 103) 84 (67 – 98) 0.6094 Parameters Baseline 12 Months P value Number of patients (n) 8 8 Age [Years; median (range)] 53 (40 – 65) 53 (40 – 65) Serum CREA (µmol/L) 85 (61 – 98) 95 (59 - 119 0.0703 Serum CYSC (mg/L) 0.76 (0.60 – 1.10) 0.81 (0.50 – 1.10) >0.9999 MDRD¥ (mL/min/1.73 m2) 82 (68 – 91) 73 (55 – 94) 0.1484 Gentian CYSC‡ (mL/min/1.73 m2) 120 (68 – 171) 111 (70 – 230) 0.9453 CKD-EPI CREA§ (mL/min/1.73 m2) 91 (72 – 100) 80 (59 – 100) 0.0391* CKD-EPI CYSC# (mL/min/1.73 m2) 98 (69 – 118) 100 (69 – 135) 0.7266 CKD-EPI CREA-CYSC† (mL/min/1.73 m2) 97 (74 – 109) 92 (59 – 121) 0.7969 Data are presented as median (IQR); IQR, interquartile range. CREA, creatinine; ¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); ‡CYSC, cystatin C; #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012); significant differences between the median concentrations were determined using the Wilcoxon signed-rank test; *p<0.5, **p<0.01.

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Figure 4.3 Effect of 3 months of FF on serum CREA, CYSC and eGFR calculated by the different equations after 3 months of FF therapy. Scatter plots showing individual participant’s levels of (A) serum CREA, (B) serum CYSC, and eGFR calculated by (C) MDRD, (D) Gentian, (E) CKD-EPI CREA, (F) CKD-EPI CYSC and (G) CKD-EPI CREA-CYSC at baseline (0) and 3 months of FF therapy. Significant differences between median concentrations were analysed using the Wilcoxon signed rank test, p<0.05 was considered to be statistically significant.

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Figure 4.4 Effect of 6 months of FF on serum CREA, CYSC and eGFR calculated using the CREA- and CYSC-based equations. Scatter plots showing individual participant’s levels of (A) serum CREA, (B) serum CYSC, and eGFR calculated by (C) MDRD, (D) Gentian, (E) CKD-EPI CREA, (F) CKD-EPI CYSC and (G) CKD-EPI CREA-CYSC at baseline (0) and 6 months of FF therapy. Significant differences between median concentrations were analysed using the Wilcoxon signed rank test; p<0.05 was considered to be statistically significant.

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Figure 4.5 Effect of 12 months of FF on serum CREA, CYSC and eGFR calculated using CREA- and CYSC-based equations. Scatter plots showing individual participant’s levels of (A) serum CREA, (B) serum CYSC, and eGFR calculated by (C) MDRD, (D) Gentian, (E) CKD-EPI CREA, (F) CKD-EPI CYSC and (G) CKD-EPI CREA-CYSC at baseline (0) and 12 months of FF therapy. Significant differences between median concentrations were analysed using the Wilcoxon signed rank test, p<0.05 was considered to be statistically significant.

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Next, changes in serum CREA, CYSC and eGFR calculated using both parameters were examined in nine participants (6 male and 3 female), median age 53 years (range 42 – 65 years) during 6 months of FF therapy. The results obtained at baseline, 3 and 6 months after commencing treatment with FF are summarised in Table 4.4. The Friedman test indicated that there was a statistically significant difference between the median serum CREA and CYSC concentrations at the different time points during the 6 months of FF therapy (p=0.0266 and p=0.0476, respectively). Post hoc analysis using Dunn’s multiple comparison test showed a significant increase in median serum CREA after 3 months (p=0.0286, p=9), but not after 6 months. The median serum CYSC was increased by 12.0% after 3 months, but was slightly reduced after 6 months of treatment, although the level remained higher than baseline value after 6 months of treatment.

The median eGFR calculated using the MDRD and CKD-EPI CREA equations did not change significantly at either 3 or 6 months (p=0.1267 and p=0.1966, respectively). However, changes in median eGFR calculated using the Gentian, CKD-EPI CYSC and CKD-EPI CREA-CYSC at the different time points during the 6 months FF therapy was statistically significant (p=0.0476, p=0.0476 and p=0.0231, respectively). A significant decrease in the median eGFR was observed after 3 months with the CKD-EPI CREA-CYSC but not after 6 months of FF therapy. Individual participant results showed an initial increase in serum CREA and CYSC levels after 3 months and a slight decrease in both parameters after 6 months of treatment, although the serum CREA and CYSC concentrations after 6 months of treatment remained higher than the baseline value in most of the subjects (Figure 4.6). A reduction in the eGFR was also observed after the first 3 months of treatment and after 6 months, the eGFR was slightly increased although the values after 6 remained lower than the baseline values (Figure 4.6).

These results suggest that the impact of FF therapy on the kidney function is greater during the first 3 months of treatment, but impact tends to decrease as treatment is continued for 6 months. The results confirmed that FF-induced increase in serum CREA and CYSC is partially reversible when treatment is administered in the long- term. The increase in both serum CREA and serum CYSC (independent markers of kidney function) suggest that FF therapy genuinely impacts on the kidney function.

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Table 4.4 Changes in serum CREA, CYSC and eGFR calculated using serum CREA- and CYSC-based equations during 6 months of FF therapy

Parameters Baseline 3 Months 6 Months p-value

Number of patients (n) 9 9 9

Age [Years; median (range)] 50 (42 – 55) 50 (42 – 55) 50 (42 – 55)

Serum CREA (µmol/L) 87 (70 – 96) 97 (71 – 121)1 94 (73 – 107) 0.0266*

Serum CYSC (mg/L) 1.08 (0.69 – 1.17) 1.21 (0.84 – 1.37) 1.12 (0.79 – 1.29) 0.0476*

MDRD¥ (mL/min/1.73 m2) 78 (69 – 85) 73 (55 – 78) 70 (63 – 81) 0.1267

Gentian CYSC‡ (mL/min/1.73 m2) 72 (65 – 146) 61 (51 – 105) 68 (56 – 116) 0.0476*

CKD-EPI CREA§ (mL/min/1.73 m2) 84 (70 – 97) 81 (59 - 89) 81 (68 – 92) 0.1966

CKD-EPI CYSC# (mL/min/1.73 m2) 69 (66 – 113) 63 (55 – 93) 73 (59 – 105) 0.0476*

CKD-EPI CREA-CYSC† (mL/min/1.73 m2) 79 (70 – 106) 70 (61 – 86) 81 (66 - 94) 0.0231*

Data are presented as median (IQR); CREA, creatinine; ¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); ‡CYSC, Cystatin C; #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012); Significant difference between the median concentrations at the different time points were analysed using the Friedman test with Dunn’s multiple comparison test; *p<0.05; n=9.

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Figure 4.6 Changes in serum CREA, serum CYSC and eGFR calculated using serum CREA- and CYSC-based equations during 6 months of FF therapy. Scatter plots showing individual participant’s levels of (A) serum CREA, (B) Serum CYSC, (C) MDRD, (D) Gentian, (E) CKD-EPI CREA, (F) CKD-EPI CYSC and (G) CKD-EPI CREA- CYSC at baseline (0), 3 and 6 months after commencing FF therapy in nine individual participants. Significant difference between median concentrations was analysed using the Friedman test with Dunn’s multiple comparison test; *p<0.05, n=9. 181

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Furthermore, the impact of FF therapy on serum CREA, serum CYSC and eGFR levels during a 12 month period of treatment was examined in four (2 male and 2 female) participants, median age 51 years (range 42 – 55 years). The median (IQR) levels of serum CREA, serum CYSC and eGFR calculated using the CREA- and CYSC-based equations at baseline, 3, 6 and 12 months after commencing treatment with FF are summarised in Table 4.4. The results showed no significant difference in the median levels of all the markers during the treatment period. However, an initial increase in the median serum CREA (23.4%) and serum CYSC (43.5%) was observed after 3 months of treatment, but the levels were reduced in the course of treatment after the following nine months. Similarly, the median eGFR levels were initially reduced after 3 months of FF therapy, reflecting the increase in serum CREA and serum CYSC and then gradually increased during the following nine months of FF therapy.

Individual study participant results showed that the serum CYSC concentration returned to baseline values, but the serum CREA concentration remained slightly higher than baseline values (Figure 4.7). It was also observed that the eGFR levels returned to baseline values after 12 months of treatment when the eGFR was calculated using serum CYSC-based and the combined CREA-CYSC-based equations. The median serum CREA-based eGFR returned to values slightly lower than the baseline values after 12 months of treatment. These results suggest a complete recovery of the kidney function after 12 months of treatment when the eGFR was calculated using the CYSC-based equations and a partial recovery when the eGFR was calculated using the serum CREA-based equations (Figure 4.7).

The results have shown that FF therapy is associated with a moderate increase in serum CREA and CYSC concentrations with a greater impact on serum CREA concentration than serum CYSC concentration. The increase in both serum CREA and CYSC (an independent marker of kidney function) is indicative of a genuine impact of FF therapy on the kidney function. This study has also shown that the impact of FF therapy on the kidney function is partially reversible when eGFR assessed by the serum CREA-based eGFR equations and completely reversible when assessed using the serum CYSC-based or the combined serum CREA-CYSC- based eGFR equations.

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Table 4.5 Changes in serum CREA, CYSC and eGFR, calculated using serum CREA- and CYSC-based equations in during 12 months of FF treatment.

Parameters Baseline 3 Months 6 Months 12 Months p-value Number of patients (n) 4 4 4 4

Age (years; range) 51 (43 – 57) 51 (43 – 57) 51 (43 – 57) 51 (43 – 57)

Serum CREA (µmol/L) 77 (60 – 99) 95 (68 – 122) 90 (70 – 117) 85 (59 – 118) 0.0933

Serum CYSC (mg/L) 0.69 (0.55 – 1.09) 0.99 (0.68 – 1.50) 0.91 (0.54 – 1.31) 0.70 (0.50 – 0.86) 0.3545

MDRD¥ (mL/min/1.73 m2) 82 (69 – 93) 60 (54 – 81) 68 (57 – 78) 74 (56 – 96) 0.0819

CKD-EPI CREA§ (mL/min/1.73 m2) 91 (75 – 102) 74 (58 – 92) 76 (61 – 89) 83 (60 – 103) 0.0819

Gentian CYSC‡ (mL/min/1.73 m2) 146 (74 – 188) 83 (48 – 151) 101 (55 – 206) 134 (102 – 230) 0.3545

CKD-EPI CYSC# (mL/min/1.73 m2) 113 (74 – 126) 81 (50 - 110) 90 (57 – 127) 111 (96 – 135) 0.3545

CKD-EPI CREA-CYSC† (mL/min/1.73 m2) 106 (76 – 116) 76 (54 – 103) 83 (59 - 113) 101 (78 – 121) 0.1576

Data are presented as median (IQR). CREA, creatinine; ¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); ‡CYSC, Cystatin C; #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012); Significant difference between the median concentrations at the different time points were analysed using the Friedman test with Dunn’s multiple comparison test; p<0.05 was considered to be significant.

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Figure 4.7 Changes in serum CREA, serum CYSC and eGFR calculated by different equations during 12 months of FF therapy. Scatter plots showing the levels of (A) serum CREA, (B) Serum CYSC, (C) MDRD, (D) Gentian eGFR, (E) CKD-EPI CREA, (F) CKD-EPI CYSC and (G) CKD-EPI CREA-CYSC at baseline (0), 3, 6 and 12 months after commencing FF therapy in four participants. Significant differences between median concentrations was analysed using the Friedman test with Dunn’s multiple comparison test; n=4.

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Collectively, these results show that FF (160 mg/L) therapy is associated with a moderate increase in serum CREA and CYSC concentrations but with a greater impact on serum CREA than CYSC. The impact of FF on the markers of kidney function is highest during the first 3 months of treatment and gradually reduces as treatment time is increased. Although both biomarkers decreased as FF therapy was continued for 12 months, serum CREA remained slightly higher than baseline, while cystatin C returned to baseline values. These results suggest that FF therapy may have a greater impact on serum CREA than on serum CYSC. The eGFR calculated using both serum CREA- and CYSC-based equations was decreased during the first 3 months of FF therapy, reflecting the increase in the serum markers. While kidney function completely recovered after 12 months of treatment when assessed using serum CYSC-based equations, results from serum CREA-based equations showed that kidney function remained lower than baseline values after 12 months of FF therapy. These results suggest that kidney function may be underestimated when calculated using serum CREA-based equations compared with the CYSC-based equations.

4.3.3 Comparison between serum CREA- and serum CYSC-based equations for calculating the GFR in patients administered FF therapy The mean (±SD) of the eGFR calculated using the various equations at baseline, 3, 6 and 12 months are shown in Figure 4.10. The results revealed that serum CYSC-based eGFRs were higher than those obtained by the CREA-based equations. The Gentian CYSC equation produced relatively higher eGFR values compared with all other equations, while the lowest eGFR values were produced by the MDRD equation. The absolute mean difference between the eGFR calculated using the MDRD equation and Gentian equation was 17 mL/min/1.73 m2 at baseline (n=19), 22 mL/min/1.73 m2 in the 3 months group (n=13), 35 mL/min/1.73 m2 in the 6 months group (n=10) and 77 mL/min/1.73 m2 in the 12 months group (n=8). These results show an increase in the mean difference between the eGFR obtained using MDRD and the Gentian equation in the groups as the length of treatment was increased. The reason for this disparity is unclear.

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The eGFR derived by CKD-EPI CREA, CKD-EPI CYSC and CKD-EPI CREA- CYSC equations were similar (i.e. 87 ± 16 mL/min/1.73 m2, 87 ± 17 mL/min/1.73 m2 and 87 ± 16 mL/min/1.73 m2, respectively) with absolute mean difference of <1 mL/min/1.73 m2 between them. The mean difference between eGFR calculated using these three equations and eGFR calculated using the MDRD equation was approximately 7 mL/min/1.73 m2. Also, the mean difference between these three equations and the MDRD equation increased as treatment time was increased. For instance, in the 3 months Group, the absolute mean difference between CKD- EPI CREA, CKD-EPI CYSC and CKD-EPI CREA-CYSC ranged from 3 – 5 mL/min/1.73 m2, while in the 6 months and 12 months groups the absolute mean difference ranged from 4 – 10 mL/min/1.73 m2 and 8 – 22 mL/min/1.73 m2, respectively. These results suggest that as kidney function changes following FF therapy the ability of each of these equations to determine eGFR differed increasingly.

The prevalence of the CKD stages in the various groups of participants classified using the MDRD, CKD-EPI CREA, Gentian, CKD-EPI CYSC and CKD-EPI CREA- CYSC equations are presented in Table 4.6. None of the participants in this study was classified as having severely decreased kidney function (defined as eGFR < 30 mL/min/1.73 m2) using any of the equations. In all the groups, the MDRD equation showed the lowest number of participants with normal kidney function (i.e. GFR > 90 mL/min/1.73 m2). At baseline, 21% (4 out of 19) of the participants were classified with normal kidney function (i.e. GFR > 90 mL/min/1.73 m2) using the MDRD equation. This was significantly lower than the percentage obtained using the CKD-EPI CREA equation (53%, n=10, p=0.0436). The percentage of participants with normal kidney function was the same (42%, n=8) with Gentian, CKD-EPI CYSC and CKD-EPI CREA-CYSC equations, although the difference with MDRD equation was not statistically significant (p=0.1627).

Also, in all the groups, the MDRD equation showed the highest proportion of participants with mild kidney disease (defined as eGFR 60 – 89 mL/min/1.73 m2) compared with all the other equations. Both the CREA-based equations and the combined CREA-CYSC-based equation showed a higher proportion of participants with mild kidney disease than CYSC-based equations. In Group B, the prevalence of mild kidney disease obtained with the MDRD equation (80%, n=10) was

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This study also examined the discrepancy between eGFR derived using the MDRD equation (routinely used in the clinics) and eGFR calculated using the other equations. At baseline, results of the MDRD equation showed that six participants (31.5%, n=19) had a mild kidney disease (eGFR 60 - 89 mL/min/1.73 m2), but reclassification with the CKD EPI CREA indicated that all the six participants had normal kidney function (eGFR > 90 mL/min/1.73 m2). This suggests that the MDRD equation underestimates the GFR compared with the CKD-EPI CREA equation, although both equations are based on the serum CREA concentration. Forty-two percent (8 out of 19) discrepancy was also observed between MDRD and both CYSC-based (Gentian and CKD-EPI CYSC) equations. For instance, in six participants, the MDRD equation indicated mild to moderate kidney function whereas both CYSC-based equations showed normal kidney function. In two other cases, both the Gentian and CKD-EPI CYSC equations indicated a mild kidney disease, whereas the MDRD results were greater than 90 mL/min/1.73 m2.

It was observed that in most cases the CKD-EPI CREA-CYSC equation showed similar results with the CYSC-based equations rather than with the CREA-based equations. However, a disparity was observed in two cases, where the CKD-EPI CYSC and Gentian CYSC equations showed normal kidney function, while the CKD-EPI CREA-CYSC equation indicated mild a kidney function disease. This suggests that the CYSC-based equations would produce relatively higher GFR compared with the CKD-EPI CREA-CYSC equation. Discordant results were also observed in two other cases where the CKD-EPI CYSC and Gentian CYSC equations showed mild reduction in kidney function but the CKD-EPI CREA-CYSC equation indicated that both had normal kidney function. This pattern of discrepant results between the various equations was observed in all the groups of participants, although in Groups A – C, the CREA-based equations tended to agree with each other while the two CYSCC-based equations also agreed with each other.

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These results suggest that compared with all the other equations examined in this study, the MDRD equation which is used routinely in the clinics tends to produce lower estimates of the GFR in patients with normal kidney function, most particularly during the early stages of kidney disease. This has been attributed to the fact that the MDRD equation was derived from patients with CKD who had a mean eGFR of 39.8 mL/min/1.73 m2 (Omuse et al., 2017). The utility of the MDRD equation in screening for CKD in asymptomatic patients is questionable given that subjectively healthy individuals have eGFR in the range where the MDRD equation has been shown to be inaccurate (i.e. > 60 mL/min/1.73 m2) (Omuse et al., 2017).

3 0 0 M D R D C K D -E P I C R E A

2 5 0 C K D -E P I C Y S C )

2 G E N T IA N m

C K D -E P I C R E A -C Y S C 3

7 2 0 0

n i

m 1 5 0

(

R 1 0 0

G e 5 0

0 0 3 6 1 2 D u ra tio n o f F F T re a tm e n t (M o n th s )

Figure 4.8 Comparison of eGFR calculated using serum CREA- and CYSC-based equations after 12 months of FF therapy. The GFR was estimated using the MDRD, CKD-EPI CREA, Gentian, CKD-EPI CYSC and CKD-EPI CREA-CYSC equations at baseline (n=19), and 3 months (n=13), 6 months (n=10) and 12 months (n=8) after commencing fenofibrate (160 mg/day) therapy in patients with dyslipidaemia. FF fenofibrate; MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012); Data are presented as mean ± standard deviation.

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Table 4.6 Frequency of CKD stages in the various groups according to the equations used in this study

CKD* Stages MDRD¥ CKD-EPI Gentian CKD-EPI CKD-EPI CREA§ CYSC‡ CYSC# CREA- CYSC† Baseline (n = 19)

>90 mL/min/1.73 m2 4 (21%) 10 (53%) 8 (42%) 8 (42%) 8 (42%)

60 – 89 mL/min/1.73 m2 13 (68%) 8 (42%) 10 (53%) 10 (53%) 11 (58%)

30 – 59 mL/min/1.73 m2 2 (11%) 1 (5%) 1 (5%) 1 (5%) 0 (0%)

< 30 mL/min/1.73 m2 0 (0%) 0 (0%) 0 (5%) 0 (0%) 0 (0%)

Group A (n = 13)

>90 mL/min/1.73 m2 0 (0%) 1 (8%) 4 (31%) 4 (31%) 4 (31%)

60 – 89 mL/min/1.73 m2 9 (69%) 8 (61%) 5 (38%) 5 (38%) 7 (54%)

30 – 59 mL/min/1.73 m2 4 (31%) 4 (31%) 4 (31%) 4 (31%) 2 (15%)

< 30 mL/min/1.73 m2 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Group B (n = 10)

>90 mL/min/1.73 m2 1 (10%) 3 (30%) 5 (50%) 4 (40%) 3 (30%)

60 – 89 mL/min/1.73 m2 8 (80%) 6 (60%) 2 (20%) 4 (40%) 6 (60%)

30 – 59 mL/min/1.73 m2 1 (10%) 1 (10%) 3 (30%) 2 (20%) 1 (10%)

< 30 mL/min/1.73 m2 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Group C (n = 8)

>90 mL/min/1.73 m2 2 (25%) 3 (37.5%) 5 (62.5%) 5 (62.5%) 4 (50)

60 – 89 mL/min/1.73 m2 4 (50%) 3 (37.5%) 2 (25%) 2 (25%) 2 (25%)

30 – 59 mL/min/1.73 m2 2 (25%) 2 (25%) 1 (12.5%) 1 (12.5%) 2 (25%)

< 30 mL/min/1.73 m2 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Data shown are number (percentage of the participants in the group). *CKD, chronic kidney disease; ¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); ‡CYSC, Cystatin C; #CKD- EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD- EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C.

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4.3.4 Concordance analysis of serum CREA vs serum CYSC as biomarkers for monitoring kidney function First, the possible correlation between serum CYSC, serum CREA and eGFR calculated using the serum CREA- and CYSC-based equations was analysed at baseline and after 3, 6 and 12 months after commencing treatment with FF (160 mg/day). At baseline the Spearman’s correlation analysis was performed between the various parameters using data from all the 19 participants (Tables 4.7). The results showed that there was no significant correlation between serum CREA and serum CYSC (r=0.3503, p=0.141, n=19). As expected, a strong negative correlation was observed between serum CREA and the eGFR calculated using the MDRD (r=-0.8509; p<0.001, n=19) and the CKD-EPI CREA (r=-0.8748; p<0.001, n=19) equations. Similarly, serum CYSC correlated well with eGFR calculated using the Gentian formula (r=-0.9596; p<0.001, n=19) and the CKD-EPI CYSC equation (r=-0.9996; p<0.001, n=19).

It was observed that both serum CREA and serum CYSC correlated well with the eGFR obtained using CKD-EPI CREA-CYSC equation (r=-0.5898; p=0.008 and r=-0.8285; p<0.001, respectively), although serum CYSC showed a better correlation with the CKD-EPI CREA-CYSC than serum CREA. The serum CYSC- based equations also showed stronger correlation with the CKD-EPI CREA-CYSC equation than the CREA-based equations. Although the correlation coefficient between the serum CREA-based and serum CYSC-based equations was not statistically significant, all the equations used in this study had a weak-strong positive correlation with each other as shown in Table 4.7.

The weak positive correlation observed at baseline between serum CREA and CYSC indicates that as serum CREA increases, there is a lower likelihood of serum CYSC increasing. Serum CYSC and CYSC-based eGFRs correlated better with eGFR calculated using the CKD-EPI CREA-CYSC equation than serum CREA and eGFR calculated using the CREA-based equations.

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Table 4.7 Spearman’s correlation coefficient between serum CREA, serum CYSC and the eGFR calculated using serum CREA- and CYSC-based equations at baseline

Spearman Parameters correlation p value coefficient (r) (n=19) CREA vs CYSC 0.3503 0.141 CREA vs MDRD¥ -0.8509 <0.001*** CREA vs CKD-EPI CREA§ -0.8748 <0.001*** CREA vs CKD-EPI CYSC# -0.2878 0.232 CREA vs Gentian CYSC‡ -0.3527 0.139 CREA vs CKD-EPI CREA-CYSC† -0.5898 0.008** CYSC vs MDRD¥ -0.2321 0.339 CYSC vs CKD-EPI CREA§ -0.2203 0.365 CYSC vs CKD-EPI CYSC# -0.9596 <0.001*** CYSC vs Gentian CYSC‡ -0.9996 <0.001*** CYSC vs CKD-EPI CREA-CYSC† -0.8285 <0.001*** MDRD¥ vs CKD-EPI CREA§ 0.9736 <0.001*** MDRD¥ vs CKD-EPI CYSC# 0.2495 0.303 MDRD¥ vs CKD-EPI CREA-CYSC† 0.6324 0.004** MDRD¥ vs Gentian CYSC‡ 0.2339 0.335

Gentian CYSC‡ vs CKD-EPI CREA§ 0.2212 0.363

Gentian CYSC‡ vs CKD-EPI CYSC# 0.9578 <0.001***

Gentian CYSC‡ vs CKD-EPI CREA-CYSC† 0.8280 <0.001***

CKD-EPI CREA§ vs CKD-EPI CYSC# 0.2565 0.289

CKD-EPI CREA§ vs CKD-EPI CREA-CYSC† 0.6282 0.004**

CKD-EPI CYSC# vs CKD-EPI CREA-CYSC† 0.8662 <0.001***

¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); ‡CYSC, cystatin C; CREA, creatinine; #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C; Spearman’s correlation coefficient; *p<0.05, **p<0.01, ***p<0.001, n=19.

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In Group A, the correlation between the various parameters was examined after 3 months of FF therapy in 13 participants. The Spearman’s correlation coefficients are presented in Table 4.8. A statistically significant positive correlation was observed between serum CREA and serum CYSC (r=0.6088, p=0.030, n=13), suggesting that serum CYSC concentration increased as serum CREA concentration was increased. As expected, a statistically significant negative correlation was also observed between serum CREA concentration and the MDRD (r=-0.8950, p<0.001, n=13) and CKD-EPI CREA (r=-0.8619, p<0.001, n=13) equations, indicating that the eGFR obtained by both equations decreased as the serum CREA concentration increased. Similarly, serum CYSC showed a strong negative correlation with the Gentian eGFR (r=-1.0000, p<0.001) and the CKD-EPI CYSC equations (r=-0.9931, p<0.001).

There was no statistically significant correlation between serum CYSC concentration and the MDRD (r=-0.4558, p=0.118) and CKD-EPI CREA (r=- 0.3522, p=0.236) equations, suggesting that there was less likelihood for the eGFR calculated using both equations to decrease as serum CYSC concentration was increased. However, the correlation between serum CREA concentration and both CYSC-based eGFRs (Gentian and CKD-EPI CYSC) was statistically significant (r=-0.6088, p=0.030 and r=-0.5887, p=0.037, respectively). This suggested that the eGFR calculated using both CYSC-based equations decreased as serum CREA concentration was increased. The CKD-EPI CREA-CYSC equation showed statistically significant positive correlation with the Gentian (r=0.9269, p<0.001) and CKD-EPI CYSC (r=0.9256, p<0.001) equations and a moderate but statistically significant positive correlation with the MDRD (r=0.6833, p=0.012) and CKD-EPI CREA-CYSC (r=0.6072, p=0.030) equations. However, the correlation between the CREA-based eGFRs and the CYSC-based eGFRs was not statistically significant.

These results suggest that after 3 months of FF therapy serum CYSC increased as serum CREA was increased, although there was no significant correlation between serum CREA- and the serum CYSC-based eGFR equations. However, the CKD-EPI CREA-CYSC eGFR was more likely to increase with an increase in the CYSC-based eGFRs than with an increase in the CREA-based eGFRs.

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Table 4.8 Spearman’s correlation coefficient between serum CREA, serum CYSC and eGFR calculated using serum CREA- and CYSC-based equations after 3 months of FF therapy

Spearman Parameters correlation p value coefficient (r) (n=13) CREA vs CYSC 0.6088 0.030* CREA vs MDRD¥ -0.8950 <0.001*** CREA vs CKD-EPI CREA§ -0.8619 <0.001*** CREA vs CKD-EPI CYSC# -0.5887 0.037* CREA vs Gentian CYSC‡ -0.6088 0.030* CREA vs CKD-EPI CREA-CYSC† -0.7903 0.002** CYSC vs MDRD¥ -0.4558 0.118 CYSC vs CKD-EPI CREA§ -0.3522 0.236 CYSC vs CKD-EPI CYSC# -0.9931 <0.001*** CYSC vs Gentian CYSC‡ -1.0000 <0.001*** CYSC vs CKD-EPI CREA-CYSC† -0.9269 <0.001*** MDRD¥ vs CKD-EPI CREA§ 0.9695 <0.001*** MDRD¥ vs CKD-EPI CYSC# 0.4552 0.119 MDRD¥ vs CKD-EPI CREA-CYSC† 0.6833 0.012* MDRD¥ vs Gentian CYSC‡ 0.4558 0.118

Gentian CYSC‡ vs CKD-EPI CREA§ 0.3522 0.236

Gentian CYSC‡ vs CKD-EPI CYSC# 0.9931 <0.001***

Gentian CYSC‡ vs CKD-EPI CREA-CYSC† 0.9269 <0.001**

CKD-EPI CREA§ vs CKD-EPI CYSC# 0.3421 0.251

CKD-EPI CREA§ vs CKD-EPI CREA-CYSC† 0.6072 0.030*

CKD-EPI CYSC# vs CKD-EPI CREA-CYSC† 0.9256 <0.001**

¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); ‡CYSC, Cystatin C; CREA, creatinine; #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C; Spearman’s correlation coefficient; *p<0.05, **p<0.01, ***p<0.001, n=13.

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In Group B, the correlation between the various parameters was examined after 6 months of FF therapy in 10 participants. The Spearmen correlation coefficients between the various parameters are presented in Table 4.9. The correlation between serum CREA and serum CYSC was not statistically significant (r=0.5593, p=0.097). The results showed a strong negative correlation between serum CREA and MDRD eGFR (r=-0.9483, p<0.001) and between serum CREA and CKD-EPI CREA eGFR (r=-0.9515, p<0.001). A strong negative correlation was observed between serum CYSC and Gentian eGFR (r=-1.0000, p<0.001) and CKD-EPI CYSC eGFR (r=-0.9848, p<0.001). The correlation between serum CREA and both CYSC-based eGFRs was not statistically significant. Table 4.9 also shows that correlation between serum CYSC and both serum CREA-based eGFR equations was not statistically significant.

The correlation between serum CYSC and CKD-EPI CREA-CYSC eGFR (r=- 0.9666, p<0.001) was stronger than the correlation between serum CREA and CKD-EPI CREA-CYSC eGFR (r=-0.6606, p=0.044). As expected, a strong positive correlation was observed between MDRD eGFR and CKD-EPI CREA eGFR (r=0.9848, p<0.001) and between Gentian eGFR and CKD-EPI CYSC eGFR (r=0.9848, p<0.001). The correlation between the CREA-based eGFRs and CYSC-based eGFRs was not statistically significant. The results also revealed a comparatively stronger correlation between CKD-EPI CREA-CYSC eGFR and both CYSC-based eGFRs [Gentian (r=0.9666, p=0.001) and CKD-EPI CYSC (r=0.9636, p<0.001)] than between CKD-EPI CREA-CYSC eGFR and both CREA- based eGFRs [MDRD (r=0.5654, p=0.1649) and CKD-EPI CREA (r=0.5349, p=0.115)].

The results from this group were similar to those obtained at baseline and after 3 months of FF therapy, except that the correlation between serum CREA and serum CYSC was not statistically significant as was observed after 3 months of treatment. These results also suggest that CREA-based eGFRs would decrease as serum CREA increases and CYSC-based eGFRs would decrease as serum CYSC is increased, although the correlation between CREA-based eGFRs and CYSC-based eGFRs was no significant. CKD-EPI CREA-CYSC eGFR showed stronger correlation with cystatin CYSC-based eGFRs than with CREA-based eGFRs.

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Table 4.9 Spearman’s correlation coefficient between serum CREA, serum CYSC and eGFR calculated using serum CREA- and CYSC-based equations after 6 months of FF therapy

Spearman Parameters correlation p value coefficient (r) CREA vs CYSC 0.5593 0.097 CREA vs MDRD¥ -0.9483 <0.001*** CREA vs CKD-EPI CREA§ -0.9515 <0.001*** CREA vs CKD-EPI CYSC# -0.5030 0.144 CREA vs Gentian CYSC‡ -0.5596 0.097 CREA vs CKD-EPI CREA-CYSC† -0.6606 0.044* CYSC vs MDRD¥ -0.4512 0.191 CYSC vs CKD-EPI CREA§ -0.3708 0.289 CYSC vs CKD-EPI CYSC# -0.9848 <0.001*** CYSC vs Gentian CYSC‡ -1.0000 <0.001*** CYSC vs CKD-EPI CREA-CYSC† -0.9666 <0.001*** MDRD¥ vs CKD-EPI CREA§ 0.9848 <0.001*** MDRD¥ vs CKD-EPI CYSC# 0.4012 0.251 MDRD¥ vs CKD-EPI CREA-CYSC† 0.5654 0.093 MDRD¥ vs Gentian CYSC‡ 0.4512 0.191

Gentian CYSC‡ vs CKD-EPI CREA§ 0.3708 0.289

Gentian CYSC‡ vs CKD-EPI CYSC# 0.9848 <0.001***

Gentian CYSC‡ vs CKD-EPI CREA-CYSC† 0.9666 <0.001***

CKD-EPI CREA§ vs CKD-EPI CYSC# 0.3091 0.387

CKD-EPI CREA§ vs CKD-EPI CREA-CYSC† 0.4909 0.155

CKD-EPI CYSC# vs CKD-EPI CREA-CYSC† 0.9636 <0.001***

¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); ‡CYSC, Cystatin C; CREA, creatinine; #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin Spearman’s correlation coefficient; *p<0.05, **p<0.01, ***p<0.001, n=10.

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In Group C, the correlation between the various parameters was examined after 12 months of FF therapy in eight participants. The Spearman correlation coefficients between the various parameters are presented in Table 4.10. A non- statistically significant positive correlation was observed between serum CREA concentration and serum CYSC concentration (r=0.6667, p=0.083). Serum CREA concentration showed strong negative correlation with the MDRD (r=-1.0000, p<0.001) and CKD-EPI CREA (r=-1.0000, p<0.001) equations. The relationship between Serum CREA concentration and the serum CYSC-based eGFR equations was not statistically significant. A strong negative correlation was also observed between serum CYSC concentration and the Gentian (r=-1.0000, p<0.001) and CKD-EPI CYSC (r=-1.0000, p<0.001) equations, but the correlation between serum CYSC and serum CREA-based eGFR equations was not statistically significant.

The results also showed a strong positive correlation between the two CREA- based eGFR equations (MDRD vs CKD-EPI CREA, r=1.0000, p<0.001), and between the two CYSC-based eGFR equations (Gentian vs CKD-EPI CYSC, r=1.0000, p<0.001). A non-statistically significant moderate positive correlation was observed between the CREA-based eGFRs and CYSC-based eGFRs (r=0.6667, p=0.083). It was observed that eGFR obtained with the CKD-EPI CREA-CYSC equation correlated better with serum CYSC (r=-0.9542, p<0.001) and CYSC-based equations (r=0.9524, p=0.001) than with serum CREA (r=- 0.8333, p=0.015) and the CREA-based eGFR equations (r=0.8333, p=0.15).

These results suggest that an increase in serum CREA after 12 months of FF therapy was not likely to be accompanied by an increase in serum CYSC concentration. However, an increase in serum CREA concentration was strongly related with a decrease in eGFR estimated using the CREA-based equations but not with a decrease in eGFR calculated using the CYSC-based equations. The results also suggest that an increase in serum CYSC concentration was related to a decrease in the CYSC-based eGFRs. The results also suggest that changes in serum CYSC would impact changes in eGFR estimated using the CKD-EPI CREA-CYSC than would changes in serum CREA.

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Table 4.10 Spearman’s correlation coefficient between serum CREA, serum CYSC and eGFR calculated using serum CREA- and CYSC-based equations after 12 months of FF therapy

Parameters Spearman correlation p value coefficient (r) (n=8) CREA vs CYSC 0.6667 0.083 CREA vs MDRD¥ -1.0000 <0.001*** CREA vs CKD-EPI CREA§ -1.0000 <0.001*** CREA vs CKD-EPI CYSC# -0.6667 0.083 CREA vs Gentian CYSC‡ -0.6667 0.083 CREA vs CKD-EPI CREA-CYSC† -0.8333 0.015* CYSC vs MDRD¥ -0.6667 0.083 CYSC vs CKD-EPI CREA§ -0.6667 0.083 CYSC vs CKD-EPI CYSC# -1.0000 <0.001*** CYSC vs Gentian CYSC‡ -1.0000 <0.001*** CYSC vs CKD-EPI CREA-CYSC† -0.9542 <0.001*** MDRD¥ vs CKD-EPI CREA§ 1.0000 <0.001*** MDRD¥ vs CKD-EPI CYSC# 0.6667 0.083 MDRD¥ vs CKD-EPI CREA-CYSC† 0.8333 0.015* MDRD¥ vs Gentian CYSC‡ 0.6667 0.083 Gentian CYSC‡ vs CKD-EPI CREA§ 0.6667 0.083 Gentian CYSC‡ vs CKD-EPI CYSC# 1.0000 <0.001*** Gentian CYSC‡ vs CKD-EPI CREA-CYSC† 0.9524 0.001** CKD-EPI CREA§ vs CKD-EPI CYSC# 0.6667 0.083 CKD-EPI CREA§ vs CKD-EPI CREA-CYSC† 0.8333 0.015* CKD-EPI CYSC# vs CKD-EPI CREA-CYSC† 0.9524 0.001**

¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); ‡CYSC, Cystatin C; CREA, creatinine; #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C. Spearman’s correlation coefficient; *p<0.05, **p<0.01, ***p<0.001, n=8.

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Next, the level of agreement between the different eGFR equations was assessed using the Bland-Altman analysis. Since none of the equations used in this study can be considered as the perfect reference, the absolute bias (or mean difference) and precision (or standard deviation around the bias) between the different equations were examined. Table 4.11 shows the bias (mean difference) and precision (SD around the bias) between the different eGFR equations at baseline. At baseline, the best levels of bias were observed between the CKD-EPI CREA- CYSC and CKD-EPI CYSC equations (absolute bias = 0.3 ± 12.1 mL/min/73 m2), between the CKD-EPI CREA-CYSC and CKD-EPI CREA equations (absolute bias = 0.3 ± 15.1 mL/min/73 m2) and between the CKD-EPI CREA and CKD-EPI CYSC equations (absolute bias = 0.6 ± 26.9 mL/min/1.73 m2). On the other hand, the highest levels of bias was observed between the Gentian equation and the MDRD equation (absolute bias = 17.2 ± 42.7 mL/min/1.73 m2), and between the Gentian equation and CKD-EPI CREA equations (absolute bias = 10.5 ± 42.9 mL/min/1.73 m2).

Bland-Altman plots of the differences between the eGFR calculated using the MDRD equation and all the other equations at baseline are shown in Figure 4.9. The results show that the MDRD equation produced relatively lower eGFR values compared with all the other equations used in this study. This is demonstrated by the negative bias between the MDRD equation and all the other equations. The CKD-EPI CREA, CKD-EPI CYSC and CKD-EPI CREA-CYSC showed similar level of bias against the MDRD equation. The highest level of deviation around the mean difference, which indicates a high level of imprecision, was also observed between the CKD-EPI CREA and Gentian equations and between the MDRD and Gentian equations. Agreement between the other equations is shown in Figure 4.10. It was observed that the precision (SD around the bias) between the different eGFR equations increased as the level of eGFR increased.

The results showed larger differences at baseline between the CREA- and CYSC- based equations, suggestive of greater disparity between these equations at baseline, and lower differences between CKD-EPI CYSC and the CKD-EPI CREA- CYSC, CKD-EPI CREA and CKD-EPI CREA-CYSC and between the CKD-EPI CREA and CKD-EPI CYSC equations, indicating agreement between them.

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Table 4.11 Bland-Altman analysis showing the absolute bias (mean difference) (bold) and precision (SD around the bias) (italic) between the eGFR equations at baseline

Baseline MDRD¥ CKD-EPI GENTIAN CKD-EPI CKD-EPI (n=19) CREA§ CYSC# CREA-CYSC†

MDRD¥ XXXXXX 6.7 17.2 7.3 7

CKD-EPI 3.5 XXXXXXX 10.5 0.6 0.3 CREA§

GENTIAN 42.7 42.9 XXXXXXX 9.9 10.2

CKD-EPI 26.8 26.9 20.6 XXXXXXX 0.3 CYSC#

CKD-EPI 15.3 15.1 28.7 12.1 XXXXXXXXX CREA-CYSC† All data are expressed in mL/min/1.73 m2; ¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C.

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Figure 4.9 Bland-Altman plots between the MDRD and the other equations at baseline The Bland-Altman plots showing the difference and the mean of the eGFR estimated using the different equations. The mean difference is indicated by the centre line, whereas the outer lines represent the limits of agreement between the equations (mean ± 2SD); MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA- CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=19.

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Figure 4.10 Bland-Altman plots between the Gentian, CKD-EPI CREA, CKD- EPI CYSC, and the CKD-EPI CREA-CYSC equations at baseline The Bland-Altman plots showing the difference and the mean of the eGFR estimated using the different equations. The mean difference is indicated by the centre line, whereas the outer lines represent the limits of agreement between the equations (mean ± 2SD); MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA- CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=19.

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The level of agreement between the different eGFR equations after 3 months of FF therapy is summarised in Table 4.12. Again, the lowest levels of bias was observed between the CKD-EPI CYSC and CKD-EPI CREA-CYSC equations (2.5 ± 10.2 mL/min/1.73 m2), and between the CKD-EPI CREA and CKD-EPI CREA- CYSC equations (2.6 ± 13.9 mL/min/1.73 m2), while the highest level of bias was observed between the MDRD and the Gentian equations (22.2 ± 39.3 mL/min/1.73 m2). The differences between the MDRD and the other eGFR equations are shown in Figure 4.11, while differences between all the other equations are shown in Figure 4.12. The results showed that after 3 months of FF therapy, eGFR calculated using the MDRD equation was lower than those calculated using all the other equations. Larger differences were also observed between the CREA-based and the CYSC-based equations. It was observed that the difference between the MDRD and CKD-EPI CREA equations at baseline (6.7 ± 3.5 mL/min/1.73 m2) was similar to the value obtained after 3 months of FF therapy (6.9 ± 3.2 mL/min/1.73 m2). These results also suggest greater disagreement between the MDRD and the Gentian equation.

Table 4.12 Bland-Altman analysis showing the absolute bias (mean difference) (bold) and precision (SD around the bias) (italic) between the eGFR equations after 3 months of FF therapy

3 Months MDRD¥ CKD-EPI GENTIAN CKD-EPI CKD-EPI (n=13) CREA § CYSC# CREA-CYSC†

MDRD¥ XXXXXX 6.9 22.2 12.1 9.5

CKD-EPI CREA § 3.2 XXXXXXX 15.2 5.2 2.6

GENTIAN 39.3 39.3 XXXXXXX 10.1 12.6

# CKD-EPI CYSC 23.1 23.9 18.9 XXXXXXX 2.5

CKD-EPI CREA- 13.3 13.9 26.3 10.2 XXXXXXXXX CYSC† All data are expressed in mL/min/1.73 m2; ¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=13.

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Figure 4.11 Bland-Altman plots between the MDRD and the other equations after 3 months of FF therapy. The Bland-Altman plots showing the difference and the mean of the eGFR estimated using the different equations. The mean difference is indicated by the centre line, whereas the outer lines represent the limits of agreement between the equations (mean ± 2SD); MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA- CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=13.

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Figure 4.12 Bland-Altman analysis between the Gentian, the CKD-EPI CREA, CKD-EPI CYSC, and the CKD-EPI CREA-CYSC equations after 3 months of FF therapy The Bland-Altman plots showing the difference and the mean of the eGFR estimated using the different equations. The mean difference is indicated by the centre line, whereas the outer lines represent the limits of agreement between the equations (mean ± 2SD); MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA- CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=13.

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The Bland-Altman analyses of the bias and precision between the eGFR calculated using the different equations after 6 months of FF therapy are given in Table 4.13. The results showed once again that the larger differences were obtained between the CREA- and CYSC-based equations. The highest differences were observed between the MDRD and the Gentian equations and between the Gentian and CKD-EPI CREA equations. Lower differences were also observed between the CKD-EPI CYSC and CKD-EPI CREA-CYSC equations and between the CKD-EPI CREA and CKD-EPI CREA-CYSC equations. Figure 4.13 depicts the difference between the MDRD and all the other equations, while the differences between the other equations are shown in Figure 4.13.

The results showed that in Group C participants, the MDRD equation also produced the lowest estimates of the GFRs showed by the negative bias between the MDRD and all the other equations. The best levels of agreement were observed between the CKD-EPI CREA-CYSC equation and the CKD-EPI CYSC and the CKD-EPI CREA equations, respectively.

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Table 4.13 Bland-Altman analysis showing absolute bias (mean difference) (bold) and precision (SD around the bias) (italic) between the eGFR equations after 6 months of FF therapy

6 Months MDRD¥ CKD-EPI GENTIAN CKD-EPI CKD-EPI (n=10) CREA§ CYSC# CREA-CYSC†

MDRD¥ XXXXX 6.5 35.9 15 11.4

CKD-EPI 4.1 XXXXXXX 29.4 8.5 4.9 CREA§

GENTIAN 67.4 67.0 XXXXXXX 20.9 24.5

CKD-EPI 30.6 30.0 39.9 XXXXXXX 3.6 CYSC#

CKD-EPI 17.4 16.6 51.1 13.7 XXXXXXXX CREA-CYSC† All data are expressed in mL/min/1.73 m2; ¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C.

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Figure 4.13 Bland-Altman plots between the MDRD and the other equations after 6 months of FF therapy. The Bland-Altman plots showing the difference and the mean of the eGFR estimated using the different equations. The mean difference is indicated by the centre line, whereas the outer lines represent the limits of agreement between the equations (mean ± 2SD); MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA- CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=10.

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Figure 4.14 Bland-Altman analyses between the different eGFR equations after 6 months of FF therapy. The Bland-Altman plots showing the difference and the mean of the eGFR estimated using the different equations. The mean difference is indicated by the centre line, whereas the outer lines represent the limits of agreement between the equations (mean ± 2SD); MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA- CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=10.

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The differences between the eGFR calculated using the various equations used in this study after 12 months of FF therapy are shown in Table 4.14. The results showed that the larger differences were observed between the CREA- and CYSC- based equations. The highest level of bias between the CREA- and CYSC-based equations was observed in Group C participants, compared with all the other groups. The highest level of differences were observed between the MDRD and the Gentian equations and between the Gentian and CKD-EPI CREA equations, while the lowest level of differences were observed between the MDRD and CKD- EPI CREA equations and between the CKD-EPI CYSC and CKD-EPI CREA- CYSC equations. Figure 4.15 shows the difference between the MDRD and the other equations, while Figure 4.16 shows the differences between the other equations. The results showed that in this group the Gentian equation produced highest eGFR values while the MDRD equation generated the lowest eGFR values.

Table 4.14 Bland-Altman analysis showing the absolute bias (mean difference) (bold) and precision (SD around the bias) (italic) between the equations after 12 months of FF therapy

12 Months MDRD¥ CKD-EPI GENTIAN CKD-EPI CKD-EPI (n=8) CREA§ CYSC# CREA-CYSC†

MDRD¥ XXXXXX 5.4 77.5 27.1 18.8

CKD-EPI 3.7 XXXXXXX 72.1 21.8 13.4 CREA§

GENTIAN 106.7 105.7 XXXXXXXX 50.4 58.8

CKD-EPI 28.5 27.3 85.6 XXXXXXXX 8.4 CYSC#

CKD-EPI 19.1 17.7 90.0 11.0 XXXXXXXXX CREA-CYSC† All data are expressed in mL/min/1.73 m2; ¥MDRD, Modification of Diet in Renal Disease; §CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); #CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); †CKD-EPI CREA-CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C.

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Figure 4.15 Bland-Altman plots between the MDRD and the other equations after 12 months of FF therapy The Bland-Altman plots showing the difference and the mean of the eGFR estimated using the different equations. The mean difference is indicated by the centre line, whereas the outer lines represent the limits of agreement between the equations (mean ± 2SD); MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA- CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=8.

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Figure 4.16 Bland-Altman plots between the Gentian, the CKD-EPI CREA, CKD-EPI CYSC, and the CKD-EPI CREA-CYSC equations after 12 months of FF therapy The Bland-Altman plots showing the difference and the mean of the eGFR estimated using the different equations. The mean difference is indicated by the centre line, whereas the outer lines represent the limits of agreement between the equations (mean ± 2SD); MDRD, Modification of Diet in Renal Disease; CKD-EPI CREA, Chronic Kidney Disease Epidemiology Collaboration Creatinine (2009); CYSC, Cystatin C; CKD-EPI CYSC, Chronic Kidney Disease Epidemiology Collaboration Cystatin C (2012); CKD-EPI CREA- CYSC, Chronic Kidney Disease Epidemiology Collaboration Creatinine and Cystatin C (2012), n=8.

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Generally, the best level of agreement was observed between the CKD-EPI CYSC and CKD-EPI CREA-CYSC equations, and between the CKD-EPI CREA and the CKD-EPI CREA-CYSC equations. The results also showed that the MDRD equation produced the lowest eGFR values, while the Gentian equation produced the highest eGFR values. Hence, the highest differences were observed between the MDRD and the Gentian equations. It was also observed that the level of bias or difference between the various eGFR equations tended to increase as the duration of FF treatment was increased, with the only exception seen between the MDRD and CKD-EPI CREA equations, which showed a similar level of bias in all the groups of participants. The mean difference between MDRD and CKD-EPI CREA in all the groups ranged from 5.4 – 6.9 mL/min/1.73 m2. This low level of bias indicate agreement between MDRD and CKD-EPI CREA equations, although in all the participants, eGFR calculated using the MDRD equation was lower than eGFR by the CKD-EPI CREA equation. Such a level of agreement was not seen between cystatin C-based equations.

The disparity between Gentian eGFR and MDRD eGFR also increased as treatment time was increased i.e. 17.2 mL/min/1.73 m2 at baseline, 22.2 mL/min/1.73m2 after 3 months, 35.9 mL/min/1.73 m2 after 6 months and 77.5 mL/min/1.73 m2 after 12 months. However, a low level of bias was observed between CKD-EPI CREA and CKD-EPI CYSC, but this also increased through the groups as treatment time increased. In terms of precision, MDRD and CKD-EPI CREA demonstrated the lowest standard deviation around the bias in all the groups; 3.5 mL/min/1.73 m2 at baseline, 3.2 mL/min/1.73 m2 in the 3 months group, 4.1 mL/min/1.73 m2 in the 6 months group and 3.7 mL/min/1.73 m2 in the 12 months group. In contrast, the highest standard deviation values (low precision) occurred between Gentian eGFR and both creatinine-based eGFRs (MDRD and CKD-EPI CREA).

In summary, the CYSC-based equations produced higher GFR estimates than the CREA-based equations. The lowest eGFR values were produced by the MDRD equation, while the highest values were produced by the Gentian equation. The best level of agreement was observed between the CKD-EPI CYSC and the CKD-

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EPI CREA-CYSC followed by CKD-EPI CREA and CKD-EPI CREA-CYSC. Agreement was observed between the two CREA-based equations but not between the CYSCC-based equations. The best precision levels were obtained between the MDRD and CKD-EPI CREA equations while Gentian equation showed high variations with all other equations. The highest deviation was observed between Gentian and MDRD in the 12 months group (106.7 mL/min/1.73 m2).

4.4 DISCUSSION FF-associated creatininaemia or inferred nephrotoxicity is a well-established phenomenon, although the mechanism by which FF increases serum CREA is not fully understood (Bonds et al., 2012). Therefore, kidney function monitoring has been recommended in patients receiving FF therapy (Kim et al., 2017). Suggestions that have been made to explain the increase in serum CREA in FF therapy include an increase in metabolic production of CREA (Hottelart et al., 2002), decreased CREA secretion in renal tubules (Ansquer et al., 2008), alteration of renal haemodynamics (Lipscombe et al., 2001), and/or the direct effect of PPARα on the synthesis of vasodilatory prostaglandins in the kidney (Tsimihodimos et al., 2001). It has also been suggested that FF therapy causes analytical interference on the serum CREA methods (Hottelart et al., 1999; Ansquer et al., 2008). These together with other limitations of serum CREA such as age, gender, race, diet, exercise (Thomas et al., 2017) strengthen the case for another biomarker of kidney function in patients receiving FF therapy.

Several reports have suggested that CYSC is a better marker of kidney function than CREA (Madero et al., 2006; Inker and Okparavero, 2011; Akerblom et al., 2015). However, the effect of FF on serum CYSC level in patients with dyslipidaemia has not been fully elucidated. Here we discuss the impact of FF on CYSC levels and the utility of serum CYSC as a marker of kidney function in patients receiving FF therapy for hypertriglyceridaemia. There are four main findings from this study:

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 First, the analytical performance of the Gentian CYSC assay on Beckman Coulter AU640 analyser, a routine laboratory platform, exceeded the manufacturer’s specification.

 Secondly, this study confirmed that FF therapy increased both serum CREA and serum CYSC levels mainly during the early period of therapy in patients with dyslipidaemia, and suggested that the increase in serum CREA concentration might be partially reversible while the increase in serum CYSC concentration is completely reversible.

 Thirdly, eGFR calculated using CYSC-based equations were higher than those derived from the CREA-based equations.

 Finally, correlation between serum CREA and serum CYSC varied during the study, with a significant correlation only observed when after 3 months of FF therapy, i.e. when the impact of FF on both serum CREA and CYSC was greatest. The CREA-based eGFR equations did not correlate well with eGFR calculated using CYSC-based equations. The combined creatinine- and cystatin C-based eGFR formula showed stronger correlation and agreement with the serum CYSC and CYSC-based equations than with serum CREA and CREA-based equations.

4.4.1 Analytical performance of Gentian CYSC exceeds manufacturer’s specifications on Beckman Coulter AU640 analyser First, the analytical performance of Gentian CYSC on the Beckman Coulter AU640 analyser was examined. In order to be useful as a biomarker of kidney function in clinical practice, the Gentian CYSC assay has to be measured as part of the kidney profile using existing equipment already available in the laboratory. Most of the CYSC kits available in the market are designed to be used on specialist immunoassay platforms. In this era where cost reduction is the primary force driving healthcare reform, the use of a single dedicated analyser would not only interrupt workflow but have significant additional staffing requirements for provision of a 24-hour service that is required for most UK hospital settings (Ash, 1996; Addicott et al., 2015). As an immunoturbidimetric assay, the Gentian CYSC

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The imprecision studies showed low within-run and between-run imprecision of less than 5%. These levels of imprecision were similar to those obtained in a previous study by Flodin et al., (2007) on an Architect ci8200 analyser using CYSC reagents from Dako (Glostrup, Denmark) and are comparable to the values specified by the kit manufacturers’ of Gentian CYSC (Moss, Norway). The imprecision of the pooled serum samples were below the minimum acceptable imprecision of 3.5% initially suggested by Ricos et al., in 1999 and updated in 2014 by Minchinela et al. Acceptable linearity and recovery was also observed over the entire range tested with linear dilution of patient sample.

These results are also in agreement with the reports by Flodin and Larsson (2009) who demonstrated a maximum deviation of 4.3% between the estimated and measured CYSC values on the Abbott ci8200 analyser. A strong positive correlation was also observed between serum CYSC and serum CREA within the range of 0.70 – 6.93 mg/L of cystatin C. This study also showed that CYSC was stable in serum samples stored at -80C for 30 months, and therefore suggests that storage did not have any statistically significant impact on the level of serum CYSC. In terms of analytical performance and laboratory organisation, these results suggest that the Gentian CYSC is eminently suitable for routine clinical use and can replace serum CREA as a maker for assessing and monitoring the kidney function.

4.4.2 Impact of FF therapy on serum CYSC, serum CREA and eGFR calculated using CYSC- and CREA-based equations

In the next step, the impact of FF therapy on serum CREA; serum CYSC and kidney function (GFR) estimated using both CREA-based and CYSC-based equations was examined. This study showed moderate increases in serum CREA and serum CYSC concentrations during the first 3 months of FF (160 mg/day) therapy in patients with dyslipidaemia. Previous studies, both short-term and long- term, have shown that FF therapy is associated with an increase in both serum

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CREA and serum CYSC levels (Ansquer et al., 2008; Ncube et al., 2012; Mychaleckyj et al., 2012).

In a 6-week randomised crossover trial in healthy people, Ansquer et al., (2008) reported a statistically significant increase in plasma CREA (mean difference = 9.72 µmol/L, p<0.05, n=24) and plasma CYSC (mean difference 0.18mg/L, p=0.02, n=24) levels after six weeks of FF (160 mg/day) therapy. These reports were supported by Ncube et al., (2012), who also reported a concomitant increase in serum CREA (mean difference = 7.0 µmol/L, p<0.005, n=12) and serum CYSC (mean difference = 0.10 mg/L, p<0.01, n=12) levels after 3 months of FF (264 mg/day) therapy in hyperlipidaemic patients. The results from this study are consistent with both reports. However, in their studies FF therapy was associated with a statistically significant increase in both biomarkers, while in this study the change in serum CYSC was not statistically significant.

The disparity with the present study may be due to the nature of the participants or dose/length of FF administered. This study excluded patients who had diabetes or renal disease, unlike in the study by Ncube et al., (2012) which included participants with diabetes mellitus. Also, participants in the study by Ncube et al., (2012) were administered a higher daily dose of FF (264 mg/day) therapy. Similar to the study by Ansquer et al., (2008) all the participants in our study were administered FF (160 mg/day) therapy, although these authors had administered the drug over a shorter period (six weeks). The present study confirms that FF therapy is associated with an increase in both serum CREA and CYSC levels.

FF therapy also caused a moderate reduction in kidney function (GFR), estimated by both CREA-based and CYSC-based equations. The decrease in eGFR by the MDRD (p=0.0025, n=13), CKD-EPI CREA (p=0.0190, n=13), and CKD-EPI CREA- CYSC (p=0.0392, n=13) equations was statistically significant, while the changes in the Gentian (p=0.3568, n=13) and CKD-EPI CYSC (p=0.1988, n=13) equations were not statistically significant. The median eGFR estimated using both CREA- based and CYSC-based equations did not change by more than 10% after 3 months of FF therapy. Ncube et al., (2012) reported a similar decrease in eGFR following 3 months of treatment with FF (264mg/day) in hyperlipidaemic patients and suggested the change in eGFR indicated deterioration in kidney function.

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In their study, Ncube et al., (2012) had estimated the GFR using the MDRD equation, which is known to underestimate the GFR (Stevens et al., 2007; MacIsaac et al., 2015). However, a long-term study by Forsblom et al., (2010) had demonstrated a concomitant decrease in CREA clearance and eGFR by FF, although the eGFR was estimated using CREA-based equations. The results from this study showed a decrease in eGFR by both CREA-based eGFRs (MDRD and CKD-EPI CREA) and cystatin C-based eGFRs (Gentian and CKD-EPI CYSC), thus indicating a genuine impact of FF on the kidney function.

This study has also shown that the increase in both serum CREA and CYSC was reversible when treatment was continued for up to 12 months. This is consistent with reports by Park et al., (2017), who demonstrated that FF-associated increase in serum CREA was partially reversible when FF was administered for 3 – 6 years to hypertriglyceridaemic patients with hypertension. A similar report by Mychaleckyj et al., (2012) in the ACCORD study showed that FF-associated increases in serum CREA and serum CYSC were reversed 51 days after withdrawal of FF. Unlike in both reports where reversibility was achieved following discontinuation of FF therapy, in this study serum CYSC concentration and GFR estimated using CYSC-based equations were completely reversed and serum CREA concentration and eGFR calculated using CREA-based formulas were partially reversed, when treatment was administered in the long-term. The results of the present study suggest that reversal of FF-associated increase in serum CREA, CYSC and reduced kidney function estimated by both CREA-based and CYSC-based equations occurred even as treatment was continued.

4.4.3 Comparison of CYSC- and CREA-based eGFRs in patients administered FF therapy Previous studies have shown that both MDRD and CKD-EPI equations tend to underestimate eGFR in healthy populations and, more generally, in patients with normal or near normal serum CREA values (Froissart et al., 2005; Delanaye and Cohen, 2008; Glassock and Winearls, 2008). This study has shown that the eGFR calculated using CYSC-based equations were higher than those derived from CREA-based equations. The MDRD equation, which is used routinely in the

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Fombon, I.S. (2020) clinics to assess kidney function, produced the lowest median eGFR in all the groups, while the Gentian equation produced the highest values. These results are consistent with the report by Han et al., (2012) who showed that CYSC-based eGFR was higher than CREA-based values in live kidney donors. In a recent study, Szopa et al., (2015) also reported that eGFR calculated by the CYSC-based formula was higher than CREA-based eGFR in patients with Type 2 diabetes.

This study has also shown that the MDRD equation identified the lowest proportion of participants with normal kidney function (i.e. eGFR >90 mL/min/1.73 m2) and the highest proportion with mild kidney disease (i.e. eGFR: 60 – 89 mL/min/1.73 m2), compared with all other eGFR equations. This therefore confirms that the MDRD equation underestimated the GFR in these patients. Since reference GFR was not measured in this study due to the complexity of the assay, further studies would be required using measured GFR values to establish the differences between the CREA- and CYSC-based equations compared with the measured GFR.

The CKD-EPI CREA equation was proposed by the Levey’s group (2009) and expected to perform better than the MDRD equation, especially in the higher GFR range (eGFR>60 mL/min/1.73 m2). Indeed, this study showed that in all the groups, median eGFR was higher when calculated using the CKD-EPI CREA than with MDRD equation. More participants were identified as having normal kidney function (GFR > 90 mL/min/1.73 m2) with the CKD-EPI CREA equation than with the MDRD equation. The number of participants with moderately reduced kidney function (GFR < 60 mL/min/1.73 m2) was similar in all the groups when GFR was calculated using both equations. This similarity in the prevalence of participants with CKD (i.e. GFR < 60 mL/min/1.73 m2) may be due to the low number of participants in this study. The results from this study suggest that the CKD-EPI CREA equation performed better than the MDRD equation in monitoring kidney function in dyslipidaemic patients with normal or mild kidney impairment administered FF therapy. However, both MDRD and CKD-EPI CREA equations remain dependent on the limitations of serum CREA and their precision has been reported as imperfect (Levey et al., 2009).

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Previous studies have shown that eGFRs obtained from various CYSC-based equations differed widely (Lee et al., 2014; Cheuiche et al., 2019). Lee et al., (2014) demonstrated that eGFR estimated using six CYSC-based equations differed widely in patients with CKD. The reason for this variation is still unclear. The CYSC-based equations used in their study included: Larson et al., 2004; Hoek et al., 2003; Le Bricon et al., 2000; Filler and Lepage (2003); Orebero-cystatin derived using DAKO CYSC reagents (Filler and Lepage, 2003) and Orebro- cystatin derived using Gentian CYSC reagents (Filler and Lepage, 2003). In this study, the Gentian eGFR was higher than the CKD-EPI CYSC values in all the groups of participants. However, the prevalence of CKD by stages was similar between the two equations, except in Group D where the Gentian equation showed slightly higher proportion with normal kidney function and therefore a lower proportion with mild kidney disease than the CKD-EPI equation. The reason for the differences in the equations is unclear. However, unlike the CKD-EPI CYSC equation, which has been tested in various populations, data supporting the use of the Gentian equation is scarce, making the option for this equation less reliable for use in the clinics (Inker et al., 2012; Yang et al., 2017).

Recent studies have reported the superior diagnostic accuracy of serum CYSC based equations over other markers and equations in monitoring kidney function in various disease conditions (Yang et al., 2017). The reason for such superiority relies on the fact that the concentration of serum CYSC is less influenced by muscle mass than serum CREA (Seronie-Vivien et al., 2008). This study has shown that the eGFR calculated using the CYSC-based equations were higher than those calculated using CREA-based equations in all the groups of participants. It has been suggested that serum CYSC-based eGFR underestimates kidney function in advanced CKD stages because CYSC does not increase along with the CKD stage Uemura et al., (2011). This study could not confirm this suggestion because the participants in this study did not have advanced CKD. However, the results showed that eGFR differed widely between CREA-based and CYSC-based equations for participants with eGFR<60 mL/min/1.73 m2. However, in Group B participants, it was observed that both CYSC equations showed a higher proportion of participants with CKD (GFR<60 mL/min/1.73 m2) than the CREA-based equations. It was also observed across

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Fombon, I.S. (2020) the groups that some participants classified by the MDRD equation as having stage 3 kidney disease (i.e. eGFR<60mL/min/1.73 m2) were reclassified by the CYSC-based equations as having stage 2 kidney disease (i.e. eGFR 60 – 89 mL/min/1.73 m2) and vice versa. The reason for such discrepancies is not clear.

The median eGFR calculated using the CKD-EPI CREA equation was comparable to the values obtained using CKD-EPI CYSC and CKD-EPI CREA-CYSC equations. The authors of both CKD-EPI CYSC and CKD-EPI CCREA-CYSC equations, which were derived from an impressive sample size (5592 subjects in the development dataset and 1119 in the validation dataset) had demonstrated better performance of the combined serum CREA and CYSC equation compared to the individual CKD-EPI equations (Inker et al., 2012). Recent studies confirmed the good performances of the two CKD-EPI CYSC-based equations in an elderly population (n=394; median age, 80 [range74-79] years) recruited from nephrology clinics and the community (Kilbride et al., 2013) and in 670 kidney transplant recipients from 3 centres undergoing GFR measurements (Mason et al., 2013). Mindikoglu et al., (2013) showed that the equation was superior to conventional equations for estimating GFR in patients with liver cirrhosis; however, the diagnostic performance was not as good as reported in non-cirrhotic subjects. In another study, Obiols et al., (2013) demonstrated that the CKD-EPI CREA-CYSC equation was more accurate and precise in hypertensive patients with higher GFR.

In this study, the CKD-EPI CREA-CYSC formula also showed the lowest proportion of participants with GFR<60 mL/min/1.73 m2 in all the groups, except in Group C where the values were similar to those obtained using the CREA-based formulas. These results suggest that the CKD-EPI CREA-CYSC-based equation performed better than all the other equations used in this study to monitor kidney function in patients with dyslipidaemia. It is important to emphasise that in this study the GFR was not measured, so further investigation we be required to confirm this finding.

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4.4.4 Agreement between serum CREA- and CYSC-based equations for determining eGFR in patients administered FF Finally, the possible correlation and agreement between the various eGFR equations was examined. This study showed a significant positive linear correlation between serum CREA and serum CYSC only after 3 months of FF therapy (r=0.6088, p=0.030, n=13), corresponding to when the greatest increase in serum CREA and CYSC levels was observed. No statistically significant linear correlation was observed between the two biomarkers in the other groups. As expected, serum CREA showed strong negative correlation with the eGFR calculated using the CREA-based equations, while serum CYSC also correlated well with the eGFR calculated using the CYSC-based equations. There was also no significant correlation between the CREA-based equations and CYSC-based equations in all the groups, except in Group A participants where a moderate but statistically significant correlation was observed between serum CREA and eGFR by the CKD-EPI CYSC equation (r=-0.5887, p=0.037) and the Gentian equation (r=-0.6088, p=0.030). These results suggest that a significant increase serum CREA would be accompanied by significant changes in the eGFR estimated using the CYSC-based equations.

The results from this study are in contrast with previous reports by Randers et al., (1998) and Elnokeety et al., (2017). Randers et al., (1998) investigated serum CYSC, serum and urine CREA and GFR by 99mTc-DTPA clearance in 66 patients with various kidney diseases and 61 patients on dialysis and reported a significant linear correlation between serum CYSC and serum CREA in those with GFR higher than 30 mL/min/1.73 m2. Similarly, in a recent study involving 80 patients with type 2 diabetes, Elnokeety et al., (2017) reported a significant linear correlation between serum CYSC and serum CREA. Unlike both studies in which the participants consisted of patients with an underlying kidney disease or diabetes, the present study did not include participants with any form of kidney impairment. It is unclear whether such disparity with this study is related to the participants involved in this study.

The Bland-Altman analyses showed low bias and high precision (or low SD around the bias) between the MDRD and CKD-EPI CREA equations, which suggest a

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Fombon, I.S. (2020) good level of agreement between the CREA-based equations. Such level of agreement was not observed between the two CYSC equations, in fact, very high bias results and low precision was observed between Gentian eGFR and all the other equations, with exceptionally low precision between Gentian eGFR and eGFR calculated using the CREA-based equations. These results suggest that the Gentian equation differed widely from the other equations in estimating the GFR. However, the CKD-EPI CREA-CYSC equation showed better concordance with all the CKD-EPI equations although precision was poor (>10 mL/min/1.73 m2) in all the Groups. It has been suggested in a previous study by Delanaye et al., (2013) that bias is dependent on the GFR level. In this study, the level of GFR varied greatly with the different equations and therefore we cannot verify the validity of that suggestion. It is also important to note that a perfect concordance between the various equations does not necessarily mean that the results of these equations are correct. Indeed, in the absence of a measured GFR, it is possible that all five equations overestimated or underestimated the GFR.

This study have shown that while the CREA-based equation tends to underestimate the GFR, the eGFR appears to be overestimated by Gentian equation, making the CKD-EPI CREA-CYSC equation the best option for calculating the eGFR. The CKD-EPI CREA-CYSC equation has also been shown in this study to correlate well with both serum CREA and serum CYSC. However, this equation requires measuring both serum CYSC and serum CREA concentrations and therefore less cost-effective for routine clinical practice. The results also showed low bias and high precision between the CKD-EPI CYSC and CKD-EPI CREA equations and between the CKD-EPI CYSC equation and CKD- EPI CREA-CYSC equations in all the groups. It can therefore be concluded that serum CYSC and the CKD-EPI CYSC equation is the best alternative to the use of serum CREA and the MDRD equation for monitoring the kidney function in patients administered FF for dyslipidaemia.

Some limitations to this study have been well recognised:

 First, this study was conducted on a small number of participants, mainly patients with dyslipidaemia who required FF therapy and whose kidney function was relatively normal. As such, these results cannot be generalised

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over a larger scale, but calls for larger prospective studies to establish the best equation for monitoring kidney function in this cohort.

 Second, the GFR was not measured by a gold standard method, as this is technically complex and expensive for use in the clinical practice. This means the accuracy of the serum CYSC- and CREA-based eGFR equations could not be tested against the measured GFR.

 Third, serum CREA concentration was determined by the Jaffé reaction method which is less specific and has less precision compared with the enzymatic method. However, the calibration of the Beckman Coulter Jaffe creatinine method used in this study is traceable to an isotope dilution mass spectrometry (IDMS) reference method using the National Institutes of Standards and Technology (NIST) Standard Reference Material 967, although traceability does not address non-specificity. The Jaffé method is routinely used in the clinics because it is cheap and readily available.

 Lastly, the study participants were all Caucasian adults and as such do not include any variations that may occur due to ethnicity.

4.4.5 Summary This study has shown that the performance of the Gentian CYSC assay on a routine laboratory platform (AU640 analyser) meets the manufacturers’ specifications. The results have also demonstrated that FF therapy is associated with a moderate increase in serum CREA and CYSC concentrations in patients with dyslipidaemia. This study has also confirmed that the increase in serum CREA and CYSC induced by FF therapy is reversible, when treatment is administered in a long term (i.e. 12 months).

The study has also shown that the increase in serum CREA and CYSC was accompanied by a reduction in kidney function estimated using both CREA- and CYSC-based equations, thereby indicating that FF therapy genuinely impacts the kidney function. While the kidney function recovered completely after 12 months therapy, when assessed with CYSC-based equations, it was only partially recovered with the CREA-based equations. Serum CYSC-based eGFRs were higher than serum CREA-based eGFRs, suggesting that the MDRD equation,

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A good level of agreement and correlation was observed between the CKD-EPI CREA-CYSC equation and all the other equations (except the Gentian equation) making it a good replacement for the MDRD equation. However, the use of this equation in clinical practice would not be cost effective, because it would require testing both serum CREA and CYSC concentrations each time. The CKD-EPI CYSC equation which also showed a good level of agreement with all the other equations emerged as a better option and together with serum CYSC would provide an alternative to serum CREA and the MDRD equation for assessing and monitoring the kidney function in patients administered FF therapy for dyslipidaemia.

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Chapter 5

Mechanisms of FF cytotoxicity in kidney and muscle cell lines

5.1 - INTRODUCTION In this chapter, the cytotoxicity of FF in kidney and muscle cells was investigated. Reports from in vitro studies on the toxicity of FF in different cell lines have been contradictory. While increasing evidence suggest that FF exerts anti-proliferative and pro-apoptotic properties in some cell lines, some studies have demonstrated a protective effect of FF in other tissues (Lian et al., 2018; Wang et al., 2018). For instance, FF has been shown to not only slow the progression of diabetic retinopathy and other microvascular complications in patients with Type 2 diabetes (Keating, 2011; Noonan et al., 2013; Czupryniak et al., 2016), but also protect against retinopathy, nephropathy, and cardiac pathological changes in Type 1 diabetes (Chen et al., 2013; Cheng et al., 2016; Zhang et al., 2016). In other studies, FF inhibited aldosterone-induced apoptosis in adult rat ventricular myocytes via stress-activated kinase-dependent mechanisms (De Silva et al., 2009). Zuo et al., (2015) described a renoprotective role of FF in fatty acid-induced apoptosis in renal proximal tubular cells (HK-2). A similar report by Thongnuanjan et al., (2016) showed that FF reduced apoptosis induced by cisplatin (anticancer drug) in renal proximal tubular cells (LLC-PK1 and HK-2). On the other hand, evidence has shown that FF inhibited the proliferation of various cell lines including human hepatocellular carcinoma HepG2 (Jiao and Zhao, 2002), prostate cancer (DU-145 and LNCap) (Wybieralska et al., 2011; Zhao et al., 2013) and neuroblastoma cell line (SH-SY5Y) (Su et al., 2015). Recent studies by Li et al., (2014) showed that FF inhibited the proliferation of breast cancer cell lines via activation of the nuclear factor kappa B (NFκB) pathway. NFκB is a protein complex that controls DNA transcription and cell survival. In human, NF-кB is made up of five subunits [p50/p105, p52/p100, ReLA (p65), RelB and c-Rel], with the p65 being the most abundant form (Nakanishi and Toi, 2005). In a quiescent state, NFκB (p65) is inactive in the cytoplasm where it combines with the inhibitor of κB (IκB), mainly IκBα, which is regulated by IκB

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Fombon, I.S. (2020) kinase (IKKα/β). Under the effect of some stimuli, IκBα is phosphorylated by IKKα/β, and then undergoes ubiquitination and degradation to release p65. Afterwards, p65 translocates into the nucleus and promotes transcription of genes. Li et al., (2014) demonstrated an increase in nuclear p65, accompanied by upregulation of phosphorylated IKKα/B and down-regulation of phosphorylated IκBα in the cytoplasm of MDA-MB-231 breast cancer cells exposed to FF. Similarly, Lian et al., (2018) demonstrated that FF induced cell death in human prostate cancer (PC-3) cells. Other mechanisms of FF-induced cell death have also been described and tend to vary in different tissues (Koltai, 2015). The impact of FF on the kidneys and the potential mechanisms of FF-induced cytotoxicity have not been described in the literature. Since the kidneys and skeletal muscles are involved in the metabolism and excretion of FF and its active metabolite, mainly fenofibric acid, and the skeletal muscles are the primary source of creatinine, FF toxicity to the kidney and skeletal muscles was investigated (in vitro) using cell line models, to establish the cause of FF-associated creatininaemia.

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5.2 – SPECIFIC AIMS In this chapter, the mechanism of FF cytotoxicity in the kidney and muscle was investigated using renal proximal tubular cells (NRK52E), skeletal muscle cells (L6) and human kidney cells (HEK293), and the intracellular events involved were characterised. The specific aims included:

1. To determine and compare the cytotoxicity of FF with 5-FU (positive control) in NRK52E, L6 and HEK293 cell lines using the MTS assay.

2. To evaluate the role of oxidative stress in FF-induced cell death in NRK52E, L6 and HEK293 cell lines

3. To investigate the molecular mechanism of FF-induced cell death by western blot analysis.

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5.3 - RESULTS

5.3.1 FF induces morphological changes in NRK52E, L6 and HEK293 cell lines To determine the cytotoxicity of FF in kidney and muscle cells, changes in the morphology of NRK52E, L6 and HEK293 cells was examined after the cells were exposed to different concentrations of FF (250, 500 and 1000 µM) or control (untreated) cells grown in 75 cm2 culture flasks for 24 hours. As shown in Figure 5.1, FF induced morphological changes in the cell lines tested and consistently reduced the density of the cells in a concentration-dependent manner. Control (untreated) NRK52E cells showed a confluent monolayer of cells, which appeared polyhedral and exhibited classic cobblestone morphological characteristics with intact cell-to-cell contact, showing cell processes, strong adherence, and high contrast. When exposed to 250 µM FF, the NRK52E cells increased in size (hypertrophied), but retained their shape and cell-to-cell contact. With 500 µM FF treatment, over fifty percent of the NRK52E cell population showed fibroblast-like appearance after 24 h exposure. The transformed cells became elongated or spindle-shaped, and lost their cobblestone growth pattern. With 1000 µM FF treatment, more than seventy percent of the NRK52E cells had lost their cell-to- cell contact, became rounded and detached from the plate and appeared dead and floating in the medium.

In the L6 cell line, Control (untreated) cells formed a monolayer of spindle-shaped cells with normal nuclei and in close contact with one another. After exposure to 250 µM FF for 24 h, morphological changes observed included gradual loss of their characteristic spindle shape, loss of cell-to-cell contact, formation of intracytoplasmic vacuolations, and shrinkage of cell body. The cells treated with 500 µM FF became rounded, with massive vacuolations in the cytoplasm, shrinkage of cell body, decrease in cell volume, cell contrast and number of adherent cells. Over sixty percent of the cells appeared dead and floating in the medium. With the 1000 µM FF treatment, more than ninety percent of the cells appeared dead, completely detached and floating in the medium.

The HEK293 cells also showed morphological changes when exposed to 250 µM FF for 24 h. The FF-treated cells displayed nuclear condensation, became round

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Fombon, I.S. (2020) and lucent. However, there were also many cells (about 60%) that morphologically, appeared to be unaffected by FF treatment in that they were similar in appearance to the control (untreated) cells, although they were detached. With 500 µM FF treatment, about seventy percent of the HEK293 cells exhibited signet-ring cell morphology with the increase in FF concentration (500 and 1000 µM). After treatment with 1000 µM FF for 24 h, about 80 percent of the cells were observed to be rounded, completely detached and floating in the medium. These results suggest that FF maybe toxic to NRK52E, L6 and HEK293 cell lines.

Untreated cells 250 µM FF 500 µM FF 1000 µM FF

NRK52E

L6 HEK293

Figure 5.1 Representative photomicrograph showing morphological changes in NRK52E, L6 and HEK293 cells exposed to FF. Equal number of cells (2.5 x 106/10 mL medium) for each cell line were seeded and allowed to attach for 24 h. Cells were then exposed to FF (0 – 1000 µM) for 24 h and images were taken using inverted phase contrast microscopy (magnification 200x). All three cell lines showed apoptotic morphological changes with increasing FF doses (n=3).

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5.3.2 FF reduces cell viability of NRK52E, L6 and HEK293 cells Next, the effect of FF on cell viability of NRK52E, L6 and HEK293 cell lines was assessed in the cells cultured for 72 hours in the presence of FF, compared with the cytotoxicity of 5-fluorouracil (5-FU) within the range (0 – 500 µM), used as positive control for the experiments. 5-FU is an age-old anticancer drug currently used to treat various types of cancer. As shown in Figure 5.2, FF and 5-FU decreased cell viability of all three cell lines in a dose-dependent manner that also depended on the cell line. As expected, all the cells were more sensitive to 5-FU than FF (Figure 5.2A-C). The HEK293 cells showed greater sensitivity to 5-FU

(IC50 = 10.98 ± 2.40 µM) than L6 cells (IC50 = 14.30 ± 0.14 µM) and NRK52E cells

(IC50 = 43.36 ± 0.41 µM) as shown by the half maximal inhibitory concentration

(IC50) values for FF and 5-FU calculated from the dose-log response curves (Table 5.1). The L6 cells were more sensitive to FF than NRK52E and HEK293 cells (Figure 5.3A).

To confirm that ethanol used to prepare the drug solutions did not significantly affect cell viability, NRK52E and L6 cells were exposed to ethanol (0 – 855 mmol/L) for 72 hours and cell viability was measured using the MTS assay. Ethanol up to 5% (855 mmol/L) did not produce any significant inhibitory effect on cell viability of the NRK52E and L6 cells (Figure 5.3B). The maximum amount of ethanol in the drug solution was 3.6% (616 mmol/L). These results suggest that within the therapeutic plasma range (13.8 – 83.1 µM) (Cizkova et al., 2016) FF is not toxic to kidney and muscle cells. However, at very high doses, FF may be toxic to kidney and muscle cells in a dose-dependent manner, with the muscle cells being more sensitive to FF than kidney cells.

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Figure 5.2 Dose-response curves of the effect of FF and 5-FU on viability of (A) NRK52E, (B) HEK293 and (C) L6 cells. Cells (5.0  103 cells /well) in 96-well plates were treated with increasing concentrations of FF and 5-FU (0 – 500 µmol) for 72 hours and cell viability was measured using the MTS assay. Black curve: cells incubated with FF, red curve: cells incubated in 5-FU, black dotted line represents point of 50% viability. Data are expressed as mean ± standard deviation of three independent experiments performed in triplicate.

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Table 5.1 Half-maximal inhibitory concentration (IC50)* of FF compared with 5-FU in NRK52E, HEK293 and L6 cell lines after 72 hours of treatment

Cell lines FF (µM) 5-fluorouracil (µM)

NRK52E >1000 43.4 ± 0.4

HEK293 >1000 11.0 ± 2.4

L6 267.2 ± 30.8 14.3 ± 0.1

*IC50 values were calculated from linear regression of the dose-log response curves after 72 hours of exposure to FF and 5-FU, determined by the MTS assay. Values expressed are mean ± SD of at least 3 independent experiments performed in triplicate.

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Figure 5.3 Dose-response curves of the effect of FF and ethanol on cell viability of NRK52E, HEK293 and L6 cells.

Cells (5.0  103/well) in 96-well plates were exposed to various treatments for 72 hours and cell viability was determined using MTS assay and change in absorbance measured at 500 nm. (A) Effect of FF on the viability of NRK52E cells (black curve), L6 cells (red curve) and HEK293 cells (green curve) after 72 hours of treatment. (B) Effect of ethanol (vehicle) on the viability of NRK52E cells (black curve) and L6 cells (red curve) after 72 hours of treatment. Data are expressed as Mean ± SD of three independent experiments performed in triplicate.

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5.3.3 FF induces the accumulation of ROS in NRK52E, L6 and HEK293 cells To understand the mechanism by which FF induced differential cell death in the NRK52E, L6 and HEK293 cells, this study investigated whether FF treatment was associated with accumulation of oxidative stress in the cells. Oxidative stress caused by reactive oxygen species (ROS) has been shown to lead to cellular damage and cell death. ROS activity was measured in NRK52E, L6 and HEK293 cells exposed to different concentrations of FF (0, 250, 500 and 1000 µM) for 6 hours using the DCFDA assay. The DCFDA forms a fluorescent compound, DCF, in response to ROS oxidation. Cells treated with 30 µM H2O2 were used as positive control for the experiments. The results are presented in Figure 5.4 as percentage changes in ROS activity compared with control (untreated) cells in each cell line.

The results showed that FF increased intracellular ROS activity in all the three cell lines in a dose-dependent manner. In the NRK52E cell line, ROS activity increased significantly by up to 30% in the cells exposed to 1000 µM FF, compared with control (untreated) cells (Figure 5.4A, p<0.001). In the L6 cell line, ROS activity also increased by 12%, 24% and 29% when the cells were exposed to 250, 500 and 1000 µM FF, respectively, although the difference compared with control (untreated) cells was not statistically significant (Figure 5.4B). Similarly, in the HEK293 cell line, ROS activity increased by 10%, 15% and 27% in cells exposed to 250, 500 and 1000 µM FF, respectively, compared with control (untreated) cells (Figure 5.4C, p=0.003).

These results suggest that FF reduces cell viability of NRK52E, L6 and HEK293 cell lines by increasing intracellular ROS production (oxidative stress) leading to significant damage to the cell structure.

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Figure 5.4 Increase in intracellular ROS in (A) NRK52E, (B) L6 and (C) HEK293 cells in response to FF toxicity. Cells were treated with different concentrations of FF (250, 500 and 1000 µM) for 6 hours and intracellular ROS activity was determined using DCFDA assay. DCF fluorescent intensity at 500 nm was measured relative to control (untreated cells). Cells treated with H2O2 (30 µM) were used as positive control for the experiments. Data are expressed as mean ± SEM of 3 independent experiments performed in triplicate; *p < 0.05, **p < 0.01, ***p < 0.001 vs control.

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5.3.4 Effect of FF on Bax and Bcl-2 expression in NRK52E, L6 and HEK293 cells In order to investigate whether FF reduced the viability of NRK52E, L6 and HEK293 cells by mediating apoptosis-inducing effects in the cells, the impact of FF on Bcl-2 family proteins expression was determined by western blot analysis. Given that Bax is associated with the apoptosis-promoting process, and Bcl-2 is associated with the apoptosis-inhibiting process, both of which are key regulators of apoptosis, the effects of FF on their protein expression was analysed. Bax and Bcl-2 in whole cell extracts from NRK52E, L6 and HEK293 cells exposed to various concentrations of FF (0, 250, 500 and 1000 µM) for 72 hours were evaluated.

Immunoblots and densitometric analysis of the Bax and Bcl-2 relative to β-actin expression for the three cell lines are shown in Figure 5.5, Figure 5.6 and Figure 5.7. In all the cell lines, a slight increase in the Bax expression was observed in the cells exposed to 250 µM FF and 500 µM FF, but no change in Bax expression in the cells exposed to 1000 µM FF. The increase in Bax expression in the HEK293 cells exposed to 250 µM FF and 500 µM FF was statistically significant (Figure 5.7, p=0.019 and 0.0049, respectively). Similarly, a slight increase in Bcl-2 expression was observed in the cells exposed to 250 µM FF and 500 µM FF in all the three cell lines, but no change in Bcl-2 expression in the cells exposed to 1000 µM FF.

These results suggest that FF caused cell death in these cell lines by inducing oxidative stress via intracellular ROS accumulation, which in turn causes an imbalance in Bax and Bcl-2 proteins leading to the activation of the caspase complexes and apoptosis. The data also suggests that at a high concentration (exposure to 1000 µM FF), cell death was induced via a Bax/Bcl-2 independent pathway, possibly by autophagy or necrosis.

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Figure 5.5 Expression of Bax and Bcl-2 in NRK52E cells exposed to FF. Cells were treated as indicated relative to control cells for 72 h. Protein expression was determined by western blot analysis. Data is expressed relative to actin and control samples. (A) Sample western blot and chemiluminescent analysis of Bax relative to actin expression (B) Sample western blot and chemiluminescent analysis of Bcl-2 relative to actin expression; Data are expressed as mean ± SD of 3 independent experiments performed in triplicate; *p < 0.05, ****p < 0.0001.

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Figure 5.6 Expression of Bax and Bcl-2 in L6 cells exposed to FF. Cells were treated as indicated relative to control cells for 72 h. Protein expression was determined by western blot analysis. Data is expressed relative to actin and control samples. (A) Sample western blot and chemiluminescent analysis of Bax relative to actin expression (B) Sample western blot and chemiluminescent analysis of Bcl-2 relative to actin expression; Data are expressed as mean ± SD of 3 independent experiments performed in triplicate; *p < 0.05, ***p < 0.001.

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` Figure 5.7 Expression of Bax and Bcl-2 in HEK293 cells exposed to FF. Cells were treated as indicated relative to control cells for 72 h. Protein expression was determined by western blot analysis. Data is expressed relative to actin and control samples. (A) Sample western blot and chemiluminescent analysis of Bax relative to actin expression (B) Sample western blot and chemiluminescent analysis of Bcl-2 relative to actin expression; Data are expressed as mean ± SD of 3 independent experiments performed in triplicate; *p<0.05, **p<0.01, ****p < 0.0001.

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5.3.5 Impact of FF on the expression of NFκB in NRK52E, L6 and HEK293 cell lines Since NFκB has a well established role in balancing between cell survival and apoptosis (Srinivas et al., 2016; Miyata et al., 2018), the effect of FF treatment on the level of its pathway related protein (NFκB p65) was investigated. In normal quiescent cells, NFκB p65 is inactive in the cytoplasm where it combines with IκB proteins, mainly IκBα, which is regulated via activation of IκB Kinase (IKK), mainly IKKα/ꞵ. Under some stimuli, IκBα is phosphorylated by IKKα/ꞵ, and then undergoes ubiquitination to release p65. The p65 released translocates to nucleus and promotes the transcription of target genes. The expression of NFκB p65 in NRK52E, L6 and HEK293 cells exposed to FF (250, 500 and 1000 µM) versus untreated (control) cells was assessed. Immunoblots and densitometric analysis of the NFκB p65 relative to β-actin expressions for the three cell lines are shown in Figure 5.8.

The results showed no significant change in the expression of NFκB p65 protein in the NRK52E cells exposed to the different concentrations of FF (Figure 5.8A). However, in the L6 cell line, NFκB p65 expression was significantly increased in the cells exposed to 250 µM FF (p<0.0001) and 500 µM FF (p<0.0001), although the level of NFκB p65 expression tended to decrease with increase in FF concentration (Figure 5.8B). The level of NFκB p65 in the L6 cells treated with 500 µM FF was still significantly higher than in control (untreated) cells (p=0.0073). NFκB p65 expression in L6 cells treated with 1000 µM FF was similar to the level in control (untreated) cells. In the HEK293 cell line, NFκB p65 expression was significantly decreased in the cells exposed to 250 µM FF and 1000 µM compared with control cells (Figure 5.8C, p<0.0001 and p=0.0342, respectively).

These results suggest that FF-induced cytotoxicity via activation of the NFκB signalling pathway. This study also suggest that in the NRK52E and HEK293 cell lines, and at a higher concentration (i.e. 1000 µM FF) in the L6 cell line, in which NFκB p65 expression was unchanged or decreased in all the cell lines compared with the control, cytotoxicity was induced by mechanisms independent of the NFκB p65 signalling pathway, possibly by necrosis.

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Figure 5.8 Effect of FF on NFκB p65 expression in NRK52E, L6 and HEK293 cell lines.

Cells were treated as indicated relative to control cells for 72 h and NFκB p65 expression was determined by western blot analysis. Chemiluminescent blots of NFκB p65 and actin expression in (A) NRK52E, (B) L6 and (C) HEK293 cell lines. Normalized band intensity relative to actin; Data are expressed as mean ± SEM of 3 independent experiments performed in triplicate; *p<0.05, **p<0.01, ****p< 0.0001.

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5.3.6 Impact of FF on MAPK signalling proteins in NRK52E, L6 and HEK293 cell lines The impact of FF on MAPK signalling pathway in NRK52E, L6 and HEK293 cell lines was investigated. Since activation of MAPK signalling such as c-Jun N- terminal kinase (JNK) and p38 MAPK pathways is associated with induction of apoptosis by stress agents, changes in JNK and p38 MAPK protein expressions in NRK52E, L6 and HEK293 cells exposed to FF (0 – 1000 µM) for 72 hours was examined. Figure 5.9 shows immunoblots and densitometric analysis of the JNK relative to β-actin expressions for the NRK52E, L6 and HEK293 cell lines exposed to the different concentrations of FF. The JNK protein was activated in all three cell lines when the cells were exposed to 1000 µM FF. At lower concentrations (i.e. 250 and 500 µM), FF exposure did not have any significant impact on JNK protein expression in the NRK52E cell line (Figure 5.9A). However, in the L6 and HEK293 cell lines, JNK expression was significantly reduced when the cells were exposed to 250 µM FF (Figure 5.9B, p=0.0007 and Figure 5.9C, p=0.0011, respectively).

These results indicate that with the lower concentrations, FF suppressed JNK expression in the L6 and HEK293 cell line, but with the higher concentration (i.e. 1000 µM); FF activated the JNK signalling pathway and induced toxicity of NRK52E, L6 and HEK293 cells. Immunoblots and densitometric analysis of the p38 protein relative to β-actin expressions for the NRK52E, L6 and HEK293 cell lines exposed to the different concentrations of FF are presented in Figure 5.10. The results showed a slight increase in p38 expression in the NRK52E cells exposed to the various concentrations of FF (Figure 5.10A), while in the L6 cell line a statistically significant increase in p38 expression compared with the control cells was observed in the different treatment groups (Figure 5.10B). However, in the HEK293 cell line, p38 expression was decreased compared with baseline levels (Figure 5.10C).

Collectively, these results suggest that FF induced toxicity in the L6 cell line occurred via the activation of the MAPK (JNK and p38) signalling pathway. The data also suggests that in the NRK52E and HEK293 cell lines, which showed no change or decrease in the expression of JNK and p38 FF induced toxicity via MAPK-independent mechanism.

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Figure 5.9 Expression of JNK protein in NRK52E, L6 and HEK293 cell lines treated with FF. Cells were treated as indicated relative to control cells for 72 h and NFκB p65 expression was determined by Western blot analysis. Chemiluminescent blots of JNK and actin expression in (A) NRK52E, (B) L6 and (C) HEK293 cell lines. Normalized band intensity relative to actin; Data are expressed as mean ± SEM of 3 independent experiments performed in triplicate; **p<0.01, ***p<0.001, ****p<0.0001.

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Figure 5.10 Effect of FF on MAPK p38 expression in NRK52E, L6, and HEK293 cell lines. Cells were treated as indicated relative to control cells for 72 h and p38 expression was determined by western blot analysis. Chemiluminescent blots of NFκB p65 and actin expression in (A) NRK52E cell line, (B) L6 cell line, and (C) HEK293 cell line. Normalized band intensity relative to actin. Data are expressed as mean ± SEM of 3 independent experiments performed in triplicate; *p<0.05, **p<0.01.

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5.4 DISCUSSION In this study the toxicity of FF to the kidney and muscle was investigated as the potential cause of the creatininaemia observed in patients administered FF therapy for dyslipidaemia. Evidence from previous studies in human HepG2 hepatoma cell line (Jiao and Zhao, 2002) and breast cancer cell lines (Li et al., 2014) showed that FF induced toxicity to the cells in a dose-dependent manner. In a recent report, Li et al., (2014) showed that FF was toxic to breast cancer cell lines. They reported that FF toxicity was dose- and cell line-dependent with the triple negative cell lines showing greater sensitivity to FF than other breast cancer cell lines. Reports on the toxicity of FF to kidney cells are scarce in the literature. In this study the impact of FF on cell viability of NRK52E, L6 and HEK293 cell lines was examined. The results showed that FF reduced cell viability of NRK52E, L6 and HEK293 cells in a dose- and cell line-dependent manner, with the L6 cell line showing greater sensitivity to FF therapy than the kidney cell lines (NRK52E and HEK293).

5.4.1 FF induces ROS accumulation in NRK52E, L6 and HEK293 cell lines Cytotoxicity is a complex process that is often mediated through DNA-adduct, oxidative stress, transcriptional factors and stress signalling leading to cell death. Recent studies have shown that FF inhibits cell growth by inducing intracellular ROS accumulation leading to cellular imbalance and change in mitochondrial membrane potential (MMP). This in turn causes the release of cytochrome C and subsequent activation of caspase, and result to cell death by apoptosis (Drukala et al., 2010; Zhao et al., 2013; Lian et al., 2018). Evidence from studies in human hepatoma HepG2 cell line have shown that treatment with FF increased intracellular ROS activity and decreased glutathione (GSH, an important cellular antioxidant) leading to impairment in mitochondrial function in the HepG2 cells (Jiao and Zhao, 2002). Jiao and Zhao (2002) suggested that ROS accumulation was upstream of the signals for cell death in HepG2 cells treated with FF.

In a similar study, Wybieralska et al., (2011) showed that FF promoted intracellular ROS accumulation in DU-145 prostate cancer cell line leading to suppression of ‘contact-stimulated’ cell motility. A recent study by Zhao et al., (2013) also 245

Fombon, I.S. (2020) demonstrated that FF exerted apoptosis properties in a prostate cancer cell line (LNCaP) by inducing oxidative stress. The FF-induced increase in intracellular ROS activity in these cells was accompanied by a decrease in the activity of superoxide dismutase (SOD) and malondialdehyde (MDA) (Zhao et al., 2013). Consistent with these reports, this study showed that FF induced intracellular ROS accumulation in a dose-dependent manner in the NRK52E, L6 and HEK293 cell lines. Preliminary results from this study also showed an increase in SOD activity in response to FF exposure (Appendix). Increase in SOD activity is considered as an adaptive response to assault by a toxic agent. However, in a sustained exposure to the assault, SOD activity decreased with increased ROS, leading to oxidative stress. Increased ROS accumulation also causes increased lipid peroxidation and protein carbonylation, which results in cellular imbalance, change of MMP and the release of cytochrome C (Su et al., 2019). Finally, release of cytochrome C causes activation of the caspase complex, which in turn leads to cell apoptosis (Latham et al., 2001). The results from this study suggest that oxidative stress due to intracellular ROS accumulation is a potential mechanism by which FF induces cell death in the kidney and muscle cells.

5.4.2 FF induces cytotoxicity of kidney and muscle cells via activation of NFκB and MAPK signalling pathway In order to elucidate the mechanism of FF-induced cell death in NRK52E, L6 and HEK293 cell lines, Bax and Bcl-2 expression in the cells exposed to different doses of FF for 72 hours was analysed by western blotting. The mitochondrial pathway of apoptosis, and particularly MMP, is regulated by proteins belonging to the Bcl-2 family (Leibowitz and Yu, 2010). Bax and Bcl-2 are key regulators of the cell apoptosis pathway. The balance between anti-apoptotic and pro-apoptotic proteins is crucial for the survival of the cell. Increased Bax/Bcl-2 ratio has been associated with cell death via the mitochondrial pathway of apoptosis (Leibowitz and Yu, 2010).

Previous studies have shown that FF down-regulated Bcl-2 expression and caused apoptosis in a mantle cell lymphoma cell line (Zak et al., 2010) and a prostate cancer cell line (Zhao et al., 2013). In a recent study, Li et al., (2014)

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Fombon, I.S. (2020) detected notable decrease in Bcl-xl expression and increase in Bad expression, but Bcl-2 expression was not significantly changed in the breast cancer cells. It has been suggested that Bad binds more strongly to Bcl-xl than Bcl-2, and it can reverse the anti-apoptosis activity of Bcl-xl, but not that of Bcl-2 (Hirai and Wang, 2001). This study showed that cell exposure to FF caused an increase in Bax expression in the kidney and muscle cells. However, at higher concentrations cell death appeared to occur via other mechanisms that are independent of the Bcl-2 family protein signalling pathway, possibly by necrosis. The results from this study did not show a strong relationship between Bax and Bcl-2 in the cell lines. Studies in a breast cancer cell line had also shown that FF-induced apoptosis was regulated by more intimate relationship between Bcl-xl and Bad than between Bcl- 2 and Bad (Hirai and Wang, 2001; Li et al., 2014). Further studies are recommended to detect the expression of other pro-apoptotic and anti-apoptotic proteins in NRK52E, L6 and HEK293 cells exposed to FF in order to fully understand the role of apoptotic processes in FF-induced cell death in these cell lines.

Finally, the possibility of ROS accumulation in the cells leading to the activation of the NFκB and MAPK signalling pathways was examined. Increased ROS production has also been associated with activation of various cellular signalling pathways and transcription factors including MAPK, nuclear factor (erythroid- derived 2)-like 2 (Nrf2)/Kelch like-ECH-associated protein 1(Keap1), NFκB and tumour suppressor p53, which can activate cell survival and/or cell death processes such as autophagy and apoptosis (Covarrubias et al., 2008; Bae, et al., 2011; Kaminskyy and Zhivotovsky, 2014; Zhang et al., 2016). Evidence of an interrelation between ROS and NFκB has been reported in the literature (Morgan and Liu, 2010). A series of studies have also shown that exogenously generated ROS influenced the activation of the NFκB pathway by inhibiting the phosphorylation of IκBα by IKK, thereby preventing their ubiquitination and degradation and inhibiting the activation of the NFκB pathway (Schoonbroodt and Piette, 2000; Takada et al., 2003). In addition, IKK is also reported as a primary target for ROS in influencing NFκB and the s-glutathionylation of IKKꞵ by ROS results in inhibition of IKKꞵ activity (Raynaert et al., 2006).

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Cumulative evidence from recent studies has shown that FF has bidirectional modulatory effects on NFκB activity in different cell lines. In breast cancer cells, Li et al., (2014) reported that FF activated the NFκB pathway by increasing the DNA binding of NFκB via down-regulation of phosphorylated-IκBα, which sequesters NFκB protein in the cytoplasm. But Zak et al., (2010) reported that NFκB was down-regulated in FF-induced apoptosis in mantle cell lymphoma. In a similar report, Liang et al., (2013) showed that FF inhibited the NFκB pathway in lung cancer by decreasing DNA binding of NFκB, without increasing IκBα protein. This study showed that FF significantly up-regulated NFκB p65 expression in L6 cell line. The expression of NFκB p65 in the NRK52E cell line did not change significantly, but was significantly down-regulated in the HEK293 cell line. Both NRK52E and HEK293 cell lines were less sensitive to FF treatment as shown by the MTS assay. Consistent with the findings of Li et al., (2014), the results from this study suggests that FF induced cell death in the L6 cell line by increasing ROS production and activation of the NFκB pathway.

The MAPK cascades are major intracellular signal transduction pathways associated with various cellular processes such as cell growth, differentiation, development, cell cycle, survival and cell death (Ravingerova et al., 2003). Members of the MAPK cascades include the extracellular signal-related kinases (ERK1/2), the c-Jun N-terminal kinases (JNK), the p38 kinase (p38), and the big MAP kinase 1 (BMK1/ERK5) pathways (Pritchard and Hayward, 2013). Since cellular stress is the main inducer of the JNK and p38 MAPK pathways (Zhang et al., 2016), we investigated the effect of FF on these protein expressions. Evidence from previous studies suggests that FF is associated with the inhibitory effect of JNK and activation of p38 expression in different cell lines. In a recent study, Lian et al., (2018) reported that FF induced apoptotic cell death of prostate cancer (PC- 3) cells via a mechanism that involved increased p38 MAPK and AMPK phosphorylation but did not significantly change the phosphorylation levels of PI3K, AKT and JNK, although inhibition of p38 MAPK or AMPK by their respective inhibitors did not change the effect of FF-induced cell death. In other studies, FF inhibited JNK and p38 expression thereby protecting proximal tubular cells from cisplatin-induced cytotoxicity (Thongnuanjan et al., 2016). Consistent with these reports the results from this study showed that at lower concentrations of FF, JNK

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5.5 SUMMARY This study has demonstrated using cell line models that within plasma therapeutic concentrations, FF is non toxic in kidney (NRK52E and HEK293) and muscle (L6) cell lines, however, at higher concentrations; FF has been shown to be toxic to the cells in a dose-dependent manner. FF toxicity was also cell line-dependent, with L6 cells showing greater sensitivity to FF than the NRK52Eand HEK293 cells. It can be concluded that the potential mechanism of FF cytotoxicity involves ROS accumulation and oxidative stress leading to cytotoxicity via an imbalance in proapoptosis protein (Bax) and antiapoptosis protein (Bcl-2), together with activation of the NFκB and MAPK signalling pathways. The results from this study have also shown that at very high doses, FF may induce cell death by other mechanisms such as necrosis. Preliminary data from flow cytometry analysis of cells exposed to different concentrations of FF (0,250, 500, 1000 µmol/L) and stained using propidium iodide and Annexin V-FITC showed that high concentrations of FF caused cell death via mechanism(s) that was not associated with apoptosis (Appendix). Further studies are recommended to examine whether necrosis is involved in FF-induced cell death.

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Chapter 6

Discussion, conclusion and future work

6.0 – DISCUSSION AND CONCLUSION The main aim of this study was to investigate FF toxicity and its impact on clinical biomarkers with the following objectives; first to examine the impact of FF (160 mg/day) therapy on routine clinical biomarkers including markers of kidney function, liver function, lipid profile, muscle damage, inflammatory markers and markers of thyroid function. Secondly, to examine the utility of serum CYSC as a marker of kidney function in patients administered FF therapy and finally to investigate the mechanism of FF toxicity in kidney and skeletal muscle using cell line models i.e. kidney (NRK52E and HEK293) and skeletal muscle (L6) cells.

First, it was hypothesised that FF impacted clinical biomarkers in a non-selective manner. This study has shown that FF (160 mg/day) therapy had different impacts on routinely tested clinical biomarkers including lipid profile, kidney function, liver function, thyroid function, markers of inflammation, and muscle damage (Figure 6.1). FF therapy is used primarily to reduce high levels of serum TRIG and increase low levels of HDL cholesterol, but may also reduce high levels of LDL cholesterol in patients with mixed dyslipidaemia (Farnier, 2008). As expected, FF therapy reduced serum TRIG levels whilst increasing HDL cholesterol levels. Serum TRIG levels were moderately reduced during the first 12 months of therapy, whilst serum HDL cholesterol level was significantly increased after 3 months (p=0.0200, n=13) and moderately reduced after 6 months of FF treatment. The impact of FF on lipids has been described extensively in the literature (Staels et al., 1998; Farnier, 2008). However, in this study FF therapy did not have any significant impact on CHOL and LDL levels. This study reconfirms the reports of large scale and long-term follow-up trials with FF such as the FIELD study and recent studies on the impact of FF on the lipid profile (Keech et al., 2005; Ncube et al., 2012).

This study also confirmed that FF therapy is associated with nephrotoxicity. Several studies (short-term and long-term) have reported an increase in serum

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CREA and CYSC levels during FF therapy (Hottelart et al., 2002; Keech et al., 2005; Ansquer et al., 2008; Ncube et al., 2012). The results from this study have shown that FF therapy caused an increase in CREA, urea and CYSC levels, mainly during the first 3 months of therapy, although the increases in these biomarkers of kidney function were clinically moderate. These findings are consistent with those reported by Ansquer et al., (2008) in a 6-week randomised crossover trial in healthy adults and later supported by Ncube et al., (2012) in a recent study involving patients with hyperlipidaemia, respectively. However, in this study the increase in CYSC level was not statistically significant. The difference with the other studies may be attributed to the nature of participants and dose/length of FF therapy. Unlike the study by Ncube et al., (2012), which included patients with diabetes, the participants in this study were apparently healthy individuals with normal baseline kidney function. Also, in the study by Ncube et al., (2012) the participants were administered FF at 264 mg/day, while the participants in this study had received FF at 160 mg/day. The concomitant increase in these three independent markers of kidney function (CREA, BUN and CYSC) together with a decrease in eGFR is indicative of a decrease in kidney function. This study therefore supports the theory that FF therapy has a genuine impact on kidney function, rather than a change in CREA metabolism or analytical interference of serum CREA assay as previously postulated (Hottelart et al., 2002; Ansquer et al., 2008).

This study further confirmed that the increase serum CREA and CYSC is reversible even when FF therapy is continued in the long-term. Recent studies have reported the reversibility of CREA levels after discontinuing FF therapy (Davis et al., 2011, Park et al., 2017). In the ACCORD study, Mychalekyj et al., (2012) demonstrated that FF-associated increase in CREA and CYSC levels was reversed 51 days after discontinuation of treatment. However, it has been unclear whether the reversibility is complete or partial. The results from this study have demonstrated that FF-associated increase in serum CREA and serum CYSC was reversible after 12 months of treatment even when treatment was not stopped. This study also showed that the increase in serum CREA was only partially reversible, while serum CYSC was completely reversible. The data from this study

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Fombon, I.S. (2020) suggests that a decrease in kidney function is the major cause of FF-associated increase in serum CREA and CYSC concentrations.

This study has also shown that FF therapy reduced serum UA levels in patients with dyslipidaemia. UA is an endogenous antioxidant which has a beneficial effect as an oxygen scavenger (Jung et al., 2018). It is present in normal humans in much higher concentrations than vitamin C and is known to cover two-thirds of free radical scavenging capacity in plasma (Jung et al., 2018). UA is particularly effective in quenching hydroxyl, superoxide, peroxide and peroxynitrite radical and is thought to serve a protective physiological role by preventing lipid peroxidation (Mahajan et al., 2009). However, hyperuricaemia has been linked with hypertension, diabetes and dyslipidaemia, and has become a public health burden worldwide, because of its high prevalence and clinical significance. In this study FF therapy significantly reduced serum UA level in dyslipidaemic patients by 16% (p<0.001, n=13) after three months of FF therapy. The results from this study are similar to those of Waldman et al., (2018) who reported a 20.2% (p<0.001, n=9795) reduction in serum UA concentration in diabetic patients after six weeks of FF therapy. The decrease in serum UA has been associated with the inhibition of the URAT1 protein, which is responsible for the transport of UA from the kidneys into the blood. Inhibition of URAT1, therefore, leads to an increase in UA excretion (Uetake et al., 2010) and a decrease in serum UA levels. However, the clinical significance of this decrease in serum UA in patients with dyslipidaemia is unclear.

On the impact of FF on liver function, this study did not support reports that link FF therapy with hepatotoxicity, which is often indicated by increased serum aminotransferases (Thulin et al., 2008; Kobayashi et al., 2009). Contrary to such reports, this study showed a moderate reduction in serum levels ALT and a significant decrease in serum AST and GGT at various periods during FF therapy, although serum levels of ALB and TBIL did not change significantly. The results also showed significant reduction in serum ALP concentration throughout the study period. This is consistent with previous reports by Gandhi et al., (2014) who showed a similar decrease in liver enzymes in patients with metabolic syndrome administered fibrate therapy. The mechanism underlying this decrease in serum ALP is not defined. However, changes in hepatic fat deposition and reduction in

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Fombon, I.S. (2020) the rate of release of ALP by the liver, as a result of a reduction in the rate of hepatic bile acid secretion are potential mechanisms that have been described (Day et al., 1993; Mikhailindis et al., 1998).

FF and other fibrates have been linked with various forms of myopathy when administered as monotherapy or in combination with statins (Wu et al., 2009). In this study, there was no evidence of muscle toxicity as serum levels of CK and LDH (enzymes associated with muscle damage) remained comparable with baseline values. It is important to note that myopathy associated with FF therapy is rare and is often associated with dose/length of treatment and other conditions including hypothyroidism (overt or subclinical form), chronic renal failure and chronic myelogenous leukaemia (Erdur et al., 2011; Kato et al., 2011; Wang and Wang, 2018). While chronic kidney failure is believed to contribute to FF toxicity by causing accumulation of the drug, the mechanism by which hypothyroidism encourages rhabdomyolysis is not fully understood. Hypothyroidism is also closely linked with both dyslipidaemia and kidney dysfunction (Yuan et al., 2015). In this study FF administered at 160 mg/day did not have any significant impact on thyroid function. This finding suggests that the decrease in kidney function observed in the study is not linked to changes in thyroid function. This study did not also show any changes in the inflammatory marker, CRP, following FF therapy.

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Figure 6.1 Impact of FF therapy on routine clinical biomarkers. Participants were administered FF (160 mg/day). ALB, Albumin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, gamma-glutamyl transferase; AST, aspartate aminotransferase; TBIL, total bilirubin, CRP, C reactive protein; CHOL, total cholesterol; TRIG, triglyceride, HDL, high-density lipoprotein; LDL, low-density lipoprotein; LDH, lactate dehydrogenase; CK, creatine kinase; CREA, creatinine; CYSC, cystatin C; NA, sodium; K, potassium; UA, uric acid; TSH, thyroid stimulating hormone; FT4, free T4; FT3, free T3; N, normal; ↓, decreased; ↑, increased biomarker.

Secondly, it was hypothesised that CYSC, a low molecular weight protein, is a better marker than CREA for monitoring kidney function in patients receiving FF therapy for dyslipidaemia. Serum CYSC has been touted as a better marker of kidney function than CREA, which is routinely used in clinical practice (Hojs et al., 2008; Bolignano et al., 2010). The use of CREA as a marker of eGFR is reportedly marred by several limitations including variations with age, sex, race, diet, muscle mass, medication use, catabolic state, tubular secretion and extrarenal excretion in severe renal impairment (Thomas et al., 2017). It is also known that the level of serum CREA only rises above the normal upper reference range when the GFR has be reduced by more than 50%, making it unsuitable as a marker of early detection of kidney injury as required when assessing drug toxicity (Perrone et al.,

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1992). This study has shown that the performance of Gentian CYSC, measured by the particle-enhanced turbidimetric immunoassay (PETIA) technique, exceed the manufacturer’s specifications with acceptable ranges of precision, linearity and assay stability. Unlike other CYSC assays which are based on the particle- enhanced nephelometric immunoassay (PENIA) technique designed to be used on specialist platforms, the Gentian CYSC is designed to be measured on routine laboratory platforms as part of the kidney profile.

Recent studies have reported the superior diagnostic accuracy of serum CYSC- based equations over other markers and equations in monitoring kidney function (Yang et al., 2016). This study showed that CYSC-based equations (i.e. Gentian and CKD-EPI CYSC) produced higher estimates of eGFR than the CREA-based equations (MDRD and CKD-EPI CREA) and the combined equation (CKD-EPI CREA-CYSC). Consequently, a higher number of participants were classified as having normal kidney function after FF therapy with the CYSC-based equations than with the CREA-based equations. This suggests that either the CREA-based equations underestimated the eGFR as suggested in previous reports (Froissart et al., 2005; Delanaye and Cohen, 2008) or that the CYSC-based equations overestimated the eGFR (Han et al., 2012). Since the GFR was not measured by a reference method due to the limitations of its use in clinical practice, it cannot be concluded whether the CYSC-based equations preformed better than the CREA- based equations. Also, there was no significant correlation between the CREA- based equations and CYSC-based equations. The combined CREA-CYSC-based formula showed significant correlation and degree of agreement with both CYSC- based and CYSC-based equations. Hence, the combined equation emerged as a better replacement for the routinely used MDRD equation as suggested in previous reports by Inker et al., (2012).

Furthermore, it was hypothesised that FF is toxic to the kidneys and skeletal muscles both of which are involved in the metabolism and excretion of FF, and that toxicity to the skeletal muscles and kidney injury accounts for the significant increase in serum CREA (Staels et al., 1998; Forsblom et al., 2010). This study has shown that within therapeutic concentrations, FF is not toxic to the kidney (NRK52E and HEK293) and skeletal muscle (L6) cells, however, at higher

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Fombon, I.S. (2020) concentrations, FF becomes toxic to the cells in a dose- and cell line-dependent manner. The results showed that muscle cells were more sensitive to FF than the kidney cells. FF toxicity has been reported in recent studies using the human HepG2 hepatoma cell line (Jiao and Zhao, 2002) and breast cancer cell lines (Li et al., 2014). Consistent with this study, both reports showed that FF-induced toxicity to the HepG2 and breast cancer cells was dose-dependent. Li et al., (2014) also demonstrated that FF-induced toxicity in breast cancer cell lines was cell line- dependent.

As a potential mechanism of FF induced cytotoxicity in the NRK52E, HEK293 and L6 cell lines (Figure 6.2), this study showed that FF induced intracellular ROS accumulation probably caused an oxidative stress, which resulted in an imbalance in the proapoptosis (Bax) and antiapoptosis (Bcl-2) protein ratio in the cells. An increase in Bax and/or a decrease in Bcl-2 lead to the activation of caspase 3 and modulation of apoptosis (Yang et al., 2002; Zak et al., 2010). Previous studies have shown that FF down-regulated Bcl-2 expression and caused apoptosis in mantle cell lymphoma cell line (Zak et al., 2010), prostate cancer cell line (Zhao et al., 2013) and breast cancer cell line (Li et al., 2014). Oxidative stress also caused the modulation of the NFκB (p65) and MAPK (p38 and JNK) signalling pathways that culminated in the cell death possibly by apoptosis.

To conclude, this study has shown that FF therapy impacts differently on routine clinical biomarkers. Apart from its effect in reducing high levels of serum TRIG and increasing low levels of HDL cholesterol, FF also reduces high levels of serum UA, AST, GGT and ALP, particularly in those with higher baseline values. This study has also confirmed that FF reduces kidney function as demonstrated by a concomitant increase in serum CREA, BUN and CYSC (independent markers of kidney function) and decrease in eGFR. This study therefore recommends that kidney function is closely monitored in patients administered FF therapy. This study has also shown that CYSC-based equations produce higher GFR estimates compared with the CREA-based equations. Future studies using a reference method to measure the GFR has been recommended in order to determine which marker performs better. Finally this study has also shown that FF toxicity is dose- and cell line-dependent. The skeletal muscle (L6) cells appear to be more sensitive to FF than the kidney (NRK52E and HEK293) cells. The potential

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Fombon, I.S. (2020) mechanism of FF cytotoxicity involves ROS accumulation and oxidative stress leading to cell death via an imbalance in proapoptosis protein (Bax) and antiapoptosis protein (Bcl-2), together with activation of the NFκB and MAPK signalling pathways.

Tissue esterases

Fenofibric acid (Active metabolite)

ROS

Oxidative stress

Bcl-2 MAPK

Bax Bcl-2 NFκB p38 JNK

p65

Apoptosis

+ Necrosis

Figure 6.2 Molecular mechanism of FF-induced toxicity in muscle and kidney cells. The overall molecular mechanism of FF cytotoxicity in NRK52E, L6 and HEK293 cells involves many steps. Briefly, FF induced intracellular ROS accumulation, which in turn created oxidative stress inside the cell. Oxidative stress caused an imbalance in Bax and Bcl-2 protein expression leading to activation of the caspase complexes and apoptosis. FF cytotoxicity also modulated the NFκB (p65) and MAPK (p38 and JNK) signalling pathways leading to cell death (apoptosis).

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6.1 Limitations and recommendations for future work There were some limitations to this study; therefore recommendations have been made for future work as follows:

First, this study was conducted on a small number of participants, mainly patients with dyslipidaemia who required FF therapy and whose kidney function was relatively normal. Also, the study participants were all Caucasian adults and as such do not include any variations that may occur due to ethnicity. As such, these results cannot be generalised over a larger scale, but calls for larger prospective studies to establish the impact of FF therapy on routine clinical biomarkers.

Second, serum creatinine concentration was determined by the Jaffé reaction method which is less specific and has less precision compared with the enzymatic method. However, the calibration of the Beckman Coulter Jaffe creatinine method used in this study is traceable to an isotope dilution mass spectrometry (IDMS) reference method using the National Institutes of Standards and Technology (NIST) Standard Reference Material 967, although traceability does not address non-specificity. The Jaffé method is routinely used in the clinics because it is cheap and readily available.

Third, we did not measure GFR by a gold standard method, as this is not feasible on a daily basis. This means we were unable to assess the accuracy of serum cystatin C- and creatinine-based eGFRs formulas. We recommend further studies using a reference method for measuring GFR in order to determine the best GFR equation(s) for monitoring kidney function in this cohort, using endogenous markers.

This study has shown that FF cytotoxicity in the kidney and muscle cells was associated with intracellular ROS accumulation leading to oxidative stress, which in turn caused cell death (apoptosis) via modulation of various signalling pathways including regulation of the Bcl-2 family proteins, NFκB and MAPK signalling pathways. Although further evidence for this is required, this study provides a solid foundation for understanding FF toxicity. The detailed mechanisms should be uncovered in future studies including measurement of necrosis in the cells exposed to FF.

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REFERENCES

Abbas, A., Saraf, S., Ramachandran, S., Raju, J., and Ramachandran, S. (2012) Factors associated with fibrate-induced creatinine elevation: observations in an out-patient setting. World Journal of Nephrology and Urology, 1(2-3), pp. 51-58.

Abbink, F.C., Laarman, C.A., Braam, K.I., van Wijk, J.A., Kors, W.A., Bouman, A.A., Spreeuwenberg, M.D., Stoffel-Wagner, B. and Bokenkamp, A. (2008) Beta-trace protein is not superior to cystatin C for the estimation of GFR in patients receiving corticosteroids. Clinical Biochemistry, 41(5-5), pp. 299 – 305.

Abdel-Maksoud, M.F., Eckel, R.H., Hamman, R.F. and Hokanson, J.E. (2012) Risk of coronary heart disease is associated with triglycerides and high-density lipoprotein cholesterol in women and non-high-density lipoprotein cholesterol in men. Journal of Clinical Lipidology, 6, pp. 374 – 381. Addicott, R., Maguire, D., Honeyman, M. and Jabbal, J. (2015) Workforce planning in the NHS. The Kingsfund, 2015, pp. 1 – 46.

Addicott, R., Maguire, D., Honeyman, M. and Jabbal, J. (2015) Workforce Planning In The NHS, pp. 1 – 46.

Ahmad, J., Odin, J., Hayashi, P., Chalasani, N., Fontana, R., Barnhart, H., Cirulli, E., Kleiner, D. and Hoofnagle, J. (2017) Identification and Characterization of Fenofibrate- Induced Liver Injury. Digestive Diseases and Sciences, 62(12), pp. 3596 - 3604.

Åkerblom, A., Helmersson-Karlqvist, J., Flodin, M. and Larsson, A. (2015) Comparison between Cystatin C- and Creatinine-Estimated Glomerular Filtration Rate in Cardiology Patients. Cardiorenal Medicine, 5(4), pp. 289 - 296.

Al-Mawali, A. (2015). Non-Communicable Diseases: Shining a Light on Cardiovascular Disease, Oman’s Biggest Killer. Oman Medical Journal, 30(4), pp. 227 - 228.

Andersson, C., Lyass, A., Vasan, R.S., Massaro, J.M., D'Agostino, R.B. and Robins, S.J. (2014) Long-term risk of cardiovascular events across a spectrum of adverse major plasma lipid combinations in the Framingham Heart Study. American Heart Journal, 168(6), pp. 878 – 883. Ansquer, J.C., Dalton, R.N., Causse, E., Crimet, D., Le Malicot, K. And Foucher, C. (2008) Effect of FF on kidney functions: a 6-week randomised crossover trial in healthy people. American Journal of Kidney Diseases, 51(6), pp. 904-913.

259

Fombon, I.S. (2020)

Argyropoulos, C.P., Chen, S.S., Ng, Y.H., Roumelioti, M.E., Shaffi, K., Singh, P.P. and Tzamaloukas, A.H. (2017) Rediscovering Beta-2 Microglobulin As a Biomarker across the Spectrum of Kidney Diseases. Frontiers in Medicine, 4(73), pp. 1 – 25.

Ash, K.O. (1996) Impact of cost cutting on laboratories: new business strategiesfor laboratories. Clinical Chemistry, 42(5), pp. 822 – 826.

Asvold, B. O., Bjoro, T. & Vatten, L. J. (2011) Association of thyroid function with estimated glomerular filtration rate in a population-based study: the HUNT study. European Journal of Endocrinology, 164(1), pp. 101–105.

Asztalos, B., Horvath, K., McNamara, J., Roheim, P., Rubinstein, J. and Schaefer, E. (2002) Comparing the effects of five different statins on the HDL subpopulation profiles of coronary heart disease patients. Atherosclerosis, 164(2), pp. 361 - 369.

Attridge, R., Frei, C., Ryan, L., Koeller, J. and Linn, W. (2013) Fenofibrate-associated nephrotoxicity: A review of current evidence. American Journal of Health-System Pharmacy, 70(14), pp. 1219 - 1225.

Bae, Y.S., Oh, H., Rhee, S.G. and Yoo, Y.D. (2011) Regulation of reactive oxygen species generation in cell signalling. Molecules and Cells, 32 (6), pp. 491 – 509.

Bagshaw, S.M. and Bellomo, R. (2010) Cystatin C in acute kidney injury. Current opinion in Critical Care, 16(6), pp. 533-539.

Baigent, C., Blackwell, L., Emberson, J., Holland, L.E., Reith, C., Bhala, N., Peto, R., Barnes, E.H., Keech, A., Simes, J. and Collins, R. (2010) Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised controlled trials. Lancet, 376, pp. 1670 - 1681.

Ballantyne, C., Houri, J., Notarbartolo, A., Melani, L., Lipka, L., Suresh, R., Sun, S., LeBeaut, A.P., Sager, P.T. and Veltri, E.P. (2003) Effect of ezetimibe coadministered with atorvastatin in 628 patients with primary hypercholesterolemia: a prospective, randomized, double-blind trial. Circulation, 107(19), pp. 2409 – 2415.

Banfi, G. And Del Fabbro, M. (2006) Serum creatinine values in elite athletes competing in 8 different sports: comparison with sedentary people. Chemical Chemistry, 52(2), pp. 330 – 331.

Bargnoux, A.S., Pieroni, L., Cristol, J.P., Kuster, N., Delanaye, P., Carlier, M.C., Fellahi, S., Boutten, A., Lombard, C., Gonzalez-Antuna, A., Delatour, V. and Cavalier, E. (2017)

260

Fombon, I.S. (2020)

Multicenter evaluation of cystatin C measurement after assay standardization. Clinical Chemistry, 63(4) pp. 833 – 841.

Barker, B. J., Goodenough, R. R. and Falko, J. M. (2003) FF monotherapy induced rhabdomyolysis. Diabetes Care, 26(8), pp. 2482–2483.

Barter, P.J. and Rye, K-A. (2008) Is there a role for fibrates in the management of dyslipidaemia in the metabolic syndrome? Arteriosclerosis, Thrombosis and Vascular Biology, 28(1), pp. 39 – 46.

Bastow, M., Durrington, P. and Ishola, M. (1988) Hypertriglyceridemia and hyperuricemia: Effects of two fibric acid derivatives (bezafibrate and fenofibrate) in a double-blind, placebo-controlled trial. Metabolism, 37(3), pp. 217 - 220.

Beker, B., Corleto, M., Fieiras, C. and Musso, C. (2018) Novel acute kidney injury biomarkers: their characteristics, utility and concerns. International Urology and Nephrology, 50(4), pp. 705 - 713.

Belfort, R., Berria, R., Cornell, J. and Cusi, K. (2010) FF reduces inflammation markers independent of its effects on lipids and glucose metabolism in patients with metabolic syndrome. Journal of Endocrinology & Metabolism, 95(2), pp. 829 – 836.

Bernard, P.H., Lamouliatte, H., Le Ball, B., Bioulac-Sage, P., Quinton, A. and Balabaud, C. (1994) Chronic active hepatitis associated with antinuclear antibodies induced by FF. Gastroenterologie Clinique et Biologie, 18, pp. 1048 – 1049.

Birjmohun R. S., Hutten B. A., Kastelein J. J. and Stroes E. S. (2005) Efficacy and safety of high-density lipoprotein cholesterol-increasing compounds: a meta-analysis of randomized controlled trials. Journal of the American College of Cardiology, 45, pp. 185 – 197.

Boden, W.E., Probstfield, J.L., Anderson, T., Chaitman, B.R., Desvignes-Nickens, P., Koprowicz, P.K., McBride, R., Teo, K. and Weintraub, W. (2011) Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy. New England Journal of Medicine, 365(24), pp. 2255 – 2267.

Bodor, E. and Offermanns, S. (2009) Nicotinic acid: an old drug with a promising future. British Journal of Pharmacology, 153(S1), pp. S68 - S75.

Boitier, E., Gautier, J.C.and Roberts, R. (2003) Advances in understanding the regulation of apoptosis and mitosis by peroxisome proliferator-activated receptors in pre-clinical

261

Fombon, I.S. (2020) models: relevance for human health and disease. Comparative Hepatology, 3(2), pp. 1 – 15.

Bolignano, D., Coppolino, G., Lacquaniti, A. and Buemi, M. (2010). From kidney to cardiovascular diseases: NGAL as a biomarker beyond the confines of nephrology. European Journal of Clinical Investigation, 40, pp. 273-276.

Bonds, D.E., Craven, T.E., Buse, J., Crouse, J.R., Cuddihy, R. and Elam, M. (2012) FF- associated changes in renal function and relationship to clinical outcomes among individuals with type 2 diabetes: the Action to Control Cardiovascular Risk in Diabetes (ACCORD) experience. Diabetologia, 55(6), pp. 1641 – 1650.

Bougarne, N., Weyers, B., Desmet, S., Deckers, J., Ray, D., Staels, B. and De Bosscher, K. (2018) Molecular Actions of PPARα in Lipid Metabolism and Inflammation. Endocrine Reviews, 39(5), pp. 760 - 802.

Bove, M., Fogacci, F. and Cicero, A.F.G. (2017) Pharmacokinetic drug evaluation of ezetimibe + simvastatin for the treatment of hypercholesterolemia. Expert Opinion on Drug Metabolism & Toxicology, 13(10), pp. 1099 – 1104.

British Cardiac Society, British Hyperlipidaemia Association, British Hypertension Society, endorsed by the British Diabetic Association (1998) Joint British recommendations on prevention of coronary heart disease in clinical practice. Heart, 80(supplement 2), pp. S1 – S29.

British Heart Foundation (2015). Heart Statistics. http://www.bhf.org.uk/research/heart- statistics.aspx (Last reviewed and updated 13 February 2015 and accessed 21 February 2015).

Broeders, N., Knoop, C., Antoine, M., Tielemans, C. (2000) Fibrate-induced increase in blood Urea and creatinine: is gemfibrozil the only innocuous agent? Nephrology, Dialysis, Transplantation, 15(12), pp. 1993 – 1999.

Brown, W.V. (2007) Expert commentary: the safety of fibrates in lipid-lowering therapy. American Journal of Cardiology, 99, pp. S19 - S21.

Brown, W.V., Dujovne, C.A., Farquhar, J.W. Feldman, E.B., Grundy, S.M., Knopp, R.H., Lasser, N.L., Mellies, M.J., Palmer, R.H., Samuel, P. et al., (1986) Effects of FF on plasma lipids. Double-blind, multicenter study in patients with type IIA or IIB hyperlipidaemia. Arteriosclerosis, 6(6), pp. 670 – 678.

262

Fombon, I.S. (2020)

Browning, J.D. and Horton, J.D. (2004) Molecular mediators of hepatic steatosis and liver injury. The Journal of Clinical Investigation, 114 (2), pp. 147 – 152.

Canaris, G.J., Manowitz, N.R., Mayor, G. and Ridgway, E. C. (2000) The Colorado thyroid disease prevalence study. Archives of Internal Medicine, 160(4), pp. 526–534.

Capell, W.H., De Souza, C.A., Poirier, P., Bell, M.L., Stauffer, B.L., Weil, K.M., Hernandez, T.L. and Eckel, R.H. (2003) Short-term triglyceride lowering with FF improves vasodilator function in subjects with hypertriglyceridemia. Arteriosclerosis, Thrombosis, and Vascular Biology, 23(2), pp. 307 – 313.

Carlson, L.A. (2005) Nicotinic acid: the broad-spectrum lipid drug. A 50th anniversary review. Journal of Internal Medicine, 258(2), pp. 94 – 114.

Casal, M., Nolin, T. and Beumer, J. (2019) Estimation of Kidney Function in Oncology. Clinical Journal of the American Society of Nephrology, 14(4), pp. 587 - 595.

Castellote, J., Ariza, J., Rota, R., Girbau, A. and Xiol, X. (2008) Serious drug-induced liver disease secondary to ezetimibe. World Journal of Gastroenterology, 14(32), pp. 5098 – 5099.

Catapano, A.L., Graham, I., De Backer, G., Wiklund, O., Chapman, M.J., Drexel, H., Hoes, A.W., Jennings, C.S., Landmesser, U., Pedersen, T.R., Reiner, Z., Riccardi, D., Taskinen, M-R., Tokgozoglu, L., Monique Verchuren, V.M., Vlachopoulos, C., Wood, D.A. and Zamorano, J.L. (2016) ESC/EAS Guidelines for the management of dyslipidaemias. The Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS). European Heart Journal, 37, pp. 2999 – 3058.

Chang, Y. and Robidoux, J. (2017) Dyslipidaemia management update. Current Opinions in Pharmacology, 33, pp. 47 – 55.

Chapman, M.J. (1987) Pharmacology of FF. The American Journal of Medicine, 83(5), pp. 21 – 25.

Chapman, M.J. (2003) Therapeutic action in atherogenic dyslipidaemia and future perspectives. Atherosclerosis, 171, pp. 1-13.

Chapman, M.J. (2006). The effects of fibrates on lipid metabolism and inflammation in type 2 diabetes. Diabetes and Vascular Disease Research, 3, pp. S6-S9.

263

Fombon, I.S. (2020)

Chapman, M.J., Ginsberg, H.N., Amarenco, P., Andreotti, F., Boren, J., Catapano, A.L., Descamps, O.S., Fisher, E., Kovanen, P.T., Kuivenhoven, J.A., Lesnik, P., Masana, L., Nordestgaard, B.G., Ray, K.K., Reiner, Z., Taskinen, M-R., Tokgozoglu, L., Tybjaerg- Hansen, A. and Watts, G.F. (2011) Triglyceride-rich lipoproteins and high-density lipoprotein cholesterol in patients at high risk of cardiovascular disease: evidence and guidance for management. European Heart Journal, 32, 1345 – 1361.

Chatrenet, P., Regimbeau, C., Ramain, J.P., Penot, J. and Bruandent, P. (1993) Chronic active cirrhogenic hepatitis induced by FF. Gastroenterologie Clinique et Biologie, 17, pp. 612 – 613.

Chelland, C.S., Moffatt, R.J. and Stamford, B.A. (2008) Smoking and smoking cessation - the relationship between cardiovascular disease and lipoprotein metabolism: a review. Atherosclerosis, 201(2), pp. 225 – 235.

Chen, Y., Hu, Y., Lin, M., Jenkins, A.J., Keech, A.C., Mott, R., Lyons, T.J. and Ma, J-X. (2013) Therapeutic effects of PPARalpha agonists on diabetic retinopathy in type 1 diabetes models. Diabetes, 62(1), pp. 261 – 272.

Cheng, Y., Zhang, J., Guo, W., Li, F., Sun, W., Chen, J., Zhang, C., Lu, X., Tan, Y., Feng, W., Fu, Y., Liu, GC, Xu, Z. and Cai, L. (2016) Up-regulation of Nrf2 is involved in FGF21- mediated fenofibrate protection against type 1 diabetic nephropathy. Free Radical Biology & Medicine, 93, pp. 94 – 109.

Cheuiche, A., Queiroz, M., Azeredo-da-Silva, A. and Silveiro, S. (2019) Performance of Cystatin C-Based Equations for Estimation of Glomerular Filtration Rate in Diabetes Patients: A Prisma-Compliant Systematic Review and Meta-Analysis. Scientific Reports, 9(1), pp. 1 - 10

Cohen, D.E. (2008) Balancing cholesterol synthesis and absorption in the gastrointestinal tract. Journal of Clinical Lipidology, 2(2), S1 – S3.

Cook, J. (1971) Creatinine assay in the presence of protein. Clinica Chimica Acta, 32(3), pp. 485 - 486.

Cooper, D., Murrell, D., Roane, D. and Harirforoosh, S. (2015) Effects of formulation design on niacin therapeutics: mechanism of action, metabolism, and drug delivery. International Journal of Pharmaceutics, 490(1-2), pp. 55 - 64.

264

Fombon, I.S. (2020)

Covarrubias, L., Hernandez-Garcia, D., Schnabel, D., Salas-Vidal, E. and Castro- Obregon, S. (2008) Function of reactive oxygen species during animal development: passive or active? Developmental Biology, 380(1) pp. 1 – 11.

Criqui, M.H. (1996) Alcohol and coronary heart disease: consistent relationship and public health implications. Clinical Chemica, Acta, 246, pp. 51 – 57.

Czupryniak, L., Joshi, S., Gogtay, J. and Lopez, M. (2016). Effect of micronized fenofibrate on microvascular complications of type 2 diabetes: a systematic review. Expert Opinion on Pharmacotherapy, 17(11), pp. 1463 - 1473.

D’Agostino, R.B., Vasan, R.S., Pencina, M.J., Wolf, P.A., Cobain, M., Massaro, J.M. and Kannel, W.B. (2008) General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation, 117(6), pp. 743 – 753.

Dalen, J.E.and Devries, S. (2014) Diets to prevent coronary heart disease 1957 – 2013: what have we learned? American Journal of Medicine, 127, pp. 364 – 369.

Davidson, M.H., Armani, A., Mckenny, J.M. and Jacobson, T.A. (2007) Safety considerations with fibrate therapy. American Journal of Cardiology, 99(6A), pp. 3C-18C.

Davidson, M.H., Clark, J.A., Glass, L.M. and Kanumalla, A. (2006) Statin safety: an appraisal from the adverse event reporting system. American Journal of Cardiology, 97(8A), pp. 32C – 43C.

Davidson, M.H., McGarry, T., Bettis, R., Melani, L., Lipka, L.J., LeBeaut, A.P., Suresh, R., Sun, S., Veltri, E.P. (2002) Ezetimibe coadministered with simvastatin in patients with primary hypercholesterolemia. Journal of the American College of Cardiology, 40, pp. 2125 – 2134.

Davignon, J. (2004) Beneficial cardiovascular pleiotropic effects of statins. Circulation, 109(23 Supplement 1), pp. III-39 – III-43.

Davis, T.M., Ting, R., Best, J.D., Donoghoe, M.W., Drury, P.L., Sullivan, D.R., Jenkins, A.J., O'Connell, R.L., Whiting, M.J., Glasziou, P.P., Simes, R.J., Kesaniemi, Y.A., Gebski, V.J., Scott, R.S. and Keech, A.C. (2011) Effects of FF on renal function in patients with Type 2 diabetes mellitus: the FF Intervention and Event Lowering in Diabetes (FIELD) study. Diabetologia 54(2), pp. 280 – 290.

Day, A., Feher, M., Chopra, R. and Mayne, P. (1993) The effect of bezafibrate treatment on serum alkaline phosphatase isoenzyme activities. Metabolism, 42(7), pp. 839 - 842.

265

Fombon, I.S. (2020)

De Backer, G., Abrosioni, E., Borch-Johnsen, K., Brotons, C., Cifkova, R., Dallongeville, J., Ebrahim, S., Faergeman, O., Graham, I., Mancia, G., Manger Cats, V., Orth-Gomer, K., Perk, J., Pyorala, K., Rodicio, J.L., Sans, S., Sansov, V., Sechtem, U., Thomsen, T. and Wood, D. (2003) Executive summary: European guidelines on cardiovascular disease prevention in clinical practice. Third Joint Task Force for of European and other societies on cardiovascular disease prevention in clinical practice. European Heart Journal, 24(17), pp. 1601 – 1610.

De La Serna, G. and Cadarso, C. (1999) Fenofibrate decreases plasma fibrinogen, improves lipid profile, and reduces uricemia. Clinical Pharmacology & Therapeutics, 66(2), pp. 166 - 172.

De Silva, D.S., Wilson, R.M., Hutchinson, C., Ip, F.C., Garcia, A.G., Lancel, S., Ito, M., Pimental, D.R. and Sam, F. (2009) Fenofibrate inhibits aldosterone-induced apoptosis in adult rat ventricular myocytes via stress-activated kinase-dependent mechanisms. American Journal of Physiology. Heart and Circulatory Physiology, 296(6), pp. H1983 - H1993.

De Vries, A.P.J. and Rabelink, T.J. (2013) A possible role of cystatin C in adipose tissue homeostasis may impact kidney function estimation in metabolic syndrome. Nephrology, Dialysis Transplantation, 28(7), pp. 1628 – 1630.

Debus, B., Kirsanov, D., Yaroshenko, I., Sidorova, A., Piven, A. and Legin, A. (2015) Two low-cost platforms for quantitative creatinine analysis in urine. Analytica Chimica Acta, 895(2015), pp. 71 – 79.

Delanaye, P. and Cohen, E. (2008) Formula-Based Estimates of the GFR: Equations Variable and Uncertain. Nephron Clinical Practice, 110(1), pp. c48 - c54.

Delanghe, J.R. and Speekaert, M.M. (2011) Creatinine determination according to Jaffe – what does it stand for? Nephrology Dialysis Transplantation, 4(2), pp. 83 – 86.

Desager, J., Hulhoven, R. and Harvengt, C. (1980) Uricosuric Effect of Fenofibrate in Healthy Volunteers. The Journal of Clinical Pharmacology, 20(10), pp. 560 - 564.

Devarajan, P. (2010) Neutrophil gelatinase-associated lipocalin: a promising biomarker for human acute kidney injury. Biomarkers Medicine, 4, pp. 265-280.

Di Angelantonio E, Sarwar N, Perry P, Kaptoge, S., Ray, K.K., Thompson, A., Wood, A.M., Lewington, S., Sattar,N., Packard, C.J., Collins, R., Thompson, S.G. and Danesh, J.

266

Fombon, I.S. (2020)

(2009) Major lipids, apolipoproteins, and risk of vascular disease. The Journal of American Medical Association, 302(18), pp. 1993 – 2000.

Doumas, B.T., Watson, W.A. and Biggs, H.G. (1971) Albumin standards and the measurement of serum albumin with bromcresol green. Clinica Chimica Acta, 31, pp. 87- 96.

Drexel, H., 2009. Statins, fibrates, nicotinic acid, cholesterol absorption inhibitors, anion- exchange resins, omega-3 fatty acids: which drugs for which patients? Fundamental & Clinical Pharmacology, 23(6), pp. 687 - 692.

Drukala, J., Urbanska, K., Wilk, A., Grabacka, M., Wybieralska, E., DeValle, L., Madeja, Z. and Reiss, K. (2010) ROS accumulation and IGF-IR inhibition contribute to FF/PPARalpha-mediated inhibition of glioma cell motility in vitro. Molecular Cancer, 9(159), pp. 1 – 15.

Dunbar R. L. and Gelfand J. M. (2010) Seeing red: flushing out instigators of niacin- associated skin toxicity. The Journal of Clinical Investigation, 120(8), pp. 2651 – 2655.

Earley, A., Miskulin, D., Lamb, E.J., Levey, A.S., Uhlig, K. (2012) Estimating equationsfor glomerular filtration rate in the era of creatinine standardization: a systematic review. Annals of Internal Medicine, 156(11), pp. 785 – 795.

Eckfeldt, J.H., Karger, A.B., Miller, W.G., Rynders, G.P. and Inker, L.A. (2015) Performance in measurement of serum cystatin C by laboratories participating in the College of American Pathologists 2014 CYS survey. Archives of Pathology and Laboratory Medicine, 139, pp. 888 – 893.

Elnokeety, M., Shaker, A. and Fayed, A. (2017) Creatinine, cystatin, and combined-based equations in assessment of renal functions in type 2 diabetic Egyptian patients. The Egyptian Journal of Internal Medicine, 29(3), pp.105 – 111.

Elwyn, G., Seagrove, A., Thorne, K. and Cheung, W.Y. (2005) Ethics and research governance in a multicentre study: add 150 days to your study protocol. British Medical Journal, 330, pp. 847

Emberson, J.R., Kearney, P.M., Blackwell, L., Newman, C., Reith, C., Bhala, N., Holland, L., Peto, R., Keech, A., Collins, R., Simes, J. and Baigent, C. (2012) Lack of effect of lowering LDL cholesterol on cancer: meta-analysis of individual data from 175,000 people in 27 randomised trials of statin therapy. PLoS One, 7, pp. 1 - 10.

267

Fombon, I.S. (2020)

Erdur, F., Soyoral, Y., Emre, H., Begenik, H., Canbaz, E. and Erkoc, R. (2012) Fenofibrate-induced rhabdomyolysis in a patient with chronic renal failure due to nephrotic syndrome: A rare case report. Clinical Biochemistry, 45(1-2), pp. 162 - 164.

Eslami, L., Merat, S., Malekzadeh, R., Nasseri-Moghaddam, S. and Aramin, H. (2013) Statins for non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Cochrane Database System Reviews, 12, pp. CD008623.

Fabiny, D. and Ertingshausen, G. (1971) Automated Reaction-Rate Method for Determination of Serum Creatinine with the CentrifiChem. Clinical Chemistry, 17(8), pp. 696 - 700.

Facchini, F.S., Hollenbeck, C.B., Jeppesen. J., Chen, Y.D. and Reaven, G.M. (1992) Insulin resistance and cigarette smoking. Lancet, 339, pp. 1128 – 1130.

Farnier, M. (2008) Update on the clinical utility of FF in mixed dyslipidaemias: mechanisms of action and rational prescribing. Vascular Health and Risk Management, 4(5), pp. 991 – 1000.

Farnier, M., Bonnefous, F., Debbas, N. And Irvine, A. (1994) Comparative efficacy and safety of micronized FF and simvastatin in patients with primary type IIa or IIb hyperlipidaemia. Archives of Internal medicine, 154(4), pp. 441 -449.

Farnier, M., Ducobu, J. and Bryniarski, L. (2010) Efficacy and safety of adding FF 160 mg in high-risk patients with mixed hyperlipidaemia not controlled by pravastatin 40 mg monotherapy. The American Journal of Cardiology, 106(6), pp. 787 – 792.

Farnier, M., Ducobu, J. and Bryniarski, L. (2011) Long-term safety and efficacy of FF/pravastain combination therapy in high risk patients with mixed hyperlipidaemia not controlled by pravastatin monotherapy. Current Medical Research and Opinion, 27(11), pp. 2165 – 2173.

Fazio, S. and Linton, M.F. (2004) The role of fibrates in managing hyperlipidemia: mechanisms of action and clinical efficacy. Current Atherosclerosis Reports, 6(2), pp. 148 – 157.

268

Fombon, I.S. (2020)

Fegan, P.G., Shore, A.C., Mawson, D., Tooke, J.E., MacLeod, K.M. (2005) Microvascular endothelial function in subjects with Type 2 diabetes and the effect of lipid-lowering therapy. Diabetic Medicine, 22(12), pp. 1670 – 1676.

Feher, M.D., Hepburn, A.L., Hogarth, M.B., Ball, S.G. and Kaye, S.A. (2003) FF enhances urate reduction in men treated with allopurinol for hyperuricaemia and gout. Rheumatology, 42(2), pp. 321 -325.

Ferguson, M.A. and Waikar, S.S. (2012) Established and emerging markers of kidney function. Clinical Chemistry, 58(4), pp. 680 – 689.

Fernandez-Miranda, C., Perez-Carreras, M., Colina, F., Lopez-Alonso, G., Vargas, C. and Solis-Herruzo, J.A. (2008) A pilot trial of FF for the treatment of non-alcoholic fatty liver disease. Digestive and Liver Disease, 40(3), pp. 200 – 250.

Filler, G. and Lepage, N. (2003) Should the Schwartz formula for estimation of GFR be replaced by cystatin C formula? Pediatric Nephrology, 18(10), pp. 981 - 985.

Filler, G., Bokenkamp, A., Hofmann, W., Le Bricon, T., Martinez-Bru, C. and Grubb, A. (2005) Cystatin C as a marker of GFR–history, indications, and future research. Clinical Biochemistry, 38, pp. 1 – 8.

Filler, G., Priem, F., Lepage, N., Sinha, P., Vollmer, I., Clark, H., Keely, E., Matzinger, M., Akbari, A., Althaus, H. and Jung, K. (2002) Beta-trace protein, cystatin C, beta(2)- microglobulin, and creatinine compared for detecting impaired glomerular filtration rates in children. Clinical Chemistry, 48(5), pp. 729 – 736.

Fletcher, B., Berra, K., Ades, P., Braun, L.T., Burke, L.E., Durstine, J.L., Fair, J.M., Fletcher, G.F., Goff, D., Hayman, L.L., Hiatt, W.R., Miller, N.H., Krauss, R., Kris-Etherton, P., Stone, N., Wilterdink, J. and Winston, M. (2005) Managing abnormal lipids. Circulation, 112, pp. 3184 – 3209.

Flodin, M. and Larsson, A. (2009) Performance evaluation of a particle-enhanced turbidimetric cystatin C assay on the Abbott ci8200 analyser. Clinical Biochemistry, 42(9), pp. 873 – 876.

Flodin, M., Jonsson, A.S., Hansson, L.O., Danielsson, L.A. and Larsson, A. (2007) Evaluation of Gentian Cystatin C reagent on Abbott ci8200 and calculation of glomerular filtration rate expressed in mL/min/1.73 m2 from the cystatin C values in mg/L. Scandinavian Journal of Clinical and Laboratory Investigation, 67(5), pp. 560 – 567.

269

Fombon, I.S. (2020)

Florentin, M., Liberopoulos, E. and Elisaf, M. (2007) Ezetimibe-associated adverse effects: what the clinician needs to know. International Journal of Clinical Practice, 62(1), pp. 88 - 96.

Forsblom, C., Huikka, A., Leinonen, E.S., Sundvall, J., Groop, P.H. and Taskinen, M.R. (2010) Effect of long-term FF treatment on markers of renal function in type 2 diabetes: the FIELD Helsinki substudy. Diabetes Care, 33(2), pp. 215 – 220.

Fossati, P., Prencipe, L. and Berti, G. (1980) Use of 3, 5-dichloro-2- hydroxybenzenesulfonic acid/4-aminophenazone chromogenic system in direct enzymic assay of uric acid in serum and urine. Clinical Chemistry, 26(2), pp. 227 - 231.

Fox, C.S., Larson, M.G., Leip, E.P., Culleton, B., Wilson, P.W. and Levy, D. (2004) Predictors of new-onset kidney disease in a community-based population. Journal of American Medical association, 291(7), pp. 844-850.

Fredrickson, D.S. and Lees, R.S. (1965) A system for phenotyping hyperlipoproteinaemia. Circulations, 31, pp. 321 – 327.

Frick, M.H., Elo, O., Haapa, K., Heinonen, O.P., Heinsalmi, P., Helo, P., Huttunen, J.K., Kaitaniemi, P., Koskinen, P., Manninen, V., Maenpaa, H.and Malkonen, M. (1987) Helsinki Heart Study: primary-prevention trial with gemfibrozil in middle-aged men with dyslipidaemia. New England Journal of Medicine, 317, 1237 – 1245.

Froissart, M., Rossert, J., Jacquot, C., Paillard, M. and Houillier, P. (2005). Predictive Performance of the Modification of Diet in Renal Disease and Cockcroft-Gault Equations for Estimating Renal Function. Journal of the American Society of Nephrology, 16(3), pp. 763 - 773.

Fulcher, J. and Keech, A. (2012) The case for more intensive use of statins. Therapeutic Advances in Chronic Disease, 3(5), pp. 201- 210.

Fulcher, J., O’Connell, R., Voysey, M., Emberson, J., Blackwell, L., Mihaylova, B., Simes, J., Collins, R., Kirby, A., Colhoun, H., Braunwald, E., La Rosa, J., Pedersen, T.R., Tonkin, A., Davis, B., Sleight, P., Franzosi, M.G., Baigent, C. and Keech, A. (2015) Efficacy and safety of LDL-lowering therapy among men and women: meta-analysis of individual data from 174,000 participants in 27 randomised trials. Lancet, 385, pp. 1397 – 1405.

Gandhi, N., Lenton, R., Bhartia, M., Abbas, A., Raju, J. and Ramachandran, S. (2014) Effect of fibrate treatment on liver function tests in patients with the metabolic syndrome. SpringerPlus, 3(14), pp. 1 – 7.

270

Fombon, I.S. (2020)

Ganotakis, E., Tsimihodimos, V., Bairaktari, E., Rizos, E., Athyros, V., Seferiades, C. and Elisaf, M. (2002) Effects of various fibrates on serum alkaline phosphatase activity. Atherosclerosis, 165, pp. 187 -188.

Gauthier, J.M. and Massicotte, A. (2015) Statins and their effect on cognition: let’s clear up the confusion. Canadian Pharmacists Journal (Ott), 148(3), pp. 150 – 155.

Ge, L., Wang, J., Qi, W., Miao, H., Cao, J., Qu, Y., Li, B. and Song, B. (2008) The Cholesterol Absorption Inhibitor Ezetimibe Acts by Blocking the Sterol-Induced Internalization of NPC1L1. Cell Metabolism, 7(6), pp. 508 - 519.

Ghonem, N.S., Assis, D.N. and Boyer, J.L. (2015) On fibrates and cholestasis: a review. Hepatology, 62(2), pp. 635 – 643.

Gille, A., Odor, E.T., Ahmed, K. and Offermanns, S. (2008) Nicotinic acid: Pharmacological effects and mechanism of action. Annual Review of Pharmacology and Toxicology, 48, pp. 79 – 106.

Glassock, R. and Winearls, C. (2008) Screening for CKD with eGFR: Doubts and Dangers. Clinical Journal of the American Society of Nephrology, 3(5), pp. 1563 - 1568.

Goldenberg, I., Benderly, M. and Goldbourt, U. (2008) Update on the use of fibrates: focus on bezafibrate. Vascular Health and Risk Management, 4(1), pp. 131 – 141.

Gotto, A.M. (1990) Interrelationship of triglycerides with lipoprotein and high-density lipoproteins. The American Journal of Cardiology, 66(6), pp. A20 – A23.

Gowda, S., Desai, P.B., Kulkarni, S.S., Hull, V.V., Math, A.A.K. and Vernekar, S.N. (2010) Markers of renal function tests. North American Journal of Medical Sciences, 2(4), pp. 170 – 173.

Grubb, A., Horio, M., Hansson, L.O., Bjork, J., Nyman, U., Flodin, M., Larsson, A., Bokenkamp, A., Yasuda, Y., Blufpand, H., Lindstrom, V., Zegers, I., Althaus, H., Blirup- Jensen, S., Itoh, Y., Sjostrom, P., Nordin, G., Christensson, A., Klima, H., Sunde, K., Hjort-Christensson, P., Armbruster, D. and Ferrero, C. (2014) Generation of a new cystatin C-based estimating equation for glomerular filtration rate by use of 7 assays standardised to the international calibrator. Clinical Chemistry, 60(7), pp. 974 - 986.

Grubb, A., Nyman, U., Bjorak, J., Lindstrom, V., Rippe, B., Sterner, G. and Christensson, A. (2005) Simple cystatin C based equations for glomerular rate compared with modification of diet in renal disease prediction equation for adults and use of Schwartz

271

Fombon, I.S. (2020) and the Counhan-Barratt prediction equations for children. Clinical Chemistry, 51, pp. 1420-1431.

Guay, G.R.P. (1999) Micronised FF: a new fibric acid hypolipidaemic agent. The Annals of Pharmacotheray, 33(10), pp. 1083 – 1103.

Guay, G.R.P. (2002) Update on FF. Cardiovascular Drug Reviews, 20(4), pp. 281 – 302.

Gullu, S., Emral, R., Bastemir, M., Parkes, A. and Lazarus, J. (2005) In vivo and in vitro effects of statins on lymphocytes in patients with Hashimoto’s thyroiditis. European Journal of Endocrinology, 153(1), pp. 41 - 48.

Hammersley, D. and Signy, M. (2017) Ezetimibe: an update on its clinical usefulness in specific patient groups. Therapeutic Advances in Chronic Disease, 8(1), pp. 4 – 11.

Han, H., Choi, K., Yang, S. and Han, W. (2012) Clinical Assessment of Follow-Up Cystatin C-Based eGFR in Live Kidney Donors. Korean Journal of Urology, 53(10), pp. 721 – 725.

Han, H.H., Choi, K.H., Yang, S.C. and Han, W.K. (2012) Clinical assessment of follow-up cystatin C-based eGFR in live kidney donors. Korean Journal of Urology, 53, pp. 721 – 725.

Hansson, G.K. (2005) Inflammation, atherosclerosis and coronary artery disease. New England Journal of Medicine, 352(16), pp. 1685 -1695.

Hao, Y., Zhang, H., Yang, X., Wang, L. and Gu, D. (2012) Effects of fibrates on C-reactive protein concentrations: a meta-analysis of randomized controlled trials. Clinical Chemistry and Laboratory Medicine (CCLM), 50(2), pp. 391 – 397.

Harper, C.R. and Jacobson, T.A. (2008) Managing dyslipidaemia in chronic kidney disease. Journal of the American College of Cardiology, 51(25), pp. 2375 – 2384.

Heemskerk, M., van den Berg, S., Pronk, A., van Klinken, J., Boon, M., Havekes, L., Rensen, P., van Dijk, K. and van Harmelen, V. (2014) Long-term niacin treatment induces insulin resistance and adrenergic responsiveness in adipocytes by adaptive downregulation of phosphodiesterase 3B. American Journal of Physiology-Endocrinology and Metabolism, 306(7), pp. E808 - E813.

Hendrani, A., Adesiyun, T., Quispe, R., Jones, S.R., Stone, N.J., Blumenthal, R.S. and Martin, S.S. (2016) Dyslipidemia management in primary prevention of cardiovascular disease: Current guidelines and strategies. World Journal of Cardiology, 8(2), pp. 201 – 210.

272

Fombon, I.S. (2020)

Herget-Rosenthal, S., Bokenkamp, A. and Hofmann W. (2007) How to estimate GFR- serum creatinine, serum cystatin C or equations? Clinical Biochemistry, 40, pp. 153 – 161.

Hermansson, J. and Kahan, T. (2017) Systematic Review of Validity Assessments of Framingham Risk Score Results in Health Economic Modelling of Lipid-Modifying Therapies in Europe. PharmacoEconomics, 36(2), pp. 205 - 213.

Hinton, W., McGovern, A., Coyle, R., Han, T.S., Sharma, P., Correa, A., Ferreira, F. and de Lusignan, S. (2018) Incidenceand prevalence of cardiovascular disease in English primary care: a cross-sectional and follow-up study of the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). British Medical Journal Open, 8, pp. 1 - 9.

Hirai, I. and Wang, H.G. (2001) Survival-factor-induced phosphorylation of Bad results in its dissociation from Bcl-x(L) but not Bcl-2. Biochemical Journal, 359(Pt 2), pp. 345 – 352.

Hiukka, A., Westerbacka, J., Leinonen, E.S., Watanabe, H., Wiklund, O., Hulten, L.M., Salonen, J.T., Tuomainen, T.P., Yki-Jarvinen, H., Keech, A.C., Taskinen, M.R. (2008) Long-term effects of FF on carotid intima-media thickness and augmentation index in subjects with type 2 diabetes mellitus. Journal of the American College of Cardiology, 52(25), pp. 2190 – 2197.

Hoek, F., Kemperman, F.W.W. and Krediet, R.T. (2003) A comparison between cystatin C, plasma creatinine and the Cockcroft and Gault formula for the estimation of glomerular filtration rate. Nephrology Dialysis Transplantation, 18(10), pp. 2024 - 2031.

Hoek, F.J., Kemperman, F.A. and Krediet, R.T. (2003) A comparison between cystatin C, plasma creatinine and the Cockcroft and Gault formula for the estimation of glomerular filtration rate. Nephrology Dialysis Transplatation, 18, 2024-2031.

Hoffmann, A., Nimtz, M. and Conradt, H. (1997) Molecular characterization of β-trace protein in human serum and urine: a potential diagnostic marker for renal diseases. Glycobiology, 7(4), pp. 499 - 506.

Hogue, J.C., Lamarche, B., Tremblay, A.J., Bergeron, J., Gagne, C., Couture, P. (2008) Differential effect of atorvastatin and FF on plasma oxidized low-density lipoprotein, inflammation markers, and cell adhesion molecules in patients with type 2 diabetes mellitus. Metabolism, 57(3), pp. 380 – 386.

Hojs, R., Bevc, S., Ekart, R., Gorenjak, M. And Puklavec, L. (2008) Serum cystatin C- based equation compared to serum creatinine-based equations for estimation of

273

Fombon, I.S. (2020) glomerular filtration rate in patients with chronic kidney disease. Clinical Nephrology, 70(1), pp. 10-17.

Hojs, R., Bevc, S., Ekart, R., Gorenjak, M. And Puklavec, L. (2006) Serum cystatin C as an endogenous marker of renal function in patients with mild to moderate impaired kidney function. Nephrology Dialysis Tranplantation, 21, pp. 1855-1862.

Hojs, R., Bevc, S., Ekart, R., Gorenjak, M. And Puklavec, L. (2004) Serum cystatin C as an endogenous marker of renal function in the elderly. International Journal of Clinical Pharmacy, 24, pp. 49-54.

Hojs, R., Bevc, S., Ekart, R., Gorenjak, M. and Puklavec, L. (2011) Kidney function estimating equations in patients with chronic kidney disease. The International Journal of Clinical Practice, 65(4), pp. 458 – 464.

Horder, M. and Gerhardt, W. (1985) The Scandinavian Committee on Enzymes. Scandinavian Journal of Clinical and Laboratory Investigation. Supplementum. 139, pp. 13 -17.

Hottelart, C., el Esper, N., Achard, J.M., Pruna, A. And Fournier, A. (2002) FF increases creatininaemia by increasing metabolic production of creatinine. Nephron, 92(3), pp. 536- 541.

HPS2-THRIVE Collaborative Group (2013) HPS2-THRIVE randomized placebo-controlled trial in 25 673 high-risk patients of ER niacin/laropiprant: trial design, pre-specifi ed muscle and liver outcomes, and reasons for stopping study treatment. European Heart Journal, 34, pp. 1279 – 1291.

Hudson, J. and Nolin, T. (2018) Pragmatic Use of Kidney Function Estimates for Drug Dosing: The Tide Is Turning. Advances in Chronic Kidney Disease, 25(1), pp. 14 - 20.

Hunter, D. (2006) Proportional ethical review and the identification of ethical issues, Journal of Medical Ethics, 33, pp. 241 – 245.

Ilanne-Parikka, P., Eriksson, J.G., Lindstrom, J., Peltonen, M., Aunola, S., Hamalainen, H., Keinanen-Kiukaanniemi, S., Laakso, M., Valle, T.T., Lahtela, J., Uusitupa, M., Tuomilehto, J. (2008) Finnish Diabetes Prevention Study Group. Effect of lifestyle intervention on the occurrence of metabolic syndrome and its components in the Finnish Diabetes Prevention Study. Diabetes Care, 31, pp. 805 – 807.

274

Fombon, I.S. (2020)

Inker, L. and Okparavero, A. (2011) Cystatin C as a marker of glomerular filtration rate. Current Opinion in Nephrology and Hypertension, 20(6), pp. 631 - 639.

Inker, L.A. (2014) Albuminuria: time to focus on accuracy. American Journal of Kidney Diseases, 63, 378 – 381.

Inker, L.A., Fan, L. and Levey, S.A. (2015) Comprehensive Clinical Nephrology. 5th edn. (Elsevier Saunders, Philadelphia, PA, 2015).

Inker, L.A., Schmid, C.H., Tighiouart, H., Eckfeldt, J.H., Feldman, H.I., Greene, T., Kusek, J.W., Manzi, J., Van Lente, F., Zhang, Y.L., Coresh, J. and Levey, A.S. (2012) Estimating glomerular filtration rate from serum creatinine and cystatin C. The New England Journal of Medicine, 367(1), pp. 20-29.

Inker, L.A., Schmid, C.H., Tighiouart, H., Greene, T., Kusek, J.W., Menzi, J., van Lente, F., Zhang, Y.L., Coresh, J. and Levey, A.S. (2012) A new equation to estimate glomerular filtration rate from creatinine and cystatin C. New England Journal of Medicine, 367, pp. 20 - 29.

Ishigami, M., Ogasawara, F., Nagao, K., Hashimoto, H., Kimura, Y., Kioka, N. and Ueda, K. (2018) Temporary sequestration of cholesterol and phosphatidylcholine within extracellular domains of ABCA1 during nascent HDL generation. Scientific Reports, 8(6170), pp. 1 – 10.

Istvan, E. (2003) Statin inhibition of HMG-CoA reductase: a 3-dimensional view. Atherosclerosis Supplements, 4(1), pp. 3 - 8.

Jacob, S. S., Jacob, S., Williams, C. and Deeg, M. A. (2005) Simvastatin, FF, and rhabdomyolysis. Diabetes Care, 28(5), pp. 1258.

Jacobson, T.A., Ito, M.K., Maki, K.C., Orringer, C.E., Bays, H.E., Jones, P.H., McKenney, J.M., Grundy, S.M., Gill, E.A., Wild, R.A., Wilson, D.P. and Brown, W.V. (2015) National Lipid Association Recommendations for patient-centered management of dyslipidaemia: Part 1 – Full report. Journal of Clinical Lipidology, in press.

Jain, K.S., Kathivarin, M.K., Rahul, S. and Chamanal, J. (2007) The biology and chemistry of hyperlipidaemia. Bioorganic and Medical Chemistry, 15, pp. 4674 – 4699.

Jameson, K., Amber, V., D’Oca, K., Mills, D., Giles, A. and Ambegaonkar, B. (2013). Impact of lipid-lowering therapy on the prevalence of dyslipidaemia in patients at high-risk

275

Fombon, I.S. (2020) of cardiovascular events in UK primary care – a retrospective database study. International Journal of Clinical Practice, 67 (12), pp. 1228-1237.

Jiao, H.L. and Zhao, B.L. (2002) Cytotoxic Effect of Peroxisome Proliferator Fenofibrate on Human HepG2 Hepatoma Cell Line and Relevant Mechanisms. Toxicology and Applied Pharmacology, 185(3), pp.172 - 179.

Jones, M., Denieffe, S., Griffin, C., Tinago, W. and Fitzgibbon, M. (2017) Evaluation of cystatin C in malignancy and comparability of estimates of GFR in oncology patients. Practical Laboratory Medicine, 8, pp. 95 - 104.

Jung, J-Y., Choi, Y., Suh, C-H., Yoon, D. and Kim, H-A. (2018) Effects of FF on uric acid level in patients with gout. Scientific Reports, 8(16767), pp. 1 – 9.

Kaminskyy, V.O. and Zhivotovsky, B. (2014) Free radicals in cross talk between autophagy and apoptosis. Antioxidants & Redox Signalling, 21(1), pp. 86 – 102.

Kassim, T., Hermes, J-M., Abdussalam, A., Aly, A.and Chandra, S. (2018) FF: A nonlithogenic means of recurrent drug-induced pancreatitis. Case Reports in Gastrointestinal Medicine, 2018, pp. 1 – 5.

Kato, K., Nagase, A., Masuda, M., Kato, Y., Onodera, K., Kawakami, T., Higuichi, M., Iwasaki, Y., Taniguchi, M. and Furukawa, H. (2011) Rhabdomyolysis associated with FF monotherapy in a patient with chronic myelogenous leukaemia. Case Reports in Gastroenterology, 5(2), pp. 492 – 496.

Katsiki, N., Nikolic, D., Montalto, G., Banach, M., Mikhailidis, D.P. and Rizzo, M. (2013) The role of fibrate treatment in dyslipidaemia: an overview. Current Pharmaceutical Design, 19(17), pp. 3124 – 3131.

Keating, G.M. (2011) Fenofibrate: a review of its lipid-modifying effects in dyslipidaemia and its vascular effects in type 2 diabetes mellitus. American Journal of Cardiovascular Drugs: Drugs, Devices, and other Interventions, 11, pp. 227 – 247.

Keating, G.M. and Croom, K.F. (2007) FF: a review of its use in primary dyslipidaemia, the metabolic syndrome and type 2 diabetes mellitus. Drugs, 67, pp. 121-153.

Keech, A., Simes, R.J., Barter, P., Best, J., Scott, R., Taskinen, M.R., Forder, P., Pillai, A., Davis, T., Glasziou, P., Drury, P., Kesaniemi, Y.A., Sullivan, D., Hunt, D., Colman, P., d’Emden, M., Whiting, M., Ehnholm, C. and Laakso, M. (2005) Effects of long-term FF

276

Fombon, I.S. (2020) therapy on cardiovascular events in 9795 people with type 2 diabetes mellitus (the FIELD study): randomised controlled trial. Lancet, 366(9500), pp. 1849 – 1861.

Kei, A. and Elisaf, M.S. (2012) Nicotinic acid: Do we know how it works after 55 years of clinical experience? World Journal of Pharmacology, 1(3), pp. 50 – 54.

Kelly, G.A., Kelly, K.S. and Franklin, B. (2006) Anaerobic exercise and lipids and lipoproteins in patients with cardiovascular disease: a meta-analysis of randomised controlled trials. Journal of Cardiopulmonary Rehabilitation, 26(3), pp. 131 – 139.

Kelly, R.B. (2010) Diet and Exercise in the management of hyperlipidaemia. American Family Physician, 81(9), pp. 1097 – 1102.

Kersten, S. (2014) Integrated physiology and systems biology of PPARα. Molecular Metabolism, 3(4), pp. 354 -371.

Kersten, S., Seydoux, J., Peters, J.M., Gonzalez, F.J., Desvergne, B. and Wahli, W. (1999) peroxisome proliferator-activated receptor alpha mediate the adaptive response to fasting. The Journal of Clinical Investigation, 103(11), pp. 1489 – 1498.

Khoury, S.R., Evans, N.S. and Ratchford, E. (2019) Exercise as medicine. Vascular Medicine, 24(4), pp. 371 – 374.

Kilbride, H., Stevens, P., Eaglestone, G., Knight, S., Carter, J., Delaney, M., Farmer, C., Irving, J., O'Riordan, S., Dalton, R. and Lamb, E. (2013) Accuracy of the MDRD (Modification of Diet in Renal Disease) Study and CKD-EPI (CKD Epidemiology Collaboration) Equations for Estimation of GFR in the Elderly. American Journal of Kidney Diseases, 61(1), pp. 57 - 66.

Kim, C.J. (2006) Effects of FF on C-reactive protein levels in hypertriglyceridaemic patients. Journal of Cardiovascular Pharmacology, 47(6), pp. 758 – 763.

Kim, S., Ko, K., Park, S., Lee, D.R. and Lee, J. (2017) Effect of FF medication on renal function. Korean Journal of Family Medicine, 38(4), pp. 192 – 198.

Kiortsis, D.N, Millionis, H., Bairaktari, E. and Elisaf, M.S. (2000) Efficacy of combination of atorvastatin and micronised FF in the treatment of severe mixed hyperlipidaemia. European Journal of Clinical Pharmacology, 56(9-10), pp. 631 – 635.

Knopp, R.H., Brown, W.V., Dujovne, C.A., Farquhar,J.W., Feldman, B.E., Goldberg, A.C., Grundy, S.M., Lasser, N.L., Mellies, M.J., Palmer, R.H., Samuel, P., Schonfeld, G. And Soperko, H.R. (1987) Effects of FF on plasma lipoproteins in hypercholesterolaemia and

277

Fombon, I.S. (2020) combined hyperlipidaemia. The American Journal of Medicine, 83(5) Supplement 2, pp. 50 – 59.

Kobayashi, A., Suzuki, Y., Kuno, H., Sugai, S., Sakakibara, H. and Shimoi, K. (2009) Effects of FF on plasma and hepatic transaminase activities and hepatic transaminase gene expression in rats. The Journal of Toxicological Sciences, 34(4), pp. 377 – 387.

Kodama, S., Tanaka, S., Saito, K., Shu, M., Sone, Y., Onitake, F., Suzuki, E., Shimano, H., Yamamoto, S., Kondo, K., Ohashi, Y., Yamada, N. and Sone, H. (2007) Effect of aerobic exercise training on serum levels of high-density lipoprotein cholesterol: a meta- analysis. Archives of Internal Medicine, 167(10), pp. 999 – 1008.

Koh, K.K., Han, S.H., Quon, M.J., Yeal Ahn, J. and Shin, E.K. (2005) Beneficial effects of fenofibrate to improve endothelial dysfunction and raise adinopectin levels in patients with primary hypertriglyceridemia. Diabetes Care, 28(6), pp. 1419 – 1424.

Koltai, T. (2015) Fenofibrate in cancer: mechanisms involved in anticancer activity. F1000Research, 4(55), pp.1 - 22.

Kostapanos, M.S., Florentin, M. and Elisaf, M.S. (2013) FF and the kidney: an overview. European Journal of Clinical Investigation, 43(5), pp. 522-531.

Kostapanos, M.S., Kei, A. and Elisaf, M.S. (2013) Current role of FF in the prevention and management of non-alcoholic fatty liver disease. World Journal of Hepatology, 5(9), pp. 470 – 478.

Kraus, W.E., Houmard, J.A., Duscha, B.D., Knetzger, K.J., Wharton, M.B., McCartney, J.S., Bales, C.W., Henes, S., Samsa, G.P., Otvos, J.D., Kulkarni, K.R. and Slentz, C.A. (2002) Effects of the amount and intensity of exercise on plasma lipoproteins. New England Journal of Medicine, 347(19), pp. 1483 – 1492.

Krysiak, R., Gdula-Dymek, A. and Okopien, B. (2011) Effect of Simvastatin and Fenofibrate on Cytokine Release and Systemic Inflammation in Type 2 Diabetes Mellitus With Mixed Dyslipidemia. The American Journal of Cardiology, 107(7), pp. 1010 - 1018.

Krysiak, R., Gilowski, W., Szkrobka, W. and Okopien, B. (2014) Different Effects of Fenofibrate on Metabolic and Cardiovascular Risk Factors in Mixed Dyslipidemic Women With Normal Thyroid Function and Subclinical Hypothyroidism. Cardiovascular Therapeutics, 32(6), pp. 264 - 269.

278

Fombon, I.S. (2020)

Kubota, T., Yano, T., Fujisaki, K., Itoh, Y. and Oishi, R. (2005) FF induces apoptotic injury in cultured human hepatocytes by inhibiting phosphorylation of Akt. Apoptosis, 10(2), pp 349-358.

Ladue, J.S. and Wroblewski, F. (1956) Serum glutamic pyruvic transaminase SGP-T in hepatic disease: a preliminary report. Annals of Internal medicine, 45(5), pp. 801 – 811.

Lamers, C., Schubert-ZZsilavecz, M. and Merk, D. (2012) Therapeutic modulators of peroxisome proliferator-activated receptors (PPAR): a patent review (2008 – present). Expert Opinion on Therapeutic Patents, 22(7), pp. 803 – 841.

LaRosa, J.C., He, J. and Vupputuri, S. (1999) Effect of statins on risk of coronary disease: a meta-analysis of randomised controlled trials. The Journal of the American Medical Association, 282(24), pp. 2340 – 2346.

Larsson, A., Malm, J., Grubb, A. and Hansson, L.O. (2004) Calculation of glomerular filtration rate expressed in ml/min from plasma cystatin C values in mg/l. Scandinavian Journal of Clinical and Laboratory Investigation, 64, pp. 25-30.

Larsson, A., Malm, J., Grubb, A. and Hansson, L.O. (2004) Calculation of glomerular filtration rate expressed in mL/min from plasma cystatin C values in mg/L. Scandinavian Journal of Clinical and Laboratory Investigation, 64(1), pp. 25 - 30.

Latham, P., Lund, E.K., Brown, J.C. and Johnson, I.T. (2001) Effects of cellular redox balance on induction of apoptosis by eicosapentaenoic acid in HT29 colorectal adenocarcinoma cells and rat colon in vivo. Gut, 49(1), pp.97 - 105.

Lavie, C., Ozemek, C., Carbone, S., Katzmarzyk, P. and Blair, S. (2019) Sedentary Behaviour, Exercise, and Cardiovascular Health. Circulation Research, 124(5), pp. 799 - 815.

Le Bricon, T., Thervet, E., Froissart, M., Benlakehal, M., Bousquet, B., Legendre, C. and Erlich, D. (2000) Plasma Cystatin C Is Superior to 24-h Creatinine Clearance and Plasma Creatinine for Estimation of Glomerular Filtration Rate 3 Months after Kidney Transplantation. Clinical Chemistry, 46(8), pp. 1206 - 1207.

Lee, H., Rhee, H., Seong, E., Lee, D., Lee, S. and Kwak, I. (2014) Comparison of glomerular filtration rates calculated by different serum cystatin C-based equations in patients with chronic kidney disease. Kidney Research and Clinical Practice, 33(1), pp. 45 - 51.

279

Fombon, I.S. (2020)

Leibowitz, B. and Yu, J. (2010) Mitochondrial signalling via the Bcl-2 family. Cancer Biology & Therapy, 9(6), pp.417 – 422.

Levey, A., Coresh, J., Greene, T., Stevens, L., Zhang, Y., Hendriksen, S., Kusek, J. and Van Lente, F. (2006) Using Standardized Serum Creatinine Values in the Modification of Diet in Renal Disease Study Equation for Estimating Glomerular Filtration Rate. Annals of Internal Medicine, 145(4), pp. 247 - 254.

Levey, A.S. and Inker, L.A. (2017) Assessment of glomerular filtration rate in health and disease: A state of the art review. Clinical Pharmacology and Therapeutics, 102(3), pp. 405 – 419.

Levey, A.S., Inker, L.A. and Coresh, J. (2014) GFR estimation: from physiology to public health. American Journal of Kidney Diseases, 63(5), pp. 820 – 834.

Levey, A.S., Stevens, L.A., Schmid, C.H., Zhang, Y.L., Castro, A.F., Feldman, H.I., Kusek, J.W., Eggers, P., Van Lente, F., Greene, T. and Coresh, J. (2009) A new equation to estimate glomerular filtration rate. Annals of Internal Medicine, 150(9), pp. 604 – 612.

Li, L., Sun, T., Zhang, P., Tian, J. and Yang, K. (2011) Statins for primary prevention of venous thromboembolism. Cochrane Database System Reviews, 12, pp. CD008203.

Li, T. and Chiang, J.Y.L. (2009) Regulation of bile acid and cholesterol metabolism by PPARs. PPAR Reseach, 2009(501739), pp. 1 -15.

Li, T., Zhang, Q., Zhang, J., Yang, G., Shao, Z., Luo, J., Fan, M., Ni, C., Wu, Z. and Hu, X. (2014) FF induces apoptosis of triple-negative breast cancer cells via activation of NFκB pathway. BMC Cancer, 14, pp. 1-13.

Lian, X., Wang, G., Zhou, H., Zheng, Z., Fu, Y. and Cai, L. (2018) Anticancer properties of FF: a repurposing use. Journal of cancer, 9(9), pp. 1527 – 1537.

Liang, C., Wang, I., Kuo, H., Yeh, H., Lin, H., Liu, Y., Hsu, W., Huang, C. and Chang, C. (2011). Long-Term Use of Fenofibrate Is Associated with Increased Prevalence of Gallstone Disease among Patients Undergoing Maintenance Hemodialysis. Renal Failure, 33(5), pp. 489 – 493.

Liang, C., Wang, I., Kuo, H., Yeh, H., Lin, H., Liu, Y., Hsu, W., Huang, C. and Chang, C., 2011. Long-Term Use of Fenofibrate Is Associated with Increased Prevalence of Gallstone Disease among Patients Undergoing Maintenance Hemodialysis. Renal Failure, 33(5), pp. 489 - 493.

280

Fombon, I.S. (2020)

Liang, H., Kowalczyk, P., Junco, J., Klug-De Santiago, H., Malik, G., Wei, S. and Slaga, T. (2013) Differential effects on lung cancer cell proliferation by agonists of glucocorticoid and PPARα receptors. Molecular Carcinogenesis, 53(9), pp. 753 - 763.

Libby, P., (2001) Current Concepts of the Pathogenesis of the Acute Coronary Syndromes. Circulation, 104(3), pp. 365 - 372.

Libby, P., Ridker, P.M. and Maseri, A. (2002) Inflammation and atherosclerosis. Circulation, 105(9), pp. 1135 – 1143.

Lipka, L.J. (2003) Ezetimibe: A first-in-class, novel cholesterol absorption inhibitor. Cardiovascular Drug Reviews, 21(4), pp. 293 – 312.

Lipscombe, J., Lewis, G.F., Cattran, D. and Bargman, J.M. (2001) Deterioration in renal function associated with fibrate therapy. Clinical Nephrology, 55(1), pp. 39-44.

Little, T. (2015) Method validation essentials, limit of blank, limit of detection, and limit of quantitation, BioPharm International 28 (4), pp. 48 – 51.

Lloyd-Mostyn, R., 2000. National service framework for coronary heart disease. British Medical Journal, 321(7261), pp. 634 - 634.

Loomba, R.S., Arora, R (2010) Prevention of cardiovascular disease utilizing fibrates – a pooled meta-analysis. American Journal of Therapeutics, 17(6), pp. E182 – e188.

Lopez-Giacoman, S. and Madero, M. (2015) Biomarkers in chronic kidney disease, from kidney function to kidney damage. World Journal of Nephrology, 4(1), pp. 57 – 73.

MacIsaac, R., Ekinci, E., Premaratne, E., Lu, Z., Seah, J., Li, Y., Boston, R., Ward, G. and Jerums, G. (2015) The Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation does not improve the underestimation of Glomerular Filtration Rate (GFR) in people with diabetes and preserved renal function. BMC Nephrology, 16(1), pp. 1 – 13.

MacIsaac, R.J., Tsalamndris, C., Thomas, M.C., Premaratne, E., Panagiotopoulos, S., Smith, T.J., Poon, A., Jenkins, M.A., Ratnaike, S.I., Power, D.A. and Jerums, G. (2006) Estimating glomerular filtration rate in diabetes: a comparison of cystatin C and creatinine- based methods. Diabetologia, 49, pp. 1686-1689.

Madero, M., Sarnak, M. and Stevens, L. (2006) Serum cystatin C as a marker of glomerular filtration rate. Current Opinion in Nephrology and Hypertension, 15(6), pp. 610 - 616.

281

Fombon, I.S. (2020)

Maeda, K., Noguchi, Y. and Fukui, T. (2003) The effect from cessation from cigarette smoking on the lipid and lipoprotein profiles: a meta-analysis. Preventive Medicine, 37, pp. 283 – 290.

Maison, P., Mennen, L., Sapinho, D., Balkau, B., Sigalas, J., Chesnier, M.C. and Eschwege, E. (2002) A pharmacoepidemiological assessment of the effect of statins and fibrates on fibrinogen concentration. Atherosclerosis, 160(1), pp. 155 – 60

Mallick, A.A. and O’Callaghan, F.J.K. (2009) Research governance delays a multicentre non-interventional study. Journal of the Royal Society of Medicine, 120, pp. 195-198.

Malmendier, C., Lontie, J., Delcroix, C., Dubois, D., Magot, T. and De Roy, L. (1989) Apolipoproteins C-II and C-III metabolism in hypertriglyceridemic patients Effect of a drastic triglyceride reduction by combined diet restriction and fenofibrate administration. Atherosclerosis, 77(2-3), pp. 139 - 149.

Mammen, A. (2016) Statin-Associated Autoimmune Myopathy. New England Journal of Medicine, 374(7), pp. 664 - 669.

Marchesini, G., Brizi, M., Bianchi, G., Tomassetti, S., Bugianesi, E., Lenzi, M., McCullough, A.J., Natale, S., Forlani, G. And Melchionda, N. (2001) Nonalcoholic fatty liver disease: a feature of the metabolic syndrome. Diabetes, 50(8), pp. 1844 – 1850.

Masson, I., Maillard, N., Tack, I., Thibaudin, L., Dubourg, L., Delanaye, P., Cavalier, E., Bonneau, C., Kamar, N., Morelon, E., Moranne, O., Alamartine, E. and Mariat, C. (2013) GFR Estimation Using Standardized Cystatin C in Kidney Transplant Recipients. American Journal of Kidney Diseases, 61(2), pp. 279 - 284.

McCullough, P.A., Ahmed, A.B., Zughaib, M.T., Glanz, E.D. and Di Loreto, M.J. (2011) Treatment of hypertriglyceridaemia with fibric acid derivatives: impact on lipid subfractions and translation into a reduction in cardiovascular events. Reviews in Cardiovascular Medicine, 12(4), pp. 173 – 185.

McDonald, K.B., Garber, B.G. and Perreault, M.M. (2002) Pancreatitis associated with simvastatin plus FF. The annals of Pharmacotherapy, 36(2), pp. 275 – 279.

McFarland, A.J., Anoopkumar-Dukie, S., Arora, D.S., Grant, G.D., McDermott, C.M., Perkins, A.V. and Davey, A.K. (2014) Molecular mechanism underlying the effects of statins in the central nervous system. International Journal of Molecular Sciences, 15, 20607 – 20637.

282

Fombon, I.S. (2020)

McGuinness, B., O'Hare, J., Bullock, R., Malouf, R., Craig, D. and Passmore, P. (2010) Statins for the treatment of dementia. Alzheimer's & Dementia, 6, pp.S567 - S567.

McKeage, K. and Keating, G. (2011) FF – a review of its use in dyslipidaemia. Drugs, 71(14), pp. 1917 – 1946.

Menezes, F.G., Neves, A.C.O., de Lima, D.F., Lourenco, S.D., da Silva, L.C. and de Lima, K.M.G. (2015) Bioorganic concepts involved in the determination of glucose, cholesterol and triglyceride in plasma using the enzymatic colorimetric method. Quimica Nova, 38(4), pp. 588-594.

Middleton, K., Anton, S. and Perri, M. (2013). Long-Term Adherence to Health Behavior Change. American Journal of Lifestyle Medicine, 7(6), pp. 395 - 404.

Mikhailidis, D.P., Ganotakis, E.S., Spyropoulos, K.A., Jagroop, I.A., Byrne, D.J. and Winder, A.F. (1998) Prothrombotic and lipoprotein variables in patients attending a cardiovascular risk management clinic: response to ciprofibrate or lifestyle advice. International Angiology, 17, pp. 225 - 233.

Mills, E.J., Rachlis, B., Wu, P., Devereaux, P.J., Arora, P. and Perri, D. (2008) Primary prevention of cardiovascular mortality and events with statin treatments: a network meta- analysis involving more than 65,000 patients. Journal of the American College of Cardiology, 52, pp. 1769 – 1781.

Min, Y.J., Choi, Y.H., Hyeon, C.W., Cho, J.H., Kim, K.J., Kwon, J.E., Kim, E.Y., Lee, W-S., Lee, K.J., Kim, S-W., Kim, T.H. and Kim, C.J. (2012) FF reduces C-reactive protein levels in hypertriglyceridaenic patients with high risks for cardiovascular diseases. Korean Circulation Journal, 42(11), pp. 741 – 746.

Minchinela, J., Perich, C., Fernandez-Calle, P., Alvarez, V., Domenech, M., Simon, M., Biosca, C., Boned, B., Cava, F., Garcia-Lario, J-V. And Fernandez-Fernandez, P. (2014) Biological variation database and quality specifications for imprecision, bias and total error (desirable and minimum) - the update 2014. Biologica Variation Database 2014 Update.

Mindikoglu, A., Dowling, T., Weir, M., Seliger, S., Christenson, R. and Magder, L. (2013) Performance of chronic kidney disease epidemiology collaboration creatinine-cystatin C equation for estimating kidney function in cirrhosis. Hepatology, 59(4), pp. 1532 - 1542.

Minnich, A., Tian, N., Byan, L. and Bilder, G. (2001) A potent PPARalpha agonist stimulates mitochondrial fatty acid beta-oxidation in liver and skeletal muscle. American Journal of Physiology-Endocrinology and Metabolism, 280(2), pp. E270 – E279.

283

Fombon, I.S. (2020)

Mora, E.R., Gavin, J.A.L., Gali, O.B., Comalat, A.F., Ciutat, L.F. and Toro, O.A. (2009) The impact of statin use in clinically localised prostate cancer treated with radical prostatectomy. Actas Urologicas Espanolas, 33(4), pp. 351 – 355.

Morgan, M. and Liu, Z. (2010) Crosstalk of reactive oxygen species and NF-κB signaling. Cell Research, 21(1), pp.103-115.

Muhlestein, J.B., May, H.T., Jensen, J.R., Horne, B.D., Lanman, R.B., Lavasani, F., Wolfert, R.L., Pearson, R.R., Yannicelli, H.D., Anderson, J.L. (2006) The reduction of inflammatory biomarkers by statin, fibrate, and combination therapy among diabetic patients with mixed dyslipidemia: the DIACOR (Diabetes and Combined Lipid Therapy Regimen) study. Journal of the American College of Cardiology, 48(2), pp. 396 – 401.

Mulvey, C., Ferguson, J., Tabita‐Martinez, J., Kong, S., Shah, R., Patel, P., Master, S., Usman, M., Propert, K., Shah, R., Mehta, N. and Reilly, M. (2012) Peroxisome Proliferator–Activated Receptor‐α Agonism With Fenofibrate Does Not Suppress Inflammatory Responses to Evoked Endotoxemia. Journal of the American Heart Association, 1(4), pp. 1 – 11.

Murty, M.S.N., Sharma, U.K., Pandey, V.B. and Kankare, S.B. (2013) Serum cystatin C as a marker of renal function in detection of early acute kidney injury. Indian Journal of Nephrology, 23(3), pp. 180 – 183.

Mychaleckyj, J.C., Craven, T., Nayak, U., Buse, J., Crouse, J.R., Elam, M., Kirchner, K., Lorber, D., Marcovina, S., Sivitz, W., Sperl-Hillen, J., Bonds, D.E. and Ginsberg, H.N. (2012) Reversibility of FF therapy-induced renal function impairment in ACCORD type 2 diabetes participants. Diabetes Care, 35, pp. 1008-1014.

Myers, G.L., Miller, W.G., Coresh, J., Fleming, J., Greenberg, N., Greene, .T, Hostetter, T., Levey, A.S., Panteghini, M., Welch, M. and Eckfeldt, J.H. (2006) Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program. Clinical Chemistry, 52(1), pp. 5 – 18.

Nagao, K., Tomioka, M. and Ueda, K. (2011) Function and regulation of ABCA1– membrane meso-domain organization and reorganization. Federation of European Biochemical Societies Journal, 278, pp. 3190 – 3203.

Najib, J. (2002) Fenofibrate in the treatment of dyslipidaemia: A review of the data as they relate to the new suprabioavailable tablet formulation. Clinical Therapeutics, 24(12), pp. 2022 – 2050.

284

Fombon, I.S. (2020)

Nakanishi, C. and Toi, M. (2005) Nuclear Factor-кB inhibitors as sensitisers to anticancer drugs. Nature Reviews, 5, pp. 297-309. National Kidney Disease Education Program (2011). http://www.nkdep.nih.gov/ labprofessionals/estimate_report_gfr.htm.

NCEP (2001) Expert Panel on Detection, Evaluation and Treatment of High Cholesterol in Adults. Executive summary of the Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation and treatment of cholesterol in adults (adult treatment panel III). The Journal of American Medical Association, 285, pp. 2486 – 2497.

NCEP (2002) Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation, 106(25), pp: 3143 -3421.

Ncube, V., Starkey, B. and Wang, T. (2012) Effect of FF treatment for hyperlipidaemia on serum creatinine and cystatin C. Annals of Clinical Biochemistry, 49, pp. 491-493.

NICE (2014) Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. NICE, clinical guidance, 181, issued 2014; last modified January, 2015.

Nicholls, S.J., Lundman, P., Harmer, J.A., Cutri, B., Griffiths, K.A., Rye, K.A., Barter, P.J. and Celermajer, D.S. (2006) Consumption of saturated fat impairs the anti-inflammatory properties of high-density lipoproteins and endothelial function. Journal of the American College of Cardiology, 48(4), pp. 715 – 720.

Nielsen, S.F., Nordestgaard, B.G. and Bojesen, S.E. (2012) Statin use and reduced cancer-related mortality. New England Journal of Medicine, 367, pp. 1792 – 1802.

Nirosha, K., Divya, M., Vamsi, S. and Sadiq, M. (2014) A review of hyperlipidaemia. International Journal of Novel Trends in Pharmaceutical Sciences, 4(5), pp. 81 – 92.

Noble, J.E. and Bailey, M.J.A. (2009) Quantitation of protein. Methods in Enzymology, 463, pp. 73 – 95.

Noguchi, Y., Tatsuno, I., Suyama, K., Shibata, T., Yoshida, T., Otsuka, Y., Fuse, M., Takeo, C. and Saito, Y. (2004) Effect of Fenofibrate on Uric Acid Metabolism in Japanese Hyperlipidemic Patients. Journal of Atherosclerosis and Thrombosis, 11(6), pp. 335 - 340.

285

Fombon, I.S. (2020)

Noonan, E.J., Jenkins, A.J., Ma, J-X., Keech, A.C., Wang, J.J. and Lamoureux, E.L. (2013). An update on the molecular actions of FF and its effects on diabetic retinopathy and other microvascular end points in patients with diabetes. Diabetes, 62, pp. 3968- 3975.

Noureldein, M., Abd El-Razek, R., El-Hefnawy, M. and El-Mesallamy, H. (2015) Fenofibrate reduces inflammation in obese patients with or without type 2 diabetes mellitus via sirtuin 1/fetuin A axis. Diabetes Research and Clinical Practice, 109(3), pp. 513 - 520.

O’Donnell, M.J., Xavier, D., Liu, L., Zhang, H., Chin, S.L., Rao-Melacini, P., Rangarajan, S., Islam, S., Pais, P., McQueen, M.J., Mondo, C., Damasceno, A., Lopez-Jaramillo, P., Hankey, G.J., Dans, A.L., Yusoff, K., Truelsen, T., Diener, H.C., Sacco, R.L., Ryglewicz, D., Czlonkowska, A., Wiemar, C., Wang, X. and Yusuf, S. (2010) Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 contries (the INTERSTROKE study): a case-control study. Lancet, 376(9735), pp. 112 – 123.

Obiols, J., Bargnoux, A., Kuster, N., Fesler, P., Piéroni, L., Badiou, S., Dupuy, A., Ribstein, J. and Cristol, J. (2013) Validation of a new standardized cystatin C turbidimetric assay: Evaluation of the three novel CKD-EPI equations in hypertensive patients. Clinical Biochemistry, 46(15), pp. 1542 - 1547.

Omuse, G., Maina, D., Mwangi, J., Wambua, C., Kanyua, A., Kagotho, E., Amayo, A., Ojwang, P. and Erasmus, R. (2017) Comparison of equations for estimating glomerular filtration rate in screening for chronic kidney disease in asymptomatic black Africans: a cross sectional study. BMC Nephrology, 18(1), pp. 1 – 8.

Pahan, K. (2006) Lipid-lowering drugs. Cellular and Molecular Life Sciences, 63(10), pp. 1165 – 1178.

Pahan, K. (2006) Lipid-lowering drugs. Cellular and Molecular Life Sciences, 63(10), pp. 1165 - 1178.

Pandor, A., Ara, R., Tumur, I., Wilkinson, A., Paisley, S., Duenas, A., Durrington, P. and Chilcott, J. (2009) Ezetimibe monotherapy for cholesterol lowering in 2,722 people: systematic review and meta-analysis of randomized controlled trials. Journal of Internal Medicine, 265(5), pp. 568 - 580.

Park, G., Jung, M., Kim, Y., Cho, I., Won, H., Shin, S., Lee, W., Lee, K., Kim, S., Kim, T. and Kim, C. (2017) Effect of Long-term Fenofibrate Therapy on Serum Creatinine and Its

286

Fombon, I.S. (2020)

Reversibility in Hypertriglyceridemic Patients with Hypertension. Journal of Lipid and Atherosclerosis, 6(2), p.89 – 96.

Parker, B.A., Capizzi, J.A., Grimaldi, A.S., Clarkson, P.M., Cole, S.M., Keadle, J., Chipkin, S., Pescatello, L.S., Simpson, K., White, C.M. and Thompson, P.D. (2013) Effect of statins on skeletal muscle function. Circulation, 127(1), pp. 96 – 103.

Paumelle, R. and Staels, B. (2008) Cross-talk between statins and PPARα in cardiovascular diseases: clinical evidence and basic mechanisms. Trends in Cardiovascular Medicine, 18, pp. 73 – 78.

Pearse, B., Smith, C. and Owen, D. (2000) Clathrin coat construction in endocytosis. Current Opinion in Structural Biology, 10(2), pp. 220 - 228.

Pearse, B.M., Smith, C.J. and Owen, D.J. (2000) Clathrin coat construction in endocytosis. Current Opinion in Structural Biology, 10, pp. 220 – 228.

Pedersen, T. (2010) Pleiotropic Effects of Statins. American Journal Cardiovascular Drugs, 10, pp. 10 - 17.

Pedersen, T.R. (2010) Pleiotropic effects of statins: evidence against benefits beyond LDL-cholesterol lowering. American Journal of Cardiovascular Drugs, 10(Supplement 1), pp. 10 – 17.

Perrone, R.D., Madias, N.E. and Levey, A.S. (1992) Serum creatinine as an index of renal function: new insights into old concepts. Clinical Chemistry, 38, pp. 1933-1953.

Peto, R., Emberson, J., Landray, M., Baigent, C. Collins, R., Clare, R. and Califf, R. (2008) Analyses of cancer data from three ezetimibe trials. New England Journal of Medicine, 359, pp. 1357 – 1366.

Phan, B., Dayspring, T. and Toth, P. (2012) Ezetimibe therapy: mechanism of action and clinical update. Vascular Health Risk Management, 8, pp. 415 – 427.

Pinal-Fernandez, I., Casal-Dominguez, M. and Mammen, A.L. (2018) Statins: pros and cons. Medicina Clinica, 150(10), pp. 398 – 402.

Playford, D.A., Watts, G.F., Best, J.D. and Burke, V. (2002). Effect of fenofi brate on brachial artery fl ow-mediated dilatation in type 2 diabetes mellitus. The American Journal of Cardiology, 90(11), pp. 1254 – 1257.

287

Fombon, I.S. (2020)

Prigent, A. (2008) Monitoring Renal Function and Limitations of Renal Function Tests. Seminars in Nuclear Medicine, 38(1), pp. 32 - 46.

Pritchard, A.L. and Hayward, N.K. (2013) Molecular pathways: Mitogen-activated protein kinase pathway mutations and drug resistance. Clinical Cancer Research 19(9), pp. 2301 – 2309.

Pucci, L., Triscornia, S., Lucchesi, D., Fotino, C., Pellegrini, G., Pardini, E., Miccoli, R., Del Prato, S. and Penno, G. (2007) Cystatin C and estimates of renal function in diabetic patients. Clinical Chemistry, 53(3), pp. 480 -488.

Raghow, R. (2017) Statins Redux: A re-assessment of how statins lower plasma cholesterol. World Journal of Diabetes, 8(6), pp. 230 – 234.

Ramasamy, I. (2016) Update on the molecular biology of dyslipidemias. Clinica Chimica Acta, 454, pp. 143 - 185.

Rana, J.S., Visser, M.E., Arsenault, B.J., Després, J.P., Stroes, E.S., Kastelein, J.J., Wareham, N.J., Boekholdt, S.M. and Khaw, K.T. (2010) Metabolic dyslipidemia and risk of future coronary heart disease in apparently healthy men and women: the EPIC- Norfolk prospective population study. International Journal of Cardiology, 143(3), pp. 399 – 404. Randers, E., Kristensen, H., Erlandsen, E. and Danielsen, S. (1998) Serum cystatin C as a marker of the renal function. Scandinavian Journal of Clinical and Laboratory Investigation, 58(7), pp. 585 - 592.

Raval, A.D., Hunter, T., Stuckey, B. and Hart, R.J. (2011) Statins for women with polycystic ovary syndrome not actively trying to conceive. Cochrane Database System Reviews, 10, pp. 1 - 40.

Ravingerova, T., Barancik, M. and Strniskova, M. (2003) Mitogen-activated protein kinase: A new therapeutic target in cardiac pathology. Molecular and Cellular Biochemistry, 247(1), pp. 127 – 138.

Reiner, Z., Catapano, A.L., De Backer, G., Graham, I., Taskinen, M-R., Wiklund, O., Agewell, S., Algria, E., Chapman, M.J., Durrington, P., Erdine, S., Halcox, J., Hobbs, R., Kjekshus, J., Filardi, P.P.,Riccardi, G., Storey, R.F. and Wood, D. (2011) ESC/EAS Guidelines for the management of dyslipidaemias. The Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS). European Heart Journal, 32, pp. 1769 – 1818.

288

Fombon, I.S. (2020)

Reklou, A., Katsiki, N., Karagiannis, A. and Athyros, V. (2019) Effects of Lipid Lowering Drugs on Arterial Stiffness: One More Way to Reduce Cardiovascular Risk?. Current Vascular Pharmacology, 18(1), pp. 38 - 42.

Remick, J., Weintraub, H., Setton, R., Offenbacher, J., Fisher, E. and Schwartzbard, A. (2008) Fibrate therapy: an update. Cardiology in Review, 16(3), pp. 129 – 141.

Reynaert, N., van der Vliet, A., Guala, A., McGovern, T., Hristova, M., Pantano, C., Heintz, N., Heim, J., Ho, Y., Matthews, D., Wouters, E. and Janssen-Heininger, Y. (2006) Dynamic redox control of NF- B through glutaredoxin-regulated S-glutathionylation of inhibitory B kinase beta. Proceedings of the National Academy of Sciences, 103(35), pp. 13086 - 13091.

Ricos, C., Alvarez, V., Cava, F., Garcia-Lario, J.V., Hernandez, A., Jimenez, C.V., Minchinela, J., Perich, C. and Simon, M. (1999) "Current databases on biologic variation: pros, cons and progress." Scandinavian Journal of Clinical Laboratory Investigation, 59, 491-500.

Ridker, P.M. (2003) Clinical application of C-reactive protein for cardiovascular disease detection and prevention. Circulation, 107(3), pp. 363 – 369.

Robinson, J., Ballantyne, C., Grundy, S., Hsueh, W., Parving, H., Rosen, J., Adewale, A., Polis, A., Tomassini, J. and Tershakovec, A. (2009) Lipid-Altering Efficacy and Safety of Ezetimibe/Simvastatin Versus Atorvastatin in Patients With Hypercholesterolemia and the Metabolic Syndrome (from the VYMET Study). The American Journal of Cardiology, 103(12), pp. 1694 - 1702.

Rosenson, R., Huskin, A., Wolff, D., Helenowski, I. and Rademaker, A. (2008) Fenofibrate reduces fasting and postprandial inflammatory responses among hypertriglyceridemia patients with the metabolic syndrome. Atherosclerosis, 198(2), pp. 381 - 388.

Rosenson, R.S., Kuskin, A.L., Wolff, D.A., Hellenowski, I.B. and Rademaker, A.W. (2008) FF reduces fasting and postprandial inflammatory responses among hypertriglyceridemia patients with the metabolic syndrome. Atherosclerosis, 198(2), pp. 381–388.

Rosner, M.H. and Bolton, W.K. (2006) Renal function testing. American Journal of Kidney Diseases, 47(1), pp. 174 – 183.

Ross, R. (1999) Atheriosclerosis – an inflammatory disease. The New England Journal of Medicine, 340 (2), pp. 115 – 126.

289

Fombon, I.S. (2020)

Rossebo, A.B., Pedersen, T.R., Boman, K., Brudi, P., Chambers, J.B., Egstrup, K., Gerdts, E., Gohlke-Barwolf, C., Holme, I., Kesaniemi, Y.A., Malbecq, W., Nienaber, C.A., Ray, S., Skjaerpe, T., Wachtell, K. and Willenheimer, R. (2008) Intensive lipid lowering with simvastatin and ezetimibe in aortic stenosis. New England Journal of Medicine, 359(13), pp. 1343 – 1356.

Rubins, H.B., Robins, S.J., Collins, D., Fye, C.L., Anderson, J.W., Elam, M.B., Faas, F.H., Linares, E., Schaefer, E.J., Schectman, G., Wilt, T.J. and Wittes, J. (1999) Gemfibrozil for the secondary prevention of coronary heart disease in men with low levels of high-density lipoprotein cholesterol: Veterans Affairs High-Density Lipoprotein Cholesterol Intervention Trial Study Group. New England Journal of Medicine, 341, pp. 410 – 418.

Rule, A., Larson, T., Bergstralh, E., Slezak, J., Jacobsen, S. and Cosio, F. (2004) Using Serum Creatinine To Estimate Glomerular Filtration Rate: Accuracy in Good Health and in Chronic Kidney Disease. Annals of Internal Medicine, 141(12), pp. 929 – 937.

Rule, A.D., Bregstraalh, E.J., Slezak, J.M., Bergert, J. and arson, T.S. (2006) Glomerular filtration rate estimated by cystatin C among different clinical presentations. Kidney International, 69, pp. 399-405.

Russell, D.W. (2003) The enzymes, regulation, and genetics of bile acid synthesis, Annual Review of Biochemistry, 72, pp. 137 – 174.

Safeer, R.S. and Lacivita, C.L. (2000) Choosing drug therapy for patients with hyperlipidaemia. American Family Physician, 61(11), pp. 3371 – 3382.

Sandilands, E.A, Dhaun, N., Dear, J.W. and Webb, D.J. (2013) Measurement of renal function in patients with chronic kidney disease. British Journal of Clinical Pharmacology, 76(4), pp. 504 – 515.

Sasakabe, T., Wakai, K., Kawai, S., Hishida, A., Naito, M., Suzuki, S ., Nindita, Y., Arisawa, K., Kita, Y ., Hara, M ., Kuriyama, N ., Hirata, A., Mikami, H., Oze, I., Kubo, M ., Tanaka, H . and Hamajima, N. (2018) Modification of the Associations of Alcohol Intake With Serum Low-Density Lipoprotein Cholesterol and Triglycerides by ALDH2 and ADH1B Polymorphisms in Japanese Men. Journal of Epidemiology, 28(4), pp. 185 – 193. Sasaki, J., Yamamoto, K. And Ageta, M. (2002) Effects of FF on high-density lipoprotein particle size in patients with hyperlipidaemia: A randomised double-blind, pacebo- controlled, multicenter, crossover study. Clinical Therapeutics, 24 (10), pp. 1614 – 1626.

290

Fombon, I.S. (2020)

Satarasinghe, R.L., Ramesh, R., Riyaaz, A. A. A., Gunarathne, P. A. K. G. and de Silva, A. P. (2007) Hypothyroidism is a predisposing factor for FF-induced rhabdomyolysis- patient report and literature review. Drug Metabolism and Drug Interactions, 22(4), pp. 279 – 283.

Scaldaferri, F., Pizzoferrato, M., Ponziani, F.R., Gasbarrini, G. and Gasbarrini, A. (2013) Use and indications of cholestyramine and bile acid sequestrants. Internal and Emergency Medicine, 8(3), pp. 205 – 210.

Schaffner, W. and Weissmann, C. (1973) A rapid,sensitive, and specific method for the determination of protein in dilute solution. Annals of Biochemistry, 56, pp. 520-514.

Schneider, A., Stange, E.F., Ditschuneit, H.H.and Ditschuneit, H. (1985) FF treatment inhibits HMG-CoA reductase activity in mononuclear cells from hyperlipoproteinemic patients. Atherosclerosis, 56(3), pp. 257 – 262.

Schoonbroodt, S., Ferreira, V., Best-Belpomme, M., Boelaert, J., Legrand-Poels, S., Korner, M. and Piette, J. (2000) Crucial Role of the Amino-Terminal Tyrosine Residue 42 and the Carboxyl-Terminal PEST Domain of IκBα in NF-κB Activation by an Oxidative Stress. The Journal of Immunology, 164(8), pp. 4292 - 4300.

Schultz, B., Patten, D. and Berlau, D. (2018) The role of statins in both cognitive impairment and protection against dementia: a tale of two mechanisms. Translational Neurodegeneration, 7(1), pp. 1 – 14.

Schumann, G. and Klauke, R. (2003) New IFCC reference procedures for the determination of catalytic activity concentrations of five enzymes in serum: preliminary upper reference limits obtained in hospital subjects. Clinica Chimica Acta, 327, pp. 69 – 79.

Schumann, G., Bonora, R., Ceriotti, F., Ferard, G., Ferrero, C.A., Franck, P.F., Gella, F.J., Weidemann, G. And Siekmann, L. (2002) IFCC Reference Procedures for the Measurement of Catalytic Activity Concentrations of Enzymes at 37C. Part 4. Reference Procedure for the Measurement of Catalytic Concentration of Alanine Aminotranferase. Clinical Chemistry and Laboratory Medicine, 40(7), pp. 718 – 724.

Schwartz, G., Furth, S., Cole, S., Warady, B. and Muñoz, A. (2006) Glomerular filtration rate via plasma iohexol disappearance: Pilot study for chronic kidney disease in children. Kidney International, 69(11), pp. 2070 - 2077.

291

Fombon, I.S. (2020)

Seegmiller, J.C., Eckfeldt, J.H. and Lieske, J.C. (2018) Challenges in measuring glomerular filtration rate: a clinical laboratory perspective. Advances in Chronic Kidney Disease, 25(1), pp. 84 – 92.

Séronie-Vivien, S., Delanaye, P., Piéroni, L., Mariat, C., Froissart, M. and Cristol, J. (2008) Cystatin C: current position and future prospects. Clinical Chemistry and Laboratory Medicine, 46(12), pp. 1664 – 1686.

Shattat, G. (2014). A Review Article on Hyperlipidemia: Types, Treatments and New Drug Targets. Biomedical and Pharmacology Journal, 7(2), pp. 399 - 409.

Shen, W.J., Azhar, S. and Kraemer, F.B. (2018) SR-B1: a unique multifunctional receptor for cholesterol influx or efflux. Annual Review of Physiology, 80, pp. 95 – 116.

Shiplak, M.G., Matsushita, K., Arnlov, J., Inkre, L.A., Katz, R., Polkinghorne, K.R., Rothenbacher, D., Sarnak, M.J., Astor, B.C., Coresh, J., Levey, A.S. and Gansevoort, R.T. (2013) Cystatin C versus creatinine in determining risk based on kidney function. New England Journal of Medicine, 369, pp 932 – 943.

Sica, D.A. (2009) Fibrate therapy and renal function. Current Atherosclerosis Reports, 11(5), pp. 338 – 342.

Slentz, C.A., Houmard, J.A., Johnson, J.L., Bateman, L.A., Tanner, C.J., McCartney, J.S., Duscha, B.D. and Kraus, W.E. (2007) Inactivity, exercise training, and detraining, and plasma lipoproteins. STRRIDE: a randomised, controlled study of exercise intensity and amount. Journal of Applied Physiology, 103(2), pp. 432 – 442.

Smith, H.W. (1951) The kidney: structure and function in health and disease. New York: Oxford University Press, inc. 1951.

Song, W-L. and FitzGerald, G.A. (2013) Niacin, an old drug with a new twist. Journal of Lipid Research, 54, pp. 2586 – 2594.

Soveri, I., Berg, U., Björk, J., Elinder, C., Grubb, A., Mejare, I., Sterner, G. and Bäck, S. (2014) Measuring GFR: A Systematic Review. American Journal of Kidney Diseases, 64(3), pp. 411 - 424.

Spanaus, K.S., Kollerits, B., Ritz, E., Hersberger, M., Kronenberg, F. and von Eckardstein, A. (2010) Serum creatinine, cystatin C, and beta-trace protein in diagnostic staging and predicting progression of primary nondiabetic chronic kidney disease. Clinical Chemistry, 56, pp. 740 – 749.

292

Fombon, I.S. (2020)

Staels, B., Dallongeville, J., Auwerx, J., Schoonjans, K., Leitersdorf, E. and Fuchart, J-C. (1998) Mechanism of action of fibrates on lipid and lipoprotein metabolism. Circulation, 98(19), pp. 2088 – 2093.

Stancu, C.and Sima, A. (2001) Satins: mechanism of action and effects. Journal of Cellular and Molecular Medicine, 5(4), pp. 378 – 387.

Steinmetz, K.L. and Schonder, K.S. (2005) Colesevelam: Potential uses for the newest bile resin. Cardiovascular Drug Reviews, 23(1), pp. 15 – 30.

Stender, S., Schuster, H., Barter, P., Watkins, C. and Kallend, D. (2005) Comparison of rosuvastatin with atorvastatin, simvastatin and pravastatin in achieving cholesterol goals and improving plasma lipids in hypercholesterolaemic patients with or without the metabolic syndrome in the MERCURY I trial. Diabetes, Obesity and Metabolism, 7(4), pp. 430 - 438.

Stevens, L., Coresh, J., Feldman, H., Greene, T., Lash, J., Nelson, R., Rahman, M., Deysher, A., Zhang, Y., Schmid, C. and Levey, A. (2007) Evaluation of the Modification of Diet in Renal Disease Study Equation in a Large Diverse Population. Journal of the American Society of Nephrology, 18(10), pp. 2749 - 2757.

Stevens, L.A. and Levey, A.S. (2009) Measured GFR as a confirmatory test for estimated GFR. Journal of the American Society of Nephrology, 20(11), pp. 2305 – 2313.

Stevens, L.A., Coresh, J., Green, T. And Levey, A.S. (2006) Assessing kidney function – measured and estimated glomerular filtration rate. New England Journal of Medicine, 354 (23), pp. 2473-2483.

Stolk, M.F.J., Becx, M.C.J.M., Kuypers, K.C. and Seldenrijk, C.A. (2006) Severe hepatic side effects of ezetimibe. Clinical Gastroenterology and Hepatology, 4(7), pp. 908 – 911.

Su, C., Shi, A., Cao, G., Tao. T., Chen, R., Hu, Z., Shen, Z., Tao, H., Cao, B., Hu, D. and Bao, J. (2015) FF suppressed proliferation and migration of human neuroblastoma cells via oxidative stress dependent of TXNIP upregulation. Biochemical and Biophysical Research Communications, 460(4), pp. 983-988 Su, L., Zhang, J., Gomez, H., Murugan, R., Hong, X., Xu, D., Jiang, F. and Peng, Z. (2019) Reactive Oxygen Species-Induced Lipid Peroxidation in Apoptosis, Autophagy, and Ferroptosis. Oxidative Medicine and Cellular Longevity, 2019, pp.1-13.

Sullivan, D. and Lewis, B. (2011) A classification of lipoprotein disorders: implications for clinical management. Clinical Lipidology, 6(3), pp. 327 – 338.

293

Fombon, I.S. (2020)

Szopa, M., Kapusta, M., Matejko, B., Klupa, T., Koblik, T., Kiec-Wilk, B., Borowiec, M. and Malecki, M.T. (2015) Comparison of glomerular filtration rate estimation from serum creatinine and cystatin C in HNF1A-MODY and other types of diabetes. Journal of Diabetes Research, 183094, pp. 1 – 5.

Takada, Y., Mukhopadhyay, A., Kundu, G., Mahabeleshwar, G., Singh, S. and Aggarwal, B. (2003) Hydrogen Peroxide Activates NF-κB through Tyrosine Phosphorylation of IκBα and Serine Phosphorylation of p65. Journal of Biological Chemistry, 278(26), pp. 24233 - 24241.

Thomas, D., Zachariah, S., Eltom, A. and Osman, A. (2017) Limitations of serum creatinine as a marker of renal function. Scholars Academic Journal of Pharmacy, 6, pp. 168 – 170.

Thompson, G.R. (2004) Management of dyslipidaemia. Heart, 90, pp. 949 – 955.

Thompson, P., Panza, G., Zaleski, A. and Taylor, B. (2016) Statin-Associated Side Effects. Journal of the American College of Cardiology, 67(20), pp. 2395 - 2410.

Thongnuanjan, P., Soodvilai, S., Chatsudthipong, V. and Soodvilai, S. (2016) Fenofibrate reduces cisplatin-induced apoptosis of renal proximal tubular cells via inhibition of JNK and p38 pathways. The Journal of Toxicological Sciences, 41(3), pp. 339 – 349.

Thulin, P., Rafter, I., Stockling, K., Tomkiewicz, C., Norjavaara, E., Aggerbeck, M., Hellmold, H., Ehrenborg, E., Andersson, U., Cotgreave, I. and Glinghammar, B. (2008) PPARα regulates the hepatotoxic biomarker alanine aminotransferase (ALT1) gene expression in human hepatocytes. Toxicology and Applied Pharmacology, 231(1), pp. 1 - 9.

Tietz, N., Burtis, C., Duncan, P., Ervin, K., Petitclerc, C., Rinker, A., Shuey, D. and Zygowicz, E., (1983) A reference method for measurement of alkaline phosphatase activity in human serum. Clinical Chemistry, 29(5), pp. 751 - 761.

Ting, R., Keech, A., Drury, P., Donoghoe, M., Hedley, J., Jenkins, A., Davis, T., Lehto, S., Celermajer, D., Simes, R., Rajamani, K. and Stanton, K. (2011) Benefits and Safety of Long-Term Fenofibrate Therapy in People With Type 2 Diabetes and Renal Impairment: The FIELD Study. Diabetes Care, 35(2), pp. 218 - 225.

Ting, R-D. and Keech, A. (2013) Fenofibrate and renal disease. Clinical Lipidology, 8(6), pp. 669 – 680.

294

Fombon, I.S. (2020)

Tolman, K.G., and Rej, R. (1999) Liver function. In: Burtis, C.A., Ashwood, E.R., eds. Tietz textbook of clinical chemistry. 3rd ed. Philadelphia (PA): Saunders, pp. 1158-1159.

Tomkiewicz, C., Muzeau, F., Edgar, A., Barouki, R. and Aggerbeck, M. (2004) Opposite regulation of the rat and human cytosolic aspartate aminotransferase genes by fibrates. Biochemical Pharmacology, 67(2), pp. 213 - 225.

Tsimihodimos, V., Kakafika, A., and Elisaf, M. (2001) Fibrate treatment can increase serum creatinine levels. Nephrology Dialysis Transplantation, 16(6), pp. 1301.

Tuteja, S., Pyrsopoulos, N.T., Wolowich, W.R., Khanmoradi, K., Levi, D.M., Selvaggi, G., Weisbaum, G., Tzakis, A.G. and Schiff, E.R. (2008) Simvastatin-ezetimibe-induced hepatic failure necessitating liver transplantation. Pharmacotherapy, 28(9), pp. 1188 – 1193.

Tziomalos, K. and Athyros, V. (2006) Fenofibrate: a novel formulation (Triglide?) in the treatment of lipid disorders: a review. International Journal of Nanomedicine, 1(2), pp. 129 - 147.

Uemura, O., Ushijima, K., Nagai, T., Yamada, T., Yamakawa, S., Hibi, Y., Hayakawa, H., Nabeta, Y., Shinkai, Y., Koike, K. and Kuwabara, M. (2011) Measurements of serum cystatin C concentrations underestimate renal dysfunction in pediatric patients with chronic kidney disease. Clinical and Experimental Nephrology, 15(4), pp. 535 - 538.

Uetake, D., Ohno, I., Ichida, K., Yamaguchi, Y., Saikawa, H., Endou, H. and Hosoya, T. (2010) Effect of Fenofibrate on Uric Acid Metabolism and Urate Transporter 1. Internal Medicine, 49(2), pp. 89 - 94.

Uusitupa, M., Peltonen, M., Lindstrom, J., Aunola, S., Ilanne-Parikka, P., Keinanen- Kiukaanniemi, S., Valle, T.T., Eriksson, J.G., Tuomilehto, J. (2009) Finnish Diabetes Prevention Study Group. Ten-year mortality and cardiovascular morbidity in the Finnish Diabetes Prevention Study—secondary analysis of the randomized trial. PLoS One, 4(5), pp. 1 – 8.

Varady, K.A. and Jones, P.J.H. (2005) Combination Diet and exercise interventions for the treatment of dyslipidaemia: an effective preliminary strategy to lower cholesterol levels? The Journal of Nutrition, 135, pp. 1829 – 1835.

Vergès, B. (2015) Pathophysiology of diabetic dyslipidaemia: where are we? Diabetologia, 58(5), pp. 886 – 899.

295

Fombon, I.S. (2020)

Verma, N. (2016) Introduction to hyperlipidaemia and its treatment: a review. International Journal of Current Pharmaceutical Research, 9(1), pp. 6 – 14.

Waldman, B., Ansquer, J., Sullivan, D., Jenkins, A., McGill, N., Buizen, L., Davis, T., Best, J., Li, L., Feher, M., Foucher, C., Kesaniemi, Y., Flack, J., d'Emden, M., Scott, R., Hedley, J., Gebski, V. and Keech, A. (2018) Effect of fenofibrate on uric acid and gout in type 2 diabetes: a post-hoc analysis of the randomised, controlled FIELD study. The Lancet Diabetes & Endocrinology, 6(4), pp. 310 - 318.

Walker, A., Kaplon, R., Lucking, S., Russell-Nowlan, M., Eckel, R. and Seals, D. (2012) Fenofibrate Improves Vascular Endothelial Function by Reducing Oxidative Stress While Increasing Endothelial Nitric Oxide Synthase in Healthy Normolipidemic Older Adults. Hypertension, 60(6), pp. 1517 - 1523.

Walker, E.A., Kaplon, R.E., Lucking, S.M.S., Russell-Nowlan, M.J., Eckel, R.H. and Seals, D.R. (2012) FF improves vascular endothelial function by reducing oxidative stress while increasing eNOS in healthy normolipidaemic older adults. Hypertension, 60(6), pp. 1517 – 1523.

Wang, D. and Wang, Y. (2018) FF monotherapy-induced rhabdomyolysis in a patient with hypothyroidism. Medicine, 97(4), pp. 1 – 3.

Wang, N., Zou, C., Zhao, S., Wang, Y., Han, C. and Zheng, Z. (2018) FF exerts protective effects in diabetic retinopathy via inhibition of the ANGPTL3 pathway. Investigative Ophthalmology and Visual Science, 59(10), pp. 4210 – 4217.

Wang, Y. and Xu, D. (2017) Effects of aerobic exercise on lipids and lipoproteins. Lipids in Health and Disease, 16(132), pp. 1 – 8.

Wang, Y., Simar, D. and Fiatarone Singh, M.A. (2009) Adaptation to exercise training within skeletal muscle in adults with type 2 diabetes or impaired glucose tolerance: a systematic review. Diabetes Metabolism Research and Reviews, 25(1), pp. 13 – 40.

Ward, N., Watts, G. and Eckel, R. (2019) Statin Toxicity. Circulation Research, 124(2), pp. 328 - 350.

Weng, T.C., Yang, Y.H., Lin, S.J. and Tai, S.H. (2010) A systematic review and meta- analysis on therapeutic equivalence of statins. Journal of Clinical Pharmacy and Therapeutics, 35(2), pp. 139 – 151.

296

Fombon, I.S. (2020)

White CA, Rule AD, Collier CP, Akbari A, Lieske JC, Lepage N, Doucette, S. and Knoll, G.A. (2015) The impact of interlaboratory differences in cystatin C assay measurement on glomerular filtration rate estimation. Clinical Journal of the American Society of Nephrology, 6(9), 2150 – 2156.

White, C., Rule, A., Collier, C., Akbari, A., Lieske, J., Lepage, N., Doucette, S. and Knoll, G. (2011) The Impact of Interlaboratory Differences in Cystatin C Assay Measurement on Glomerular Filtration Rate Estimation. Clinical Journal of the American Society of Nephrology, 6(9), pp. 2150 - 2156.

Wi J, Kim JY, Park S, Kang SM, Jang Y, Chung N, Shim WH, Cho SY, Lee SH. (2010) Optimal pharmacologic approach to patients with hypertriglyceridemia and low high- density lipoprotein-cholesterol: randomized comparison of FF 160 mg and niacin 1500 mg. Atherosclerosis, 213(1), pp. 235 – 240.

Williams, H. (2013) Dyslipidaemia: pathophysiology and types. Clinical Pharmacist, 5, pp. 189 – 194.

Wilson, P.W.F., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H. and Kannel, W.B. (1998) Prediction of coronary heart disease using categories. Circulation, 97(18), pp. 1837 – 1847.

Wong, N.D. (2014) Epidemiological studies of CHD and the evolution of preventive cardiology. Nature Reviews Cardiology, 11, pp. 276 – 289.

World Health Organisation (2015). Cardiovascular diseases (CVDs). Fact sheet No. 317 updated January 2015 [online]. Available from URL: http://www.who.int/mediacentre/factsheets/fs317/en/index/html# [accessed 22 February 2015].

World Health Organization (2019) Cardiovascular diseases (CVDs). http:// www.who.int/mediacentre/factsheets/fs317/en/ (accessed 06 August 2019).

Wroblewski, F. and LaDue, J.S. (1956) Serum glutamic pyruvic transaminase in cardiac with hepatic disease. Proceedings of the Society for Experimental Biology and Medicine, 91(4), pp. 569-571.

Wu, J., Song, Y., Li, H. and Chen, J. (2009) Rhabdomyolysis associated with fibrate therapy: review of 76 published cases and a new case report. European Journal of Clinical Pharmacology, 65(12), pp. 1169 – 1174.

297

Fombon, I.S. (2020)

Wuyts, B., Bernard, D., Van Den Noortgate, N., Van De Walle, J., Van Vlem, B., De Smet, R., De Geeter, F., Vanholder, R. and Delanghe, J. (2003) Reevaluation of Formulas for Predicting Creatinine Clearance in Adults and Children, Using Compensated Creatinine Methods. Clinical Chemistry, 49(6), pp. 1011 - 1014.

Wuyts, B., Bernard, D., Van den, Noortgate,N., Van de Walle, J., Van Vlem, B., De Smet, R., De Geeter, F., Vanholder, R. and Delanghe, J.R. (2003) Reevaluation of formulas for predicting creatinine clearance in adults and children, using compensated creatinine methods. Clinical Chemistry, 46 (6 pt 1), pp. 1011-1014.

Wybieralska, E., Szpak, K., Gorecki, A., Bonarek, P., Miekus, K., Drukala, J., Majka, M., Reiss, K., Madeja, Z. and Czyz, J. (2011) Fenofibrate attenuates contact-stimulated cell motility and gap junctional coupling in DU-145 human prostate cancer cell populations. Oncology Reports, 26(2), pp. 447 – 453.

Yang, B., Johnson, T., Thomas, G., Watson, P., Wagner, B., Furness, P. and El Nahas, A. (2002) A shift in the Bax/Bcl-2 balance may activate caspase-3 and modulate apoptosis in experimental glomerulonephritis. Kidney International, 62(4), pp. 1301 - 1313.

Yang, F., Li, D., Di, Y., Zhang, Y., Zang, Y., Ren, J., Yan, L., Zhou, Z., Liu, H. and Xu, Z., (2017) Pretreatment Serum Cystatin C Levels Predict Renal Function, but Not Tumor Characteristics, in Patients with Prostate Neoplasia. BioMed Research International, 2017, pp. 1 - 8.

Yang, L.P. and Keating, G.M (2009) Fenofibric acid: in combination therapy in the treatment of mixed dyslipidaemia. American Journal of Cardiovascular Drugs, 9(8), pp. 401 – 409.

Yang, S., Liu, J., Zhang, X., Hu, C., Zhang, W., Sun, L. and Zhang, H., 2016. Diagnostic Accuracy of Serum Cystatin C for the Evaluation of Renal Dysfunction in Diabetic Patients: A Meta-Analysis. Therapeutic Apheresis and Dialysis, 20(6), pp. 579 - 587.

Ye, J., Kiage, J.N., Arnett, D.K., Bartolucci, A.A. and Kabagambe, E. (2011) Short-term effect of FF on C-reactive protein: a meta-analysis of randomised controlled trials. Diabetology & Metabolic Syndrome, 3(24), pp. 1 – 11.

Yuan, Z., Zhao, M., Zhang, B., Zhang, H., Zhang, X., Guan, Q., Ning, G., Gao, L., Xue, F. and Zhao, J. (2015) Thyroid function modifies the association between ratio of triglyceride to HDL-density lipoprotein cholesterol and renal function: a multicenter cross-sectional study. Scientific Reports, 5(11052), pp. 1 – 9.

298

Fombon, I.S. (2020)

Zak, Z., Gelebart, P. and Lai, R. (2010) Fenofibrate induces effective apoptosis in mantle cell lymphoma by inhibiting the TNFα/NF-κB signaling axis. Leukaemia, 24(8), pp. 1476 - 1486.

Zhang, J., Wang, X., Vikash, V., Ye, Q., Wu, D., Liu, Y. and Dong, W. (2016) ROS and ROS-Mediated Cellular Signaling. Oxidative Medicine and Cellular Longevity, 2016, pp.1- 18.

Zhao, H., Zhu, C., Qin, C., Tao, T., Li, J., Cheng, G., Li, P., Cao, Q., Meng, X., Ju, X., Shao, P., Hua, L., Gu, M. and Yin, C. (2013) Fenofibrate down-regulates the expressions of androgen receptor (AR) and AR target genes and induces oxidative stress in the prostate cancer cell line LNCaP. Biochemical and Biophysical Research Communications, 432(2), pp. 320 - 325.

Zhou, Q. and Liao, J.K. (2010) Pleiotropic effects of statins - Basic research and clinical perspectives. Circulation Journal, 74(5), pp. 818 – 826.

Zodda, D., Giammona, R. and Schifilliti, S. (2018) Treatment strategy for dyslipidaemia in cardiovascular disease prevention: focus on old and new drugs. Pharmacy, 6(10), pp. 1 – 16.

Zuo, N., Zheng, X., Liu, H. and Ma, X. (2015) Fenofibrate, a PPARα agonist, protect proximal tubular cells from albumin-bound fatty acids induced apoptosis via the activation of NFκB. International Journal of Clinical & Experimental Pathology, 8(9), pp. 10653 – 10661.

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APPENDIX

Comprehensive List of Participants showing sample provisions

Patient Sex Age Baseline Group A Group B Group C Comments No. (Years) (3 months) (6 Months) (12 Months) B01 F 63 Yes Yes Yes No

B02 M 43 Yes Yes Yes No

B03 M 37 Yes Yes Yes No

B04 M 58 Yes Yes Yes Yes

B05 M 52 Yes Yes Yes Yes

B06 F 50 Yes Yes Yes Yes

B07 F 38 Yes No No No Drop out

B08 M 50 Yes Yes No No

B09 M 70 Yes No No No Drop out

B10 M 51 Yes Yes Yes No

B11 M 52 Yes Yes No No

T01 F 53 Yes Yes No Yes

T02 F 40 Yes Yes Yes Yes

T03 M 62 Yes No Yes Yes

T04 M 47 Yes Yes Yes No

T05 M 65 Yes Yes No Yes

T06 M 47 Yes No No No Drop out

T07 M 34 Yes No No No Drop out

T08 M 48 Yes No No Yes

N-number 19 13 10 8 4

Yes means participant provided samples, No means participant did not provide sample and Drop out means participants who provided only the baseline sample and no follow-up sample Patient numbers: ‘B’ for participants at BHT and ‘T’ for participants at TST. BHT, Burton Hospital’s NHS Foundation Trust, TST, Taunton and Somerset NHS Foundation Trust 300

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NRES Committee South West - Exeter Whitefriars Level 3 Block B Lewins Mead Bristol BS1 2NT

Telephone: 01173421390 Fax:01173420445 10 December 2014

Prof. Tim Reynolds Burton Hospitals NHS Foundation Trust Queen's Hospital Belvedere Rd, Burton-on-Trent DE13 0RB

Dear Prof. Reynolds

Study title: Role of cystatin C in the evaluation of renal function in patients receiving fenofibrate treatment for dyslipidaemia. REC reference: 14/SW/1104 Protocol number: FAC/CysC/01 IRAS project ID: 150171

Thank you for your letter of 03 November 2014, responding to the Committee’s request for further information on the above research and submitting revised documentation.

The further information has been considered on behalf of the Committee by the Vice Chair, Dr Nicole Dorey and Professor Malcolm Cowburn.

We plan to publish your research summary wording for the above study on the HRA website, together with your contact details. Publication will be no earlier than three months from the date of this favourable opinion letter. The expectation is that this information will be published for all studies that receive an ethical opinion but should you wish to provide a substitute contact point, wish to make a request to defer, or require further information, please contact the REC Manager, Mrs Kirsten Peck, nrescommittee.southwest- [email protected] . Under very limited circumstances (e.g. for student research which has received an unfavourable opinion), it may be possible to grant an exemption to the publication of the study.

A Research Ethics Committee established by the Health Research Authority

Confirmation of ethical opinion

On behalf of the Committee, I am pleased to confirm a favourable ethical opinion for the above research on the basis described in the application form, protocol and supporting documentation as revised, subject to the conditions specified below.

Management permission or approval must be obtained from each host organisation prior to the start of the study at the site concerned.

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Management permission ("R&D approval") should be sought from all NHS organisations involved in the study in accordance with NHS research governance arrangements.

Guidance on applying for NHS permission for research is available in the Integrated Research Application System or at http://www.rdforum.nhs.uk.

Where a NHS organisation’s role in the study is limited to identifying and referring potential participants to research sites ("participant identification centre"), guidance should be sought from the R&D office on the information it requires to give permission for this activity.

For non-NHS sites, site management permission should be obtained in accordance with the procedures of the relevant host organisation.

Sponsors are not required to notify the Committee of approvals from host organisations

Registration of Clinical Trials

All clinical trials (defined as the first four categories on the IRAS filter page) must be registered on a publically accessible database. This should be before the first participant is recruited but no later than 6 weeks after recruitment of the first participant.

There is no requirement to separately notify the REC but you should do so at the earliest opportunity e.g. when submitting an amendment. We will audit the registration details as part of the annual progress reporting process.

To ensure transparency in research, we strongly recommend that all research is registered but for non-clinical trials this is not currently mandatory.

If a sponsor wishes to request a deferral for study registration within the required timeframe, they should contact [email protected]. The expectation is that all clinical trials will be registered, however, in exceptional circumstances non registration may be permissible with prior agreement from NRES. Guidance on where to register is provided on the HRA website.

It is the responsibility of the sponsor to ensure that all the conditions are complied with before the start of the study or its initiation at a particular site (as applicable).

Ethical review of research sites

A Research Ethics Committee established by the Health Research Authority NHS sites The favourable opinion applies to all NHS sites taking part in the study, subject to management permission being obtained from the NHS/HSC R&D office prior to the start of the study (see "Conditions of the favourable opinion" below). Approved documents The final list of documents reviewed and approved by the Committee is as follows:

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Document Version Date Covering letter on headed paper 31 October 2014 Other [e-mail from Ivo Sama] 28 October 2014 Other [Response to ethics review letter] Other [New PIS ] 2 18 November 2014 Other [uPDATED CONSENT FORM - VER 3] 3 10 December 2014 Participant information sheet (PIS) 2 20 October 2014 REC Application Form [REC_Form_08092014] 08 September 2014 Referee's report or other scientific critique report [Early report from University on Project [issues fully addressed]] 01 01 May 2014 Research protocol or project proposal [Protocol] 01 29 August 2014 Response to Request for Further Information 03 November 2014 Summary CV for Chief Investigator (CI) [Dr Reynolds CV] 04 September 2014 Summary CV for student [Iwo Fombon CV] 01 29 August 2014 Statement of compliance The Committee is constituted in accordance with the Governance Arrangements for Research Ethics Committees and complies fully with the Standard Operating Procedures for Research Ethics Committees in the UK. After ethical review Reporting requirements The attached document “After ethical review – guidance for researchers” gives detailed guidance on reporting requirements for studies with a favourable opinion, including: • Notifying substantial amendments • Adding new sites and investigators • Notification of serious breaches of the protocol • Progress and safety reports • Notifying the end of the study The HRA website also provides guidance on these topics, which is updated in the light of changes in reporting requirements or procedures. A Research Ethics Committee established by the Health Research Authority

User Feedback

The Health Research Authority is continually striving to provide a high quality service to all applicants and sponsors. You are invited to give your view of the service you have received and the application procedure. If you wish to make your views known please use the feedback form available on the HRA website: http://www.hra.nhs.uk/about-the- hra/governance/quality- assurance/

HRA Training

We are pleased to welcome researchers and R&D staff at our training days – see details at http://www.hra.nhs.uk/hra-training/

14/SW/1104 Please quote this number on all correspondence

With the Committee’s best wishes for the success of this project.

Yours sincerely

Mrs Joan Ramsay Vice Chair

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Fombon, I.S. (2020)

Email: [email protected]

Enclosures: “After ethical review – guidance for researchers” [SL-AR2]

Copy to: Mrs Anne Hogg

A Research Ethics Committee established by the Health Research Authority

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Fombon, I.S. (2020)

Flow cytometric analysis of NRK52E cells exposed to various concentrations of fenofibrate for 4 hours and stained with Annexin V-FITC/PI. A) Control unstained cells, B) Control cells stained with Annexin V only, C) Control cells stained with PI only, D) Control untreated cells, E) Test cells exposed to 250 µM FF, F) Test cells exposed to 500

µM FF, G) Test cells exposed to 1000 µM FF, H) Control cells exposed to 25 µM H2O2. FF, fenofibrate

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Fombon, I.S. (2020)

Flow cytometric analysis of L6 cells exposed to various concentrations of fenofibrate for 4 hours and stained with Annexin V-FITC/PI. a) Control unstained cells, b) Control cells stained with Annexin V only, c) Control cells stained with PI only, d) Control untreated cells, e) Test cells exposed to 250 µM FF, f) Test cells exposed to 500 µM FF, g) Test cells exposed to 1000 µM FF, h) Control cells exposed to 25 µM H2O2. FF, fenofibrate

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Fombon, I.S. (2020)

Flow cytometric analysis of HEK293 cells exposed to various concentrations of fenofibrate for 4 hours and stained with Annexin V-FITC/PI. i) Control unstained cells, ii) Control cells stained with Annexin V only, iii) Control cells stained with PI only, iv) Control untreated cells, v) Test cells exposed to 250 µM FF, vi) Test cells exposed to 500 µM FF, vii) Test cells exposed to 1000 µM FF, viii) Control cells exposed to 25 µM H2O2. FF, fenofibrate

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Fombon, I.S. (2020)

Protein expression of SOD in fenofibrate treated NRK52E, L6 and HEK293 cells. Cells were exposed to different concentrations of fenofibrate as indicated for 72 hours and SOD protein expression was determined by Western blot analysis. Data represents immunoblot of SOD and GAPDH expression for the three cell lines.

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