Potential of Polyphenols As Therapeutic Options in Type 2 Diabetes Elham Nikbakht

Potential of Polyphenols as Therapeutic Options in Type 2 Diabetes

Author Nikbakht Nasrabadi, Elham

Published 2018-04

Thesis Type Thesis (PhD Doctorate)

School School of Medical Science

DOI https://doi.org/10.25904/1912/3203

Downloaded from http://hdl.handle.net/10072/384277

Griffith Research Online https://research-repository.griffith.edu.au

Potential of Polyphenols as Therapeutic Options in Type 2 Diabetes

Elham Nikbakht Bachelor of Food Science Master of Nutritional Sciences

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

School of Medical Science Griffith University Menzies Health Institute Queensland

April 2018

Abstract

Type 2 diabetes (T2D) is a complex phenotype mainly characterised by prolonged unsettled hyperglycemia caused by pancreatic beta cell (β-cell) dysfunction and progressive insulin resistance. In addition, T2D is a well-recognised inflammatory disease due to the significant role of metabolic inflammation, triggered by over-nutrition, in β- cell dysfunction initiation. The aetiology of T2D is influenced by the interactions of environmental and genetic factors. Although the exact mechanism by which diabetes- associated genes interact with each other and environmental influences is not fully understood, it has been indicated that several genes and lifestyle risk factors are involved in the development of T2D, each being a small contributor. The prevalence of diabetes is expected to rise in the adult population, affecting approximately 600 million by 2035.

Untreated T2D is associated with long-term macrovascular and microvascular complications. In fact, there is a significant disability burden associated with T2D along with a considerable cost to the healthcare system. Annual healthcare costs of diabetes were estimated 245 billion US dollars in the United States in 2012 and 14.6 billion

Australian dollars in Australia in the year 2010. With substantial treatment costs, the increasing prevalence of diabetes represents a significant social and economic issue.

Thus, the development of novel therapeutic approaches for T2D and complications accompanying it, or prevention of diabetes in at-risk individuals is an essential component of future medical research strategies.

Pharmacotherapy and lifestyle modifications are the primary therapeutic approaches for management of T2D. However, due to the side effects accompanying the

i use of medications, as well as the difficulty in maintaining health-related behaviour changes, non-pharmacological interventions are attracting scientific attention, with certain natural supplements and dietary components such as polyphenolic antioxidants showing promise as a replacement or complement to pharmacotherapy for T2D. The anti- diabetic effects of polyphenolic antioxidants such as anthocyanins (ACNs) have been demonstrated in several in vitro, in vivo, human interventional trials, and observational studies. ACNs are abundant in red to blue coloured fruits and vegetables as well as plant- derived beverages and are broadly distributed in the human diet. Polyphenolic compounds including ACNs are found to attenuate important hallmarks of T2D such as hyperglycaemia and hyperlipidaemia, in addition to having the ability to act as anti- inflammatory agents, hence potentially controlling obesity-induced inflammation and associated disorders. Moreover, various subclasses of polyphenols have demonstrated gene expression regulatory roles in different experimental models. As the overall aim, this doctoral study sought to investigate the potential of dietary polyphenols as therapeutic options for type 2 diabetic and pre-diabetic adults. The studies conducted as a part of this thesis were based on the hypothesised effects of polyphenolic antioxidants, including

ACN, on physical measurements; biochemical parameters; pro-inflammatory biomarkers; and genetic signals including micro-RNAs (miRNAs) and associated messenger-RNAs (mRNAs), in T2D and pre-diabetic adults. This thesis incorporates a systematic review and meta-analysis followed by an intervention trial, with four main research questions being addressed in four studies presented in Chapters 3, 5, 6 and 7.

A systematic review and meta-analysis was carried out to fulfil the first research question on the current evidence for the relationship between polyphenols intervention, flavonoids in particular, and metabolic markers of diabetes in T2D adults of various

ii ethnicities. A clinical trial was conducted with healthy, type 2 diabetic, and pre-diabetic volunteers, 25-75 years of age and the mixtures of ethnicities including Australians,

European Australians, Asian Australians and Arab Australians.

Cochrane Library, PubMed, Scopus, and Cumulative Index to Nursing and Allied

Health Literature were searched for relevant studies. Randomised controlled trials investigating the efficacy of a dose of flavonoids or flavonoid substances on fasting blood glucose (FBG), haemoglobin A1c (HbA1c), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) of adults with

T2D were included in the initial screening. Rosendal scale was used to assess the quality of included trials and meta-analysis was undertaken using the random-effect model.

Pooling of glycaemic and lipid profile data in a systematic review and meta-analysis manner (Chapter 3), demonstrated that regular dietary intake of flavonoid compounds is associated with the significant reduction of FBG, TC and TG in adults with T2D.

An open-label clinical trial enrolling type 2 diabetics, people with T2D risk and healthy individuals was conducted to investigate the efficacy of dietary ACN supplementation (320mg/day over the course of four weeks) on diabetes-associated inflammatory biomarkers and relevant biochemical and physical parameters. In addition, the dietary inflammatory potential was estimated by calculating the dietary inflammatory index (DII) score for each individual, then comparing across groups. The anti- inflammatory potential of polyphenolic antioxidants investigated through the ACN intervention trial in Chapter 5, demonstrated that a high intake of ACN might be beneficial in modulating the elevated level of pro-inflammatory biomarkers (interleukin-

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6, interleukin-18, and tumour necrosis factor-α) in T2D adults. Post ACN intervention biochemical analysis demonstrated significant decreases in FBG, LDL-cholesterol and uric acid in diabetic at-risk participants. Blood pressure was lowered in pre-diabetic and

T2D groups following four weeks of ACN supplementation, but anthropometric data, including body mass index and waist-hip ratio, did not show any significant changes in the groups. Investigation of the dietary inflammatory potential of participants revealed that there is a significant difference in the dietary pattern and composition of healthy individuals compared to participants with T2D. In fact, the DII score calculation for the

T2D group was consistent with an anti-inflammatory diet.

The epigenetic effects of the same treatment protocol (four weeks of 320 mg ACN daily) on the miRNA profile of T2D and non-diabetic individuals were investigated in

Chapter 6 of this thesis. Differential analysis of miRNA profiles pre- and post- supplementation revealed that the expression of ten miRNAs in the non-diabetic group

(miR-1827, miR-574-5p, miR-603, miR-199a-3p+miR-199b-3p, miR-519d-3p, miR-

216b-5p, miR-206, miR-548d-5p, miR-1264 and miR-493-3p) and five miRNA in T2D group (miR-944, miR-593-3p, miR-548b-3p, miR-93-5p and miR-1827) were significantly altered post-supplementation. Further, in Chapter 7, significantly altered miRNAs in the T2D group were investigated for their predicted target genes. Well- recognised diabetes-associated genes were found to be regulated by these miRNAs. Five genes (GLIS3, HNF4A, JAZF1, WFS1 and NOTCH2) were investigated for their pre- and post- ACN supplementation expression in healthy, T2D and T2D-at-risk groups, in which

GLIS3 and HNF4A shown to be significantly down-regulated after supplementation in

T2D-at-risk individuals. Modulation of pro-inflammatory biomarkers and alteration of the miRNA profile and associated T2D genes in participants with or without T2D in our

iv study suggests the interaction of ACN metabolites with diabetes signalling pathways and transcription factors. This could be useful in the development of novel therapeutic approaches for T2D and complications accompanying it.

Statement of Originality

This thesis describes original research conducted by Elham Nikbakht at Griffith

University, School of Medical Science. This work has not previously been submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made within the thesis itself.

Elham Nikbakht

27th April 2018

Table of Content

Abstract ...... i Statement of Originality ...... vi Table of Content ...... vii Table of Figures ...... xi Table of Tables ...... xii List of Abbreviations ...... xiii Acknowledgements ...... xvi Publications, Presentations and Awards in Support of this Thesis ...... xix Chapter 1 ...... 1 Review of the Literature ...... 1 1.1 Background ...... 1 1.2 Type 2 diabetes, characteristics and prevalence ...... 1 1.2.1 Pathophysiology of type 2 diabetes, beta cell dysfunction and insulin resistance ...... 2 1.3 Type 2 diabetes, an inflammatory condition...... 5 1.3.1 Inflammatory potential of dietary composition ...... 8 1.3.2 Pro-inflammatory biomarkers in type 2 diabetes ...... 9 1.3.2.1 C-reactive protein ...... 9 1.3.2.2 Interleukin-6 ...... 10 1.3.2.3 Interleukin-8 ...... 10 1.3.2.4 Interleukin-18 ...... 11 1.3.2.5 Interleukin-1 receptor alpha ...... 11 1.3.2.6 Tumour necrosis factor alpha ...... 12 1.3.2.7 Resistin ...... 12 1.4 Genetic aspect of type 2 diabetes ...... 13 1.4.1 Role of micro-RNAs in type 2 diabetes pathophysiology ...... 14 1.4.2 Specific micro-RNAs in type 2 diabetes ...... 17 1.4.3. Involvement of genetic signals in type 2 diabetes occurrence ...... 17 1.5 Polyphenolic antioxidant ...... 18 1.5.1 Anthocyanin ...... 21 1.6 Type 2 diabetes amelioration by dietary polyphenol consumption ...... 22 1.7 Polyphenols as an anti-inflammatory agent ...... 23

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1.8 Application of polyphenols in the regulation of diabetes associated micro-RNAs ...... 25 1.9 Effect of polyphenolic compounds on diabetes-related genes ...... 26 1.10 Conclusion and directions for future research ...... 27 Chapter 2 ...... 29 Hypothesis & Aims ...... 29 2.1 General hypothesis ...... 29 2.2 Research questions, aims and hypothesis ...... 31 2.3 Significance of the research ...... 34 2.4 Thesis orientation and outline of chapters ...... 35 Chapter 3 ...... 38 Effects of Flavonoids on Metabolic Profile of Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis of Randomized Controlled Trials…………...38 3.1 Abstract ...... 40 3.2 Introduction ...... 41 3.3 Materials and Methods ...... 43 3.3.1 Search strategy ...... 43 3.3.2 Study eligibility and selection ...... 44 3.3.3 Quality assessment and Data extraction ...... 46 3.3.4 Data Synthesis and meta-analysis ...... 47 3.3.5 Sensitivity and subgroup analysis ...... 47 3.4 Results ...... 48 3.4.1 Quality and characteristics of included studies ...... 48 3.4.2 Information on supplement protocol ...... 49 3.4.3 Results of Individual Studies ...... 50 3.4.4 Meta-analysis results ...... 52 3.4.5 Sensitivity and subgroup analysis ...... 55 3.5 Discussion ...... 60 3.6 Conclusion ...... 64 Chapter 4 ...... 67 Methods and Materials ...... 67 4.1 Human clinical trial study design ...... 67 4.2 Volunteer recruitment and screening ...... 67 4.3 Inclusion and exclusion criteria ...... 68 4.4 Supplement information ...... 70

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4.5 Dietary assessment ...... 72 4.6 Dietary inflammatory index calculations ...... 72 4.7 Physical measurement ...... 73 4.8 Blood collection and processing ...... 74 4.9 Biochemistry assay ...... 74 4.10 Analysis of pro-inflammatory biomarkers by Bio-Plex ...... 75 4.11 Interleukin-6 analysis ...... 76 4.12 RNA isolation ...... 76 4.13 micro-RNA expression profile ...... 77 4.14 Normalisation of micro-RNA expression ...... 78 4.15 micro-RNA: messenger-RNA target interactions ...... 78 4.16 Reverse transcription ...... 81 4.17 Primer efficiency ...... 81 4.18 Real-time polymerase chain reaction ...... 83 4.19 Statistical analysis ...... 84 Chapter 5 ...... 86 Potential of anthocyanin as an anti-inflammatory agent: A human clinical trial on type 2 diabetic, diabetic at-risk and healthy adults...... 86 5.1 Abstract ...... 89 5.2 Introduction ...... 90 5.3 Methods and materials ...... 92 5.4 Results ...... 93 5.4.2 Dietary intake, dietary inflammatory index score and its correlation with biomarkers of inflammation ...... 94 5.4.3 Effect of anthocyanin intervention on physical parameters ...... 99 5.4.4 Effect of dietary anthocyanin consumption on fasting blood glucose ...... 99 5.4.5 Anthocyanin intervention and serum lipids ...... 99 5.4.6 Effect of anthocyanin intake on uric acid...... 100 5.4.7 Effect of anthocyanin intervention on pro-inflammatory biomarkers ...... 103 5.5 Discussion ...... 105 5.6 Conclusion, limitations and direction for future studies ...... 109 Chapter 6 ...... 112 Effect of anthocyanin supplementation on the micro-RNA profile of diabetic and non-diabetic adults...... 112 6.1 Abstract ...... 114 6.2 Introduction ...... 115

6.3 Methods and materials ...... 119 6.4 Results ...... 120 6.5 Discussion ...... 130 6.6 Conclusion, limitations, and direction for future studies ...... 134 Chapter 7 ...... 136 Nutritional genomic effects of dietary anthocyanin on micro-RNA target ...... 136 genes’ expression in type 2 diabetic, pre-diabetic, and healthy individuals ...... 136 7.1 Abstract ...... 138 7.2 Introduction ...... 139 7.3 Methods and materials ...... 143 7.4 Results ...... 144 7.5 Discussion ...... 147 7.6 Conclusion ...... 153 Chapter 8 ...... 154 General Discussion and Conclusion ...... 154 References...... 161 Appendices ...... 185 Appendix 1. Human Research Ethics Committee ...... 185 Appendix 2. Australian New Zealand Clinical Trial Registry ...... 187 Appendix 3. Flyer advertisements to recruit healthy and non-diabetic volunteers... 188 Appendix 4. Flyer advertisements to recruit type 2 diabetic volunteers...... 189 Appendix 5. Invitation to participate in a research project ...... 190 Appendix 6. Volunteers Initial Screening ...... 193 Appendix 7. Consent Form ...... 196 Appendix 8. Analysis of the Anthocyanin Capsule ...... 198 Appendix 9. Food Questionnaire, Antioxidant Questionnaire, Food diary form and Instructions ...... 200

Table of Figures

Figure 1.1. Stimulus-secretion coupling in beta cells...... 4 Figure 1.2. Development of obesity-induced inflammation in T2D condition...... 7 Figure 1.3. The involvement of miRNAs in insulin release in pancreatic ß-cells and ß- cells fate...... 16 Figure 1.4. Polyphenol-rich foods absorption...... 19 Figure 1.5. The structure of the flavonoid backbone...... 20 Figure 1.6. The chemical structure of the flavonoid subgroups...... 21 Figure 2.1. Overview of the thesis...... 37 Figure 3.1. PRISMA flowchart and study flow diagram of a systematic literature search of flavonoids and type 2 diabetes...... 45 Figure 3.2. Forest plot of the effect of flavonoid consumption on FBG...... 52 Figure 3.3. Forest plot of the effect of flavonoid consumption on HbA1c...... 53 Figure 3.4. Forest plot of the effect of flavonoid consumption on TC...... 53 Figure 3.5. Forest plot of the effect of flavonoid consumption on LDL...... 54 Figure 3.6. Forest plot of the effect of flavonoid consumption on HDL...... 54 Figure 3.7. Forest plot of the effect of flavonoid consumption on TG...... 55 Figure 4.1. Anthocyanin intervention design...... 70 Figure 5.1. Diet inflammatory index (DII) score comparison of three groups………….95 Figure 5.2. Spearman correlation between diet inflammatory index (DII) score of healthy participants and biomarkers of inflammation……………………………………………………96 Figure 5.3. Spearman correlation between diet inflammatory index (DII) score of pre- diabetic participants and biomarkers of inflammation…………………………………….…..97 Figure 5.4. Spearman correlation between diet inflammatory index (DII) score of type 2 diabetes participants and biomarkers of inflammation…………………………………………98 Figure 5.5. Biomarkers of inflammation pre- and post- anthocyanin supplementation.104 Figure 6.1. Type 2 diabetes development……………………………………………………..117 Figure 7.1. Relative expression of GLIS3, HNF4A, JAZF1, WFS1, and NOTCH2 genes before and after four weeks of anthocyanin intervention across three groups of participants...... 145

Table of Tables

Table 3.1. Characteristics of the six included double-blind randomised placebo- controlled trials evaluating the effect of flavonoids and flavonoid substances on type 2 diabetes risk factors……………………………………………………………………..51 Table 3.2. Subgroup analysis of included randomised controlled trials in a meta-analysis of the effect of flavonoids and flavonoid subclasses on type 2 diabetes risk factors. .... 57 Supplemental Table 3.1. Methodology assessment summary and Rosendal score of studies included in systematic review and meta-analysis of the effect of flavonoids intake on the management of type 2 diabetes...... 65 Table 4.1. Predicted miRNA: mRNA interactions by three prediction tools...... 80 Table 4.2. Primer pair sequences of messenger-RNA transcripts used for real-time polymerase chain reaction...... 82 Table 5.1. Baseline and post-intervention measures for healthy, type 2 diabetes at-risk and type 2 diabetes individuals...... 101 Supplemental Table 5.1. Summary of the participants’ group mean daily dietary composition and comparison between the three groups...... 110 Supplemental Table 5.2. The Mean DII score and comparison between the three groups of healthy, T2D at-risk, and T2D…………………………………………………………………111 Table 6.1. Demographic and baseline characteristics of healthy and type 2 diabetes individuals...... 121 Table 6.2. Comparison of pre-and post-intervention measures for healthy individuals versus type 2 diabetes individuals...... 124 Table 6.3. Comparison of top 15 up-regulated or down-regulated micro-RNAs at baseline and post-intervention of two groups...... 127 Table 6.4. Top 15 up-regulated or down-regulated micro-RNAs in healthy and type 2 diabetes individuals before and after the intervention...... 129 Table 7.1. Demographic characteristics and analysis of baseline and post- supplementation measurements of body mass index, fasting blood glucose, and five messenger-RNAs relative expressions in three groups of healthy, pre-diabetic, and type 2 diabetes...... 146

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List of Abbreviations

ACN Anthocyanin

ADP Adenosine diphosphate

ADSF Adipocyte-secreted factor

ANOVA Analysis of variance

ATP Adenosine triphosphate

β-cells Beta cells

BMI Body mass index

BP Blood pressure

Ca Calcium

CCL2 CC-chemokine ligand 2

CDS coding region of a gene

DNA Deoxyribonucleic acid cDNA complementary DNA

CRP C-reactive protein

CT Cycle threshold

CVD Cardiovascular disease

DII Diet inflammatory index

DM Diabetes mellitus

EDTA Ethylene diamine tetra-acetic acid

ELISA Enzyme-linked immunosorbent assay

FBG Fasting blood glucose

HDL High-density lipoprotein

IκK Inhibitor of κ kinase

ICAM Intercellular Adhesion Molecule 1

IDF International Diabetes Federation

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IFN-γ Interferon-gamma

IL-1Rα Interleukin-1 receptor antagonist

IL-1β Interleukin-1β

IL-4 Interleukin-4

IL-6 Interleukin-6

IL-8 Interleukin-8

IL-18 Interleukin-18

GLP-1 Glucagon-like peptide-1

K Potassium

PKR Protein kinase R

LDL Low-density lipoprotein

LPS Lipopolysaccharide

Mg Magnesium mRNA Messanger-RNA miRNA micro-RNA

Na Sodium

NCD Non-communicable disease

NGT Normal glucose tolerance

NF-κβ Nuclear transcription factor kappa β

PKA Protein kinase A

RNA Ribonucleic acid

ROS Reactive oxygen species

SD Standard deviation

SE Standard error

SNP Single nucleotide polymorphism

SOCS Suppressor of cytokine signalling

SST Serum separator tubes

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T2D Type 2 diabetes

TC Total cholesterol

TG Triglyceride

TNF-α Tumour necrosis factor-α

UCP2 Uncoupling protein 2

UCSC University of California, Santa Cruz

UTR Untranslated region

VCAM Vascular cell adhesion molecule

WHO World Health Organization

WHR Waist-hip ratio

Acknowledgements

With immense gratitude, I would like to acknowledge the guidance of my principal supervisor and role model Dr Natalie Colson. Her constant support and encouragement have always been the source of inspiration for me to peruse my dream as a researcher. Thanks for being always approachable, helpful and kind Natalie. Your guidance, advices, scientific and emotional supports throughout this challenging journey have been gratefully received. I wish to thank my associate supervisors Assoc Prof Indu

Singh for her support, guidance, and patience. Ma’am your continued support and encouragement have provided me with invaluable foundations as a researcher. I would like to express my gratitude to Prof Lauren Williams for being my associate supervisor and very helpful and speedy in providing feedback on my numerous drafts that were invaluable to the progression of my research and thesis, and finally I would like to thank

Dr Nic West, my third associate supervisor, for his guidance, valuable expertise, and critical perspectives.

I would like to acknowledge the support of Griffith University in providing me with the scholarships and resources to complete this research. I wish to thank the Head of

School, Prof Mark Forwood and Deputy Head Research Prof Nigel McMillan for their support throughout my PhD. A special thanks to Assoc Prof Neil Harris for supporting my element transition. I wish to acknowledge my fellow colleagues, friends and lab members within the School of Medical Science who have assisted me throughout various stages of my PhD project. Your technical assistance, willingness to help, constant encouragement and emotional support is always appreciated; Dr Lada Vugic, Dr Avinash

Reddy, Dr Jelena Vider, Dr Paulina Janeczek, Mrs Tia Griffith, Dr Esam Gaiz, Dr Lauren

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Wendt , Dr Lana Biviol, Mrs Ernesta Sofija, Ms Nicola Wiseman and Mrs Anahita

Aboonabi. I also wish to thank my friends, in particular, Dr Rimante Ronto, Dr Najlaa

Aljefree, and Dr Saman Khalesi. Thank you for accompanying me on this journey in parallel to your own. Everything seems less of a problem when you have a great friend to talk it over with. Your emotional therapy, caffeine support, and friendship made this journey possible, amongst all the challenges. I wish to thank Mrs Denise McMillan for her help in proofreading my thesis, and additionally, I would like to thank the participants of this study for their contribution. Their valuable input was a major factor in accomplishing this study.

A special thank you to my dear family that has been an unwavering support. Their continued love and support have always helped me to achieve my goals. I don’t even know how to begin to thank my parents who have been a constant source of encouragement throughout my life. To my dad, my hero, who instilled in me a strong value of knowledge and learning. To my mom, for her love, advice and emotional support.

Thanks, mom and dad for always believing in me. Thank you for being the most caring parents one could hope for. I hope I’ve made you proud! And to my dear brothers, Ehsan and Amir. Thank you for being the best brothers a girl could ask for. Your endless love, support, and long distance humorous entertainment was the best therapy when times were tough and I was missing you all!

Finally and most of all, to the love of my life, husband and best friend, Dr Ali

Ahani. None of this would have been possible without your love, support and sacrifices.

Thank you for the endless patience and understanding throughout this journey. Thanks for believing in me and encouraging me to achieve even more than what I thought I

xvii possibly could, and thanks for being the best husband ever. I am very lucky to have you in my life and I will be forever grateful!

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Publications, Presentations and Awards in Support of this Thesis

The candidate has produced four manuscripts from the research included in this thesis. The manuscripts are co-authored with other researchers and include one systematic review and meta-analysis of the literature and three original research papers. Manuscripts are under preparation for publication. The contribution of the research candidate to each publication is outlined at the front of the relevant chapter. In order of appearance, the four manuscripts prepared are as follows:

1. Elham Nikbakht, Lauren T. Williams, Indu Singh, Nicholas P. West, Natalie Colson. Effects of Flavonoids on Metabolic Profile of Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

2. Elham Nikbakht, Indu Singh, Jelena Vider, Lauren T. Williams, Lada Vugic, Almottesembellah Gaiz, Natalie Colson. Potential of anthocyanin as an anti- inflammatory agent: A human clinical trial on type 2 diabetic, diabetic at-risk and healthy adults.

3. Elham Nikbakht, Jelena Vider, Ping Zhang, Indu Singh, Lauren T. Williams, Natalie Colson. Effect of anthocyanin supplementation on the microRNA profile of diabetic and non-diabetic adults.

4. Elham Nikbakht, Indu Singh, Lada Vugic, Lauren T. Williams, Natalie Colson. Nutritional genomic effects of dietary anthocyanin on diabetes-associated genes in type 2 diabetic, pre-diabetic, and healthy individuals.

A part of this research has been presented at two international conferences/forums.

The details of the presentations are listed in order of recency:

1. Elham Nikbakht, Jelena Vider, Ping Zhang, Lauren T. Williams, Indu Singh, Natalie Colson. Targeting microRNAs in type 2 diabetes adults: anthocyanin intervention as an alternative molecular therapy. International Student Research Forum, Griffith University, Gold Coast, Australia, 2018.

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2. Elham Nikbakht, Nicholas P. West, Lauren T. Williams, Indu Singh, Natalie Colson. Anthocyanin intervention as an alternative molecular therapy in type 2 diabetic adults: the therapeutic role of microRNAs. International Student Research Forum, Omaha, Nebraska, USA, 2017.

In addition to above-mentioned manuscripts and presentations, the PhD candidate has published eight peer-reviewed articles, two as a first author and six as a co-author during the candidature in her field of research. The publications are two original research articles and six systematic reviews and meta-analyses. The contribution of the candidate to each publication is outlined after the relevant paper and they are as follows in the in order of recency:

1. Elham Nikbakht, Rosita Jamaluddin, Mohd Redzwan Sabran, Saman Khalesi. Oral administration of Lactobacillus casei Shirota can ameliorate the adverse effect of an acute aflatoxin exposure in Sprague Dawley rats. (accepted for publication in the International Journal for Vitamin and Nutrition Research) Candidate contribution to this paper involved: Study design and experimental methodology, Conducting the experiments, Data analysis and interpretation, Manuscript writing, preparation and revision.

2. Hossein Khosravi-Boroujeni, Elham Nikbakht, Ernesta Paukste, Saman Khalesi. Sesame and hypertension: a systematic review and meta-analysis of control trials. 2017. Journal of the Science of Food and Agriculture. Candidate contribution to this paper involved: Study design, Initial and final screening of the manuscripts, Quality assessment of the included papers, Data extraction and report, Manuscript writing, preparation and revision.

3. Nikbakht. E, Khalesi. S, Singh. I, Williams. L.T, West. N.P, Colson. N. Effect of probiotics and synbiotics on blood glucose: a systematic review and meta-analysis of controlled trials. 2016. European Journal of Nutrition. Candidate contribution to this paper involved: Study design, Systematic literature search, Initial and final screening of the manuscript, Quality assessment of the included papers, Data extraction and report, Meta-analysis of the included studies, Graph, table and figure preparation, Manuscript writing, preparation and revision.

4. McKean. J, Naug. H, Nikbakht. E, Amiet. B, Colson. N. Probiotics and Subclinical Psychological Symptoms in Healthy Participants: A Systematic Review and Meta-Analysis. 2016. Journal of alternative and complementary medicine (New York, N.Y.).

Candidate contribution to this paper involved: Quality assessment of the included papers, Data extraction and report, Manuscript preparation and revision.

5. Sesame fractions and lipid profiles: A systematic review and meta-analysis of controlled trials. Saman Khalesi, Ernesta Paukste, Elham Nikbakht, Hossein Khosravi-Boroujeni. 2016. The British journal of nutrition. Candidate contribution to this paper involved: Study design, Initial and final screening of the manuscripts, Quality assessment of the included papers, Data extraction and report, Manuscript writing, preparation and revision.

6. Colson. N, Naug. HL, Nikbakht. E, Zhang. P, McCormack. J. The impact of MTHFR 677 C/T genotypes on folate status markers: a meta-analysis of folic acid intervention studies. 2015. European Journal of Nutrition. Candidate contribution to this paper involved: Quality assessment of the included papers, Data extraction and report.

7. Mohd Redzwan. S, Abd Mutalib. MS, Wang. JS, Ahmad. Z, Kang. MS, Abdul. Rahman N, Nikbakht-Nasrabadi. E, Jamaluddin. R. Effect of supplementation of fermented milk drink containing probiotic Lactobacillus casei Shirota on the concentrations of aflatoxin biomarkers among employees of Universiti Putra Malaysia: A randomised, double-blind, cross-over, placebo-controlled study. 2015. The British journal of nutrition. Candidate contribution to this paper involved: Study design and experimental methodology, Conducting the experiments, Manuscript writing.

8. Saman Khalesi, Jing Sun, Nicholas Buys, Arash Jamshidi, Elham Nikbakht- Nasrabadi, Hossein Khosravi-Boroujeni. Green tea catechins and blood pressure: a systematic review and meta-analysis of randomised controlled trials. 2014. European Journal of Nutrition. Candidate contribution to this paper involved: Quality assessment of the included papers, Data extraction and report.

The candidate received one award, two scholarships and one travel grants during her candidature. The details of the award, scholarships and grant are listed in order of recency:

2017 Health Group Completion Assistance Griffith University Postgraduate Research Scholarship (Stipend and Tuition Fees)

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2017 Travel Grant, participate and represent Griffith University at International Student Research Forum (ISRF) held at University of Nebraska Medical Centre, Omaha, Nebraska, USA- Griffith University

2015 Highly Commended 3 Minutes Thesis Presentation Award- Griffith University

2014 Vice Chancellor Griffith University International Postgraduate Research Scholarship (Stipend and Tuition Fees)

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Chapter 1

Review of the Literature

1.1 Background

The dramatic increase in the prevalence of diabetes mellitus (DM) over the past few decades, driven by inappropriate dietary patterns and an unhealthy lifestyle, has made this global pandemic a significant public health issue (1, 2). DM is a complex metabolic disorder, characterised by a loss of control of blood glucose levels, resulting in hyperglycaemia (3).

Increased thirst and hunger, along with frequent fatigue and urination, are symptoms of undiagnosed or poorly controlled DM (3). Untreated DM may lead to several macrovascular, and microvascular, complications, including cardiovascular disease, renal failure, foot ulcers, eye damage, and diabetic ketoacidosis and nonketotic hyperosmolar coma (3, 4). Type 2 diabetes (T2D) is the most prevalent form of diabetes, accounting for 90-95% of diabetic cases

(3).

1.2 Type 2 diabetes, characteristics and prevalence

T2D, formerly known as non-insulin dependent diabetes, is classified as a chronic or non-communicable disease (NCD) (3). The mechanism of its development is reduced production of insulin by the pancreatic beta cells (β-cells), resulting from dysfunctional β-cells and/or insufficient response to endogenous insulin by cells in insulin resistance (3, 5). Although

T2D has a strong genetic component, its expression is influenced by lifestyle factors, such as obesity and sedentary lifestyle (6). The increasing prevalence of overweight and obesity

1 worldwide, resulting from urbanisation and modernisation, has led to the international T2D pandemic (7).

Diabetic prevalence varies between populations, reflecting the relative contribution of genetic predisposition and environmental influences. In 2011, the International Diabetes

Federation (IDF) estimated 332 million people to be suffering from T2D worldwide (6). In addition to diagnosed cases and T2D sufferers, estimates are that one-quarter of the adult western population has the pre-diabetic stage of impaired glucose tolerance and/or metabolic syndrome, which puts them at increased risk of developing T2D (7). T2D prevalence is expected to rise globally, with the World Health Organization (WHO) predicting nearly 600 million T2D sufferers by 2035 (8). This number equates to three new diabetic cases every ten seconds, or ten million new T2D individuals per year (8). While diabetes was once considered a condition of middle or elder age, it now commonly affects younger adults and even children

(8).

1.2.1 Pathophysiology of type 2 diabetes, beta cell dysfunction and insulin resistance

Blood glucose levels are controlled by the three hormones, insulin, glucagon, and epinephrine (9). Liver and muscle are the main sites of glucose metabolism (10). Glucose converts to glycogen upon entering the liver and muscle via a process called glycogenesis (10).

When the concentration of blood glucose is low, glucagon and epinephrine are released to convert glycogen to glucose, in a process called glycogenolysis (11). In a high blood glucose condition, the insulin hormone is released to transfer glucose into the cells, thereby maintaining a normal level of blood glucose (12). On entering the cells, glucose produces adenosine triphosphate (ATP), which provides energy for various metabolic processes (13). Glucose is

2 thus a unique nutrient as it is a primary source of energy (13), and has the ability to initiate insulin secretion and sensitivity (12). Therefore maintaining a blood glucose level within the normal range is crucial to normal physiology. Reduction in insulin sensitivity and β-cell dysfunction are highly associated with hyperglycaemia (12). As hyperglycaemia develops, insulin resistance and β-cell dysfunction occur (12). Several theories have explained the relationship between pancreatic β-cell dysfunction, insulin resistance, and hyperglycaemia leading to T2D. One of the most accepted theories involves insulin granules and calcium and potassium channels (6). Insulin granule exocytosis requires higher amounts of intracellular calcium, coming from calcium influx through plasmalemmal voltage-gated calcium channels.

Their opening is controlled by the ATP-sensitive potassium (KATP) channel, which also plays a key role in insulin secretion by linking cell metabolism to the membrane potential. This channel is opened at low to normal concentrations of blood glucose. The K+ efflux through the open pore keeps the membrane hyperpolarised and prevents electrical activity, opening the calcium channel, causing calcium influx and insulin secretion. In a hyperglycaemia condition, glucose uptake and metabolism are enhanced by β-cells, which increase metabolically generated ATP and decrease MgADP (magnesium adenosine diphosphate). The alteration in adenine nucleotide concentrations closes KATP channels, thus initiating electrical activity, calcium influx, and eventually insulin secretion (6). In addition, glucose metabolism reinforces insulin secretion downstream of KATP-channel closure and calcium influx (6). Given this, insulin secretion and sensitivity are highly dependent on glucose metabolism (Figure 1.1).

Figure 1.1. Stimulus-secretion coupling in beta cells. Glucose is taken up via the glucose transporter Glut1 and phosphorylated by glucokinase (GCK). Further metabolism, especially in the mitochondria, results in the generation of ATP at the expense of ADP. This leads to KATP-channel closure. Sulphonylureas (SU) inhibit the channel by direct binding, bypassing metabolism. The increased membrane resistance (Rm↑) resulting from KATP-channel closure allows small background inward currents, such as those associated with the spontaneous opening of T type calcium (Ca2+) channels to depolarize the β-cells (Ψ↓). This leads to regenerative activation of voltage-gated L type and P/Q type Ca2+ channels and sodium (Na+) channels, which produces action potential firing. The associated Ca2+ influx triggers the exocytosis of insulin granules (SG). Incretins such as glucagon-like peptide-1 (GLP-1) potentiate exocytosis by both protein kinase A (PKA)-dependent and epac2-dependent mechanisms. Plus signs indicate stimulation, and minus signs inhibition, of the indicated processes (6).

Insulin resistance, the reduction in insulin-stimulated glucose uptake, is usually present in pre-diabetes and throughout the development of T2D (6). Pancreatic β-cells react to insulin resistance by increasing insulin secretory activity, via enhancing their cell mass (14). T2D develops when the functional expansion of pancreatic islets fails to compensate for the degree of insulin resistance (15, 16). Thus, as long as pancreatic β-cells of insulin-resistant individuals are compensating for the increased demand for insulin, they do not develop T2D (16).

However, the significant role of genetic factors needs to also be considered in the pathophysiology of this condition.

1.3 Type 2 diabetes, an inflammatory condition

Chronic low-grade metabolic inflammation in T2D individuals has given impetus to further investigation of immuno-metabolism linking inflammation to β-cell dysfunction, insulin resistance, and eventually T2D (17). In fact, T2D is an inflammatory condition, triggered by disordered metabolisms, activating inflammatory signalling cascades inside the cells (18).

Metabolic inflammation in T2D, induced by over-nutrition, is different to classic inflammation in several ways. Classic inflammation is the body’s immune response for self- protection in reaction to injury and is characterised by redness, swelling, heat, and pain (19).

In this type of inflammation, the metabolic rate is normally increased to help fight the foreign invaders, whereas metabolic inflammation is linked to a reduced rate of metabolism, as it is caused by metabolic surplus (19). Moreover, metabolic inflammation, often called obesity- induced inflammation, initiates through the production of pro-inflammatory molecules by metabolic cells (20).

To date, various pro-inflammatory biomarkers have been suggested to be involved in the initiation and promotion of metabolic inflammation in the T2D condition. In addition to hyperglycaemia and hyperlipidaemia, cytokines, chemokines, oxidative stress and reactive oxygen species (ROS), have been proposed as pro-inflammatory biomarkers mediating the

5 inflammatory condition (21). These pro-inflammatory molecules induce inflammation through specific intracellular signalling pathways, involving nuclear factor kappa B (NF-κB), an inhibitor of κ kinase (IκK), protein kinase R (PKR), and c-Jun NH2-terminal kinase (JNK) signalling molecules (21-23). These pathways could interact with insulin signalling via serine/threonine inhibitory phosphorylation of insulin receptor substrate (IRS) (24). Apart from over-intake of certain dietary components and inactivity, age, smoking and psychological stress are known to be associated with chronic metabolic inflammation and T2D development (25).

Investigating the pathophysiology of metabolic inflammation onset leading to T2D demonstrates that the over-intake of specific nutrients, such as glucose and free fatty acids, may stress the pancreatic islets and insulin-sensitive tissue such as adipose tissue, liver and muscle (18). Increased glucose and free fatty acid uptake by endothelial cells in hyperglycaemia and hyperlipidaemia conditions cause an excess production of ROS by mitochondria. Increased ROS may inflict oxidative damage and activate inflammatory signalling cascades inside the endothelial cells, leading to the local production and release of pro-inflammatory molecules, including cytokines and chemokines (Figure 1.2) (18, 26).

Subsequently, the production of interleukin 1 receptor antagonist (IL-1RA) by pancreatic β- cells is decreased, which results in recruitment of immune cells, contributing to tissue inflammation (18). The release of cytokines and chemokines, such as tumour necrosis factor alpha (TNF-α), interleukin 1 beta (IL-1β), and CC-chemokine ligand 2 (CCL2) from adipose tissue into the circulation, promotes inflammation in the other tissues, including the islets (14,

18) (Figure 1.2).

Figure 1.2. Development of obesity-induced inflammation in T2D condition. Excess of glucose and free fatty acids inflict stress on insulin-sensitive tissues and pancreatic cells, causing excess production of reactive oxygen species (ROS) in mitochondria. The increased amount of ROS induces oxidative damage to the corresponding cells, activates inflammatory signalling cascades inside the cells, and leads to the local stimulation of pro- inflammatory biomarkers. Consequently, cytokines and chemokines, such as interleukins (IL) and tumour necrosis factors (TNF), are released from insulin-sensitive tissues into the circulation, promoting inflammation in other tissues, including the pancreatic islets (14, 18).

Another mechanism linking obesity-induced inflammation to the T2D occurrence is through increased immune cell infiltration in the adipose tissue in obesity/metabolic syndrome

(27). Infiltrating macrophages, as well as other immune cells and adipocytes, participate in the release of cytokines and biologically active molecules such as interleukins and tumour necrosis factors, implicated in the initiation of inflammation and associated insulin resistance (28, 29).

Therefore, reducing macrophage infiltration could be a potential approach in battling obesity- induced inflammation and related disorders such as T2D.

1.3.1 Inflammatory potential of dietary composition

As previously explained, obesity-induced inflammation is one of the major environmental risk factors underlying T2D onset and development (20). Certain dietary patterns or over-intake of specific nutrients is linked to the incidence of chronic metabolic inflammation and associated diseases, including T2D (30). The surplus of nutrients such as lipids and glucose induces stress to the insulin-sensitive tissues and β-cells, which increases the production of ROS in mitochondria, leading to oxidative stress in corresponding cells (31,

32). Oxidative stress activates inflammatory signalling cascades inside the cells, which causes local recruitment of pro-inflammatory molecules, including cytokines and chemokines (33).

Given this, diet could act in an anti-inflammatory, as well as in a pro-inflammatory manner.

For example, a diet high in red meat, full cream dairy products, and reﬁned grains, such as the

Western-type diet, has shown to act as a pro-inflammatory agent, being associated with higher levels of inflammatory biomarkers, such as C-reactive protein, and interleukin-6 (34, 35). In contrast, a diet rich in fruit, green vegetables, whole grains, and fish, such as the Mediterranean- type diet, has shown to be associated with a low level of pro-inflammatory molecules (36, 37).

In order to estimate the overall inflammatory potential of the diet, a dietary inflammatory index (DII) was developed by researchers at University of South Carolina based upon an extensive literature search, incorporating in vitro, in vivo, human clinical trials, observational, and epidemiological studies of the effect of diet on inflammation (38, 39). The

DII has been shown to be associated with specific inflammatory markers, including C-reactive proteins and some cytokines, and it has been confirmed in follow-up studies (39-42).

1.3.2 Pro-inflammatory biomarkers in type 2 diabetes

Considering the pathophysiology of T2D, the elevated level of circulating pro- inflammatory biomarkers including C-reactive proteins (hs-CRP), interleukins, tumour necrosis factor and resistin may be an indicator and/or a predictor of the T2D condition (25).

Some of the well-established T2D-associated pro-inflammatory biomarkers are explained in the following sections.

1.3.2.1 C-reactive protein

High sensitivity C-reactive protein, or hs-CRP, is a protein of hepatic origin found in the bloodstream (43). An accumulating body of evidence suggests the involvement of hs-CRP in the pathophysiology of T2D (44). The association of elevated hs-CRP with hyperglycaemia and insulin resistance, has been postulated by experimental and cross-sectional data (45-47).

Hence, it may be used to predict T2D development in pre-diabetic individuals (47, 48).

Although the mechanisms underlying how an elevated level of CRP increases the risk of T2D occurrence are not fully understood, it has been suggested that increased CRP may modify insulin secretion and resistance through an altered innate immune response, due to heightened systemic inflammation (49, 50). In addition, CRP elevation is associated with the endothelial production of E-selectin, intercellular adhesion molecule-1 (ICAM-1), and vascular cell-adhesion molecule-1 (VCAM-1), which are important mediators of impaired vascular reactivity, reduced insulin delivery, and increased peripheral insulin resistance (50, 51). Thus,

CRP elevation plays a major role in T2D development and related subclinical disorders. An

9 elevated level of CRP may stimulate the secretion of interleukin-6 by macrophages and T cells

(44, 52).

1.3.2.2 Interleukin-6

Interleukin-6 (IL-6), a multifunctional cell signalling protein, is a cytokine secreted by

T cells and macrophages and is produced by many types of cells and tissues, including fibroblasts, endothelial cells, monocytes, and adipose tissue (21). The elevation of IL-6 in inflammatory conditions such as T2D is well established through the literature in cell culture studies as well as animal and human studies (53, 54). IL-6 secretion is greatly associated with the production of CRP (44, 52, 55). In addition, this cytokine plays a key role in the link between obesity-induced inflammation and insulin resistance, through its effect on insulin signalling pathways, leading to an impaired biological effect of insulin (53). Although the exact mechanisms of action are yet to be investigated, it may be due to its interaction between suppressor of cytokine signalling (SOCS) proteins and the insulin receptor, decreasing insulin action (56, 57). Therefore, in addition to inflammation initiation, the chronic increase in circulating IL-6 could contribute to insulin resistance and eventually T2D (21).

1.3.2.3 Interleukin-8

Interleukin-8 (IL-8), is a chemokine, belonging to the family of small cytokines produced by macrophages, epithelial cells, endothelial cells, and smooth muscles (58). The circulating level of IL-8 is reported to be increased in T2D individuals compared to non- diabetics (59, 60). In fact, IL-8 plays a major role in neutrophil recruitment and degranulation, and is a key mediator associated with obesity and inflammation (61, 62). The secretion of IL-

8 is increased by oxidative stress, leading to the recruitment of inflammatory molecules (63).

IL-8 expression is controlled by NF-κB signalling pathways, therefore NF-κB regulation might be suggested as an anti-IL-8 therapy, which could be used to alleviate inflammatory conditions such as T2D (63).

1.3.2.4 Interleukin-18

Interleukin-18 (IL-18) is a cytokine known as interferon-gamma inducing factor (64).

IL-18 is affiliated with the IL-1 superfamily and is produced mainly by macrophages (64).

Lipopolysaccharide (LPS) and Fas ligand are immunological stimuli of IL-18, inducing their effect via caspase-1 dependent and independent conversion (65, 66). IL-18 has been shown to induce inflammatory reactions, which suggests its role in certain inflammatory disorders such as T2D (67). Concordantly, a positive correlation between IL-18 and fasting blood glucose in

T2D patients, and insulin resistance in individuals with or without T2D, is reported in the literature (67, 68).

IL-18 may exert its effect on insulin resistance through a direct effect on insulin signalling, a secondary response to insulin resistance, or an indirect effect via induction of tumour necrosis factor alpha (TNF-α) in the target tissue (69). TNF-α is known to suppress insulin signalling pathways, inducing insulin resistance (69).

1.3.2.5 Interleukin-1 receptor alpha

Interleukin-1 receptor alpha (IL-1Rα), is a cytokine receptor which binds to IL-1α (70).

IL-1Rα is an important mediator involved in many cytokine-induced immune and inflammatory responses, thus plays a significant role in inflammation-associated disorders such

11 as T2D (70). One of the well-known pathways in which IL-1Rα induces its inflammatory effect is through the NF-κB signalling pathway (71). In fact, the physical interaction between IL-1 and its receptor (IL-1Rα) stimulates Phosphoinositide 3-kinase, one of the key component of

NF-κB activation (71). Therefore, inflammation could be controlled through inhibition of NF-

κB signalling pathway, by limiting physical interaction between IL-1 and IL-1Rα.

1.3.2.6 Tumour necrosis factor alpha

Tumour necrosis factor alpha (TNF-α), a cytokine involved in metabolic inflammation, is produced by a variety of cell-types, mainly macrophages and lymphocytes (21). Two transcription signalling pathways, including NF-κB and JNK pathways which are involved in inflammation and insulin resistance, are activated by TNF-α (72). The pathophysiological role of TNF-α on insulin resistance, through the phosphorylation of the insulin receptor substrate-1

(IRS-1) protein on serine residues, is established in the literature (73). The phosphorylation of the IRS-1 protein on serine residues prevents interaction with the insulin receptor beta subunit, stopping the insulin signalling pathways leading to the development of insulin resistance (21).

1.3.2.7 Resistin

Resistin, or adipocyte-secreted factor (ADSF), is an adipose-derived hormone similar to cytokines, suggested to play a role in T2D onset and development (74). Resistin has been shown to increase the expression of several pro-inflammatory cytokines, including interleukins and tumour necrosis factors, by enhancement of transcriptional events through the NF-κB signalling pathway (75). Correspondingly, interleukins might stimulate the upregulation of resistin (75). In addition to interleukins, LPS are shown to stimulate resistin secretion (76).

Resistin is known to contribute to insulin resistance, leading to the onset of T2D (77).

In an in vitro study using cultured adipocytes, increased resistin was shown to reduce insulin- stimulated glucose transport, inhibiting adipocyte differentiation (78, 79). Hence, resistin may be a link between adipose tissue, obesity, and insulin resistance (78). In an in vivo study, resistin-knockout mice demonstrated lower fasting glycaemia and increased insulin sensitivity, which was associated with a reduced liver glucose production (80). In humans, however, controversial findings have been observed regarding the association of resistin with obesity and insulin resistance. Some studies have found high expression of resistin in adipose tissue, while others did not find any correlation between resistin level, body mass index (BMI), and insulin resistance (21, 80).

1.4 Genetic aspect of type 2 diabetes

As previously mentioned, T2D development could be influenced by lifestyle factors, however, the genetic component of T2D needs to be acknowledged (6). Obesity derived from inappropriate dietary patterns and sedentary lifestyle, small or large birth weight and stress, is a well-documented lifestyle risk proposed to impact the development of T2D (81). While environmental risk factors play a major role in T2D development, they do not impact all individuals in the same way (82). Even with similar lifestyle risk factors, some individuals are more susceptible to developing T2D than others, and this susceptibility appears to be inherited

(82). Several genes are suggested to be involved in T2D development, each providing a minor or major contribution to the occurrence and progression of the condition. A link has been demonstrated between insulin secretion, impaired ß-cell function, and genetic causes of T2D in both polygenic and monogenic T2D (6).

In addition to messenger-RNAs (mRNAs), short, single-stranded, non-protein-coding

RNAs (named micro-RNAs (miRNAs)) have also been shown to be widely involved in a post- transcriptional network of T2D gene expression, hence play a key role in the pathogenesis of this condition (83). Various experiments have demonstrated an altered profile of miRNA expression in insulin target tissues in diabetic models (84-86). This suggests an involvement of miRNAs in insulin production, secretion, action, and eventual T2D occurrence.

1.4.1 Role of micro-RNAs in type 2 diabetes pathophysiology

miRNAs play a key role in the regulation of protein-encoding diabetes-associated mRNAs (87). They are endogenous, short (approximately 18-23 nucleotides in length), non- coding RNA molecules, which function as regulators of gene expression by binding to the 3’

UTR region of mRNAs and destabilising them or inhibiting their translation (88). Therefore, unlike mRNAs, miRNAs are not translated into protein, and they exert their biological effects through post-transcriptional control of protein-coding mRNAs (89). Since each miRNA has multiple target genes, miRNAs may regulate at least 50% of human protein-coding mRNAs

(89). miRNAs have important molecular roles in normal physiology as well as in disease processes such as T2D (90).

Pre-miRNAs or miRNA precursors are characterized by their hairpin structures.

However, a large amount of similar hairpins can be folded in many genomes (91). Therefore, miRNAs can be classified into real or pseudo miRNAs. There are some computational methods using machine learning approaches such as Ab initio method or support vector machine to distinguish between real vs. pseudo pre-miRNAs (91, 92). Almost all current methods use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins

(91). These approaches are able to distinguish pre-miRNAs from sequence segments with pre- miRNA-like hairpin structures (91, 92). Classifying real vs. pseudo pre-miRNAs is important in the understanding of the nature of miRNAs in addition to developing new prediction methods to discover new miRNAs.

Several studies have demonstrated remarkable roles of miRNAs in insulin production and secretion, pancreatic islet development, ß-cell differentiation, and insulin resistance (84-

86). Furthermore, they are found to be involved in diverse aspects of both glucose homeostasis and lipid metabolism implicated in T2D, in addition to many functional aspects of adipocyte differentiation, which potentially contribute to the pathogenesis of obesity and its related medical complications (93-95). Different mechanisms have been proposed for the post- transcriptional regulatory role of miRNAs in relation to T2D. One of the widely accepted mechanisms is through the involvement of miRNAs in ß-cell membrane electrical excitation, initiated by an increase in the ATP/ADP ratio, as well as insulin synthesis, exocytosis processes, and ß-cell mass formation (96, 97).

Uncoupling protein 2 (UCP2) in pancreatic ß-cells decreases the level of ATP, resulting in a reduction of the ATP/ADP ratio, which subsequently diminishes glucose-stimulated insulin secretion (96). UCP2 is a direct target of miR-15a in pancreatic ß-cells. Prolonged stimulation of MIN6 cells, a mouse pancreatic beta cell line, with glucose downregulates miR-

15a, causing an increase in UCP2 and a reduction of insulin secretion (97). miR-9 reduces sirtuin 1 (silent mating type information regulation 2 homologs), known as SIRT1, in ß-cells and decreases glucose-stimulated insulin secretion (98), possibly through enhanced expression of UCP2 (96-98). miR-29a and miR-29b also negatively control insulin release by reducing monocarboxylate transporter 1 (MCT1 (SLC16A1)), which acts as a substrate for

15 mitochondrial oxidation to increase the cytosolic ATP/ADP ratio and trigger insulin release in

ß-cells (99). miR-124a targets forkhead box A2 (FOXA2), regulating the KATP channel subunits, Kir6.2 and Sur-1, and pancreatic development (Figure 1.3) (100).

Figure 1.3. The involvement of miRNAs in insulin release in pancreatic ß-cells and ß-cells fate. KATP channel: ATP-sensitive potassium channel; MAP4K4: MAPKKKK4; MCT1: monocarboxylate transporter 1; Mtpn: myotrophin; Onecut2: one cut homeobox 2; Pdcd4: programmed cell death 4; PDK1: phosphoinositide-dependent protein kinase 1; Rab27a: member RAS oncogene family; Vamp2: vesicle-associated membrane protein 2 (87).

In addition to the miRNAs described in Figure 1.3, there are several other miRNAs known to play significant roles in insulin granule exocytosis and insulin resistance, as well as pancreatic cell fate and pancreas formation, where their dysregulation may contribute to the onset and progression of T2D in human (87). Moreover, animal and human studies have identified a diverse set of miRNAs, which act as a signalling network to alter the regulation of immune and metabolic activity (94). Given their regulatory role in transcriptional networks, miRNAs may offer tangible targets for treating metabolic conditions and might be useful to predict disease and indicate progression.

1.4.2 Specific micro-RNAs in type 2 diabetes

Blood miRNA analysis of T2 diabetic patients shows that seven miRNAs including miR-9, miR-29a, miR-30d, miR-34a, miR-124a, miR-146a and miR-375 were significantly upregulated compared with individuals with normal glucose tolerance (NGT) (101). In addition, five of the above named miRNAs including miR-9, miR-29a, miR-34a, miR-146a, and miR-375 were significantly elevated compared with levels in individuals with pre-diabetes

(101). Zampetaki et al. (2010) reported lower levels of plasma miRNAs (miR-20b, miR-21, miR-24, miR-15a, miR-126, miR-191, miR-197, miR-223, miR-320, and miR-486) in T2 diabetic patients, but a modest increase in miR-28-3p (102). Importantly, a downregulation in miR-15a, miR-29b, miR-126, and miR-223 and an upregulation in miR-28-3p levels in plasma indicated the manifestation of the disease, indicating their potential value for identifying T2D.

Karolina et al. (2011) identified miR-144, miR-146a, miR-150 and miR-182 in the blood of diabetic patients as the signature miRNAs for identifying T2D (103). Furthermore, Pescador et al. (2013) indicated that three serum miRNAs including miR-138, miR-376a, and miR-15b are potential biomarkers for distinguishing obese patients from obese-T2 diabetic and T2 diabetic patients; meanwhile, the combination of miR-503 and miR-138 can distinguish diabetic from obese-diabetic patients (104).

1.4.3. Involvement of genetic signals in type 2 diabetes occurrence

As previously mentioned, gene-environment interactions and epigenetic mechanisms have a significant role in the occurrence of T2D (82). The exact mechanism by which diabetes- associated genes interact with each other and environmental risk factors is not fully understood, however, it has been indicated that several genes are involved in the development of T2D, each

17 being a small contributor (82). To date, more than 36 genes, have been found to contribute to the T2D occurrence, which together accounts for 10% of the total heritable component of T2D

(105). As an example, certain alleles of TCF7L2, involved in insulin secretion, increase the risk of T2D occurrence by 1.5 times (106).

It has been indicated that most of the T2D-associated genes are involved in pancreatic

ß-cell function (106). Genes that are linked to insulin resistance, obesity, glucose sensing, or other aspects of T2D appear to play a smaller role in T2D development (106). One potential reason for the dominance of ß-cell genes in the preliminary list of diabetes-associated loci is the existence of greater numbers of genes crucial for physiological ß-cell function than those involved with insulin sensitivity (107). Furthermore, several genes discovered by genome-wide association studies (GWAS) are implicated in cell-cycle regulation, thus proposed to act by influencing pancreatic ß-cell mass during development (107). Given the role of genetic signals in T2D development, therapeutic approaches targeting mRNAs represent a novel molecular therapy in controlling T2D.

1.5 Polyphenolic antioxidant

Polyphenols encompass a wide variety of phytochemicals, found abundantly in plant- based foods including fruits and vegetables and their by-products, such as chocolate, tea, wine, olive oil, and other extracts (108). The absorption of polyphenolic compounds, demonstrated in Figure 1.4, could occur directly through either the stomach or the small intestine, or, upon reaching the large intestine, undergo intensive gut microbiota metabolism, and subsequent absorption to the bloodstream (109). Polyphenols have been shown to stimulate the growth and activity of probiotics in the large intestine, and act as prebiotics, hence contributing to

18 probiotics’ beneficial health effects (109). Digested polyphenols coming from the stomach, small intestine, or after microbial transformation in the large intestine, reach the liver through the enterohepatic circulation and undergo biotransformation in the liver (109). Most of the food including polyphenols and other antioxidants are often metabolised by the liver (109). The resulting metabolites are circulated by blood to various peripheral tissues and depending on the half-life of metabolites they exert different beneficial effects at different times (Figure 1.4)

(109).

Figure 1.4. Polyphenol-rich foods absorption. Polyphenols may absorb directly through the stomach or small intestine or may reach the large intestine, undergo intensive gut microbiota metabolization, and get absorbed into the bloodstream afterwards (109).

A diet rich in polyphenol antioxidants exerts various beneficial effects on human health.

Antioxidants have been demonstrated by various researchers to provide a preventive and therapeutic effect in cancers, inflammation, diabetes, obesity and age-related disorders (109-

112). Beneficial health effects of polyphenolic compounds result from non-specific mechanisms, due to the broad antioxidant activity; or specific mechanisms, in which they interact with key signalling proteins (113). Moreover, evidence from in vitro experiments suggests the regulatory role of polyphenols on metabolic enzymes, nuclear receptors, gene expression, and multiple signalling pathways (114).

Polyphenolic compounds can exist as non-flavonoid polyphenols or simple phenolic acids, including hydroxybenzoic or hydroxycinnamic acids, or as flavonoid polyphenols (108).

Flavonoid polyphenols are C6-C3-C6 molecules with two aromatic rings of A and B, connected to an oxygenated heterocycle (ring C) (Figure 1.5) (109).

Figure 1.5. The structure of the flavonoid backbone consists of two aromatic carbon rings of A and B connected with a three-carbon chain that forms the C ring which is an oxygenated heterocyclic ring (115).

Classifying flavonoids based on the oxidation degree of their C ring divides them into the six subclasses of anthocyanins, flavonols (flavan-3-ols), flavones, flavanols (catechins), flavanones, and isoflavones (109). The structure of the flavonoid subclasses is shown in Figure

1.6.

Figure 1.6. The chemical structure of the flavonoid subgroups. Subgroups of flavonoids are classified based on the functional groups on each ring, generic structure of the C ring, and the position at which the B ring is attached to the C ring (116).

1.5.1 Anthocyanin

Anthocyanin (ACN), a subgroup of flavonoids, is the most abundant water-soluble phytochemical, responsible for the red to blue-purple colour present in fruit, vegetable and plant-based food products (117). The ability to form flavium cations makes ACNs a distinctive group among flavonoids (117, 118). ACNs are glycosylated polyhydroxy or polymetoxy derivatives of 2-phenyl benzo pyrylium or flavylium salt, which can be found in different berry types, such as blueberries, raspberries, and strawberries, in addition to cherries, blackcurrants, purple grapes, and red wine (119). Roughly 600 naturally occurring ACNs have been identified, with various molecular structures (118). They have different chemical structural moieties and bonding, even though the main chemical composition is the same in all ACN.

Their difference in the activity comes from slightly variable chemical reactions (118). Despite

21 the minor structural differences, ACN compounds share a similar and wide range of biological features, including antioxidant (120), anti-microbial (121), anti-convulsant (122), anti-cancer

(123), anti-obesity (122, 124), and anti-inflammatory effects (125, 126). Regular consumption of ACN is reported to have an inverse correlation with the incident of numerous chronic diseases such as coronary heart disease and T2D (127, 128).

1.6 Type 2 diabetes amelioration by dietary polyphenol consumption

The anti-diabetic effects of polyphenolic phytochemicals have been demonstrated in several in vitro, in vivo, and human interventional trials, and observational studies (109, 129-

132). Polyphenols are found to attenuate important hallmarks of T2D such as hyperglycaemia and hyperlipidaemia (133). Glucotoxicity and lipotoxicity caused by hyperglycaemia and hyperlipidaemia exert detrimental effects on a pancreatic β-cell function, which eventually contributes to impaired glucose homeostasis (133, 134). Polyphenols have been proposed to protect cells from glucotoxicity and lipotoxicity, which would be instrumental in improving insulin sensitivity and alleviation of T2D (135).

The liver is an important organ highly involved in T2D onset by playing a crucial role in hepatic insulin resistance development, associated with hyperglycaemia and dyslipidaemia

(135). Plant polyphenols have been found to enhance hepatic glucokinase activity, increasing glucose utilisation, promoting energy storage in glycogen form, and leading to the suppression of hepatic glucose output, hence attenuating T2D (135).

Another suggested mechanism in which polyphenols lower the blood glucose level is by inhibiting disaccharidases such as α-amylase and α-glucosidase in the intestinal lumen (135).

Disaccharidase inhibition may limit the digestion of polysaccharides in the diet, which reduces the absorption of simple sugars. Furthermore, as skeletal muscle represents a significant mass of tissue, an increase in the uptake of glucose in this type of tissue leads to a decreased amount of glucose in the blood. In this regard, polyphenolic antioxidants might exert glucose-lowering effects by improving glucose uptake in the muscle mass as well as adipose tissues (135).

Moreover, polyphenolic antioxidants are known to have a defensive role in protecting cells from potential biological threats, by disrupting energy transduction pathways, such as mitochondrial oxidative phosphorylation (136). The defensive effect of polyphenols results from mitochondrial proton gradient dissipation and/or inhibition of the electron transport chain or ATP synthase (136-138). The mitochondrial effect reduces ATP/AMP ratio which activates

AMP-activated kinase (AMPK) (139, 140). As in vitro and in vivo studies demonstrate, several polyphenols stimulate glucose uptake and oxidation, fatty acid catabolism, and inhibition of adipogenesis through this mechanism (141-143).

1.7 Polyphenols as an anti-inflammatory agent

Dietary polyphenols have been found to have the potential to act as anti-inflammatory agents, hence potentially controlling obesity-induced inflammation and associated metabolic disorders including T2D (144, 145). Polyphenolic compounds may exert anti-inflammatory effects through blocking mitogen-activated protein kinase (MAPK) pathways or inhibiting

NFκB activity, which regulates the expression of inflammatory cytokines and chemokines

(146-149).

In addition, polyphenolic compounds have been proposed to be involved in the direct activation of peroxisome proliferator-activated receptor gamma (PPARγ) (150-152). PPARγ is an important regulator of various aspects of lipid metabolism and adipocyte differentiation, which is also implicated in the inhibition of inflammatory genes, including inducible nitric oxide synthase (iNOS), NFκB and MAPK (153, 154). PPARγ is a target of Thiazolidinediones

(TZD), a widely used class of antidiabetic drugs (155).

Another important role of polyphenols in inhibiting obesity-induced inflammation and associated insulin resistance is via decreasing macrophage infiltration (156). Immune cells such as infiltrating macrophages are elevated as a result of metabolic inflammation, contributing to the release of pro-inflammatory biomarkers (157). Cyanidin-3-O-b-glucoside, found in ACNs, has been shown to reduce macrophage infiltration in mice by downregulation of CC chemokine receptor 2 (CCR2). Cyanidin-3-O-b-glucoside has also been shown to inhibit IκBα (nuclear factor kappa B-α) phosphorylation, suppressing NFκB activity and reducing LPS-stimulated

TNF-α and IL-6 expression in cell-culture experiments (156, 158).

Dietary ACNs have shown the ability to inhibit the secretion of pro-inflammatory cytokines after inducing inflammation in cellular and animal models (159-161). They have been demonstrated to suppress the induced secretion of inflammatory-related molecules, including vascular endothelial growth factor and intracellular adhesion molecule-1 (162, 163).

The inhibition or suppression of pro-inflammatory molecules by ACNs were associated with an inhibition of NF-κB activation (159, 163). It has been suggested that breakdown products of ACNs may be responsible for this effect. In fact, metabolites of ACNs that serve as redox buffer are shown to suppress oxidative stress, thus dampening the inflammatory response by

24 direct ROS scavenging. As a result, the secretion of pro-inflammatory signalling molecules and mediators will be decreased, which eventually contributes to the prevention or partial reversal of inflammation and associated conditions (164).

1.8 Application of polyphenols in the regulation of diabetes associated micro-RNAs

A regulatory role of various subclasses of polyphenols, including ACN, on miRNAs has been previously confirmed in in vitro (165-167), and in vivo animal studies (168, 169). Several miRNAs have been found to be affected by polyphenolic antioxidants, regulating different biological functions, such as apoptosis, inflammation, cancer, and lipid metabolism (165, 170,

171). Milenkovic and colleagues (2013) demonstrated modulation of the expression of more than 100 miRNAs by different polyphenols in a cell culture model (172). Administration of polyphenol antioxidants (green tea extracts) to human breast cancer cells, has shown the down- regulation of miR-27 (167). In an in vivo animal study, chronic administration of proanthocyanidins or docosahexaenoic acid down-regulated the increased miR-33a and miR-

122 in dyslipidemic obese rats (173). Yavari et al. (2016) demonstrated up-regulation of miR-

146a expression after the administration of a high polyphenolic regime to diabetic rats (170).

They indicated that the expression of miR-146a was negatively correlated with the NF-κB inflammatory pathway, as up-regulation of miR-146a inhibited NF-κB signals (170).

Extending the findings to human clinical studies, long-term intervention with grape extract containing polyphenols resulted in modulation of inflammatory-related miRNAs (miR-

21, miR-181b, miR-663, miR-30c2, miR-155, and miR-34a), and associated cytokines in T2D participants (171). It has been proposed that all polyphenols, despite their structural differences,

25 exert a similar effect on miRNA profiles, however, exposure time and concentration of the compounds seem to affect the expression profile differently (172).

Although the mechanisms by which polyphenols regulate the expression of miRNAs are not fully understood, it has been suggested that polyphenols might modulate miRNA transcription by affecting cell-signalling pathways such as NF-κB (168). Furthermore, it must be acknowledged that most of the available data are findings from in vitro experiments, and further animal studies and human clinical trials are required to confirm the reported miRNA regulatory effect of polyphenols.

1.9 Effect of polyphenolic compounds on diabetes-related genes

Polyphenolic compounds may influence the expression of certain genes associated with

T2D development. These genes include glucose transport regulatory genes, insulin action and secretion genes, and genes responsible for lipid metabolism, inflammation, and vascular function (174-176). Gene expression modulatory effects of polyphenolic antioxidants have been supported by several in vitro, in vivo, and human studies, as well as mRNA analysis of human peripheral blood mononuclear cell (PBMC) (174, 176, 177). In a cell culture model using rat adipocytes, administration of ACN up-regulated adipocyte-specific genes (LPL, aP2,

UCP2). In addition, Leptin, adiponectin and their associated mRNAs increased with ACN administration, resulting from increased phosphorylated MAPK (178). In a rodent study, intake of ACN-rich extracts by animals fed with a high-fat diet, led to the alteration of substrate metabolism in the liver, by modulation of specific nuclear hormone receptors and transcription factors such as liver X receptor and sterol regulatory element binding protein (SREBP)-1c

(179). In the same study, ACN intervention resulted in down-regulation of genes involved in

26 oxidative stress and endoplasmic reticulum (ER) stress in the liver, and down-regulation of pathways implicated in inflammatory responses in the liver and muscles (179).

The mechanisms of ACN attenuating obesity, obesity-induced inflammation and associated diseases including T2D could be through up-regulation of the thermogenic mitochondrial uncoupling protein 2 (UCP-2) and the lipolytic enzyme hormone-sensitive lipase

(HSL), in addition to down-regulation of the nuclear factor plasminogen activator inhibitor-1

(PAI-1) (178).

1.10 Conclusion and directions for future research

According to global estimations, diabetes prevalence is expected to rise in the adult population, affecting approximately 600 million by 2035 (180). The AusDiab Follow-up Study

(Australian Diabetes, Obesity and Lifestyle Study) in 2005 indicated that 1.7 million

Australians are suffering from T2D (181). This number is projected to increase to 3.3 million by 2031 (182), representing a doubling of the prevalence since 2005. More than half of the adult population in Australia are categorised as overweight or obese, which puts them at higher risk of developing T2D (183). Among Aboriginal and Torres Strait Islander Australians, the rate of death from diabetes is 17 times greater than that of the general population (180).

Furthermore, T2D ranked second in the Australian Guidelines for the assessment of cardiovascular disease risk factors, (184).

T2D individuals are a substantial cost to the healthcare system. Diabetes was associated with 245 billion US dollars in healthcare expenditure in the United States in the year 2012 and

14.6 billion Australian dollars in Australia in 2010 (185, 186). With substantial treatment costs, the increasing prevalence of diabetes represents a significant social and economic issue. Thus, prevention of diabetes and its complications is an essential component of future public health strategies for all nations including Australia (187).

ACNs are widely distributed in the human diet and consumed in significant amounts form plant-based food. They have a great potency of health-promoting effects and can be consumed as a functional food. ACNs have a unique therapeutic advantage, being responsible for regulation of pro-inflammatory markers as well as genetic signals associated with T2D.

Therefore, the therapeutic use of ACNs in amelioration of T2D, by targeting pro-inflammatory biomarkers, miRNA profile and associated genes, potentially offers an effective therapeutic strategy against T2D.

Chapter 2

Hypothesis & Aims

2.1 General hypothesis

The anti-diabetic effects of polyphenolic antioxidants have been demonstrated in several in vitro, in vivo, as well as human randomised controlled trials and observational studies.

Polyphenols are found to attenuate important hallmarks of T2D such as hyperglycaemia and hyperlipidaemia in many small independent studies with multiple variables. In addition, dietary polyphenols are shown to have the potential to act as anti-inflammatory agents, control obesity- induced inflammation and associated metabolic disorders in various experimental models including in vitro, in vivo and human studies. The metabolites of polyphenols, serve as redox buffers and are shown to suppress oxidative stress, thus dampening the inflammatory response by direct reactive oxygen species (ROS) scavenging. As a result, the secretion of pro- inflammatory signalling molecules and mediators is decreased, which eventually contributes to the prevention or partial reversal of inflammation and associated conditions. Furthermore, a regulatory role of various subclasses of polyphenols on micro-RNAs (miRNAs) has been previously confirmed in in vitro, in vivo animal studies and human. Several miRNAs have been found to be affected by polyphenolic antioxidants, regulating different biological functions, such as lipid metabolism and inflammation. Polyphenolic compounds may influence the expression of certain genes associated with T2D development. Gene expression modulatory effects of polyphenolic antioxidants have been supported by several in vitro, in vivo, and human studies.

All the evidence from the literature cited above, are from various research groups on small scale studies at different times, not providing enough cumulative data to draw a conclusion whether polyphenols hold the potential to reduce the risk of diabetes and its associated complications. The studies conducted as a part of this thesis were based on the hypothesised effects of polyphenolic antioxidants, including anthocyanin (ACN), on diabetic risk factors; biochemistry; pro-inflammatory biomarkers; and genetic signals including miRNAs and associated messenger-RNAs (mRNAs), in type 2 diabetic (T2D) and pre-diabetic adults.

Plant polyphenols’ ability to control diabetes onset in at-risk population or alleviate its progression in T2D adults, has been well documented by several observational studies and clinical trials (188-190). Polyphenolic compounds including flavonoids and ACN have shown the ability to reduce diabetes risk factors by affecting hyperglycaemia and hyperlipidaemia, thus improving insulin sensitivity and beta cell (ß-cell) function (14). Furthermore, it has been proposed that new approaches which inhibit metabolic inflammation at the cellular level, may be instrumental in controlling T2D onset and progression (18). The anti-inflammatory properties of polyphenols have been established in several in vitro and in vivo studies (162,

163, 191). It has been demonstrated that polyphenolic antioxidants could target diabetes associated pro-inflammatory biomarkers, hence avoiding the onset and/or progression of an inflammation-mediated condition such as T2D. Other evidence has suggested the regulatory role of polyphenols on miRNAs and miRNAs-associated genes in diabetic and pre-diabetic conditions by affecting various metabolic pathways. miRNAs exert their effect through a post- transcriptional regulatory role on target mRNAs. Moreover, it has been suggested that miRNAs and mRNAs play a significant role in inflammation initiation and pathogenesis of metabolic

30 disorders such as T2D, therefore, present an ideal therapeutic target at the molecular level in

T2D and pre-diabetic adults.

2.2 Research questions, aims and hypothesis

The overall aim of this thesis is to investigate the efficiency of dietary ACN on diabetes- related clinical parameters, pro-inflammatory biomarkers, miRNA profile, and miRNA target genes in healthy, pre-diabetic, and T2D adults. This thesis incorporates a systematic review and meta-analysis followed by an interventional trial with specific aims, research questions and hypothesis that aim to meet the overall objective of the thesis. Research questions, specific aims and hypothesises are as follows:

RQ1: What is the current evidence for a relationship between flavonoid intervention and

metabolic markers of diabetes in type 2 diabetes adults?

RQ2: What is the effect of anthocyanin supplementation on physical measurements,

biochemical parameters, and biomarkers of inflammation in type 2 diabetic, diabetic-at-

risk, and healthy participants?

RQ3: What is the effect of anthocyanin intervention on the miRNA profile of individuals

with or without type 2 diabetes?

RQ4: What is the relationship between dietary anthocyanin supplementation and the

expression of miRNA target genes in diabetic, pre-diabetic, and healthy individuals?

Aim 1: Study 1 was a systematic review and meta-analysis of randomised controlled trials, with the aim of investigating the effectiveness of flavonoids and flavonoid subclasses on diabetic-associated biochemistry parameters, including fasting blood glucose (FBG), haemoglobin A1c (HbA1c), total cholesterol (TC), low-density lipoprotein (LDL), high- density lipoprotein (HDL), and triglycerides (TG). Six studies met the inclusion criteria which were appraised in depth and reported in our paper. This study was prepared in the format of a manuscript (Manuscript 1) and is presented in chapter 3.

Hypothesis 1: The antioxidative effects of flavonoids have been well documented throughout the literature, based on which it can be hypothesised that flavonoid intervention may have positive effects on several metabolic markers associated with type 2 diabetes.

Aim 2: Study 2 was a human clinical trial and applied an intervention approach to examine the efficacy of dietary ACN on biomarkers of inflammation, in addition to biochemical and physical parameters, within three groups of healthy, diabetic-at-risk, and T2D participants.

Forty-seven participants were recruited for the trial of whom forty met the inclusion criteria.

Targeting biomarkers of inflammation suggests a potential therapeutic approach to ameliorate

T2D and associated complications. Considering the major role of diet in the incidence of inflammation, the inflammatory potential of the participants’ diet was calculated through the diet inflammatory index (DII) to investigate the association between diet and inflammatory status of healthy, T2D, and pre-diabetic participants. This study was prepared in the format of a manuscript (Manuscript 2) and is described in Chapter 5.

Hypothesis 2: Based on the hypothesised antioxidant effects of ACN, a subgroup of flavonoids, it is expected that ACN supplementation may improve physical characteristics, biochemical parameters and regulate circulating blood levels of inflammatory markers in type 2 diabetes individuals, diabetic-at-risk and healthy participants.

Aim 3: Study 3 aimed to explore participants’ miRNA profile and if miRNAs could be influenced by ACN supplementation in relation to T2D. In addition to the miRNA profile, pre- and post-supplementation physical and biochemical characteristics of participants were assessed and compared at the end of the study. Eighteen participants (nine having T2D and nine non-diabetic participants) were included in this study. This study was prepared in the format of a manuscript (Manuscript 3) and is described in Chapter 6.

Hypothesis 3: Antioxidants including polyphenols have previously shown to positively influence gene expression in healthy, as well as diabetic individuals. Based on these previous studies it can be hypothesised that four weeks ACN supplementation may positively alter the miRNA profile of diabetic and non-diabetic participants.

Aim 4: Study 4 examined nutritional genomic effects of dietary ACN on the relative expressions of miRNA target genes in T2D, pre-diabetic, and healthy individuals. The expression of five genes was investigated in 38 participants. These genes were predicted to be regulated by miRNAs that were significantly expressed in the T2D group in the previous study.

Three online miRNA: target prediction tools were used to identify potential miRNA: mRNA interactions. The chosen mRNAs were established as diabetic-associated genes. This study was prepared in the format of a manuscript (Manuscript 4) and is described in Chapter 7.

Hypothesis 4: Based on the findings from the literature and the results from the previous studies included this thesis, it can be hypothesised that four weeks of ACN supplementation may exert anti-diabetic effect by regulating the expression of genes that are associated with the development of T2D.

2.3 Significance of the research

Clinical methods for managing T2D and its associated complications are well documented and recognised. Nevertheless, due to the adverse effects of pharmacotherapies, non-pharmacological treatment approaches using natural supplements, including phenolic antioxidants, have gained the attention of scientists in recent years (192, 193). Furthermore, to develop advanced therapeutic strategies which are more efficient than the previous ones, there is a considerable need to choose ideal prognostic, diagnostic and therapeutic target biomarkers, which are significantly associated with pathophysiological stages of diabetes development at the cellular and molecular level.

In this research, the therapeutic use of dietary polyphenols, such as flavonoids and their subclasses, on diabetic clinical markers, including glycaemic control and lipid profile, of T2D adults was investigated in a systematic review and meta-analysis fashion. The findings of this study can be used to confirm the reported desirable effects of flavonoid consumption on T2D risk factors and biochemistry markers in other randomised controlled trials. Moreover, given the involvement of inflammation in T2D development, diabetes-associated pro-inflammatory biomarkers were measured pre- and post- ACN intervention in healthy, T2D, and pre-diabetic individuals to investigate if the current treatment protocol was successful in inhibiting pro-

34 inflammatory biomarkers, hence controlling T2D. Further, this intervention trial extended its approach to nutritional genomic and molecular therapies, analysing the expression of miRNAs and targeted mRNAs to investigate the potential of polyphenolic antioxidants such as ACN on regulating genetic factors post-transcriptionally. The outcome of this research will provide a unique contribution to the body of knowledge involving novel therapeutic markers, pro- inflammatory biomarkers, miRNAs and mRNAs, and presents ACN as a dietary constituent to potentially control T2D development and progression.

2.4 Thesis orientation and outline of chapters

The thesis structure is outlined in Figure 2.1. Following the literature review, hypothesis and aims, this thesis is structured as a series of manuscripts for publication.

Chapter 1 presents a synthesis of the current literature on relevant topics, including the prevalence and characteristics of T2D, T2D association with metabolic inflammation, a genetic aspect of T2D, polyphenolic antioxidants including ACN, and the application of ACN in the regulation of T2D-associated pro-inflammatory biomarkers, and miRNA profile and their target genes. Chapter 2 introduces the research hypothesis, research aims and questions, significance of the research, and finally the outline of the thesis. Chapter 3 presents findings of

Study 1, a systematic review, and meta-analysis. Chapter 4 outlines the laboratory methods and materials used to conduct the current intervention trial. Chapter 5 presents findings of study 2, the effects of ACN consumption on pro-inflammatory biomarkers associated with T2D, in diabetic, pre-diabetic, and healthy adults. Chapter 6 presents findings of study 3, the effect of dietary ACN supplementation on the miRNA profile of diabetic and non-diabetic individuals.

Chapter 7 presents findings of study 4, the effect of ACN consumption on miRNA associated

35 mRNAs in T2D, pre-diabetic and healthy adults. Chapter 8 discusses the findings as a body of work and explores the research implications and recommendations for future research.

Chapter 1 Review of the Literature

Background, Type 2 diabetes, characteristics and prevalence, Type 2 diabetes, an

inflammatory condition, Genetic aspect of type 2 diabetes, Polyphenolic antioxidants, Type 2 diabetes amelioration by dietary polyphenols consumption, Polyphenols as an anti-inflammatory agent, Application of polyphenols in regulation of diabetic

associated micro-RNA, Effect of polyphenolic compounds on diabetic genes, Conclusion and future directions

Chapter 2 Hypothesis & Aims Research hypothesis, study aims and questions, Significance of the research, Thesis orientation and outline of chapters

Chapter 3 Effect of flavonoids on the metabolic profile of adults with type 2

diabetes: A systematic review and meta-analysis of randomized controlled trials

Chapter 4 Materials and Methods Research approach, Laboratory methods and materials

Chapter 5 Potential of anthocyanin as an anti-inflammatory agent: A human clinical trial on type 2 diabetic, diabetic at-risk and healthy adults

Chapter 6 Effect of anthocyanin supplementation on the microRNA profile of diabetic and non-diabetic adults

Chapter 7 Nutritional genomic effects of dietary anthocyanin on micro-RNA target genes’ expression in type 2 diabetes, pre-diabetic and healthy individuals

Chapter 8 General Discussion and Conclusion Integration of findings to address research questions, contributions of the research, future research directions, recommendations

Figure 2.1. Overview of the thesis.

Chapter 3

Effects of Flavonoids on Metabolic Profile of Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis of Randomized Controlled Trials

This chapter includes a co-authored paper, which I am the primary author. It is prepared for submission to the Journal of Human Nutrition and Dietetics.

Authors: Elham Nikbakht, Lauren T. Williams, Indu Singh, Nicholas P. West, Natalie Colson.

My contribution to this paper involved:

 Study design.  A systematic literature search.  Initial and final screening of the manuscript.  Quality assessment of the included papers.  Data extraction and report.  Meta-analysis of the included studies.  Graph, table and figure preparation.  Manuscript writing, preparation and revision.

Elham Nikbakht

27th April 2018

Lauren T. Williams’s contribution to the paper involved:

 Manuscript writing and revision.

Nicholas P. West’s contribution to the paper involved:

 Manuscript writing and revision.

Indu Singh’s contribution to the paper involved:

 Manuscript writing and revision.

Natalie Colson’s contribution to the paper involved:

 Study design.  Initial and final screening of the manuscript.  Quality assessment of the included papers.  Meta-analysis.  Manuscript writing and revision.

Corresponding author of paper:

Dr Natalie Colson 27th April 2018

Supervisor:

Dr Natalie Colson 27th April 2018

3.1 Abstract

Observational studies confirm the beneficial effect of flavonoid intake on type 2 diabetes

(T2D) onset. However, the efficacy of flavonoid consumption on metabolic markers of T2D participants has not been systematically examined. Therefore, we performed a systematic review and meta-analysis of randomised controlled trials (RCTs) to investigate the effect of flavonoid intake on glycemic control and lipid profile of T2D participants. Online databases of

Cochrane Library, PubMed, Scopus, and Cumulative Index to Nursing and Allied Health

Literature were searched for relevant studies. RCTs investigating the efficacy of a dose of flavonoids or flavonoid substances on fasting blood glucose (FBG), haemoglobin A1c

(HbA1c), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein

(HDL), and triglycerides (TG) of adults with T2D were included in this review. No time limitation was imposed in the literature search. Quality checks of included studies were conducted using the Rosendal scale, and meta-analysis was undertaken using the random-effect model. From 924 articles in the original search, 6 studies were included in this systematic review and meta-analysis. Results indicated that dietary intervention with flavonoids exerts significant decreases in FBG (0.44 mmol/L), TC (0.12 mmol/L), and TG (0.19 mmol/L). No significant changes were observed in HbA1c, LDL, or HDL. Subgroup analysis of the intervention duration reveals that longer intervention duration of flavonoids (≥90 days) is associated with statistically significant changes in the desired direction for almost all biomarkers of interest (except LDL). This study suggests that dietary intake of flavonoids over a long duration and on a regular basis might be beneficial in improving T2D by ameliorating the level of some of its metabolic biomarkers.

Keywords: Flavonoids, T2D, Glycemic Control, Lipid Profile, Meta-analysis

3.2 Introduction

Diabetes mellitus is the world’s fastest-growing chronic disease, affecting 422 million adults worldwide in 2014 (194). This represents a doubling in the prevalence since 1980 (194).

Type 2 diabetes (T2D) accounts for 90 to 95% of all diabetic cases (195) and is characterised by prolonged unsettled hyperglycemia, mainly as a result of progressive insulin resistance and pancreatic β-cell dysfunction (6). It is one of the major causes of mortality and morbidity either directly through clinical sequelae or indirectly through cardiovascular and kidney diseases

(196). Treating T2D poses a substantial cost to the healthcare system (197), therefore the increasing prevalence of diabetes represents a significant social and economic issue, and the management of diabetes and its complications is an important public health priority (187).

It has been medically established that improving glycemic control, primarily, and cardiovascular disease (CVD) risk factors, secondarily, are beneficial approaches in T2D improvement (198, 199). Pharmacotherapy and lifestyle modifications, including appropriate dietary habits and regular physical activity, are the primary therapeutic approaches for management of T2D (195, 200-202). Due to the side effects accompanying the use of medications (203), as well as the difficulty in maintaining health-related behaviour changes

(201, 204), non-pharmacological interventions are attracting scientific attention, with certain natural supplements and dietary components such as flavonoids showing promise as a replacement for, or complement to, pharmacotherapy for T2D (205, 206).

Flavonoids are bioactive polyphenols that constitute a diverse group of phytonutrients naturally present in plant foods and beverages including vegetables, fruits, nuts, grains, soy, tea, cocoa, chocolate, red wine, and herbs (207). Flavonoid polyphenols are C6-C3-C6

41 molecules with two aromatic rings of A and B, connected to a three-carbon chain that forms the C ring (an oxygenated heterocyclic ring) (208). Subgroups of flavonoids are classified based on the functional groups on each ring, generic structure of the C ring, and the position at which the B ring is attached to the C ring. Within each subgroup, individual compounds are characterised by specific hydroxylation and conjugation patterns (208). The function of flavonoids is structure-dependent. Some of the health functions are common across all flavonoids and some are exclusive to a specific subgroup (209). The abundant range and structural complexity of flavonoids have necessitated their classification into six subclasses; flavonols, flavones, flavanones, flavan-3-ols (and their oligomers, proanthocyanidins), isoflavones, and anthocyanins (210). Dietary flavonoids are secondary metabolites shown to exert health benefits, including antioxidant, cardioprotective, and anti-inflammatory effects, through cell signalling pathways (211, 212). Evidence from in vitro and in vivo animal studies indicate that a high concentration or intake of flavonoids or flavonoid substances derived from plants provides a useful strategy to improve hyperglycemia and regulate blood lipid profile

(213-219), and therefore may be of use in the management of diabetes.

The pooling of observational data from cohort studies in a systematic manner confirms that flavonoid consumption is associated with a reduced risk of developing T2D (188). To date, several clinical trials have assessed the efficacy of flavonoid intervention on diabetic individuals (189, 220, 221); however, clinical outcomes of these trials have not been subject to systematic review or meta-analysis. Thus, a systematic review and meta-analysis of published randomised controlled trials (RCTs) was undertaken to investigate the effectiveness of flavonoids and flavonoid subclasses on diabetic biomarkers, including fasting blood glucose

(FBG), haemoglobin A1c (HbA1c), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG). Subgroup analyses were conducted to

42 explore the possible effect of different factors such as dose, duration, and type of supplement on the same outcomes.

3.3 Materials and Methods

3.3.1 Search strategy

A systematic literature search was carried out using online databases including

Cochrane Library, PubMed (MEDLINE), Scopus, and Cumulative Index to Nursing and Allied

Health Literature (CINAHL). In order to find potentially relevant studies, the following terms and Standard Medical Subject Headings (MeSH) terms were used in all possible combinations, based on all available databases up until May 2017: Diabetes Mellitus, Diabetes Insipidus,

Type 2 Diabetes Mellitus, Noninsulin-dependent diabetes mellitus, Insulin-Resistant, Blood

Glucose, Blood Sugar, Glycated Haemoglobin, Hemoglobin A Glycosylated, Hemoglobin A1c

Protein, Lipid Profile, Total Cholesterol, Triglycerides, Low-density Lipoprotein, High-density

Lipoprotein, Flavonoids, Flavonols, Flavones, Flavanones, Flavan-3-ols Vaccinium myrtillus extract, Anthocyanins, Isoflavones, Phytoestrogens. Reference lists from relevant articles were scanned for potentially related publications.

This review was conducted in accordance with the Cochrane methodology (222). The

Preferred Reporting Items for Systematic Reviews and Meta-Analysis: The PRISMA

Statement was followed as a guideline for presenting the results of this systematic review and meta-analysis (223). The protocol for this systematic review was registered with the

International Prospective Register for Systematic Reviews (PROSPERO)

(CRD42017056072).

3.3.2 Study eligibility and selection

Randomised crossover and parallel controlled trials investigating the efficacy of a dose of flavonoids or flavonoid substances on the FBG, HbA1c, and lipid profile including TC,

LDL, HDL, and TG of adults (age ≥ 18 years) with T2D were included in this systematic review and meta-analysis. Manuscripts investigating the efficacy of flavonoid mixed in with other supplements, in which the effect of flavonoids could not be separated, were excluded. Trials were excluded if they recruited children, pregnant or lactating women, or critically ill participants. Review articles and study protocols were not included. Studies where the dose of supplemented flavonoids was not clearly described, were excluded. Only studies published in

English were considered.

Two researchers independently conducted the initial screening of titles and abstracts of the 849 articles from the search after duplicates were removed. Seventy-three full texts were reviewed and examined. The decision regarding the inclusion or exclusion of studies was made through an agreement between the two researchers. A third researcher was involved in decision-making in cases of disagreement between the first two researchers. The study flow diagram is illustrated in Figure 3.1.

# of records # of records identified through identified through

database searching other sources n=920 n=4

Identification # of records in total Records excluded (n=776): Type 1

n=924 diabetes (7); Review articles (46); In

Vitro cell studies (79); Animal Studies (63); Studies on Flavonoids’

mechanisms of actions (153); Not ## ofof recordsrecords afterafter adults (8); Pregnant women (5); Not duplicatesduplicates removedremoved n=502n=849 screenedscreened forfor randomized controlled trial (34);

Screening titletitle andand abstractabstract Healthy participants (90); Participants with CVD (209); Participants with other health conditions (82)

# of records full-text Records excluded (n=67): Pre-diabetic articles assessed for participants (11); Participants with eligibility n=73 diabetes and other conditions (3); No Eligibility clear data on at least one of the primary objectives (45); Mixed intervention (5); Study protocol (1); No control arm for T2D participants (2)

# of full-text included in qualitative synthesis n=6

Included # of full-text included in quantitative synthesis and meta-analysis n=6

Figure 3.1. PRISMA flowchart and study flow diagram of a systematic literature search of flavonoids and type 2 diabetes. An initial literature search was conducted to identify potential articles. Studies were screened based on the inclusion and exclusion criteria through the title, abstract and finally methodology. After filtering non-eligible studies, the quality of the

45 included articles was assessed according to the Rosendal scale. Studies with a Rosendal score of 50% or higher were included in this systematic review and meta-analysis.

3.3.3 Quality assessment and Data extraction

The Rosendal scale (224) was used by two researchers to assess the methodological quality of the included articles. This scale is to compare and contrast the included randomised controlled trials, using a scale that assessed a number of factors associated with the minimization of bias in areas such as subject selection, performance and data analysis (224).

Rosendal scale is developed by combining the items used in various other scaling including

Jadad scoring system (225), the PEDro scale (226) and the Delphi List (227), in addition to recommendations contained in the CONSORT statement (228) to assess the quality of randomized controlled clinical trials. An overall Rosendal score of 60% was regarded as being of excellent methodological quality (225). Studies with a Rosendal score of 50% or higher were included in this systematic review and meta-analysis. The ‘checklist of items to consider in data collection’ from the Cochrane Handbook for Systematic Review of Interventions (229) was followed to extract relevant data. Data from included studies were extracted by two reviewers. Information on study design and setting, type, and source of supplement/placebo, intensity and duration of the trial, as well as participants’ age and gender were all retrieved, matched, and discussed between the two reviewers. Disagreements were resolved by consensus and re-checking the original article. The measurements reported for FBG, HbA1c, TC, LDL,

HDL, and TG were extracted as the main outcomes. The preferred unit for reporting FBG, TC,

LDL, HDL, and TG in this study was mmol/L, and all measurements reported in mg/dl were converted to this preferred unit of measurement by dividing FBG in mg/dl by 18, TC, LDL, and HDL by 38.67, and TG by 88.57. HbA1c was reported by percentage.

3.3.4 Data Synthesis and meta-analysis

RevMan software (Cochrane Review Manager, version 5.3) was utilised to perform a meta-analysis of data, and the Cochrane Handbook for Systematic Review of Interventions

(222) was used to guide the review. The effect of flavonoids or flavonoid subgroups on FBG,

HbA1c, TC, TG, LDL, and HDL was reported as a comparison of final measurements between the intervention and control arm. In randomised trials, comparison of final measurements estimates the same quantity as the comparison of changes from baseline (222). Standard deviation (SD) for the changes from baseline was not reported in any of the included studies, making it impossible to impute correlation coefficient (r), missing SD for the changes, and finally using the mean differences between intervention and control for analysis. A

DerSimonian and Laird random-effects model was chosen for the meta-analysis as included studies were heterogeneous in terms of their methodology and design (230). The effect of each trial is presented as weight (%), and mean difference and 95% confidence interval (CI). The I2 index was used to assess the heterogeneity of the pooled effect. Values of 25%, 50%, and 75%, were used for the I2 analysis and correspond to low, moderate, and high heterogeneity, respectively (229). A P-value of less than 0.05 was considered a statistically significant effect.

3.3.5 Sensitivity and subgroup analysis

A one-by-one sensitivity analysis was performed by excluding individual studies and examining the effect of those studies on the overall result of meta-analysis for each outcome.

Subgroup analysis was limited to the design of the study, source, and intervention duration.

Parallel trials were compared with cross-over trials to determine the effect of study design on

47 the meta-analysis result. Flavonoid delivered in capsule form was compared with flavonoid delivered in fortified food to investigate a possible influence of different interventions.

Subgroup analysis of duration of supplementation was performed to compare intervention duration of ≥90 days with duration of <90 days. Heterogeneity between subgroups was assessed, with P<0.05 representing a significant difference.

3.4 Results

3.4.1 Quality and characteristics of included studies

Six trials with a total of 316 participants were included in the systematic review and meta-analysis of the effect of flavonoid consumption on metabolic parameters associated with

T2D. Included trials had a Rosendal score of 71% and above, reflecting excellent methodological quality (225), with Studies by Balzer et al. (231), Curtis et al. (232), and

Dabaghian et al. (190) having the highest Rosendal score of 87% (Supplemental Table 3.1).

No trials were excluded due to low-quality assessment score.

The characteristics of the included studies are presented in Table 3.1. Included trials were all double-blinded, randomised controlled trials, with four studies using parallel design (190, 231-

233), and two cross-over design (234, 235). Studies by Gonzalez et al. (234), and Curtis et al.

(232) recruited menopausal women with T2D, but the rest of the trials recruited T2D participants of both genders. Baseline body mass index (BMI) of participants in studies by

Gonzalez et al. (234), Balzer et al. (231), Curtis et al. (232), and Dabaghian et al. (190) were

2 2 above 30 kg/m . Li et al. (233) recruited T2D participants with a BMI of more than 25 kg/m , and Mellor et al. (235) did not report BMI. A significant change in body weight after the

48 intervention was not reported in any of the studies. Three studies (232, 233, 235) reported a method for dietary intake measurement (dietary recall/record, or FFQ) at the baseline and end of the trial to note any dietary change. In the study by Curtis et al. (232), volunteers were instructed not to consume specified flavonoid-rich foods while participating in the study.

Mellor et al. (235) advised their participants not to have any chocolate except that provided as a flavonoid supplement for consumption during the trial. Participants of studies by Dabaghian et al. (190), and Li et al. (233) were instructed to maintain their habitual diet and lifestyle.

3.4.2 Information on supplement protocol

As shown in Table 3.1, flavonoid was delivered in the form of confectionery in two studies (232, 235), in food in one study (234), and as a beverage in another (231). Two studies used capsules as the vehicle for flavonoid delivery (190, 233). The daily dose and the period of the intervention varied greatly among studies. The daily dose ranged from 16.6 mg of high polyphenol chocolate (235) to 963 mg/day of flavanol-rich cocoa drink (231), and 1496 mg/day of the rose hip (190). The study duration ranged from a minimum of 30 days (231) to a maximum of 365 days (232). Studies reported a high level of compliance with no adverse effect of the supplements, except for the study by Curtis et al. which reported 21% withdrawal. Side effects were not described in this study (232). Five studies utilised placebo as a comparable arm for the intervention, while Balzer et al. (231), used a low dose of flavanols (25 mg flavanols vs. 321 mg flavanols) for the control arm. Placebos appeared identical to the active supplements, and Balzer et al. mentioned that the control supplement was similar to the flavanol-rich cocoa in taste and all supplements were labelled anonymously (231).

3.4.3 Results of Individual Studies

An examination of individual studies revealed that only two of the included trials reported a statistically significant reduction in FBG after flavonoid consumption. Dabaghian et al. (190) showed a 16% decrease in FBG (P=0.002) after supplementing with rose hip for 3 months, while Li et al. (233) showed an 8% reduction (P=0.042) after anthocyanin intervention for 6 months. HbA1c was significantly reduced in one trial by 1.43%, by Balzer et al. (231)

(P=0.048). However, Li et al. (233) reported marginally significant improvement (P=0.06). TC was lowered significantly in the study by Li et al. (233) only (3.75%, P=0.041). LDL cholesterol was significantly decreased in two studies, (232) (5%, P=0.04), and (233) (8%,

P=0.03). HDL cholesterol level increased in two studies, (235) by 8% (P=0.05), and (233) by

16% (P=0.012). The difference between TG before and after intervention with anthocyanin as the flavonoid was highly significant in the study by Li et al. (233) (23%, P<0.01), but not in any other studies. Together, only the study by Li et al. (233) reported a significant improvement in almost all target outcomes (HbA1c was an exception with a P value of 0.06). Participants in this study were supplemented with 320 mg anthocyanin per day for 168 days. In contrast,

Gonzalez et al. (234) failed to detect an improvement in any of these biomarkers after supplying

132 mg/day of soy isoflavones to T2D participants for 84 days. The other four trials showed an improvement in at least one of the outcomes of interest.

Table 3.1. Characteristics of the six included double-blind randomised placebo-controlled trials evaluating the effect of flavonoids and flavonoid substances on type 2 diabetes risk factors. Values are presented as the mean ± standard deviation (SD). Study/ Design/ Intervention/ Flavonoid type & Daily Dose Dietary intake Intervention Participant Year/ Duration (days) Control Source (mg) measurement n (F%); age Location Control n (F%) Intervention/ Control

Gonzalez et al./ Crossover/ Isoflavones/ Soy isoflavones 132 No 26 (100%), NS- (Post- 2007/ UK 84 Placebo 26(100%) menopause) (234) Balzer et al./ Parallel/ Flavanols/ Flavanol-rich cocoa 963 No 21(62%); 63.1± 8.3/ 2008/ USA 30 Control drink 20(80%) 64.4 ±8.6 (231) Mellor et al./ Crossover/ Epicatechins/ High-polyphenol 16.6 Yes 12(42%) 42-71* 2010/ UK 56 Placebo chocolate 12(42%) (235)

Curtis et al./ Parallel/ Epicatechins+ Flavonoid-enriched 190 Yes 47(100%); 51–74* 2012/ UK 365 Isoflavones/ chocolate 46(100%) (232) Placebo

Dabaghian et al./ Parallel/ Rose hip/ Rose hip 1496 No 25(64%); 53.7±9.1/ 2015/ Iran 90 Placebo capsule 23(48%) 58.7±8.5 (220) Li et al./ Parallel/ Medox/ Anthocyanin 320 Yes 29(41%); 58.1±2.3/ 2015/ China 168 Placebo capsule 29(41%) 57.6±3.4 (189)

F: Female. NS: Not Stated. *In the original article the age range was presented for these studies.

3.4.4 Meta-analysis results

Meta-analysis of the effect of flavonoids and flavonoid subclasses on each of the outcomes are presented in the figures below. The weight of each study is represented by the size of the box, and individual variance is represented by the length of the horizontal line.

The meta-analysis of the comparison between intervention and control group indicated that FBG was significantly lower in the intervention group by -0.44 mmol/L (95% CI: -0.73, -

0.15 mmol/L; P=0.003). Moderate heterogeneity was observed for FBG analysis (I2=47%;

P=0.09) (Figure 3.2).

Figure 3.2. Forest plot of the effect of flavonoid consumption on FBG.

Meta-analysis of the effect of flavonoids on HbA1c failed to detect significant differences between intervention and control arm (-0.09% 95% CI: -0.29, 0.10%; P=0.36).

Significant heterogeneity was observed in the HbA1c analysis (I2=67%; P=0.01) (Figure 3.3).

Figure 3.3. Forest plot of the effect of flavonoid consumption on HbA1c.

A forest plot of the effect of flavonoid consumption on TC in T2D participants is presented in Figure 3.4. Total cholesterol was lowered by -0.12 mmol/L with flavonoid intervention (95% CI: -0.17, -0.08 mmol/L; P<0.00001). No level of heterogeneity was detected in TC analysis (I2=0%; P=0.46).

Figure 3.4. Forest plot of the effect of flavonoid consumption on TC.

The result of a meta-analysis of the comparison of LDL between intervention and control group after flavonoid intervention is presented in Figure 3.5. No significant difference was observed in LDL across the groups (-0.09 95% CI: -0.22, 0.04 mmol/L; P=0.15). A low heterogeneity was detected for LDL analysis (I2=34%; P=0.18).

Figure 3.5. Forest plot of the effect of flavonoid consumption on LDL.

Meta-analysis of the effect of flavonoids on HDL is shown in the figure below (Figure

3.6). No significant difference was seen in HDL between treatment and control groups (0.06 mmol/L 95% CI: -0.03, 0.16 mmol/L; P=0.18), however, a high level of heterogeneity was observed in HDL analysis (I2=92%; P<0.00001).

Figure 3.6. Forest plot of the effect of flavonoid consumption on HDL.

A forest plot of the effect of flavonoid intervention on TG is presented in Figure 3.7.

The meta-analysis revealed a significant difference between flavonoid and control group by -

0.19 mmol/L (95% CI: -0.37, -0.01 mmol/L; P=0.04) with a high level of statistical heterogeneity (I2=74%; P=0.002).

Figure 3.7. Forest plot of the effect of flavonoid consumption on TG.

3.4.5 Sensitivity and subgroup analysis

Sensitivity analysis was performed for each outcome, by excluding individual studies and evaluating the changes in the overall meta-analysis result. FBG analysis was not sensitive to any of the trials since the reduction of glucose after flavonoid intervention remained significant after excluding each study. Removing Mellor et al. (235) from the meta-analysis significantly improved the result of flavonoid effect on HbA1c (overall effect of -0.21%, 95%

CI: -0.28, -0.15; P<00001). The meta-analysis of TC was sensitive to studies by Curtis et al.

(232), and Li et al. (233). Excluding these two studies resulted in nonsignificant differences between flavonoid intervention and controls for TC. LDL analysis was highly sensitive to the study by Dabaghian et al. (190). Meta-analysis of the effect of flavonoids on LDL was significant after removing this study (overall effect of -0.14 mmol/L, 95% CI: -0.17, -0.11;

P<00001). The meta-analysis of HDL was influenced by the Balzer et al. (231) study. The HDL result was significantly higher after eliminating this study (overall effect of 0.10 mmol/L, 95%

CI: 0.01, 0.20; P=0.03). TG meta-analysis was sensitive to three studies. Excluding Balzer et al. (231), Curtis et al. (232), and Li et al. (233) reduced the statistical significance of the meta- analysis for the effect of flavonoids on TG.

Subgroup analysis of the effect of design, source, and duration of the intervention, presented in Table 3.2, highlighted a number of study characteristics affecting the meta- analysis results of this study. Supplementing flavonoids for ≥90 days significantly improved all biomarkers except for LDL cholesterol. Limiting meta-analysis to randomised controlled trials using parallel design showed a significant result for FBG, HbA1c, TC, and TG. Having a cross-over design resulted in amelioration of HDL only. Consuming fortified food as the flavonoids sources resulted in lower FBG and LDL levels without inducing any significant effect on other outcomes.

Table 3.2. Subgroup analysis of included randomised controlled trials in a meta-analysis of the effect of flavonoids and flavonoid subclasses on type 2 diabetes risk factors. Outcome Subgroups Trials, Mean difference (95% CI) of outcome Test for subgroup n difference FBG Design: Parallel n=4 -0.68 mmol/L (-0.81, -0.54; P<0.00001; I2=0%) I2=84.4%, P=0.01

Design: Cross-over n=2 -0.26 mmol/L (-0.55, 0.03; P=0.07; I2=0%) Source of flavonoids: Food based n=4 -0.38 mmol/L (-0.75, -0.01; P=0.04; I2=68%) I2=0%, P=0.61 Source of flavonoids: Supplement n=2 -0.59 mmol/L (-1.33, 0.14; P=0.12; I2=0%) Intervention duration ≥ 90 days n=3 -0.69 mmol/L (-0.82, -0.55; P<0.00001; I2=0%) I2=87.6%, P=0.004 Intervention duration < 90 days n=3 -0.23 mmol/L (-0.52, 0.05; P=0.11; I2=0%) Overall effect n=6 -0.44 mmol/L (-0.73, -0.15; P=0.003; I2=47%)

HbA1c Design: Parallel n=4 -0.22% (-0.28, -0.16; P<0.00001; I2=0%) I2=92.8%, P=0.0002

Design: Cross-over n=2 0.08% (-0.06, 0.23; P=0.26; I2=0%) Source of flavonoids: Food based n=4 -0.09% (-0.32, 0.14; P=0.44; I2=79%) I2=0%, P=1 Source of flavonoids: Supplement n=2 -0.09% (-0.56, 0.38; P=0.71; I2=0%) Intervention duration ≥ 90 days n=3 -0.22% (-0.28, -0.15; P <0.00001; I2=0%) I2=83.2%, P=0.01 Intervention duration < 90 days n=3 0.03% (-0.16, 0.22; P=0.74; I2=20%) Overall effect n=6 -0.09% (-0.29, 0.10; P=0.36; I2=67%)

TC Design: Parallel n=4 -0.13 mmol/L (-0.17,-0.09; P<0.00001; I2=0%) I2=63.7%, P=0.1 57

Design: Cross-over n=2 0.10 mmol/L (-0.17, 0.37; P=0.46; I2=0%) Source of flavonoids: Food based n=4 -0.10 mmol/L (-0.23, 0.03; P=0.13; I2=19%) I2=0%, P=0.6 Source of flavonoids: Supplement n=2 -0.00 mmol/L (-0.33, 0.32; P=0.98; I2=0%) Intervention duration ≥ 90 days n=3 -0.13 mmol/L (-0.17, -0.09; P <0.00001; I2=0%) I2=0%, P=0.42 Intervention duration < 90 days n=3 -0.01 mmol/L (-0.29, 0.27; P=0.94; I2=21%) Overall effect n=6 -0.12 mmol/L (-0.17, -0.08; P<0.00001; I2=0%)

LDL Design: Parallel n=4 -0.13 mmol/L (-0.30, 0.03; P=0.10; I2=39%) I2=40.8%, P=0.19 Design: Cross-over n=2 0.06 mmol/L (-0.18, 0.29; P=0.65; I2=0%) Source of flavonoids: Food based n=4 -0.14 mmol/L (-0.17, -0.10; P <0.00001; I2=0%) I2=0%, P=0.68 Source of flavonoids: Supplement n=2 -0.01 mmol/L (-0.63, 0.62; P=0.98; I2=80%) Intervention duration ≥ 90 days n=3 -0.12 mmol/L (-0.33, 0.10; P =0.28; I2=59%) I2=0%, P=0.4 Intervention duration < 90 days n=3 0.01 mmol/L (-0.20, 0.22; P=0.92; I2=0%) Overall effect n=6 -0.09 mmol/L (-0.22, 0.04 mmol/L; P=0.15; I2=34%) HDL Design: Parallel n=4 0.07 mmol/L (-0.07, 0.20; P=0.33; I2=95%) I2=0%, P=0.97 Design: Cross-over n=2 0.07 mmol/L (0.01, 0.13; P=0.02; I2=0%) Source of flavonoids: Food based n=4 0.03 mmol/L (-0.05, 0.12; P=0.45; I2=77%) I2=0%, P=0.42 Source of flavonoids: Supplement n=2 0.15 mmol/L (-0.12, 0.41; P=0.28; I2=97%) Intervention duration ≥ 90 days n=3 0.13 mmol/L (-0.00, 0.27; P=0.05; I2=96%) I2=54.1%, P=0.14 Intervention duration < 90 days n=3 -0.03 mmol/L (-0.20, 0.14; P=0.72; I2=79%)

Overall effect n=6 0.06 mmol/L (-0.03, 0.16, P=0.18; I2=92%)

TG Design: Parallel n=4 -0.35 mmol/L (-0.40, -0.30; P<0.00001; I2=0%) I2=94%, P<0.0001

Design: Cross-over n=2 -0.04 mmol/L (-0.18, 0.09; P=0.52; I2=0%) Source of flavonoids: Food based n=4 -0.16 mmol/L (-0.39, 0.07; P=0.17; I2=83%) I2=0%, P=0.65 Source of flavonoids: Supplement n=2 -0.26 mmol/L (-0.63, 0.11; P=0.17; I2=33%) Intervention duration ≥ 90 days n=3 -0.35 mmol/L (-0.40, -0.30; P <0.00001; I2=0%) I2=94 %, P<0.0001 Intervention duration < 90 days n=3 -0.06 mmol/L (-0.19, 0.08; P=0.41; I2=0%) Overall effect n=6 -0.19 mmol/L (-0.37, -0.01; P =0.04; I2=74%)

FBG: fasting blood glucose, HbA1c: haemoglobin A1c, TC: total cholesterol, LDL: low-density lipoprotein, HDL: high-density lipoprotein, and TG: triglycerides. Changes are presented as mean difference and 95% confidence interval. Heterogeneity (I2) is presented by percent (%). A P- value <0.05 is considered significant.

3.5 Discussion

The present study systematically analysed the effectiveness of flavonoids and flavonoid compounds on metabolic markers of T2D, including FBG and HbA1c as a primary, and TC,

LDL, HDL, and TG as a secondary outcome, within RCTs. To the best of our knowledge, this is the first systematic review and meta-analysis to investigate the effect of flavonoids of different sources and types on glycemic control and lipid profile of T2D clinical trials participants. This study provides evidence that daily dietary supplementation of flavonoids and flavonoid subclasses may induce a significant favourable effect on FBG, TC, and TG by 0.44 mmol/L, 0.12 mmol/L, and 0.19 mmol/L respectively. No significant improvement was observed in HbA1c, LDL, or HDL.

Subgroup analysis of duration of the intervention suggests that pronounced changes in almost all outcomes (except for LDL cholesterol) could be expected by longer (≥90 days) consumption of flavonoids. Therefore, having flavonoids as a dietary constituent should be considered for T2D control. Design of the included studies influenced the result of the meta- analysis. Studies using parallel design showed a greater reduction in FBG, HbA1c, TC, and TG than those using a cross-over design. This could be due to the carry-over effect of one treatment caused by an inadequate washout period. In addition, of the six included studies, only two used cross-over design, and they had a shorter study duration (<90 days) than the parallel studies.

This may, to some extent, explain the greater meta-analysis effect observed in the subgroup analysis of the parallel design. Subgroup analysis of the source of flavonoids delivery revealed that having fortified food with flavonoids could achieve a significant result for FBG and LDL.

However, studies using flavonoids in a capsule form failed to detect a significant difference in

the outcomes of interest, which might be due to the lower number of trials included in this subgroup (two trials only).

Flavonoids represent a large class of phenolic compounds present in nature ubiquitously; however, they are not equally distributed. Some of the subgroups are present in many foods, whereas others are limited to certain foods (236). In addition, the richest flavonoid compound may not necessarily be the most bioavailable one which has the highest concentration of biologically active metabolites in relation to specific health outcomes (237).

The type and bioavailability of flavonoids determine their efficacy, rather than the amount in food. As an example, despite the moderate amount of proanthocyanidins in cranberries, they exert anti-inflammatory effects due to having a substantial proportion of unique molecular structures (238). The findings from our review suggest that flavonoid type may be a key contributor to the observed effects, as different effects were observed with different supplements. In the study by Li et al. (2015), supplementing 320 mg/day of anthocyanin for

168 days resulted in significant improvement in the T2D metabolic biomarkers.

T2D is a multifactorial metabolic disorder occurring as a result of chronic hyperglycemia mainly caused by abnormal β-cell function, insulin resistance in target cells, or overproduction of the glucose at hepatic level (6). Hyperglycemia is associated with micro- and macro-vascular complications of T2D; therefore, maintaining good control of FBG in T2D patients, even at a small scale, may decrease the adverse vascular outcomes which is one of the primary objectives in managing T2D (239). Our findings suggest that flavonoids and flavonoid subclasses might exert a desirable effect on FBG. This is in line with the literature on the ability

of flavonoids to regulate blood glucose level through insulin secretion enhancement, and immune function modulation, all to control T2D (135).

Possible mechanisms by which flavonoids play a positive role in managing T2D involve more than one pathway, believed to be related to their antioxidant and anti- inflammatory properties (240). Using anthocyanins as an example, it has been reported that these flavonoids inhibit one or more of the processes involved in T2D onset and progression such as inflammation, oxidative stress, and endothelial dysfunction (241, 242). Given the established association between T2D and inflammation (25), and the potential of flavonoids to protect the body against free radicals and other pro-oxidative compounds (243, 244), it is possible that flavonoid intervention might be an effective means of managing T2D (245).

Hyperlipidemia is a frequent element of T2D (246), and observational studies suggest that lipid-lowering plays an important role in T2D therapy (247). Several mechanisms have been proposed to explain the biological action of the lipid-lowering property of flavonoids.

Some studies suggested that flavonoids are capable of improving insulin sensitivity and may suppress inflammatory activity and oxidative stress, helpful in balancing lipid metabolism

(208, 211, 245). Others indicated that a possible mechanism for the lipid-lowering effect of flavonoids involves their antioxidant content. Flavonoid antioxidants protect tissues against free oxygen radicals and lipid peroxidation (209, 248). Flavonoids might also exert beneficial effects through inhibition of lipid absorption and increased lipid oxidation which results in lipid metabolism (209, 249). Therefore, flavonoid consumption might improve lipid profiles in subjects with metabolic disorders such as T2D. Our study adds weight to the evidence for the lipid-lowering property of flavonoids specifically for TC and TG.

The findings of the current review suggest that some flavonoids or flavonoid-rich foods may modulate important risk factors of T2D, hence contribute to the management of this long- term metabolic disorder. However, this study has some limitations which need to be taken into account. Administration of flavonoid type, product, dosage, and period of the intervention varied greatly between the trials included in this meta-analysis. As well, dietary intake of flavonoids varied between populations based on the availability of dietary source and habits of different demographic groups (250, 251). RCTs included in this review were conducted in different countries with participants having different food habits, which could be considered a confounding factor in this meta-analysis. Some of the interventions and control arms differed widely. The compatibility of active supplement and control in terms of saturated fat content, the contribution of industry funding, and any other suggested biases may all lead to overstated suggestions of effectiveness (252). Furthermore, the length of the time it takes for each biomarker to change differs. For instance, FBG can alter hourly (253), whereas it might take up to three months for HbA1c to change (254). In addition, there were some limitations in subgroup analyses. For instance, subgroup analysis of dose-dependency of the effect of flavonoids and flavonoid compounds on T2D was not applicable due to the variation in the flavonoid supplements used in the included RCTs. The low number of trials in some subgroups may have affected the result of subgroup analysis as well.

Studies included in this systematic review and meta-analysis were of high quality with

316 T2D participants which is a considerable number, however, with only six studies having participants of various ethnicities and age range, supplementing flavonoid substance of different source, type and intervention duration, it is hard to draw a conclusion which can be generalised to a larger scale with regards to the best source, dosage and intervention period to maximise the beneficial effect. In addition, the evidence of the positive effect of foods rich in 63

flavonoids needs to be further explored to examine whether it is the flavonoids or other bioactive components accompanying flavonoids which are responsible for the observed effect.

Future RCTs at clinical or population level with different sources and various durations of intervention and daily doses are required to confirm the health benefits and the role of flavonoids in the management of T2D. Furthermore, participants which are matched in demographic characteristics and baseline measurements of biochemistry parameters are needed to confirm the health benefits of polyphenolic antioxidants such as flavonoid substance on metabolic markers associated with T2D.

3.6 Conclusion

The findings of this review indicate that the intake of flavonoids might be beneficial in improving T2D by lowering FBG, TC and TG. Subgroup analysis reveals that significant improvement in most of the outcomes of interest will be observed if flavonoid is consumed for at least three months. Although these outcomes may be influenced by the low number of trials, and other confounding factors, it appears that flavonoids and flavonoid subgroups have potential in assisting T2D management by ameliorating its biochemical abnormalities.

Supplemental Table 3.1. Methodology assessment summary and Rosendal score of studies included in systematic review and meta-analysis of the effect of flavonoids intake on the management of type 2 diabetes.

12 7 y

trial ation

Authors -

Stats

(References) Pre

% % Score

Blinding

Subjects

Reported

Blinding of

Described

Conditions

Eligibilit

Variability

Blinding of

Methodfor

Methodand

SampleSize

Described

Calcul

Investigators

Evaluationof

andomization

Adverse Effects

Measuresand

Reproducibility

Comparisons

NonCompleters

Between Group

Stats Described

Randomization

R Baseline Measures

Gonzalez et al./ 2007 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 80% Balzer et al./ 2008 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 87% Mellor et al./ 2010 1 1 0 1 0 1 1 1 1 NA 1 1 1 1 0 78% Curtis et al./ 2012 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 87% Dabaghian et al./2015 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 87% Li et al./ 2015 1 1 0 0 0 1 1 1 1 NA 1 1 1 1 0 71%

* 1=Yes; 2=No; NA=Not Applicable.

1- A clear description of the inclusion and exclusion criteria was provided. 2- The trials were randomized.

3- The method used to generate the random allocation sequence, including details of any restrictions (e.g. blocking, stratification) was described. 4- Sample size was justified (e.g. by power calculation). 5- Attempts were made to control and/or monitor the pre-trial condition (e.g. diet, exercise). 6- Design incorporated measures of important baseline variables. 7- There was blinding of all subjects. 8- There was blinding of all investigators involved in the trials. 9- Both the method of blinding and the evaluation of the successfulness of blinding were described. 10- Details were provided regarding the inability of subjects to complete study requirements. 11- Statistical methods used to compare groups for the primary outcome measure, and methods for additional analyses, such as subgroup analyses. and adjusted analyses were described. 12- Both point measures and measures of variability for the primary outcome measure were provided. 13- The results of between-group statistical comparisons were reported for the primary outcome measure (e.g. an estimated effect size), and its precision (e.g. 95% CI). 14- The method used to assess adverse effects was reported. 15- Reproducibility of the primary outcome measures was reported.

Chapter 4

Methods and Materials

4.1 Human clinical trial study design

The present human clinical trial was conducted at Griffith University (GU); Gold Coast,

Australia. This study enrolled 40 adults (24 males and 16 females) in an open-label design. All participants including healthy (n=14), type 2 diabetes (T2D) (n=12), and T2D-at-risk (n=14) received the same treatment (320 mg anthocyanin /per day for the course of 28 days). The study protocol was approved by the Human Research Ethics Committee of the Griffith University

(GU Ref No: MSC/07/14/HREC) (Appendix 1) and was conducted in accordance with the guideline prescribed in the Declaration of Helsinki. This trial is registered in the Australian

New Zealand Clinical Trial Registry (ANZCTR) with the number ACTRN12615000293561

(Appendix 2).

4.2 Volunteer recruitment and screening

Healthy, T2D, and pre-diabetic volunteers, 25-75 years of age, were recruited from the local community and Griffith University by means of newsletters, posters, flyer advertisements, and word of mouth between December 2015 and December 2016 (Appendix

3, 4 and 5). Diagnosis of T2D was according to the World Health Organization diagnostic criteria and Australian guidelines for diabetes (3, 255). Non-diabetic volunteers were categorised as healthy or at risk of diabetes based on their characteristics at the initial health

screening (Appendix 6). Volunteers were grouped in a diabetic-at-risk category if they met the definition of metabolic syndrome, possessing at least three of the following conditions: abdominal obesity (central obesity, defined as a waist-hip ratio above 0.85 cm for females and above 0.90 cm for males) or obesity (body mass index above 30 kg/m2), elevated blood pressure

(systolic blood pressure>120 mm/Hg, diastolic blood pressure>80 mm/Hg), elevated fasting blood glucose (FBG) (>6 mmol/L), elevated triglyceride (>1.1 mmol/L), and low level of high- density lipoprotein (HDL-cholesterol) (<1 mmol/L) (256, 257). All other volunteers were classified as healthy participants.

The sample size and power calculation was done based on the formula for comparison between groups’ mean in the clinical trials (258). Glucose changes after anthocyanin intervention in the study by Qin et al. (2009) was used as a reference mean change or effect size (259). In order to have 80% power to detect a difference between the means of each group with a significance level alpha=0.05, and considering a dropout rate of 10%, 14 volunteers per group were needed to be recruited. After volunteers screening and dropouts, we had a required minimum of 12 participants per group.

4.3 Inclusion and exclusion criteria

Volunteers included in this trial were individuals who had not had any previous history of pathological conditions such as cardiovascular, lung, or liver disease, or allergies.

Individuals were excluded if they had an excessive bleeding tendency, gastrointestinal bleeding, or major surgery in the last six weeks. Volunteers were not included if they were

pregnant, lactating, or taking any anti-inflammatory and/or anticoagulant medications. In addition, full blood examination was conducted prior to the intervention. Volunteers included in this study were those with no anaemia and normal white cell count. Those with the differential white cell population were rollout due to any current infection and inflammation.

Furthermore, volunteers completed a health questionnaire where they self-reported if they were on any medication or alternative dietary supplement. If they did not want to discontinue that for at least two weeks prior to the study and during the intervention, they were not included in the study. Individuals were excluded if they were regular smokers, or those with daily alcohol consumption in excess of one standard drink or having more than 30 minutes daily regular physical activity. All participants provided written informed consent (Appendix

7). The design of this trial is shown in Figure 4.1.

Figure 4.1. Anthocyanin intervention design.

4.4 Supplement information

All participants were assigned to four weeks of anthocyanin (ACN) intervention in capsule form at a daily dose of 320 mg ACN. ACN supplement (Medox®) is a hemicellulose

capsule, which contains powder of anthocyanins extracted from Bilberries (Vaccinium myrtillus) and Black Currants (Ribes nigrum). Each capsule contains 80 mg of ACNs, which consist of 17 different natural ACNs. The relative amount of each ACN compounds has been reported previously (259). In addition to ACN, each capsule also contained citric acid as a counter ion, and maltodextrin to stabilise the anthocyanins. An analysis of the ACN capsule is provided in Appendix 8. Participants were requested to take four capsules per day (two capsules twice daily) 30 minutes after two of their meals (breakfast and supper) for 28 days. The dose and duration of our intervention (ACN capsules, 320 mg/d, 28 days) were determined based on a previous animal study (260) and human study (164). Supplementation of 130 mg ACN per kg per day contained in black rice anthocyanin extract was shown to significantly improve the lipid profile in mice (260). The effective dose of ACN was determined to be in the range of 100.5 to 335.0 mg/d for a 70 kg human being when it was extrapolated to the humans by the method of specific surface area (164). In the study by Karlsen et al. (2007), daily intake of 320 mg ACN was found to be safe and effective in improving the inflammatory response (164). In the same study supplementation with Medox ACNs to healthy adults for three weeks decreased the plasma concentrations of several NF-κβ-regulated pro-inflammatory chemokines and immunoregulatory cytokines (164). In another clinical study, using anthocyanin-rich sweet cherries managed to decrease inflammatory biomarkers after four weeks of intervention (261).

Given the findings from past studies, four weeks of intervention duration was chosen for the present clinical trial.

Intervention adherence was monitored during the study by making phone calls to individuals on two different occasions. All participants were asked to return their supplied package on the day of post-intervention blood collection, so the leftover capsules could be collected and the consumed numbers of capsules be recorded to verify compliance. The side

effects of the supplemented ACN capsules were checked by the research team individually.

Participants were asked to maintain their habitual lifestyle and diet during the study period.

4.5 Dietary assessment

To monitor antioxidant-rich food consumption during the intervention period, dietary intake of participants was assessed using a prospective technique. Individuals were requested to complete a 3-day food diary over the period of two weekdays and one weekend day on three occasions: prior to, throughout, and at the end of the trial. In order to ensure the accuracy of food records, participants were provided with special guidelines and instructions in a face-to- face meeting. In addition to the 3-day food record, participants were asked to complete a food and antioxidant questionnaire. A copy of food questionnaire, antioxidant questionnaire, food diary form and instructions are provided in Appendix 9. Food data was analysed using

FoodWorks8® software (Xyris Software Pty Ltd., Australia) based on the Australian Food

Composition database.

4.6 Dietary inflammatory index calculations

The overall inflammatory potential of diet was estimated by the dietary inflammatory index (DII) score, calculated according to the method developed by Cavicchia (39) and

Shivappa (38). DII was calculated for 22 nutrients (carbohydrate, protein, total fat, saturated fat, monounsaturated fat (MUFA), cholesterol, alcohol, fibre, thiamine, riboflavin, niacin,

Vitamin C, Vitamin E, Vitamin A, β-carotene, total folate, magnesium, iron, zinc, omega-6 fatty acids, omega-3 fatty acids, and caffeine) to provide an overall DII score for every

individual. Nutritional parameters promoting anti-inflammatory characteristics including antioxidants, omega-3 and omega-6 fatty acids receive a minus score, whereas positive score is assigned to a pro-inflammatory dietary component such as saturated fatty acids. Therefore, lower DII scores suggest a more anti-inflammatory diet and higher ones imply a more pro- inflammatory dietary habit (38, 39).

4.7 Physical measurement

Physical characteristics of the participants, including anthropometric and blood pressure measurements were taken by a qualified nutritionist at the beginning and end of the trial. Volunteers presented to the laboratory between the hours of 6-9 am following an overnight fast, for physical measurements and blood collections. Measurements were taken in light clothing, without shoes, watches, or other accessories. Height was determined to the closest 0.1 cm with a rod stadiometer (Surgical & Medical Products, Australia). Body mass was measured using a BC- 601 digital body composition scale (Tanita Corporation, Australia).

Body mass index (BMI) was calculated by dividing the body weight in kilograms by the height in metres squared. Waist and hip circumference were measured using a graduated anthropometric measuring tape (Seca, Germany) according to the World Health Organisation

STEPwise approach applying surveillance protocol (262). Waist circumference was measured to the nearest 1 mm at the natural indent, or the umbilicus was used if the natural indent could not be observed. The hip circumference was recorded at the widest part of the hips. Waist-hip ratio was calculated as waist measurement divided by the hip measurement. Blood pressure was determined on the right arm with participants in a seated position after few minutes rest using a validated semi-automatic sphygmomanometer (Omron HEM-705CP).

4.8 Blood collection and processing

Venous blood sample collections were performed by a qualified phlebotomist using standard venepuncture techniques before (day 0) and after four weeks (day 29) of supplementation. Blood was directly drawn into evacuated blood tubes, including tri-potassium ethylene diamine tetra-acetic acid (EDTA-1.8 mg/mL concentration) anticoagulant tube

(Vacuette Greiner Bio-one, Australia) for pro-inflammatory biomarker analysis, serum separator tubes (SST) (Becton, Dickinson and Company, Australia) for biochemistry analyses and biomarkers of inflammation, and the PAXgene blood RNA tubes (PreAnalytiX, BD

Company, Australia) for miRNA profiling and gene expression. Plasma and serum samples were separated and aliquoted after 10 minutes of centrifuging SSTs at 2000×g, and then stored at -80 0C until analysis. Samples were incubated in the RNA stabilising agent contained in the

PAXgene tubes at room temperature for two hours prior to storage at -80 0C.

4.9 Biochemistry assay

Lipid profile including total cholesterol (TC), triglycerides (TG), and high-density lipoprotein (HDL) cholesterol; fasting blood glucose (FBG); uric acid; and inflammatory marker high sensitivity C-reactive protein (hs-CRP) were analysed by a COBAS Integra 400 system. This is a fully automated blood chemistry analyser capable of high throughput analysis of clinical samples, using a commercially available reagent, controls and calibrators (Roche

Diagnostics, Australia). Serum samples obtained from whole blood collected into SSTs were used as an input for COBAS analyser. Samples were thawed initially on ice and then to room temperature prior to the analysis and analysis performed according to the company’s protocol,

with calibration and controls run before samples. Low-density lipoprotein (LDL) cholesterol was calculated using the Friedewald equation (263).

4.10 Analysis of pro-inflammatory biomarkers by Bio-Plex

Pre- and post-intervention samples, were screened for pro-inflammatory biomarkers including interleukin-8 (IL-8), interleukin-18 (IL-18), interleukin-1R alpha (IL-1R), tumour necrosis factor alpha (TNF-α), resistin and leptin using Human Magnetic Luminex® Assays kit

(R&D) and Bio-Plex Analyser 200 (Bio-Rad, Texas, USA) according to the manufacturer’s instruction (R&D). Plasma samples collected into EDTA tubes were thawed on ice and spun down at 14000×g for 10 minutes at 4 0C, before two-fold dilution and further processing.

Individual sets of samples from the same participants were run in the same assay kit. As recommended by the manufacturer, a magnetic plate washer was used to guarantee higher yields of analytes.

Each analyte is quantifed based on super-paramagnetic beads coated with analyte- specific antibodies. Beads recognizing different target analytes are mixed together and incubated with the sample. Captured analytes are subsequently detected using a cocktail of biotinylated detection antibodies and a streptavidin-phycoerythrin conjugate. A magnet in the analyser captures and holds the superparamagnetic microparticles in a monolayer. Two spectrally distinct Light Emitting Diodes (LEDs) illuminate the beads. One LED identifies the analyte that is being detected and the second LED determines the magnitude of the PE-derived signal, which is in direct proportion to the amount of analyte bound. Each well is imaged with a CCD camera.

4.11 Interleukin-6 analysis

Enzyme-linked immunosorbent assay (ELISA) was performed to measure the serum level of interleukin-6 (IL-6) in pre- and post-supplementation samples. Standard procedure was followed according to the manufacturer’s protocol using a 96-well plate (ELISA kit; Abcam,

Australia). After the four-step incubation and washing procedure, an acidic stopping solution was added, and the degree of enzymatic turnover of the substrate was determined by chromatography. The reading at 450nm was obtained by Multiskan FC (Thermo scientific).

4.12 RNA isolation

Total RNA was isolated from the whole blood sample collected into PAXgene blood collection tubes using Maxwell® RSC miRNA Tissue Kit and a Maxwell® RSC Instrument for automated DNA and RNA purification (Promega, Madison, Wisconsin, United States).

Extraction was carried out according to the manufacturer’s protocol with small modifications.

Following isolation, RNA yield was quantified initially using a Nanodrop ND-1000 spectrophotometer (ThermoFisher Scientific) and then confirmed by QuantiFluor® RNA

System (Promega, Madison, Wisconsin, United States). The quality of purified RNA was checked using a labchip GX touch nucleic acid analyser (PerkinElmer, Massachusetts, USA).

A sample with RNA concentration of <40 ng/ul, and an average 260/280 nm ratio of <1.5, or average 260/230 nm ratio of <1.0, were considered unsuitable due to the low quantity and quality of isolated RNA, therefore excluded from the analysis.

4.13 micro-RNA expression profile

NanoString nCounter Human v3 miRNA Expression Assay kit containing 828 probes which includes 798 endogenous miRNAs, 6 positive control, 8 negative control, 6 ligation, 5 spike-in and 5 housekeeping genes (Nanostring Technologies, Seattle, WA, USA) was utilised as reference genes to analyse the miRNA profile of 36 paired baseline and post-intervention samples from the 18 participants. Samples were analysed according to the manufacturer’s instructions. Briefly, samples were diluted to 100 ng RNA as an input. Multiple annealing of specific tags to their target miRNA and ligation reaction were employed in the preparation of the samples. miRNAs were annealed to specific barcode probes and to the reporter probes.

NanoString technology is based on digital detection and direct molecular barcoding of target molecules or barcode probes through the use of a color-coded probe pair. The probe pair consists of a reporter probe, which carries the signal on its 5' end, and a Capture Probe which carries a biotin moiety on the 3' end. In the end, excess tags were removed by restriction digestion. In order to ensure the sequence specificity between each miRNA and its correct tag, annealing and ligation temperatures were closely monitored. Following annealing, ligation, and clean-up enzyme purification, CodeSet hybridisation was undertaken according to the manufacturer’s protocol with 5 uL aliquots from the miRNA sample preparation reaction. All hybridisation reactions were incubated at 65°C for 20 hours. During the overnight hybridisation reaction, probe pairs were present in large excess to target RNAs to ensure that each target found a probe pair.

The assay was run on the nCounter Analysis system (Nanostring Technologies, Seattle,

WA, USA) comprised of two instruments, the Prep Station used for post-hybridisation

processing, and the Digital Analyzer for data collection. The digital analyser counts the barcodes which represent the number of miRNAs.

4.14 Normalisation of micro-RNA expression

In order to eliminate variability unrelated to the samples, raw miRNA expression data were normalised by means of nSolver Analysis Software version 3 (Nanostring Technologies,

Seattle, WA, USA). Background correction was done by subtraction of negative control probes, and normalisation carried out using a combination of positive control normalisation for technical variability and CodeSet Content normalisation for assay input variability. The normalisation factor was generated by inputting the geometric mean of the positive controls used for each sample and this was applied to the raw count of the miRNA output data. In addition, miRNA expression data were normalised with the geometric mean of the top 100 miRNAs, which minimises the impact of none- or low-expressed miRNAs. Normalised data were log-2 transformed for further analysis.

4.15 micro-RNA: messenger-RNA target interactions

Computational algorithms were utilized to predict miRNA target messenger-RNA

(mRNA) and its binding sites within the 3′-UTR of a chosen mRNA (264). The best characterised features determining miRNA-target recognition are six-nucleotide (nt) seed binding sites, which perfectly complement the 5' end of the miRNA (positions 2±7) (265). In addition, a match with miRNA nucleotide 8, an A across from nucleotide 1, or both of these conditions, enhance the seed pairing and enforce miRNA-mediated repression (266). These seed-pairing rules are widely used to predict functional miRNA target-sites, normally in

combination with the secondary structure of the 3′-UTR, the neighbouring contextual information and/or evolutionary conservation (267). Three online miRNA: target prediction tools TargetScan (268), DIANA Tools (269), and miRWalk (270), were used to identify potential miRNA: mRNA interaction. These tools provide algorithms, databases and software for interpreting and archiving data in a systematic framework ranging from the analysis of expression regulation from deep sequencing data, the annotation of miRNA regulatory elements and targets (264).

TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA (271).

Predictions in the TargetScan tool are ranked based on the predicted efficacy of targeting as calculated using cumulative weighted context++ scores of the sites (268). TargetScan considers matches to human 3'-UTRs and their orthologs, as defined by the University of California,

Santa Cruz (UCSC) whole-genome alignments (271).

DIANA Tools is specifically trained on a positive and a negative set of miRNA

Recognition Elements located in both the 3'-UTR and the protein coding region of a gene

(CDS) regions (269). It provides a significant increase in sensitivity when compared against experimental proteomics data, using databases of experimentally validated targets, and offers the option to filter results based on their interaction score (272). One is the maximum score and lower values signify smaller probabilities of functional impact (272).

miRWalk is a comprehensive archive, supplying the biggest available collection of predicted and experimentally verified miRNA-target interactions with various novel and unique features (273). It hosts possible binding site interaction information, including central

pairing sites, between genes and miRNAs resulting from the miRWalk algorithm by walking with a heptamer (7nts) seed of miRNA from positions 1 to 6 (274). These different starting positions are considered because it has recently been identified that miRNAs also regulate the expression of their target genes by annealing from nucleotides 4 to 15 (274).

Five miRNAs (miR-944, miR-593-3p, miR-548b-3p, miR-93-5p, miR-1827) that were significantly expressed after 28 days of 320mg/day ACN intervention in T2D group in our previous study were studied for their target genes. Interactions that were predicted by at least two of these algorithms for the same miRNA were considered for further expression analysis.

The predicted miRNA: mRNA binding interactions for each tool are summarised in Table 4.1.

Table 4.1. Predicted miRNA: mRNA interactions by three prediction tools.

Prediction tool miRNA Predicted target mRNA

TargetScan miR-944 NOTCH2, GLIS3

miR-593-3p NOTCH2, GLIS3, WFS1

miR-548b-3p NOTCH2, JAZF1

miR-93-5p GLIS3, WFS1, JAZF1

miR-1827 NOTCH2, GLIS3, WFS1, JAZF1, HNF4A

DIANA miR-944

miR-593-3p

miR-548b-3p NOTCH2, JAZF1

miR-93-5p NOTCH2, JAZF1, WFS1

miR-1827 miRWalk miR-944

miR-593-3p NOTCH2, GLIS3

miR-548b-3p NOTCH2, GLIS3

miR-93-5p NOTCH2, WFS1

miR-1827 JAZF1, HNF4A

4.16 Reverse transcription

Isolated RNA was reverse-transcribed to cDNA using the iScript™ cDNA Synthesis kit

(Bio-rad Gladesville, NSW, Australia), according to the manufacturer’s instruction. cDNA samples were stored in 5 ng aliquots at -20 0C until required.

4.17 Primer efficiency

Primer sequences were selected based on previously published articles (275-279), and synthesised by Sigma-Aldrich P/L (Castle Hill, NSW, Australia). The specificity of each set of primer was verified by Basic Local Alignment Search Tool (BLAST) from the GenBank non- redundant nucleotide sequence database (280). In order to eliminate genomic DNA (gDNA) amplification, all assays were designed such that at least one primer spanned an exon boundary.

The efficiency of each primer pair was determined using previously published methods

(281). Briefly, the minimum concentration of primer, resulting in optimal amplification of the target gene with minimising non-specific amplification, was established. The sequence for each primer pair and the expected amplicon size is outlined in Table 4.2. Our amplicon range was between 101bp to 209bp, as the optimal PCR product size recommended for SYBR green PCR reaction

is 100bp to 200bp. It is still acceptable to use the product size between 200bp to 300bp, although it is

not optimal and it is not recommended to use the amplicon size over the 300bp (282). The assay was

tested using cDNA pool serial dilutions for each primer pair. Primer efficiency and cDNA

concentration were determined prior to the sample runs. Glyceraldehyde 3-phosphate

dehydrogenase (GAPDH) was used as an endogenous control for determining ΔCT values

which was previously tested using geNorm software (283). GeNorm program used in previous

studies determined the most appropriate reference gene from a selection, under ACN

supplementation in human (283, 284).

Table 4.2. Primer pair sequences of messenger-RNA transcripts used for real-time polymerase chain reaction. Target Primer Sequence Amplicon Size Citation (bp)

GLIS3 Forward- ACGTTTGAAGGTTGCGAGAAG 171 (275) Reverse- AGGTTTGGTGTCCAGATGCG

HNF4A Forward- CAGGCTCAAGAAATGCTTCC 101 (276) Reverse- GGCTGCTGTCCTCATAGCTT

JAZF1 Forward- CCACAGCAGTGGAAGCCTTA 209 (277) Reverse- GCTTCTCTTCCCCTCCATTCAT

WFS1 Forward- GTTCCCGACTCAATGCCACA 105 (278) Reverse- CCGCTGCGTCTCTAACACC

NOTCH2 Forward- TCAACTGCCAAGCGGATGT 191 (279) Reverse- CTTGGCTGCTTCATAGCTCC

GAPDH Forward- CCTGCACCACCAACTGCTTA 140 (285) Reverse- GGCCATCCACAGTCTTCTGAG

4.18 Real-time polymerase chain reaction

The mRNA transcripts of five genes (GLIS3, HNF4A, JAZF1, WFS1, NOTCH2) were measured by real-time polymerase chain reaction (RT-PCR) using a 72-well rotor QIAGEN

Rotor-Gene Q system. Each reaction contained 2 µL of 1/50 sample cDNA, 10 µL of Bio-Rad

SYBR Green PCR Master Mix (Bio-Rad Laboratories Pty Ltd, Gladesville, NSW, Australia), and 300 nM of each primer made up to a final volume of 20 µL with RNAse-free water. Each sample is amplified in duplicate. No-template controls were included for each primer pair, acting as a negative control. The pool of cDNA samples was used as a positive control, confirming primer pair amplification and consistency between runs. The amplification cycle consisted of an initial activation step at 95°C for 10 min, followed by 60 cycles of: 15 s at 95°C,

30 s at 56°C (annealing), 30 s at 72°C, and ramping from 72°C to 95°C rising by 1°C each step

(data was acquired at this step).

A threshold (T) of 0.03 was set in the exponential phase of the reaction above the baseline, using the amplification plot of fluorescence versus cycle number. T was kept constant between runs to allow for analysis between plates. The cycle threshold (CT) was calculated by

Rotor-Gene Q Software (QIAGEN Pty Ltd), as the cycle number of an amplifying PCR product where it crosses the fixed threshold line. The differences between the mean CT values of the duplicates for each target gene and the endogenous control gene, GAPDH, were calculated to give ΔCT used in data normalization. The ΔΔCT values were calculated by normalising the ΔCT for each sample to the ΔCT value of a pooled control sample. The relative

- ΔΔC quantitation/expression value was then presented as 2 T (286).

4.19 Statistical analysis

Data analysis was performed using the Statistical Package for Social Sciences (SPSS) software version 22 (SPSS Inc. Chicago, USA). The level of statistical significance for all analyses was set at P≤ 0.05. GraphPad Prism software version 7 (GraphPad Prism, California,

USA) was utilised to generate graphs. Data in the tables were expressed as mean ± SD (standard deviation), and in the graph as mean ± SE (standard error).

A two-way analysis of variance (ANOVA) following Tukey’s post comparison test was applied to investigate physical characteristics, biochemical parameters, and biomarkers of inflammation after four weeks of ACN intervention between three groups of healthy, pre- diabetic and diabetic participants. One-way ANOVA following Tukey’s post-hoc test was utilised to determine the differences of DII score between three groups of participants.

Spearman’s rank order correlation was performed to investigate the correlation between DII score and each of the inflammatory biomarkers in three groups of healthy, pre-diabetic and

T2D.

Non-expressed or low count miRNAs were removed from analysis according to the manufacturer’s suggested threshold. Limma-R Package software was used to detect differentially expressed miRNAs in two groups of healthy and T2D at different time points

(pre- and post-intervention). Paired sample T-Test was used to investigate the effect of ACN supplements on physical and biochemical parameters of participants in two groups of healthy and T2D (within-group analysis). Unpaired sample T-Test was applied to compare baseline measurements of two groups (between groups analysis).

Microsoft office Excel 2010 was used to calculate all the values, including ΔCT, ΔΔCT,

- ΔΔC and 2 T. A two-way analysis of variance (ANOVA) following Tukey’s post comparison test

-ΔΔC was conducted to examine the differences in relative gene expression (2 T) prior to and post

ACN supplementation between three groups of healthy, pre-diabetic, and diabetic participants.

Chapter 5

Potential of anthocyanin as an anti-inflammatory agent: A human clinical trial on type 2 diabetic, diabetic at-risk and healthy adults.

This chapter includes a co-authored paper, which I am the primary author. It is prepared to be submitted to the Journal of Functional Food.

Authors: Elham Nikbakht, Indu Singh, Jelena Vider, Lauren T. Williams, Lada Vugic, Almottesembellah Gaiz, Avinash Reddy Kundur, Natalie Colson.

My contribution to this paper involved:

 Review of the literature.  Study design and experimental methodology.  Blood collection and processing.  Pro-inflammatory biomarker analysis- Bio-Plex and ELISA.  Food data entry and dietary inflammatory index calculations.  Statistical analysis.  Graph and table preparation.  Manuscript writing, preparation and revision.

Elham Nikbakht

27th April 2018

Indu Singh’s contribution to the paper involved:

 Study design and experimental methodology.  Manuscript writing and revision.

Jelena Vider’s contribution to the paper involved:

 Experimental methodology.  Training and assisting in conducting Bio-Plex experiments.

Lada Vugic’s contribution to the paper involved:

 Assistance with physical measurements.

Almottesembellah Abdalruhman Gaiz’s contribution to the paper involved:

 Assistance with biochemistry assay.

Lauren T. Williams’s contribution to the paper involved:

 Manuscript writing and revision.

Avinash Reddy Kundur’s contribution to the paper involved:

 Study design and experimental methodology.  Assistance with biochemistry assay.

Natalie Colson’s contribution to the paper involved:

 Primary supervisor of the project.  Study design and experimental methodology.  Dietary inflammatory index calculations training.  Statistical analysis.  Manuscript writing and revision.

Corresponding author of paper:

Dr Natalie Colson 27th April 2018

Supervisor:

Dr Natalie Colson 27th April 2018

5.1 Abstract

Inappropriate dietary patterns play a significant role in metabolism impairments leading to chronic inflammation and associated disorders, including type 2 diabetes (T2D). In fact, T2D is an inflammatory condition triggered by metabolic surplus derived from over-nutrition and unhealthy diet. In contrast, a healthy dietary habit may exert anti-inflammatory effects, contributing to the management of inflammation and related diseases. The anti-inflammatory potential of polyphenolic antioxidants, including anthocyanin (ACN) has been suggested in previous studies. The current study is an open-label clinical trial enrolling T2D, people with

T2D risk and healthy individuals to investigate the efficacy of dietary ACN supplementation

(320mg/day over the course of four weeks) on diabetes-associated inflammatory biomarkers and relevant biochemical and physical parameters. In addition, the dietary inflammatory potential was estimated by calculating the dietary inflammatory index (DII) score for each individual, then comparing across groups. Analysis of pre- and post-intervention data demonstrated a significant reduction in the pro-inflammatory biomarkers interleukin-6, interleukin-18, and tumour necrosis factor-α in the T2D group. Some, but not all physical and biochemical parameters including blood pressure, fasting blood glucose, low-density lipoprotein and uric acid were significantly improved at the end of the study in T2D-at-risk participants. The DII score calculation for the T2D group was consistent with an anti- inflammatory diet. The findings from the present study provide evidence of the anti- inflammatory potential of dietary ACN in T2D participants.

Keywords: type 2 diabetes, pro-inflammatory biomarkers, anthocyanin, dietary inflammatory index, dietary pattern.

5.2 Introduction

Metabolic inflammation in type 2 diabetes (T2D), caused by over-nutrition (20), has given impetus to the further investigation of immuno-metabolism linking inflammation to beta cell (β-cell) dysfunction and insulin resistance (17). Over intake of specific nutrients, such as sugar and harmful fatty acids, inflict stress on insulin-sensitive tissues and pancreatic cells, causing excess production of reactive oxygen species (ROS) in mitochondria (31, 32). The increased amount of ROS induces oxidative damage to the corresponding cells, activates inflammatory signalling cascades inside the cells, and leads to the local stimulation of pro- inflammatory biomarkers (33). Consequently, cytokines and chemokines, such as interleukins

(IL) and tumour necrosis factors (TNF), are released from insulin-sensitive tissues into the circulation, promoting inflammation in other tissues, including the pancreatic islets (33).

Metabolic inflammation, often referred to as obesity-induced inflammation, usually begins with the overproduction of pro-inflammatory molecules by adipose tissues (20, 287). In contrast to classic inflammation, the body’s immune response for self-protection in reaction to injury (288), which results in the enhancement of the metabolic rate, metabolic inflammation is associated with reduced metabolic rate (19). Long-term overproduction of pro-inflammatory molecules and reduced metabolic rate, leading to chronic inflammation and various metabolic disorders (288), is associated with several clinically important complications, including T2D

(18). In fact, T2D is an inflammatory condition caused by dysfunctional β-cells and insulin resistance (18, 289), inducing the hyperglycaemia (6), characteristic of T2D (6, 290).

T2D is a global health burden, projected to rise from 382 million cases in 2013 to 592 million by 2035 (291). These statistics represent only those people who are clinically diagnosed, with estimates that there are millions more who remain undiagnosed and untreated

(292). Poorly treated T2D is associated with long-term macrovascular and microvascular complications, including atherosclerosis, stroke, amputation, diabetic nephropathy, neuropathy, and retinopathy (32, 291).

Lifestyle modification is fundamental to T2D treatment, but the evidence is that the majority of people do not meet recommended dietary intake (293, 294). Given the significant role of metabolic inflammation in the development of T2D, approaches that attempt to control inflammatory events to avoid the onset of diabetes in at-risk populations and prevent progression of the disease in T2D individuals have been explored (14, 18, 289). Previous experimental data on the potential anti-inflammatory effects of polyphenolic antioxidants has directed current studies to further investigate the role of these antioxidants, such as anthocyanins (ACNs), in reducing diabetes risk factors (295-297). Polyphenolic antioxidants have been proposed as a potential candidate for the management of hyperglycaemia as well as hyperlipidaemia (178, 298). The pooling of randomised controlled trial data in our last systematic review and meta-analysis also confirmed hypoglycaemic and lipid lowering effect of polyphenols, flavonoids in particular, in T2D adults. ACNs, flavonoid subclasses of polyphenolic compounds, are natural antioxidants responsible for the red, blue, and purple colours of fruits and vegetables (299). Observational studies have demonstrated the link between high intake of dietary ACNs, and alleviation of risk factors for various metabolic disorders such as T2D (300, 301). Furthermore, intervention trials have confirmed the beneficial effects of ACN consumption on oxidative stress and inflammation in participants

with different health conditions (302, 303). However, the association of dietary inflammatory potential of individuals and inflammatory biomarkers have not been examined in the previous intervention trials.

This study aimed to investigate the anti-inflammatory potential of dietary polyphenols

ACN in T2D, T2D-at-risk and healthy individuals and determine the association of diet with biomarkers of inflammation through the dietary inflammatory index (DII).

5.3 Methods and materials

For information regarding the methods and materials, please refer to the Chapter 4 at page 66. Relevant sections to this chapter are listed below:

Section 4.1 Human clinical trial study design (page 67) Section 4.2 Volunteer recruitment and screening (page 67-68) Section 4.3 Inclusion and exclusion criteria (page 68-69) Section 4.4 Supplement information (page 70-72) Section 4.5 Dietary assessment (page 72) Section 4.6 Dietary inflammatory index calculations (page 72-73) Section 4.7 Physical measurement (page 73) Section 4.8 Blood collection and processing (page 74) Section 4.9 Biochemistry assay (page 74-75) Section 4.10 Analysis of pro-inflammatory biomarkers by Bio-Plex (page 75) Section 4.11 Interleukin-6 analysis (page 76) Section 4.19 Statistical analysis (page 84-85)

5.4 Results

5.4.1 Baseline characteristics of participants

Participants’ demographic characteristics, pre- and post-intervention physical and biochemical measurements are summarized in Table 5.1. Forty volunteers were included in this study, and all completed the intervention. No adverse effects of ACN supplements were reported.

Healthy individuals had measures within the normal reference range of all physical measurements prior to the supplementation. Except for the TC (4.16±1.08 mmol/L) and LDL- cholesterol (2.8 mmol/L), which were slightly elevated at baseline, the biochemical parameters were within the normal range. T2D and T2D-at-risk volunteers had elevated levels of all physical parameters at baseline before ACN supplementation (Table 5.1). The mean BMI in

T2D-at-risk and diabetic participants were 30.27±3.44 kg/m2 and 32.59±7.37 kg/m2 respectively, which is in the obese range (>30 kg/m2) (304). The baseline level of WHR was

0.91±0.085 cm in T2D-at-risk and 0.98±0.09 cm in T2D individuals, which puts both groups in the central/abdominal obesity category (262). Systolic blood pressure (SBP) was elevated in

T2D-at-risk and T2D participants at a mean of 140±6.70 mm/Hg in T2D-at-risk and

136.33±11.49 mm/Hg in T2D participants. The mean DBP was 85.57±2.84 mm/Hg and

83±3.39 mm/Hg in diabetic-at-risk and diabetic individuals respectively. These mean SBP and

DBP figures are in the pre-hypertension status reference range (305, 306).

The mean FBG was within the normal range value in the T2D-at-risk group (5.05±1.0 mmol/L), while in T2D participants the mean of FBG was slightly elevated prior to the intervention (6.30±1.56 mmol/L). Total cholesterol was slightly elevated in both groups of

diabetics (4.01±1.26 mmol/L) and pre-diabetics (4.44±1.54 mmol/L). As shown in Table 5.1, the mean TG was elevated at baseline in T2D participants (2.08±1.36 mmol/L). In pre-diabetic individuals, the mean TG was slightly above the reference range (1.36±0.92 mmol/L). The mean LDL-cholesterol was slightly above the normal range in diabetic (2.83±1.09 mmol/L) and elevated in diabetic-at-risk groups (3.35±1.52 mmol/L). HDL-cholesterol was lower than the recommended level (<1.0) in T2D (0.75±0.27), and diabetic-at-risk populations

(0.82±0.34) (307). Uric acid was slightly elevated at the beginning of the intervention in the all groups. However, it was still within the normal range (Table 5.1).

5.4.2 Dietary intake, dietary inflammatory index score and its correlation with

biomarkers of inflammation

A summary of mean daily energy intake and dietary composition for each of the three groups and their between groups comparison is presented in Supplemental Table 5.1. The mean

DII score and comparison between the three groups of healthy, T2D-at-risk, and T2D is presented in Supplemental Table 5.2 and Figure 5.1. As demonstrated, the mean DII scores of

T2D group is significantly lower than the healthy group (P=0.04).

* P = 0 .0 4

0 .0

-0 .5

-1 .0

I D -1 .5

-2 .0

-2 .5 H e a lthy T 2 D a t ris k T 2 D

Figure 5.1. Diet inflammatory index (DII) score comparison of three groups. Data are presented as a mean ± standard error (SE). * Significant intervention effect. Lower DII scores suggest a more anti-inflammatory diet and higher ones imply a more pro- inflammatory dietary habit (38, 39).

There was a significant difference in the mean daily energy intake of healthy participants with both T2D at-risk and T2D groups. Comparison of dietary composition between three groups showed that the T2D at-risk group had a significantly higher mean of carbohydrate, total fat and MUFA intake than healthy individuals. Mean protein, total fat, saturated fat, MUFA and cholesterol intake of the T2D group was significantly higher than the healthy group. No other significant differences were observed.

Spearman correlation analysis of DII score and biomarkers of inflammation in healthy, T2D-at-risk, and T2D participants is presented in Figures 5.2, 5.3, and 5.4 respectively.

There were no significant correlations between DII score and the corresponding pro- inflammatory biomarkers in the healthy, pre-diabetic or the T2D groups.

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r = - 0 .4 1 g

p r = 0 .2 3

4 0 0 (

8 1

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3 0 0 L

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- g

F r = - 0 .2 5 p

r = 0 .2 8 4 2 0 0 0 0 (

t s

3 1 5 0 0 0 i

e R 2 1 0 0 0 0

1 5 0 0 0

-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3

D I I S c o r e D I I S c o r e )

L 2 5 0 0 0

/ P = 0 .9 8 )

P = 0 .9 2 2 .0 g L

m r = - 0 .0 4

( m r = 0 .0 3

2 0 0 0 0 /

g R

1 .5 p

(

- n

s 1 5 0 0 0

t p

1 .0 e

1 0 0 0 0 L

0 .5 5 0 0 0

-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 D I I S c o r e D I I s c o r e

Figure 5.2. Spearman correlation between diet inflammatory index (DII) score of healthy participants and biomarkers of inflammation.

1 0 8 P = 0 .8 8 P = 0 .4 3 r = 0 .0 4 r = - 0 .2 3 8 )

L 6

m /

6 g

(

)

6 4

- m

4 L

p (

2 8

L I

-6 -4 -2 0 2 4 -6 -4 -2 0 2 4

D I I S c o r e D I I S c o r e

)

m / 6 0 0 /

1 5 0 0 g

P = 0 .1 3 g P = 0 .0 9

(

r = - 0 .4 3 8 r = 0 .4 8



1 L

4 0 0 -

I 1 0 0 0

L I

2 0 0 5 0 0

-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 D I I S c o r e D I I S c o r e

1 0 1 5 0 0 0 ) P = 0 .4 3

P = 0 .6 8 L

r = 0 .1 3 m

r = - 0 .1 2 /

8 g

p (

1 0 0 0 0

 )

6 -

T g

4 p (

5 0 0 0

t s

2 i

e R

-6 -4 -2 0 2 4 -6 -4 -2 0 2 4

D I I S c o r e D I I S c o r e )

P = 0 .3 8 4 P = 0 .1 5 ) 6 0 0 0 0

L L

/ r = 0 .4 1 g

r = - 0 .3 8 m

( p

(

P 4 0 0 0 0

- e

2 s

h L

2 0 0 0 0 1

-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 D I I S c o r e D I I s c o r e

Figure 5.3. Spearman correlation between diet inflammatory index (DII) score of pre-diabetic participants and biomarkers of inflammation. 97

8 8 P = 0 .5 5 P = 0 .9 2 r = - 0 .1 8 r = - 0 .0 3

6 6

)

/ g

g 4 4

(

L I I 2 2

0 0 -5 -4 -3 -2 -1 0 -5 -4 -3 -2 -1 0 D I I S c o r e D I I S c o r e

P = 0 .6 6 1 0 0 0 2 5 0 0 P = 0 .2 9 r = - 0 .1 3 r = - 0 .3 2 2 0 0 0

8 0 0 )

)

L /

g 1 5 0 0

m 6 0 0

(

 (

1 0 0 0 8

4 0 0 R

I I 2 0 0 5 0 0

0 0 -5 -4 -3 -2 -1 0 -5 -4 -3 -2 -1 0 D I I S c o r e D I I S c o r e

P = 0 .1 6 P = 0 .6 3 2 0 r = - 0 .6 0 r = - 0 .1 5 1 5 0 0 0

) 1 5

)

g L

p 1 0 0 0 0

(

1 0 /



(

N t

T 5 s

i 5 0 0 0

e R 0 -5 -4 -3 -2 -1 0 0 D I I S c o r e -5 -4 -3 -2 -1 0 D I I S c o r e

P = 0 .4 0 8 0 0 0 0 r = - 0 .2 6 P = 0 .6 6 8 r = - 0 .1 3

6 0 0 0 0 )

6 L

)

g g

p 4 0 0 0 0

(

- s

L 2 0 0 0 0

h 2

0 0 -5 -4 -3 -2 -1 0 -5 -4 -3 -2 -1 0 D I I S c o r e D I I s c o r e

Figure 5.4. Spearman correlation between diet inflammatory index (DII) score of type 2 diabetes participants and biomarkers of inflammation. 98

5.4.3 Effect of anthocyanin intervention on physical parameters

Post-intervention measurement did not show any significant changes in anthropometric data, BMI and WHR of participants in all groups. The intervention significantly improved blood pressure in the T2D and T2D-at-risk groups. In the T2D-at-risk individuals, systolic blood pressure (SBP) reduced from 140±6.70 mm/Hg to 131.6 mm/Hg (P=0.0001), and in diabetic volunteers, it reduced to 127.33±7.54 mm/Hg from 136.33±11.49 mm/Hg at baseline

(P=0.0002). Diastolic blood pressure (DBP) decreased to 81.5±2.19 in diabetic-at-risk volunteers (P=0.0022), and to 79.75±2.52 in T2D participants (P=0.0067). Blood pressure did not differ from baseline in the group of healthy individuals.

5.4.4 Effect of dietary anthocyanin consumption on fasting blood glucose

FBG improved significantly in pre-diabetic group after 4 weeks of ACN intervention, and it reduced to 4.30±1.24 mmol/L at the end of the study (P=0.032). In the T2D group, the mean FBG slightly reduced to 6.03±1.57 by the end of the trial. FBG did not differ from baseline value for the healthy participants and remained within the reference range.

5.4.5 Anthocyanin intervention and serum lipids

Supplementing high amounts of ACN (320 mg/day) over the course of four weeks reduced total cholesterol levels in T2D-at-risk individuals. However, this reduction did not reach the statistical significance (P=0.059). The mean triglyceride and HDL-cholesterol levels

did not differ significantly in any of the three groups. LDL-cholesterol levels reduced significantly in T2D-at-risk participants (P=0.049), but it did not change in the other two groups (Table 5.1).

5.4.6 Effect of anthocyanin intake on uric acid

Uric acid reduced significantly from 334.5±91.65 umol/L to 281.0±74.66 umol/L in the pre-diabetic group (P=0.021). This marker did not change from baseline in healthy and T2D individuals.

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Table 5.1. Baseline and post-intervention measures for healthy, type 2 diabetes at-risk and type 2 diabetes individuals. Data are presented as mean ± standard deviation (SD).

Parameters Healthy Individuals T2D at-risk individuals T2D Individuals Normal Pre Post P-value Pre Post P-value Pre Post P-value Reference intervention intervention intervention intervention intervention intervention Range (305, (n=14) (n=14) (n=14) (n=14) (n=12) (n=12) 308, 309) Gender (female/ 6/8 6/8 4/8 male) Age (years) 35.2±11.02 50.1±11.3 57.7±8.4

Physical parameters BMI 18.5-24.9 23.36±1.36 23.29±1.19 0.80 30.27±3.44 30.07±3.41 0.15 32.59±7.37 32.35±7.15 0.093 (kg/m2) WHR Female< 0.85 0.76±0.03 0.75±0.03 0.31 0.85±0.05 0.84±0.05 0.095 0.87±0.05 0.84±0.05 0.22 Male <0.90 0.84±0.05 0.83±0.05 0.95±0.07 0.93±0.08 1.03±0.07 1.03±0.04

SBP 90-120 118.78±10.26 117.92±10.34 0.91 140±6.70 131.60±3.06 0.0001* 136.33±11.49 127.33±7.54 0.0002* (mm/Hg) DBP 60-80 72.92±3.65 72.21±3.42 0.63 85.57±2.84 81.5±2.19 0.0022* 83±3.39 79.75±2.52 0.0067* (mm/Hg) Biochemical parameters FBG 4-6 4.71±0.82 4.3±1.10 0.38 5.05±1.0 4.30±1.24 0.032* 6.30±1.56 6.03±1.57 0.77

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(mmol/L) TC <3.9 4.16±1.08 3.65±1.24 0.31 4.44±1.54 3.68±1.43 0.059 4.01±1.26 3.68±1.43 0.71 (mmol/L) Triglycerides <1.1 0.93±0.41 0.82±0.42 0.91 1.36±0.92 1.08±0.55 0.45 2.08±1.36 1.63±1.10 0.15 (mmol/L) LDL <2.5 2.80±1.0 2.34±1.08 0.33 3.35±1.52 2.61±1.19 0.049* 2.83±1.09 2.64±1.31 0.90 cholesterol (mmol/L) HDL >1 1.17±0.48 1.14±0.33 0.98 0.82±0.34 0.84±0.39 0.99 0.75±0.27 0.70±0.19 0.96 cholesterol (mmol/L) Uric acid 200-430 311.32±80.60 291.89±99.75 0.67 334.5±91.65 281.0±74.66 0.021* 339.2±133.97 295.1±76.92 0.11 (umol/L)

Note: WHR is presented separately for female and male; however, due to the low number of the participants in each group, analysis is done on the mean of both genders.

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5.4.7 Effect of anthocyanin intervention on pro-inflammatory biomarkers

The differences in biomarkers of inflammation before and after ACN intervention in three groups of healthy, T2D-at-risk and T2D participants are presented in Figure 5.5.

Statistically significant reduction in IL-6 (P=0.003), IL-18 (P=0.01) and TNF-α (P=0.035) were observed with intervention in T2D participants. IL-1Rα and leptin did not differ in healthy or pre-diabetic participants but showed a trend towards reducing in diabetic individuals

(P=0.067 and P=0.058 respectively). IL-8, resistin and hs-CRP did not change in any of the groups.

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P r e - in t e r v e n t io n P r e - in t e r v e n t io n P o s t - in t e r v e n t io n P o s t - in t e r v e n t io n

6 8 )

) 6 L

L 4 *

g p

p 4

(

6 -

- 2

L I I 2

0 0 H e a lth y T 2 D a t r isk T 2 D H e a lth y T 2 D a t r isk T 2 D

P r e - in t e r v e n t io n P r e - in t e r v e n t io n P o s t - in t e r v e n t io n P o s t - in t e r v e n t io n 5 0 0 2 5 0 0

4 0 0 ) 2 0 0 0

) *

m /

3 0 0 g 1 5 0 0

(

 8

2 0 0 R 1 0 0 0

I L 1 0 0 I 5 0 0

0 0 H e a lth y T 2 D a t r isk T 2 D H e a lth y T 2 D a t r isk T 2 D

P r e - in t e r v e n t io n P r e - in t e r v e n t io n P o s t - in t e r v e n t io n 1 5 0 0 0 P o s t - in t e r v e n t io n

1 0 )

) 8

L m

/ 1 0 0 0 0

m *

/ g

g 6

(

 t

- 4

s i

F 5 0 0 0

e T 2 R

0 0 H e a lth y T 2 D a t r isk T 2 D H e a lth y T 2 D a t r isk T 2 D

P r e - in t e r v e n t io n P r e - in t e r v e n t io n P o s t - in t e r v e n t io n P o s t - in t e r v e n t io n 5 0 0 0 0 5

4 0 0 0 0

4 )

)

/ g

3 g 3 0 0 0 0

(

i R

2 t 2 0 0 0 0

L h 1 1 0 0 0 0

0 0 H e a lth y T 2 D a t r isk T 2 D H e a lth y T 2 D a t r isk T 2 D

Figure 5.5. Biomarkers of inflammation pre- and post- anthocyanin supplementation. Data are presented as a mean ± SE. * Significant intervention effect. Statistically significant reduction in IL-6 (P=0.003), IL-18 (P=0.01) and TNF-α (P=0.035) was observed with intervention in T2D participants.

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5.5 Discussion

This study found a statistically significant reduction of pro-inflammatory molecules IL-

6, IL-18 and TNF-α in diabetic participants with ACN supplementation. Considering the key role of metabolic inflammation in the development of T2D, modulation of pro-inflammatory biomarkers such as interleukins and tumour necrosis factors in the present study is a promising effect to prevent progression of the disease in T2D individuals. Reduction in the release of pro- inflammatory biomarkers may diminish the stress on insulin-sensitive tissues and pancreatic cells, reducing the production of ROS in mitochondria (31, 32). This may result in the reduction of oxidative stress which is associated with insulin resistance improvement and contributes to blood glucose level reduction (289). As literature suggests, to stop inflammation, the inflammatory response should be limited via inhibition of transcription factors nuclear factor- kappa B (NF-κB) and c-jun terminal NH2-kinase (JNK) activation (310, 311). Given this, transcription factors are believed to be essential components in stimulating inflammatory responses (310). Furthermore, there is a proven link between insulin resistance and the increased production of tumour necrosis factors and interleukins through the NF-κB and JNK signalling pathways (311). Our study supports the findings of other ACN trials. Karlsen and colleagues (2007) demonstrated a reduction of several NF-κB and JNK-associated pro- inflammatory chemokines and cytokines in the plasma of healthy adults after three weeks of supplementation with ACN, suggesting the inhibitory role of flavonoid ACN on NF-κB and

JNK activation (164). A clinical trial supplementing ACN-rich sweet cherries as an intervention indicated that hs-CRP was decreased after four weeks of supplementation (261).

Zhu and colleagues (2012), reported the inhibition of inflammatory response in hypercholesteraemic individuals after 24 weeks supplementation with purified ACN (191).

Although the mechanisms underlying the inhibitory role of ACN on NF-κB and JNK signalling 105

pathways are not fully understood, it has been suggested that the end products and metabolites of ACN might be responsible for the ACN anti-inflammatory effects (164). In fact, metabolites of ACN that serve as redox buffer are shown to suppress oxidative stress, thereby stop the inflammatory response by direct ROS scavenging which eventually leads to the reduction of pro-inflammatory cytokines and chemokines (164).

A potential confounder to the present study’s findings is that the T2D group had a significantly lower DII score than the healthy participants. These data suggest an anti- inflammatory promoting diet of T2D group, which was in agreement with the reduction of pro- inflammatory molecules IL-6, IL-18 and TNF-α in the present study and what has been previously reported by Shivappa and colleagues (2015) (40). As previous studies have indicated, in addition to energy intake, dietary pattern is strongly associated with the onset and development of inflammation (38, 39). A diet high in fruit, vegetables, legumes, olive oil and fish, and low in red meat, such as the Mediterranean diet, confers anti-inflammatory properties

(36, 312), whereas the Western diet, characterised by high intake of red and processed meats, has been widely associated with higher levels of pro-inflammatory biomarkers including IL-6,

TNF-a and hs-CRP (34, 35). Specific nutrients including complex carbohydrates (313), n-3 polyunsaturated fatty acids (314), vitamin C (315), vitamin E (316), β-carotene (317), magnesium (318) and fibre (319), have consistently been associated with lower levels of inflammation. Therefore, the improvement of pro-inflammatory biomarkers in T2D participants might have been due to the anti-inflammatory dietary habit of these participants.

Post ACN intervention biochemical analysis of the current trial demonstrated significant decreases in FBG, LDL-cholesterol and uric acid in diabetic at-risk participants, 106

with no significant alterations in the other two groups. Hyperglycaemia is one of the main indicators of T2D and one of the most important risk factors for pre-diabetic individuals (320).

The hypoglycemic effect of plant polyphenols and flavonoids, including ACN, has been previously reported in the literature (298). Flavonoids from different sources have shown the potential to alleviate T2D through their effect on glucose absorption and insulin level or secretion (178, 321, 322). In the present intervention, consumption of high doses of ACN in a period of four weeks was associated with a significant reduction of FBG level in pre-diabetic adults, but not in T2D individuals. Others have found a significant effect with longer intervention duration. Dabaghian et al. (2015), demonstrated a significant improvement in FBG after three months of flavonoid intervention in T2D adults (190). Li and colleagues (2015), found a significant reduction of FBG in T2D adults after ACN supplementation for six months

(233). Lack of a significant reduction for FBG in T2D participants in the current trial might be explained through intervention duration.

Hyperlipidaemia is well recognised as one of the most important risk factors for cardiovascular disease in T2D and pre-diabetic people (323). After four weeks of ACN supplementation in the current study, LDL cholesterol significantly reduced in pre-diabetic adults but not in the T2D group. In an intervention trial of ACN supplementation with the same daily dose as our study, but longer duration (six months), significant improvements of TC, triglycerides, and LDL-cholesterol were observed in T2D adults (233). Thus, perhaps the duration of the intervention in the current study was not long enough to observe statistically significant effects on the lipid profile of T2D individuals.

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Uric acid is recognised as one of the strongest risk factors for T2D, as the level of serum urate is associated with the level of blood glucose (324). Flavonoids and their subclasses are suggested as hypouricemic agents through evidence from animal and human studies (325, 326).

Plasma urate was significantly lowered after only a few hours of cherry consumption in healthy volunteers in a study by Jacob and colleagues (326). This effect could be partially due to the inhibition of xanthine dehydrogenase and xanthine oxidase (XDH/XO) activities in the liver

(325). The XDH/XO is a complex Metallo-flavoprotein, which is able to catalyse oxypurines

(hypoxanthine and xanthine) to uric acid. Inhibition of XDH/XO activities decreases the uric acid levels and results in a hypouricemic effect (327). The finding from the present study to some extent confirmed the reported effect of the polyphenols and flavonoids in the literature as the uric acid level was significantly reduced in at-risk groups.

Blood pressure was lowered in both the pre-diabetic and T2D groups following four weeks of ACN supplementation. This is a desirable effect, as even a slight reduction of BP at population level strongly contributes to reduced risk of hypertension and cardiovascular disease. Reduction of SBP and DBP by 3.3 mm Hg and 1.4 mm Hg respectively is associated with a reduced risk of stroke and cardiovascular mortality by 22% (328). Accumulating evidence confirms the vasodilatory properties of polyphenolic antioxidants via enhancement of nitric oxide in the plasma (329), associated with relaxation of vascular tone and lowering blood pressure (330). In addition, nitric acid is known for reducing plasma pro-inflammatory cytokines (330). The result of the current study is in agreement with the suggested properties of polyphenolic antioxidants.

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5.6 Conclusion, limitations and direction for future studies

Given the improvement of some pro-inflammatory biomarkers in the T2D group, the findings from the present study may confirm the anti-inflammatory potential of ACN reported by the literature. Furthermore, anti-inflammatory dietary patterns of T2D participants could be a potential confounder to the present study’s findings and may have accelerated the anti- inflammatory effect of the ACN capsules supplemented in this trial. Some physical and clinical parameters of diabetic and pre-diabetic participants were also improved by the intervention, particularly in the at-risk group. However, in the current study, unreported changes in the lifestyle, dietary habits and even medications of participants, individuals with different ethnic background, age-range variation between groups and different duration of diabetes between

T2D volunteers, which may possibly affect the result, could not be excluded. Therefore, although the findings from the present intervention trial are of great interest, in order to confirm the health-promoting effects of ACN on T2D and T2D-at-risk populations, further investigations with a larger samples size at the population level, age-matched groups, longer duration of intervention and placebo group in addition to the intervention one are required.

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Supplemental Table 5.1. Summary of the participants’ group mean daily dietary composition and comparison between the three groups.

Healthy T2D at risk T2D (A) (B) (C)

Daily Dietary Mean±SD P-value Mean±SD P-value Mean±SD P-value intake A vs. B B vs. C C vs. A

Energy (kJ/d) 6236.3±1657 0.04* 8532.4±2015.3 0.99 8565.4±2869.8 0.05* Carbohydrate 160.09±55.2 0.02* 218.4±36.0 0.15 177.6±60.2 0.70 (g/d) Protein (g/d) 67±17.7 0.07 95.2±33.1 0.89 100.8±35.1 0.03* Total Fat 45.9±15.5 0.04* 75.1±30.8 0.88 80.6±33.0 0.01* (g/d) Saturated Fat 18.8±7.7 0.07 31.5±15.17 0.88 34.0±15.3 0.02* (g/d) MUFA (g/d) 16.9±6.5 0.04* 27.3±11.1 0.86 29.5±11.7 0.01* Cholesterol 220.06±154.6 0.14 408.6±290.4 0.64 497.4±222.6 0.02* (mg/d) Alcohol (g/d) 12.3±14.9 0.97 13.6±12.4 0.90 16.3±7.5 0.80 Fibre (g/d) 19.7±9.7 0.86 21.7±9.9 0.73 18.7±8.6 0.97 Thiamine 2.0±1.8 0.97 2.2±2.2 0.98 2.3±2.1 0.91 (mg/d) Riboflavin 2.2±1.5 0.99 2.2±0.5 0.73 2.6±1.3 0.77 (mg/d) Niacin (mg/d) 22.0±13.3 0.59 27.0±10.5 0.91 29.0±13.1 0.38 Vitamin C 70.0±43.5 0.99 70.9±32.1 0.60 56.0±34.9 0.64 (mg/d) Vitamin E 7.4±3.1 0.29 9.7±2.7 >0.99 9.7±4.5 0.30 (mg/d) Vitamin A 471.0±235.5 0.43 610.8±258.1 0.99 625.3±302.8 0.37 (ug/d) β-carotene 1913.7±1926.6 0.75 1442.2±969.4 0.61 2064.3±1700.1 0.97 (ug/d) Total Folate 479.0±264.6 0.91 514.4±136.0 0.99 517.5±222.5 0.90 (ug/d) Magnesium 279.3±89.3 0.20 349.1±73.9 0.60 310.9±119.7 0.72 (mg/d) Iron (mg/d) 8.0±2.2 0.06 11.0±2.9 0.94 10.6±3.7 0.12 Zinc (mg/d) 8.8±2.4 0.39 11.0 ±4.3 0.92 11.6±4.9 0.23 omega-6 FA 5.8±3.3 0.33 7.6±2.2 0.89 8.2 ±3.3 0.16 (g/d) omega-3 FA 1.04±0.8 0.80 1.2±0.7 0.89 1.3±0.6 0.54 (g/d)

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Caffeine (g/d) 0.8±2.2 0.49 0.2±0.3 0.96 0.1±0.07 0.36

MUFA: monounsaturated fatty acids, FA: fatty acids

Supplemental Table 5.2. The Mean DII score and comparison between the three groups of healthy, T2D at-risk, and T2D.

Participants Mean DII Score P-value Mean±SD Healthy (A) -0.28±1.35 A vs. B: 0.19

T2D at-risk (B) -1.45±1.94 B vs. C: 0.70

T2D (C) -1.97±1.28 C vs. A: 0.04*

DII: diet inflammatory index. * Significant difference between groups.

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Chapter 6

Effect of anthocyanin supplementation on the micro-RNA profile of diabetic and non-diabetic adults.

This chapter includes a co-authored paper, which I am the primary author. It is prepared to be submitted to the Molecular Nutrition & Food Research journal.

Authors: Elham Nikbakht, Jelena Vider, Ping Zhang, Lauren T. Williams, Indu Singh, Natalie Colson.

My contribution to this paper involved:

 Review of the literature.  Study design and experimental methodology.  Blood collection and processing.  Physical measurements.  RNA isolation and miRNA expression experiment.  miRNA, physical and biochemical data analysis.  Graph, table and figure preparation.  Manuscript writing, preparation and revision.

Elham Nikbakht

27th April 2018

Jelena Vider’s contribution to the paper involved:

 Training and assisting with RNA isolation and conducting miRNA expression experiment.  Assistance with miRNA data analysis.

Ping Zhang’s contribution to the paper involved:

 Assistance with miRNA data analysis.

Lauren T. Williams’s contribution to the paper involved:

 Manuscript writing and revision.

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Indu Singh’s contribution to the paper involved:

 Study design.  Manuscript writing and revision.

Natalie Colson’s contribution to the paper involved:

 Primary supervisor of the project.  Review of the literature.  Study design and experimental methodology.  Statistical analysis.  Manuscript writing and revision.

Corresponding author of paper:

Dr Natalie Colson 27th April 2018

Supervisor:

Dr Natalie Colson 27th April 2018

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6.1 Abstract

micro-RNAs (miRNAs) are an abundant class of small, non-protein coding RNAs, believed to be involved in post-transcriptional regulation of gene expression, thereby playing a significant role in the pathophysiology of various disorders, including type 2 diabetes (T2D).

Findings from in vitro and in vivo animal experiments have provided evidence on the ability of polyphenolic antioxidants to interact with cells’ signalling pathways, modulate the activity of transcription factors, and regulate the expression of miRNAs. However, there is no human clinical data to support the idea that a high intake of dietary polyphenols, including anthocyanin

(ACN), might be beneficial to regulate the miRNA profile of T2D adults. Therefore, a clinical trial was conducted on T2D and non-diabetic individuals to investigate the effect of four weeks of ACN intervention (320 mg/day) on participants’ miRNA profile, biochemical and physical parameters. NanoString nCounter Human v3 miRNA Expression Assay kit was utilised to profile miRNA and software was used to detect differentially expressed miRNAs, as fold- change (FC) values, across two groups of healthy and T2D, pre- and post-supplementation.

Ten miRNAs in the non-diabetic group including miR-1827 (FC=2.37), miR-574-5p

(FC=3.21), miR-603 (FC=7.83), miR-199a-3p+miR-199b-3p (FC=4.45), miR-519d-3p

(FC=1.87), miR-216b-5p (FC=5.43), miR-206 (FC=3.01), miR-548d-5p (FC=3.69), miR-1264

(FC=2.93) and miR-493-3p (FC= -2.30), and five in the T2D group including miR-944

(FC=3.74), miR-593-3p (FC=3.24), miR-548b-3p (FC=2.14), miR-93-5p (FC= -1.63) and miR-1827 (FC= -1.89) were significantly up-regulated or down-regulated post- supplementation. Modification of non-diabetic groups’ miRNA profile was aligned with the improvement of biochemical and physical characteristics such as fasting blood glucose, uric acid and waist-hip ratio. These findings demonstrate the potential of polyphenols to target the miRNA profile in individuals with or without T2D, suggesting ACN as an effective therapeutic

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agent, and miRNAs as a novel molecular target to manage metabolic disorders such as T2D and its associated complications.

Keywords: type 2 diabetes, polyphenols, anthocyanin, miRNA, molecular therapy

6.2 Introduction

Type 2 diabetes (T2D) is a multifactorial metabolic disorder mainly characterised by chronic hyperglycaemia resulting from pancreatic beta cell (β-cell) dysfunction, and progressive insulin resistance (6). Untreated T2D is associated with long-term complications including failure of various organs such as heart, kidneys, and eyes (4), in addition to short- term complications including hypoglycaemia and diabetic ketoacidosis (331). T2D aetiology results from the interaction between genetic predisposition and environmental influences (200), suggesting the involvement of diabetes-related genes and epigenetic mechanisms (332).

Environmental and behavioural risk factors such as inappropriate diet may alter gene expression; thus affecting the molecular phenotype, leading to T2D (Figure 1) (333). T2D is well recognised as an inflammatory condition, having altered cytokines and chemokines (18).

Environmental risk factors may trigger an inflammatory response, promoting inflammation- mediated insulin resistance and eventually T2D (18). Pro-inflammatory stimuli, including hyperglycaemia, hyperlipidaemia, oxidative stress, and other inflammatory mediators, are able to deregulate gene expression at the post-transcriptional level, without inducing any changes in the underlying DNA strand, through modification of specific sequences named micro-RNAs

(miRNAs) (Figure 1) (332, 333).

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miRNAs are an abundant class of endogenous, short (approximately 18-23 nucleotides in length), non-protein coding RNAs which play a key role in the post-transcriptional regulation of gene expression (334). The post-transcriptional regulatory role of miRNA is conducted by partial complementary binding to the 3’ untranslated regions (3’ UTR) of target messenger-RNAs (mRNAs) in a sequence-dependent manner (335). As a result, mRNA becomes double-stranded and is prevented from being translated into a protein (335). Given this, up- or down-regulation of miRNAs should eventually alter the expression of associated genes in target cells and potentially affect the function of corresponding tissues and organs

(Figure 1). It has been estimated that more than half of human genes are influenced by the post- transcriptional regulatory role of miRNAs. Thus, miRNAs are believed to participate in almost all cellular processes, being involved in the pathophysiology of various diseases (89, 336).

Over- or under-expression of certain miRNAs or dysregulation of miRNAs, have been shown to be directly linked with diabetic complications (336, 337), accordingly, miRNAs have been implicated in the pathogenesis of the metabolic and immune abnormalities underlying T2D aetiology (Figure 6.1) (83, 338, 339).

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Figure 6.1. Type 2 diabetes development. Type 2 diabetes occurs as a result of the interactive relationship between environmental risk factors and genetic susceptibility. Environmental influences such as excess intake of sugar may induce hyperglycemia, inflicting stress on insulin-sensitive tissues and pancreatic cells causing insulin resistance. Insulin resistance triggers excess production of reactive oxygen species (ROS) in mitochondria which induces oxidative damage to the corresponding cells, activates inflammatory signalling cascades inside the cells, and leads to the local stimulation of pro-inflammatory biomarkers. Chronic overproduction of pro-inflammatory biomarkers promotes inflammation in other tissues and my deregulate gene expression at the post-transcriptional level, without inducing any changes in the underlying DNA strand, through modification of specific sequences named micro-RNAs.

Indeed, a differential miRNA profile between T2D patients and healthy controls was found in whole blood (340), serum (101), plasma (102) and plasma exosomes (339). Blood miRNA analysis of T2D patients showed that seven miRNAs including miR-9, miR-29a, miR-

30d, miR-34a, miR-124a, miR-146a and miR-375 were significantly upregulated compared with the individuals with normal glucose tolerance (NGT) (101). In addition, some reports

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show that the miRNA profile in plasma or serum is deregulated in the pre-diabetic state before the development of overt T2D (341-343). Interestingly, for some miRNAs, such as miR-126 and miR-23a, the difference was significantly greater in T2D than in pre-diabetic individuals, suggesting that their levels could be correlated with the worsening of glycaemic status (342,

344). In another study Kong and colleagues (2011) found that miR-9, miR-29a, miR-34a, miR-

146a, and miR-375 were significantly elevated in T2D patients compared with individuals with pre-diabetes (101). Furthermore, Zampetaki et al. (2010) reported a lower level of plasma miR-

20b, miR-21, miR-24, miR-15a, miR-126, miR-191, miR-197, miR-223, miR-320 and miR-

486 in T2D patients, but a modest increase in miR-28-3p (102). Karolina et al. (2011) identified miR-144, miR-146a, miR-150 and miR-182 in the blood of T2D patients as the signature miRNAs for predicting of T2D (103). Considering the findings of previous studies, it can be concluded that T2D, pre-diabetics and healthy individuals have distinct miRNA profiles.

In addition, it has been demonstrated that miRNAs are involved in multiple pathways, including glucose homeostasis, β-cell differentiation, insulin signalling, immune-mediated inflammation, lipid metabolism, and food intake regulation pathways influencing T2D onset and progression (345-348). Therefore, miRNA-based therapeutic approaches for managing

T2D and associated complications offer an innovative treatment opportunity with in vitro experiments showing positive outcomes (349, 350).

Anti-diabetic effects of polyphenols and flavonoid-rich extracts through miRNA regulation have been well established in cell culture and animal experiments (172, 298, 351).

Anthocyanins (ACN), a subgroup of flavonoids, are the largest water-soluble phytochemicals, and are widely distributed in blue, purple and red coloured fruits, vegetables, leaves, and 118

flowers (299). Beneficial effects of ACNs on T2D and its complications through their antioxidant activity have been demonstrated in animal and clinical studies (188, 191, 259, 352-

354). More recently, findings from cellular and animal experiments indicated that polyphenolic compounds such as ACN could interact with cellular signalling cascades, and control the activity of transcription factors, regulating the expression of miRNAs and associated genes

(172). Therefore, targeting miRNAs by polyphenolic antioxidants represents a novel therapeutic approach to controlling metabolic disorders, including T2D. To the best of our knowledge, there is no human clinical data to support the idea that T2D individuals’ miRNA profile could be influenced by the administration of a polyphenolic antioxidant such as ACN.

Therefore, a clinical trial was conducted on T2D and healthy volunteers, supplemented with

ACN for 28 days (320 mg/day), to investigate participants’ miRNA profile prior to and post- supplementation. An innovative technology, the Nanostring n Counter analyser and Human v3 miRNA Expression Assay kit was used in this research to allow us profiling of several hundred miRNA molecules expression simultaneously. This method is significant in that it provides the opportunity to undertake computational approaches to identify differentially expressed miRNAs across the groups. This research may add to the body of knowledge on the effect of

ACN on miRNA expression in humans, with a particular focus on T2D. It may also provide insights into the possibility of using miRNAs as prognostic, diagnostic and regulatory biomarkers in managing various chronic diseases, as well as T2D.

6.3 Methods and materials

This study is part of the original intervention trial (n=40) conducted at Griffith

University, examining the effect of ACN supplementation on biomarkers of inflammation and gene expression in T2D, pre-diabetic and healthy adults. In fact, the samples used in this study 119

were randomly selected from the original trial. For the current study, whole blood miRNAs, along with biochemistry and physical parameters, were assessed at baseline and post- intervention in a cohort of 18 participants (nine T2D, and nine healthy). More information regarding the methods and materials is provided in Chapter 4, page 66. Relevant sections to this chapter are listed below:

Section 4.1 Human clinical trial study design (page 67) Section 4.2 Volunteer recruitment and screening (page 67-68) Section 4.3 Inclusion and exclusion criteria (page 68-69) Section 4.4 Supplement information (page 70-72) Section 4.5 Dietary assessment (page 72) Section 4.6 Dietary inflammatory index calculations (page 72-73) Section 4.7 Physical measurement (page 73) Section 4.8 Blood collection and processing (page 74) Section 4.9 Biochemistry assay (page 74-75) Section 4.12 RNA isolation (page 76) Section 4.13 micro-RNA expression profile (page 77-78) Section 4.14 Normalisation of micro-RNA expression (page 78) Section 4.19 Statistical analysis (page 84-85)

6.4 Results

Demographic characteristics and physical and biochemical measures of participants prior to ACN intervention are demonstrated in Table 6.1. Participants of both groups of healthy and T2D were matched in baseline physical characteristics. The average body mass index

(BMI), and waist-hip ratio (WHR) of healthy volunteers was above the healthy range at baseline (BMI=27.7±5.1 kg/m2, considered overweight, and WHR=0.89±0.12 cm, considered borderline abdominal obesity). The mean of BMI and WHR of T2D participants were 120

32.9±7.08 kg/m2 and 0.99±0.11 cm, categorised as obesity and abdominal obesity respectively.

Systolic blood pressure (SBP) of the healthy participants at baseline was considered normal, however, it was slightly elevated in the T2D group (132.4±10.7 mm/Hg). Diastolic blood pressure (DBP) was within the normal reference range in both groups. The performed laboratory biochemistry tests were within the normal reference range in healthy participants.

FBG, TG, HDL cholesterol, and hs-CRP were outside of the recommended range in T2D group prior to ACN supplementation. Analysis of baseline measurements revealed a statistically significant difference in FBG (P=0.007), and hs-CRP (P=0.022) between healthy and T2D individuals (Table 6.1).

Table 6.1. Demographic and baseline characteristics of healthy and type 2 diabetes individuals. Data are presented as the mean ± standard deviation (SD). characteristics Optimal Healthy T2D P-value Reference Individuals Individuals Range (305, 308, 309) Gender 4/5 3/6 (female/male) Age (years) 44.3±11.5 58.8±6.6

Physical parameters BMI 18.5-24.9 27.7±5.1 32.9±7.08 0.11 (kg/m2)

WHR Female> 0.85 0.80 ±0.07 0.87±0.06 0.094 (cm) Male> 0.90 0.96±0.08 1.05±0.07 SBP 90-120 124.6±16.06 132.4±10.7 0.24 (mm/Hg) DBP 60-80 80.88±9.08 82.2±3.4 0.68 (mm/Hg) Biochemical parameters FBG 4-6 4.67±0.99 6.31±1.20 0.007* (mmol/L) 121

TC <3.9 3.62±1.33 3.74±1.07 0.82 (mmol/L) Triglycerides <1.1 1.10±1.11 1.73±1.12 0.28 (mmol/L) LDL <2.5 2.43±1.21 2.55±0.91 0.81 cholesterol (mmol/L) HDL >1 0.97±0.41 0.86±0.23 0.50 cholesterol (mmol/L) Uric acid 200-430 288.38±108.65 320.72±149.35 0.62 (umol/L) hs-CRP <1 1.01±0.53 3.27±2.44 0.022* (mg/L)

BMI: body mass index; WHR: waist-hip ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; FBG: fasting blood glucose; TC: total cholesterol; LDL: low-density lipoprotein; HDL: high-density lipoprotein; hs-CRP: high sensitivity C-reactive protein. P-value is based on unpaired sample T-Test. * Significantly different values. Note: WHR is presented separately for female and male; however, due to the low number of the participants in each group, analysis is done on the mean of both genders.

The comparison of baseline and post-supplementation physical and biochemical parameters of healthy versus T2D individuals are summarised in Table 6.2. After 320 mg daily intake of ACN for 28 days, BMI mean had not changed for the T2D or the healthy group.

However, the mean WHR reduced slightly, albeit significantly, in healthy individuals

(P=0.005). As previously mentioned, mean SBP was slightly elevated in the T2D group. This value was lowered post-supplementation (125.5±6.4 mm/Hg, P=0.036). Mean DBP was also reduced significantly to 79.6±1.5 mm/Hg from 82.2 mm/Hg in the T2D group after 28 days of

ACN consumption (P=0.041). FBG reduced significantly in non-diabetic group. Mean FBG was reduced to 3.88±0.88 mmol/L from 4.67±0.99 mmol/L post-intervention (P=0.004). Lipid profile was slightly improved in both groups; however, none of the changes were statistically significant. Uric acid decreased in healthy participants to 241.39±97.88 umol/L from

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288.38±108.65 umol/L, which was statistically significant (P=0.04). hs-CRP did not differ significantly in any of the groups.

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Table 6.2. Comparison of pre-and post-intervention measures for healthy individuals versus type 2 diabetes individuals. Data are presented as the mean ± standard deviation (SD).

Characteristics Healthy Individuals T2D Individuals Optimal Pre Post P-value Pre Post P-value Reference intervention intervention intervention intervention Range (n=9) (n=9) (n=9) (n=9) Physical parameters BMI 18.5-24.9 27.7±5.1 27.6±4.9 0.40 32.9±7.08 32.8±6.9 0.10 (kg/m2) WHR Female> 0.85 0.80±0.07 0.78±0.08 0.005* 0.87±0.06 0.83±0.05 0.24 (cm) Male> 0.90 0.96±0.08 0.93±0.08 1.05±0.07 1.04±0.03

SBP 90-120 124.6±16.06 124.88±14.47 0.92 132.4±10.7 125.5±6.4 0.036* (mm/Hg) DBP 60-80 80.88±9.08 78.77±6.12 0.21 82.2±3.4 79.6±1.5 0.041* (mm/Hg) Biochemical parameters FBG 4-6 4.67±0.99 3.88±0.88 0.004* 6.31±1.20 5.95±1.28 0.56 (mmol/L) TC <3.9 3.62±1.33 2.97±1.10 0.10 3.74±1.07 3.32±0.98 0.21 (mmol/L) Triglycerides <1.1 1.10±1.11 0.76±0.36 0.27 1.69±1.13 1.34±0.55 0.18 (mmol/L)

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LDL cholesterol <2.5 2.43±1.21 1.95±1.07 0.11 2.55±0.91 2.27±0.91 0.27 (mmol/L) HDL >1 0.97±0.41 0.85±0.25 0.31 0.86±0.23 0.78±0.15 0.23 cholesterol (mmol/L) Uric acid 200-430 288.38±108.65 241.39±97.88 0.04* 320.72±149.35 268.5±69.67 0.15 (umol/L) hs-CRP <1 1.01±0.53 0.96±0.76 0.74 3.27±2.44 3.45±2.78 0.60 (mg/L)

Note: WHR is presented separately for female and male; however, due to the low number of the participants in each group, analysis is done on the mean of both genders.

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Of the total of 798 endogenous miRNA probes on the NanoString platform, 261 miRNAs had counts above the background in at least one of the participants, thus considered expressed miRNAs, suitable for further analysis. As expected, miRNAs were differentially expressed prior to ACN intervention across healthy and T2D participants. Likewise, some miRNAs were differentially expressed after ACN intervention in healthy compared to T2D participants. Analysis of baseline and post-intervention comparisons reveal that in addition to differentially expressed miRNAs, five similar miRNAs at baseline and post-intervention were expressed differently across both groups. miR-532-3p, miR-15b-5p, and miR-92a-3p were up- regulated, and miR-146a-5p and miR-100-5p were down-regulated before and after the trial in both groups (Table 6.3).

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Table 6.3. Comparison of top 15 up-regulated or down-regulated micro-RNAs at baseline and post-intervention of two groups. Data are presented as fold-change (FC) values.

Baseline comparison of healthy vs T2D individuals Post-intervention comparison of healthy vs T2D individuals

Up-regulated Down-regulated Up-regulated Down-regulated miRNA FC miRNA FC miRNA FC miRNA FC hsa-miR-532-3p* 3.27 hsa-miR-1827* -3.0 hsa-miR-92a-3p* 1.34 hsa-miR-100-5p* -3.98 hsa-miR-576-5p* 2.80 hsa-miR-146a-5p* -3.77 hsa-miR-15b-5p* 1.24 hsa-miR-23a-3p* -1.38 hsa-miR-15b-5p* 1.26 hsa-miR-100-5p* -3.78 hsa-miR-532-3p* 2.55 hsa-miR-4443* -2.24 hsa-miR-92a-3p* 1.26 hsa-miR-199a- -4.01 hsa-miR-519d-3p* 1.80 hsa-miR-128-3p* -2.17 3p+hsa-miR-199b- 3p* hsa-miR-219a-2-3p 2.83 hsa-miR-548d-5p* -4.0 hsa-miR-301b-3p* 2.21 hsa-miR-132-3p* -1.79 hsa-miR-1260b 2.22 hsa-miR-1245b- -5.40 hsa-miR-299-3p 1.50 hsa-miR-146a-5p -2.67 5p* hsa-miR-125b-5p* -4.01 hsa-miR-193b-3p 2.17 hsa-miR-525-5p -5.24 hsa-miR-761 1.53 hsa-miR-627-5p -7.49 hsa-miR-374b-5p 1.22

*Significantly expressed miRNAs- (A P-value <0.05 is considered significant).

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Table 6.4 summarizes the top 15 differentially expressed miRNAs at baseline and post- intervention (Healthy versus T2D). Based on the selected differential expression threshold (1.2 fold, P ≤0.05), ten miRNAs in the healthy group, and five miRNAs in the T2D group reached statistical significance. miR-1827, miR-574-5p, miR-603, miR-199a-3p+miR-199b-3p, miR-

519d-3p, miR-216b-5p, miR-206, miR-548d-5p and miR-1264 were up-regulated, and miR-

493-3p was down-regulated across the healthy participants. miR-944, miR-593-3p, and miR-

548b-3p were up-regulated, and miR-93-5p and miR-1827 were down-regulated in T2D participants. Among the top 15 up- or down-regulated miRNAs across both groups only miR-

1827 was common, being up-regulated in healthy participants but down-regulated in T2D individuals.

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Table 6.4. Top 15 up-regulated or down-regulated micro-RNAs in healthy and type 2 diabetes individuals before and after the intervention. Data are presented as fold-change (FC) values.

Pre vs post intervention in healthy individuals Pre vs post intervention in T2D individuals

Up-regulated Down-regulated Up-regulated Down-regulated miRNA FC miRNA FC miRNA FC miRNA FC hsa-miR-1827* 2.37 hsa-miR-493-3p* -2.30 hsa-miR-944* 3.74 hsa-miR-93-5p* -1.63 hsa-miR-574-5p* 3.21 hsa-miR-593-3p* 3.24 hsa-miR-1827* -1.89 hsa-miR-603* 7.83 hsa-miR-548b-3p* 2.14 hsa-miR-20a-5p+hsa- -1.55 miR-20b-5p hsa-miR-199a-3p+hsa- 4.45 hsa-miR-508-3p 2.61 hsa-miR-21-5p -1.50 miR-199b-3p* hsa-miR-519d-3p* 1.87 hsa-miR-543 1.72 hsa-let-7g-5p -1.59 hsa-miR-216b-5p* 5.43 hsa-miR-1285-3p 2.12 hsa-miR-193b-3p -2.11 hsa-miR-206* 3.01 hsa-miR-487a-3p 2.02 hsa-miR-486-3p -1.64 hsa-miR-548d-5p* 3.69 hsa-miR-1293 3.32 hsa-miR-1264* 2.93 hsa-miR-627-5p 7.54 hsa-miR-1276 4.50 hsa-miR-499a-3p 6.13 hsa-miR-761 1.57 hsa-miR-196a-5p 1.56

*Significantly expressed miRNAs.

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6.5 Discussion

To the best of our knowledge, this study is the first to investigate the effects of ACN intervention on both miRNA profile and associated clinical parameters of T2D and healthy volunteers. The findings of this study suggest that the expression of certain miRNAs and some clinical parameters can be influenced by a relatively high intake of polyphenolic antioxidants over the course of 4 weeks. The efficacy of ACN supplementation on SBP and DBP reached statistical significance in diabetic participants. In non-diabetic healthy participants, WHR,

FBG, and uric acid changed significantly after consuming ACN for 28 days. The miRNA profile analysis revealed that several miRNAs were influenced by ACN consumption, however, only five miRNAs in T2D participants were altered to statistical significance. These miRNAs are experimentally validated miRNAs, which are further explained in the next sections.

In the current study, we demonstrated that miR-944 was upregulated in diabetic participants after anthocyanin supplementation. There is no literature on miR-944 in relation to T2D, however, this miRNA has been shown to be involved in cell proliferation and metastasis in cancer cells (355). miR-593-3p was the next miRNA significantly upregulated post-ACN intervention in T2D individuals. Interestingly, miR-593-3p has been shown to regulate insulin-promoted glucose metabolism in hepatocellular carcinoma cells (356). In a human trial, the expression of miR-548-3p was significantly down-regulated after 12 months of vitamin D supplementation (357). This miRNA was significantly up-regulated in T2D participants after intake of 320 mg ACN daily for four weeks in the present study. miR-93-5p was down-regulated after ACN consumption in T2D volunteers in our clinical trial. It has been shown that miR-93-5p regulates interleukin 8 (IL-8), a pro-inflammatory marker, and vascular

130 endothelial growth factor (VEGF) in neuroblastoma cells (357). The only common miRNA significantly expressed in both groups post supplementation in the current study was miR-1827, down-regulated in T2D volunteers, but up-regulated in healthy participants. miR-1827 has been widely studied in cancer research. In a recent study this miRNA was introduced as a regulator of the Wnt signalling pathway in colorectal cancer, thus suggested as a potential target for therapeutic approached in cancer research (358). In fact, miR-1827 is an onco-miRNA exerting its effect through regulation of the Wnt signalling pathway and its over-expression is associated with elevated Wnt activity and cell cycle progression of cancer cells (358). Therefore, down- regulation of this miRNA in T2D participants in our study is the desired effect. Since this miRNA is introduced as an onco-miRNA, it might be turned off in our healthy group and this could be the possible reason for the opposite effect observed in the healthy group versus diabetic group. The significant variation in the expression of miR-1827 at the baseline level of the diabetic and non-diabetic group to some extent confirms that this miRNA may be switched on in T2D disorder but not normal condition. Further investigations are required to understand if this miRNA is involved in T2D signalling pathways or could possibly target T2D-associated genes.

In the past, the beneficial health effects of polyphenolic compounds such as ACN were attributed to the free radical scavenging role via their antioxidant effect (113). Recent experimental data have shown the ability of polyphenols to modulate the activity of transcription factors, interact with multiple signalling pathways, and hence regulate the expression of miRNAs and associated genes (168, 359). For the first time in 2012, Milenkovic and colleagues demonstrated the modulation of miRNA expression by polyphenol supplementation at nutritional doses (corresponding to an equivalent intake in humans of 300 mg/day for two weeks) in vivo in the liver of mice (168). They identified five miRNAs (miR-

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291b-5p, miR-296-5p, miR-30c-1, miR-467b, and miR-374) that were regulated by all polyphenols tested. Tested polyphenols were quercetin, hesperidin, naringenin, anthocyanin, catechin, and curcumin (168). In another rodent study, administration of troxerutin, a type of polyphenol and flavonoid, to the amount of 150 mg/ kg rats body weight for one month increased the expression of miR-146a, while decreasing pro-inflammatory NF-κB levels in diabetic rats (170). miR-146a is known as a negative regulator of pro-inflammatory NF-κB, hence it protects against inflammation (351). This may confirm the anti-inflammatory role of polyphenols and flavonoids including ACN through inhibition of NF-κB signals and its adaptor proteins, by targeting their regulatory miRNAs. Furthermore, long-term supplementation of grape extracts containing polyphenols (350 mg/day for one year) demonstrated modulation of inflammatory-related miRNAs (miR-21, miR-181b, miR-663, miR-30c2, miR-155, and miR-

34a), and cytokines in T2D participants with hypertension and coronary artery disease (171).

As previously mentioned, miRNAs have the ability to regulate gene expression by binding to the target genes and suppress its translation or initiate its degradation (360).

Therefore, in addition to identifying miRNAs involved in diabetes onset and development, it is important to recognise and validate the genes that those miRNAs may target. Identification and validation of the target genes for each miRNA are essential since the biological role of individual miRNAs will be dictated by the genes that they regulate (360). Moreover, previous studies demonstrated some interactions between miRNAs, which result in a more pronounced alteration in their target gene’s expression (361-363). miRNAs that have completely or partially complementary structures may form miRNA-miRNA duplexes via reverse complementary binding events (361). miRNA: miRNA interactions are a specific phenomenon and common between natural or endogenous miRNAs (361). miRNAs from similar gene families always have the same type of regulation or deregulation pattern, although different in expression level,

132 therefore, their interaction contribute to the gene expression regulation or deregulation synergically (363). There is no evidence on the interaction of miRNAs significantly expressed in the present trial. However, considering the importance of miRNAs interactions in their gene expression regulatory role, this needs to be investigated in future studies.

In addition to the significant changes at the molecular level on miRNAs, our study reports the beneficial effects of ACN supplementation on physical and biochemical risk factors of T2D. Several other studies have reported that the intake of ACNs could lower blood pressure and blood glucose, and improve the lipid profile in animal and human models with various health conditions (233, 354, 364-367). In addition, ACNs, commonly consumed phenolic antioxidants in the human diet (368), are well recognised for their antioxidant and anti- inflammatory properties against T2D (233, 354, 369, 370).

Mean FBG was significantly reduced in non-diabetic participants only in our study after four weeks of ACN supplementation. Li et al. (2015), and Hassellund et al. (2013) demonstrated the significant reduction of FBG after ACN supplementation in diabetic and pre- hypertensive participants respectively (233, 371). Moreover, Li and co-workers (2015) in the same study, supplementing diabetic participants with 320 mg daily ACN over a period of 24 weeks, showed significantly improved total cholesterol, LDL, HDL, and serum triglycerides

(233). In the present study supplementing the same daily doses of ACN after 4 weeks did not result in significant improvement of lipid profile in diabetic or healthy individuals.

Furthermore, flavonoids and their subclasses are suggested as hypouricemic agents through the evidence from animal and human studies (325, 326). Uric acid was reduced significantly in the healthy group in the current study, which to some extent confirms the desirable effect reported

133 in the literature. hs-CRP, an inflammatory marker associated with T2D (44), was not significantly changed post-ACN supplementation in T2D volunteers or healthy participants. In the randomised controlled trial conducted by Zhu et al. (191), supplementing ACN in the same daily dose as the present study for 24 weeks resulted in significantly decreased hs-CRP in participants with hypercholesterolemia. Therefore, the duration of the intervention in our study may not be sufficient to observe statistically significant changes in biochemistry tests and inflammatory marker hs-CRP in T2D participants. With regards to blood pressure, an intake of

320 mg ANC per day for 28 days significantly reduced both SBP and DBP in diabetic individuals. These findings are in agreement with the studies conducted by Dohadwala et al.

(2011), and Basu and colleagues (2010

6.6 Conclusion, limitations, and direction for future studies

The miRNA regulatory role of various subclasses of dietary polyphenols including

ACN has been previously confirmed in vitro, and in vivo in animal and a few human studies.

Our results provide evidence for the miRNA regulatory role of ACN in human metabolism, suggesting a potential therapeutic role of miRNAs for various conditions, including T2D.

Although this study provides novel findings on the effect of ACN intervention on the miRNA profile of T2D participants, it has some limitations which must be acknowledged. The current study was conducted with a relatively small sample size (n=18, 9 per group). In order to confirm the observed results, a larger sample size is required. In addition, as few significant and some non-significant changes were seen in physical or clinical measures of participants, a longer intervention duration is suggested, to detect any possible statistically and clinically significant improvements. The outcome of this study may have been affected by the genetic variation of participants. Therefore, genotyping of individuals is suggested, in order to

134 distinguish between genetically susceptible participants and non-susceptible ones to be able to generalise the outcome. Moreover, further studies are recommended to investigate if significantly expressed miRNAs in this research could interact with each other’s or truly target diabetes-associated genes, and hence regulate their expression at a post-transcriptional level.

Finally, the effect of alterations in the expression of miRNAs at the protein level needs to be investigated.

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Chapter 7

Nutritional genomic effects of dietary anthocyanin on micro-RNA target genes’ expression in type 2 diabetic, pre-diabetic, and healthy individuals

This chapter includes a co-authored paper, which I am the primary author. It is prepared for submission.

Authors: Elham Nikbakht, Indu Singh, Lada Vugic, Lauren T. Williams, Natalie Colson.

My contribution to this paper involved:

 Review of the literature.  Study design and experimental methodology.  Blood collection and processing.  Investigation of the predicted targets for specific miRNAs.  RNA extraction, cDNA synthesis, real-time PCR.  Statistical analysis.  Graph and table preparation.  Manuscript writing, preparation and revision.

Elham Nikbakht

27th April 2018

Indu Singh’s contribution to the paper involved:

 Study design.  Manuscript writing and revision.

Lauren T. Williams’s contribution to the paper involved:

 Manuscript writing and revision.

Lada Vugic’s contribution to the paper involved:

 Training and assisting with real-time PCR.

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Natalie Colson’s contribution to the paper involved:

 Primary supervisor of the project.  Study design and experimental methodology.  Assistance with the investigation of the predicted targets for specific miRNAs.  Training real-time PCR.  Assistance with statistical analysis.  Manuscript writing and revision.

Corresponding author of paper:

Dr Natalie Colson 27th April 2018

Supervisor:

Dr Natalie Colson 27th April 2018

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7.1 Abstract

Gene-environment interactions and epigenetic mechanisms play a significant role in the occurrence of type 2 diabetes (T2D). Diet is one of the most important determinants of environmental risk factors for T2D, with the phenotypic effects of over-nutrition accumulating over the course of a lifetime. Nutrients can influence gene expression by interacting with transcription factors or through the modification of gene regulatory mediators such as micro-

RNAs (miRNAs). Polyphenolic antioxidants, including anthocyanin (ACN), are well- recognised as having metabolites capable of acting as transcription factors or modulating miRNA expressions, hence regulating the expression of associated genes. In our previous study, after 28 days of ACN intervention (320 mg/day), the expression of five miRNAs (miR-

944, miR-593-3p, miR-548b-3p, miR-93-5p and miR-1827) were significantly altered in T2D individuals. These miRNAs were investigated for their target gene via the three computational algorithms TargetScan, DIANA Tools, and miRWalk. Five diabetes-associated genes (GLIS3,

HNF4A, JAZF1, WFS1, NOTCH2) that were predicted by at least two of these algorithms for the same miRNA were selected for further analysis. The effect of ACN supplementation (320 mg/day for 28 days) on the expression of these genes was investigated using real-time PCR in three groups of participants (T2D, T2D-at-risk, and healthy). After the intervention, the relative expression of GLIS3 and HNF4A significantly reduced in the pre-diabetic group (P=0.026 and

P=0.046 respectively), and HNF4A showed a trend of reduction in T2D individuals (P=0.079).

GLIS3 and HNF4A are known to be involved in diabetes onset and development, therefore, their down-regulation after 28 days of ACN intervention demonstrates a possible link between dietary polyphenol consumption and gene expression regulation. Given this, our findings may provide a novel insight into the possible regulatory role of polyphenols in diabetes-associated

138 gene expression; hence potentially contributing to the management of T2D and pre-diabetic complications.

Keywords: Type 2 diabetes, Pre-diabetes, Gene expression, Polyphenols, Anthocyanin.

7.2 Introduction

Type 2 diabetes (T2D), mainly diagnosed by chronic hyperglycaemia, is a principal cause of atherosclerotic vascular disease, kidney failure, stroke, and blindness (3, 374). It occurs due to an increased demand for insulin, brought by insulin resistance, together with dysfunctional pancreatic beta cells (β-cell) (6). A growing body of evidence suggests the close association between obesity and obesity-induced insulin resistance with the development of

T2D (6). However, not all obese or insulin-resistant individuals progress to T2D (12). The occurrence of T2D depends on the level of insulin compensation by β-cells (15). Pancreatic β- cells react to insulin resistance by the enhancement of the released insulin, sufficient to overcome the reduced efficiency of insulin action, thereby maintaining the normal level of blood glucose (15, 16). Failure to compensate for the decreased insulin sensitivity, named β- cell dysfunction, leads to the development of T2D (15, 16). Accordingly, individuals with β- cell dysfunction are at a higher risk of developing T2D, even if their blood glucose is still within the reference range (16).

The ability of β-cells to compensate for an increased insulin demand is associated with genetic signals and varies between individuals due to genetic variation or differences in the biological systems of individuals (105). Given this, first-degree relatives of T2D individuals exhibit a higher chance of developing insulin resistance and T2D (375). In addition,

139 epidemiological studies demonstrate a different prevalence of T2D among various nations and races, supporting the genetic basis of T2D (7). In vivo investigation of T2D pathophysiology through animal models demonstrated a coordinated alteration in gene expression of insulin- responsive tissues and pancreatic islets in insulin-resistant animals (376). The changes in the correlated transcripts, that either contribute to compensation for the degree of insulin resistance or facilitate the development of T2D, confirms the involvement of genetic factors in the incidence of T2D (376). Genome-wide association (GWA) studies have identified several loci at which common variants are associated with high risk of T2D development (377).

Knowing the importance of susceptible genes, the contribution of lifestyle risk factors in the development of T2D needs to be acknowledged. T2D is often referred to as a complex metabolic disorder, which results from an interactive combination between non-modifiable genetic risk factors and modifiable lifestyle influences (200, 333). A substantial increase in

T2D prevalence among a migrant population to a new environment, establishing a different lifestyle behaviour, suggests a potential gene-environment interaction (378). Modifiable environmental/lifestyle influences; including dietary pattern, physical activity, and smoking; known as epigenetic signals, may affect the expression of certain genes, thus altering the phenotypic outcome (379, 380). Among lifestyle risk factors, diet is an essential, ongoing and unavoidable occurrence which could interact with the genome in a number of ways (381).

Nutrients may act as nuclear transcription factors and alter the expression of genes directly through the interaction of stimulated receptors with a response element in the genome (382), or indirectly by the modulation of gene regulatory factors such as non-protein coding RNAs

(383). Non-coding RNAs, of which micro-RNAs (miRNAs) are an abundant class, have well- documented ability to modify the expression of genes post-transcriptionally (383-385). The post-transcriptional regulatory role of miRNA is conducted by partial complementary binding

140 to the 3’-untranslated regions (3’-UTR) of target messenger-RNAs (mRNAs) in a sequence- dependent manner (335). As a result, mRNA becomes double-stranded and is prevented from being translated into a protein (335). Due to the small size of the miRNA recognition sequence, each miRNA may target hundreds of mRNA (85). In addition, each mRNA can be targeted by several miRNAs, allowing complex and multifaceted regulatory network (386). Therefore, over- or under-expression of miRNAs could eventually alter the expression of associated mRNAs in target cells and potentially affect the function of corresponding tissues and organs

(89). It has been estimated that more than half of human genes are influenced by the post- transcriptional regulatory role of miRNAs and therefore, they are believed to participate in almost all cellular process (89).

Nutrient-gene interaction is called nutrigenomics (382). Nutritional status can influence gene expression directly and indirectly. Direct interaction occurs when activated nutrient receptors act as transcription factors (387). Following ligand binding, receptors bind to response elements in a gene’s promoter region (382). Indirectly is through modulating the availability of substrates and coenzymes which impacts upon systematic metabolism and modifies the cellular milieu (388). This has consequences for a range of modification that may impact gene expression (388). For example, vitamin E, a potent antioxidant, has shown to regulate the expression of the IL-4 gene. In the study conducted by Li-Weber and colleagues

(2002), it was shown that vitamin E suppresses IL-4 transcription in human T cells by blocking the binding of transcription factors NF-κB to the promoter binding sites (389). In another study, vitamin C and E supplementation have shown to regulate the gene expression and secretion of

IL-6 in human muscle tissue (390). Additionally, nutritional status influences the important process of DNA synthesis and repair, impacting upon the rate of genetic mutation and genomic instability leading to an altered risk of various diseases (391).

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In this regard, T2D pathophysiological studies indicated that an unhealthy diet may trigger the onset of T2D by altering the expression of diabetes-associated genes (333).

Correspondingly, intake of specific functional food may regulate the expression of T2D- associated genes, thereby alleviating T2D (392). Among functional food, polyphenolic antioxidants are well recognised for their anti-diabetic activities in various experimental models including human intervention studies through several different mechanisms (393). In addition to antioxidant (120), anti-inflammatory (125), and anti-obesity properties (122), polyphenolic compounds such as anthocyanins (ACNs) have been shown to exert nutritional genomic influences on T2D by interfering with the expression of diabetes-associated genes directly or through small, non-coding RNAs (miRNAs), hence regulating the expression of

T2D genes (156, 158, 394).

ACNs belong to the flavonoid subgroup of polyphenols. They are distinct flavonoids because they produce unique breakdown metabolites which act as a high potential redox buffer

(117, 164). These metabolites exert antioxidant and anti-inflammatory influences via direct scavenging of reactive oxygen species (ROS) (164). ROS is associated with the pathophysiology and cellular dysfunction of several disorders, including T2D (18). ACNs are abundant in red to blue coloured fruits and vegetables as well as plant-derived beverages, and are broadly distributed in the human diet (299). These polyphenolic antioxidants have demonstrated a wide range of beneficial health effects instrumental in controlling T2D. Some of these effects can be attributed to their antioxidant properties acting at the cellular level (113), and some are due to their gene regulatory potential through signalling pathways, at the molecular level (394). The gene and miRNA regulatory roles of ACNs have been demonstrated in various in vitro and in vivo studies (113, 178, 394, 395). However, to the best of our knowledge, no study to date has shown the effects of ACN intervention on the expression of

142 specific miRNAs target genes, known to be associated with T2D development such as GLIS3,

HNF4A, JAZF1, WFS1 and NOTCH2, in pre-diabetic and T2D adults. These diabetes- associated genes of interest were predicted to be regulated by miRNAs that were significantly expressed after the same treatment protocol in T2D adults in our previous study.

7.3 Methods and materials

This study is part of the original intervention trial conducted at Griffith University. The aim of this chapter is to examine the effect of ACN supplementation on T2D associated gene expression in T2D, pre-diabetic and healthy adults. In the present study, the relative expression of five selected T2D associated genes in 38 participants are determined and presented along with fasting blood glucose measurement and body mass index. More information regarding the methods and materials is provided in Chapter 4, page 66. Relevant sections to this chapter are listed below:

Section 4.1 Human clinical trial study design (page 67) Section 4.2 Volunteer recruitment and screening (page 67-68) Section 4.3 Inclusion and exclusion criteria (page 68-69) Section 4.4 Supplement information (page 70-72) Section 4.7 Physical measurement (page 73) Section 4.8 Blood collection and processing (page 74) Section 4.9 Biochemistry assay (page 74-75) Section 4.12 RNA isolation (page 76) Section 4.15 micro-RNA: messenger-RNA target interactions (page 78-81) Section 4.16 Reverse transcription (page 81) Section 4.17 Primer efficiency (page 81-82) Section 4.18 Real-time polymerase chain reaction (page 83) Section 4.19 Statistical analysis (page 84-85)

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7.4 Results

The relative expression of the five genes prior to and after ACN supplementation along with participants’ demographic characteristics, BMI, and FBG level, is summarised in Table

7.3. After four weeks of ACN intervention, the expression of the GLIS3 was down-regulated in T2D-at-risk and T2D individuals. This reduction in expression reached statistical significance in the T2D at-risk group only (P=0.026) (Figure 7.1). No changes were observed in the group of healthy people post-supplementation.

Supplementing ACN polyphenols reduced the expression of HNF4A after four 28 days significantly in the prediabetic group (P=0.046), and non-significantly in the T2D group. A trend of reduction was observed for HNF4A mRNA in the T2D group (P=0.079) (Figure 7.1).

The expression of this gene had not changed in healthy individuals at the end of the study compared to the beginning.

As shown in Table 7.1, ACN intervention for 28 days did not significantly alter the expression of the JAZF1 gene in the T2D group nor in the pre-diabetic or healthy groups

(Figure 7.1). Analysis of the relative expression of WFS1 did not reveal any significant changes after consuming high doses of ACN for 28 days in any of the groups (Figure 7.1). NOTCH2 gene pre-and post-intervention statistical analysis did not demonstrate any significant changes across all groups (Figure 7.1). The mean BMI and FBG of participants were not altered after four weeks of ACN intervention in any of the groups (Table 7.1).

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Figure 7.1. Relative expression of GLIS3, HNF4A, JAZF1, WFS1, and NOTCH2 genes before and after four weeks of anthocyanin intervention across three groups of participants. Relative expression of GLIS3 and HNF4A decreased significantly in the T2D at-risk group. - ΔΔC Data are presented as mean 2 T ± standard error (SE).

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Table 7.1. Demographic characteristics and analysis of baseline and post-supplementation measurements of body mass index, fasting blood glucose, and five messenger-RNAs relative expressions in three groups of healthy, pre-diabetic, and type 2 diabetes. Data are presented as the mean ± standard deviation (SD). Parameters Healthy Individuals T2D at-risk individuals T2D Individuals

Pre Post P-value Pre Post P-value Pre Post P-value intervention intervention intervention intervention interventio intervention (n=14) (n=14) (n=12) (n=12) n (n=12) (n=12) Gender 6/8 5/7 4/8 (female/male) Age (years) 35.2±11.4 49.2± 11.7 57.7±8.4 BMI 23.36±1.36 23.29±1.19 0.81 30.19±3.16 30.0±3.1 0.22 32.59±7.37 32.35±7.15 0.10 (kg/m2) FBG 4.71±0.82 4.3±1.10 0.40 4.99±0.89 4.32±1.12 0.11 6.30±1.56 6.03±1.56 0.78 (mmol/L)

mRNA

GLIS3 0.74±0.85 0.70±0.76 0.99 1.55±1.8 0.47±0.42 0.026* 2.41±1.27 1.65±0.73 0.16

HNFA4 0.90±0.63 0.71±0.72 0.89 1.47±1.07 0.63±0.60 0.046* 1.83±2.02 1.07±1.01 0.079

JAZF1 1.36±0.71 1.52±0.91 0.87 1.24±0.81 1.44±1.09 0.81 1.17±1.14 1.35±1.31 0.83

WFS1 1.45±0.73 1.67±1.11 0.89 1.04±1.35 1.52±1.30 0.52 0.70±0.76 0.91±0.83 0.92

NOTCH2 1.23±0.90 1.03±0.86 0.91 1.69±1.61 1.28±0.96 0.61 1.24±0.62 1.14±0.78 0.98

BMI: body mass index; FBG: fasting blood glucose. * Significantly changed values.

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7.5 Discussion

The effect of four weeks’ supplementation of ACN capsules (320 mg/day) on five genes, predicted to be regulated by miRNAs that were significantly altered in T2D participants after the same treatment protocol in our previous study (detailed in the method section), were evaluated in this intervention trial. To the best of our knowledge, this study provides, for the first time, evidence of the nutritional genomic effect of ACN on GLIS3, HNF4A, JAZF1, WFS1, and NOTCH2 gene expression in T2D, T2D-at-risk, and healthy individuals. It was observed that supplementation with polyphenolic-rich

ACN capsules reduced the expression of GLIS3 and HNF4A significantly in the pre- diabetic group. HNF4A showed a trend of reduction after four weeks of ACN intervention in T2D group. Analysis of relative expression for the other three genes (JAZF1, WFS1, and NOTCH2) demonstrated some gene expression alterations at the end of the trial; however, none of these changes reached statistical significance in any of the groups. BMI and FBG of participants did not change significantly after consuming high doses of ACN after 28 days across the groups.

Zinc finger protein Gli-similar 3 (GLIS3) is a member of the Kruppel-like family of transcription factors encoded by GLIS3 (396). GLIS3 is highly expressed in pancreatic

β-cells and plays an important role in the regulation of insulin gene transcription (396,

397). This gene is known as a susceptible risk locus for both type 1 and 2 diabetes (396).

Therefore, attempting to regulate the expression of GLIS3 might be useful in T2D prevention and control. In the current study, consumption of high doses of ACN in T2D- at-risk group significantly reduced the expression of GLIS3 after 28 days. Except for cell culture experimental data, there are no in vivo available findings on the effects of

147 polyphenolic antioxidants on GLIS3 gene expression. Oleaga and colleagues in 2012 demonstrated the under-expression of GLIS3 gene upon incubation of human colon cancer cells with coffee polyphenol (398). The expression of this gene was not significantly affected by ACN intervention in the T2D or healthy groups in our study.

Hepatocyte nuclear factor 4 alpha (HNF4A) gene is associated with the development of the liver and its metabolic function, in addition to β-cell development and activity (399). Variants in the HNF4A gene have been found to be linked with decreased insulin secretion and consequent increase in the risk of T2D (400). Furthermore, HNF4A transcripts have been shown to be involved in lipid synthesis pathways, thus promoting obesity and obesity-induced insulin resistance (401). Given this, targeting HNF4A gene might be beneficial in attenuating T2D, obesity, and other associated complications. In the current intervention trial, this gene was down-regulated significantly in the pre- diabetic group and non-significantly in T2D adults after four weeks of ACN supplementation. Previous findings suggest a possible mechanism. In the study conducted by Benn et al. (2014), expression of the HNF4A was significantly decreased in the liver of the mice fed with a high-fat diet after four weeks of polyphenol-rich blackcurrant extract consumption (402). Down-regulation of HNF4A in the Benn et al. study (2014) was positively correlated with total cholesterol and blood glucose levels (402).

Genome-wide association study (GWAS) indicated that juxtaposed zinc finger protein 1 (JAZF1) gene is linked to T2D incidence (403). JAZF1 has been shown to be differentially expressed in the islets of T2D and non-diabetic subjects (404). The expression of this gene regulates β-cell function and development (404), and down- regulation of JAZF1 is associated with an increased risk of T2D (404). After 28 days of

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ACN supplementation in the current study, JAZF1 was not significantly changed in any of the groups.

Wolfram syndrome 1 (WFS1) is a gene responsible for producing wolframin protein, and is defective in individuals with Wolfram syndrome (405). Wolframin is an important protein found in several tissues, including the pancreas and kidneys (406). This protein is responsible for regulating the amount of calcium in cells, required to perform cellular functions such as cell-to-cell communication (407). WFS1 is reported to protect cells against endoplasmic reticulum stress, one of the most important Alzheimer’s disease causes (408). In addition, WFS1 appears to be involved in β-cell function and is significantly associated with the development of T2D in a large scale (409). Thus, since under-expression of this gene is linked with several disorders such as T2D, aiming to regulate WFS1 sounds promising in managing T2D and other associated complications.

The prior study by Sakurai and co-workers (2013), testing the effect of Oligonol (fruit- derived polyphenol) on the WFS1 in an animal model, demonstrated a significant enhancement in the expression of WFS1 in mice fed with polyphenolic compounds (410).

In the current intervention, no significant changes were observed across the groups.

Neurogenic locus notch homolog protein 2 (NOTCH2) gene was identified in a meta-analysis of GWAS as a common trait in T2D (411, 412). Abnormalities in the expression of NOTCH2 have been associated with the incidence of T2D (411). In fact,

NOTCH signalling pathways are known to be involved in the pathophysiology of many diseases, including cancer and T2D. In T2D, NOTCH signalling is shown to control pancreatic cell differentiation and function, hence contributing to T2D pathogenesis. The

149 result of our clinical trial did not demonstrate any significant changes in the relative expression of NOTCH2 after four weeks of ACN supplementation. Thus far, there is no literature on the effect of polyphenolic compounds on NOTCH2 in relation to T2D, however, in vitro investigation of the effect of polyphenols on NOTCH2 showed desirable effects in cancer cells. Jin and colleagues (2013) demonstrated that the expression of

NOTCH2 at the mRNA and protein levels was significantly decreased after administration of green tea polyphenols to colorectal cancer cells (413). It has been previously reported that the downregulation of this gene plays an important role in the suppression of cancer during the development of colorectal cancer (414).

As described, genes studied for their relative expression in this chapter, were among T2D-associated genes predicted to be a target by miRNAs that were significantly expressed in our previous study. miRNAs are small non-protein coding RNAs known as important regulators of gene expression (415). They alter gene expression by targeting mRNAs for translational repression or cleavage (416). miRNAs recognize their target genes by base-pairing interactions between nucleotides 2 to 8 of the seed region of miRNA and complementary nucleotides in the 3ʹ-untranslated region (3ʹ-UTR) of mRNAs (415, 417). Each miRNA could target several mRNAs (416). Therefore, in addition, to identify miRNA target genes, it is essential to validate those genes. Validation of the target genes for each miRNA enables us to establish commonalities, allowing more precise predictions of miRNA: mRNA interactions (418). However, the most fundamental challenge in miRNA biology is to outline the rules of miRNA target recognition (418). Several experimental procedures are required to demonstrate that the miRNA, in fact, binds to the 3ʹ-UTR of its target mRNA, although currently there is no clear agreement as to what experimental procedures should be followed to demonstrate

150 that a given mRNA is a target of a specific miRNA. Kuhn et al., (2008) reviewed the criteria to validate miRNA: mRNA interaction (418). The first criteria reported by Kuhn and colleagues was to verify the miRNA: mRNA interaction. Secondly, the predicted miRNA must be co-expressed with the mRNA it targets and thirdly, the given miRNA must have a predictable effect on target protein expression. Lastly, miRNA-mediated regulation of target gene expression should equate to altered biological function (418).

Understanding and applying these criteria’s are important to validate miRNA: target interaction and demonstrate that miRNA-mediated regulation or miRNA target gene expression can be affected by polyphenolic antioxidant consumption. This may contribute to the knowledge of the pathophysiology of T2D as well as the possibility of discovering therapeutic approaches to aid T2D and diabetic at-risk individuals.

Furthermore, there are millions of single nucleotide polymorphisms (SNPs) that could possibly influence the association between miRNAs and genes (419). SNPs could have a functional role in miRNA-mediated gene regulation, thereby affecting gene expression (420). They may also affect the base pairing of miRNAs at the target site by creating or abolishing miRNA binding site, therefore influence the genes (419). For instance, it has been reported that SNPs in the canonical 7 and 8 regions of the 3ʹ-UTR that match the seed regions of miRNAs are likely to disrupt miRNA bindings (421).

Integrative analysis, using computational algorithm incorporating miRNA expression, mRNA expression and single SNPs genotype data are required in order to discover which

SNPs are involved in miRNA regulatory role (422). This is above the scope of the present research and future investigations addressing the association of specific SNPs and miRNAs are recommended.

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Observational studies have reported that a polyphenol-rich diet is associated with reduced risk of T2D in pre-diabetics and attenuation of complications in T2D individuals

(188). Experimental evidence suggests that the role of polyphenolic antioxidants in improving β-cell function, increasing insulin sensitivity and secretion, inhibiting pro- inflammatory signalling pathways and finally managing T2D, could be partially due to their gene expression regulation activities (423). High intake of dietary polyphenols was positively correlated with the regulation of metabolic pathway gene expression (424). In addition, polyphenol antioxidants have been associated with regulating inflammatory gene expression in peripheral blood cells (425). Moreover, polyphenolic compounds have been reported to exert anti-mutagenic activities (424). In addition, they have shown potential to increase DNA repair and reduce DNA damage (177, 426). The gene expression regulation property of polyphenols is attributed to their interactions with receptors and enzymes (212). This interaction might affect signal transduction, leading to modification of the redox status of cells to exert favourable effects (212, 427).

In the current study, supplementation of ACN polyphenols for 28 days (320 mg/day) has significantly modified the expression of a couple of genes of interest in a favourable direction in the pre-diabetic group, which may be beneficial to delay the onset of diabetes in at-risk individuals. However, the outcome of our study may have been influenced by its limitations. Having a relatively small sample size may have affected the result and a larger study population could have possibly led to a different outcome. The big standard deviation in the relative gene expression data could be partially due to variations in the metabolic and genetic profiles of the study participants. In addition, the length and amount of treatment protocol (28 days- 320 mg ACN/day), might not be optimal to overcome T2D and regulate its associated gene expression statistically

152 significantly. Therefore, to maximise the supplementation effects, further clinical trials are required to determine the optimal dose and type of dietary polyphenols most appropriate to regulate specific transcripts.

7.6 Conclusion

Polyphenolic compounds such as ACNs may have favourable effects on T2D through β-cell function improvements, insulin secretion stimulation, reducing oxidative stress and pro-inflammatory biomarkers, thereby controlling hyperglycaemia. The significant reduction of the relative expression of GLIS3 and HNF4A in the diabetes at- risk group after four weeks of ACN intervention showed a possible link between dietary polyphenols and gene expression regulation. GLIS3 and HNF4A are recognised to be involved in diabetes development, therefore, regulation of their activity might contribute to the attenuation of T2D and associated complications. Given this, the finding from the present research may provide a novel insight into the possible role of polyphenols in gene expression regulation and consequently in diabetic and pre-diabetic management.

However, in order to draw a precise conclusion on the effect of polyphenolic antioxidants on diabetes genes and due to the genetic variation and susceptibility of each individual, genotyping of participants is recommended.

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Chapter 8

General Discussion and Conclusion

The pronounced increase in the prevalence of type 2 diabetes (T2D) in the past few decades as a consequence of obesity and its related lifestyle changes have drawn the attention of scientists and clinicians alike. It has been medically proven that T2D can be clinically prevented in at-risk population and managed in T2D individuals by adhering to a healthy lifestyle with appropriate dietary habits, such as a diet rich in green vegetables, whole grains and fish, combined with glucose-lowering agents aimed at controlling hyperglycaemia and T2D complications. The overall aim of this thesis was to examine the potential of polyphenolic antioxidants such as flavonoids and their subclass anthocyanin (ACN) in improving T2D risk factors, including physical characteristics, glycemic and lipid profiles, moderating T2D associated pro-inflammatory biomarkers, modulating micro-RNA (miRNA) profile of T2D adults, and finally regulating associated genes in T2D and pre-diabetic individuals. A total of four studies were conducted as a part of this thesis to support the hypothesised anti-diabetic effect of polyphenolic compounds, aiming to find a natural dietary antioxidant to simultaneously target various pathways, hence exerting anti-inflammatory and gene regulatory effects in T2D and diabetic at-risk adults.

The first study combined the data from flavonoid intervention trials on T2D adults in a systematic review and meta-analysis fashion, investigating the potential of flavonoids and their subclasses on metabolic markers and risk factor of diabetes. The outcome of this

154 study suggests that regular dietary intake of polyphenols such as flavonoid compounds might significantly reduce fasting blood glucose (FBG), total cholesterol (TC), and triglycerides (TG) in adults with T2D. Analysis of intervention duration as a subgroup indicates that glycaemic and lipid profile improvement would be anticipated to increase even further with a longer supplementation period, suggesting the benefit of consumption of polyphenols and flavonoids as regular dietary constituents in the management of diabetes risk factors. In addition, the design of the included studies might have influenced the outcome of meta-analysis, as studies using parallel design showed a greater reduction in FBG, haemoglobin A1c (HbA1c), TC, and TG than those using a cross-over design.

This, to some extent, could be due to the carry-over effect of one treatment caused by an inadequate washout period. Furthermore, subgroup analysis of the source of flavonoids delivery revealed that the long term consumption of flavonoid fortified food might be more beneficial than the flavonoid supplements. Consuming fortified food as the flavonoids sources resulted in a significant reduction of FBG and LDL-cholesterol.

The potential of flavonoid intervention (ACN capsules, 320 mg/day over the four weeks) as an anti-inflammatory agent in T2D and T2D-at-risk and healthy individuals was investigated in a human clinical trial in the second study. Besides multiple pro- inflammatory biomarkers; anthropometric characteristics, blood pressure (BP), and metabolic markers associated with T2D were measured in three groups of participants and compared at the end of the study. The findings from this study indicate that a high intake of ACN might be helpful in modulating the elevated level of pro-inflammatory biomarkers including interleukin-6 (IL-6), interleukin-18 (IL-18) and tumour necrosis factor-α (TNF-α) in T2D adults and improve BP, FBG, LDL-cholesterol, and uric acid levels in diabetic at-risk individuals.

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Given the close association of inflammation and T2D occurrence, approaches that attempt to control inflammatory events are likely to avoid the onset of diabetes in at-risk populations and prevent progression of the disease in T2D individuals. The finding from the present study is of interest, as three pro-inflammatory biomarkers significantly reduced after four weeks of ACN intervention. In addition, considering the role of hyperglycaemia in T2D development and its association with micro- and macro-vascular complications; significantly reduced FBG in T2D at-risk group in this study is a desirable outcome. Moreover, a significant reduction of LDL cholesterol post ACN intervention is a favourable outcome, knowing the role of hyperlipidaemia as one of the most important risk factors for cardiovascular disease in T2D and pre-diabetic people. Among physical measurements, blood pressure was lowered in both the pre-diabetic and T2D groups following four weeks of ACN supplementation. This is a desirable effect as well, considering that even a slight reduction of BP at the population level strongly contributes to reduced risk of hypertension and cardiovascular disease. Furthermore, the group comparison of diet inflammatory potential (DII) score in this study demonstrated a significant difference between DII of T2D and healthy individuals. T2D participants had a significantly lower DII score which suggests an anti-inflammatory promoting diet of this group.

Our third study further explored the epigenetic effects of dietary ACN supplementation (four weeks of 320 mg ACN daily) on miRNA profile and physical and biochemical measurements of T2D and healthy adults. Findings from cellular and animal experiments indicated that polyphenolic compounds such as ACN could interact with cellular signalling cascades, and control the activity of transcription factors, regulating the expression of miRNAs and associated genes. Therefore, targeting miRNAs by

156 polyphenolic antioxidants represents a novel therapeutic approach to controlling metabolic disorders, including T2D. This study aimed to examine the anti-diabetic effects of flavonoid-rich extracts such as ACN through miRNA regulation. A total of 261 miRNAs for each participant were included in the final analysis, in which five in T2D and ten in the healthy group were significantly altered after the intervention. miR-944, miR-593-3p and miR-548b-3p were significantly up-regulated, and miR-93-5p and miR-

1827 down-regulated to the statistical significance in the T2D group. These significantly expressed miRNAs were further studied for their biological functions. Literature suggests miR-593-3p is involved in insulin-promoted glucose metabolism regulation, which could be associated with alleviation of T2D. miR-93-5p found to be involved in the interleukin

8 (IL-8) regulatory pathway, hence contributing to the management of T2D. The other three significantly up/down-regulated miRNAs in the T2D group in our third study have not been studied for their regulatory role in T2D. miR-548-3p was significantly down- regulated after long-term vitamin D supplementation in healthy individuals, and miR-944 and miR-1827 were extensively studied in cancer research (in vitro experiments), found to be involved in cell proliferation and the Wnt signalling pathway, respectively. The results obtained from the present study provide evidence for the miRNA regulatory role of ACN in human metabolism, suggesting a potential therapeutic role of miRNAs for various conditions, including T2D.

Nutritional genomic effects of dietary ACN (four weeks supplementation, 320 mg/day) in T2D, T2D-at-risk and healthy individuals were examined in our fourth study.

Significantly expressed miRNAs in T2D participants in our third study were investigated for their predicted target genes through the online computational algorithm tools

TargetScan, DIANA Tools, and miRWalk. Five genes that were predicted by at least two

157 algorithms for the same miRNA, were chosen for further investigation. Selected genes

(GLIS3, HNF4A, JAZF1, WFS1, and NOTCH2) which were well recognised to be involved in diabetes development, were studied for their pre- and post- ACN supplementation expression. The expression of GLIS3 was significantly down-regulated in the pre-diabetic group after four weeks of polyphenolic-rich ACN capsules intervention. GLIS3 is shown to be highly expressed in pancreatic β-cells, hence plays an important role in insulin regulation. Therefore, regulating the activity of GLIS3 might contribute to the attenuation of T2D and associated complications. GLIS3 was down- regulated after administration of coffee polyphenols in a previous in vitro study in human cancer cells. The relative expression of HNF4A was significantly reduced in the T2D-at- risk group. In addition, a trend of down-regulation was observed in the T2D group. The

HNF4A is known to be associated with decreased insulin secretion, thus increasing the risk of T2D occurrence. Given this, reducing the activity of HNF4A could be instrumental in the alleviation of T2D or the delay of onset in at-risk populations. The findings of the current study are of great interest, and to some extent confirm previous findings. The

HNF4A gene was significantly down-regulated in the mice fed with polyphenol-rich blackcurrant extract after four weeks. This study’s finding may provide a novel insight into the possible role of polyphenols in gene expression regulation in diabetic and pre- diabetic management.

The overall aim of this PhD thesis was to investigate the potential of dietary polyphenols as therapeutic options for T2D and diabetic at-risk adults. The outcome of various studies conducted in this research achieved the aim through the observed desirable effects of polyphenolic antioxidants on some, but not all of the markers measured in this study. In summary, the finding from this study suggests that the intake

158 of polyphenolic antioxidants including ACN could be a promising therapeutic approach to avoid the onset of diabetes in at-risk populations and prevent progression of the disease in T2D individuals.

Despite the observed favourable effects of ACN in diabetes risk factor alleviation including physical and biochemical parameters, pro-inflammatory marker and gene regulation, it is important to discuss the limitations of the research conducted. In the present study, unreported changes in the lifestyle, dietary habits and even medications of participants, individuals with different ethnic backgrounds, age-range variation between groups and different duration of diabetes between T2D volunteers, which may possibly affect the result, could not be excluded. In addition, our finding could be influenced by the number of participants included in each study. Significantly changed values might be achieved with a larger sample size in each study. Longer duration of intervention might result in significantly improved markers, including pro-inflammatory biomarkers in participants with metabolic syndrome or diabetic at-risk group. Moreover, investigating the inhibitory role of ACN’s metabolites in various cellular and molecular pathways associated with the incidence of T2D might be helpful to develop advanced therapeutic agents capable of targeting T2D-associated biomarkers. Furthermore, studies at the population level are required to investigate the relative metabolic profile, genetic susceptibility and environmental exposures of various races within a community and effect of alterations in expression of miRNAs at the protein level, in order to develop precise dietary intervention aiming to prevent or manage T2D. Finally, due to the possibility of differences in genetic predisposition and to personalise the intervention protocol for each individual, the genotyping of participants is suggested for future trials.

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Overall, this thesis has expanded our knowledge on the potential of polyphenolic dietary compounds such as ACN as therapeutic options in T2D and pre-diabetic conditions. Pooling of glycaemic and lipid profile data from clinical trials that assessed the efficacy of flavonoid intervention on T2D individuals to some extent confirmed the favourable effects reported in previous studies. Observations from our clinical trial confirmed the anti-inflammatory potential of polyphenolic antioxidants in T2D participants. Considering the important role of metabolic inflammation in T2D pathogenesis, modulation of pro-inflammatory biomarkers by ACN suggests a beneficial approach in the management of T2D. Previous studies investigated the effect of polyphenols on individual miRNA and gene regulation. We have examined the regulatory role of ACN on the total profile of miRNA using NanoString technology. Further, significantly altered miRNAs were investigated for their target genes, in which well recognised diabetes-associated genes were found to be regulated by these miRNAs.

Alteration of miRNA profile and associated T2D genes in participants with or without

T2D in our study suggests the interaction of ACN metabolites with signalling pathways and transcription factors, which could be useful in the development of novel therapeutic approaches for T2D and complications accompanying it.

160

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Appendices

Appendix 1. Human Research Ethics Committee

GRIFFITH UNIVERSITY HUMAN RESEARCH ETHICS COMMITTEE 01-Aug-2014

Dear Dr Singh

I write further to the additional information provided in relation to the provisional approval granted to your application for ethical clearance for your project "Full Review: Anthocyanins: Possible Antiplatelet Alternative for Aspirin Resistant Diabetic Population." (GU Ref No: MSC/07/14/HREC). The additional information was considered by Office for Research. This is to confirm that this response has addressed the comments and concerns of the HREC. Consequently, you are authorised to immediately commence this research on this basis. The standard conditions of approval attached to our previous correspondence about this protocol continue to apply.

Regards

Rick Williams Manager, Research Ethics Office for Research Bray Centre, N54 Room 0.15 Nathan Campus Griffith University ph: 07 3735 4375 fax: 07 373 57994 email: [email protected] web:

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Cc: Researchers are reminded that the Griffith University Code for the Responsible Conduct of Research provides guidance to researchers in areas such as conflict of interest, authorship, storage of data, & the training of research students. You can find further information, resources and a link to the University's Code by visiting http://policies.griffith.edu.au/pdf/Code%20for%20the%20Responsible%20Conduct%20of%2 0Research.pdf PRIVILEGED, PRIVATE AND CONFIDENTIAL This email and any files transmitted with it are intended solely for the use of the addressee(s) and may contain information which is confidential or privileged. If you receive this email and you are not the addressee(s) [or responsible for delivery of the email to the addressee(s)], please disregard the contents of the email, delete the email and notify the author immediately

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Appendix 2. Australian New Zealand Clinical Trial Registry

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Appendix 3. Flyer advertisements to recruit healthy and non-diabetic volunteers

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Appendix 4. Flyer advertisements to recruit type 2 diabetic volunteers

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Appendix 5. Invitation to participate in a research project

Gold Coast University Hospital

INVITATION TO PARTICIPATE IN A RESEARCH PROJECT PROJECT INFORMATION STATEMENT

Project Title: Antiplatelet therapies in diabetes

Investigators: Dr Indu Singh, Dr Peter Davoren, Dr Natalie Colson and Prof Alfred Lam

Dear Participant

You are invited to participate in a research project being conducted by Griffith University and Gold Coast University Hospital. This information sheet describes the project in straightforward language, or ‘plain English’. Please read this sheet carefully and be confident that you understand its contents before deciding whether to participate. If you have any questions about the project, please ask one of the investigators.

Who is involved in this research project? Why is it being conducted?

 The investigators listed at the top of the page want to compare the protective effects of aspirin and natural antioxidants (anthocyanins from fruit source), on platelet (blood cells involved in clotting of blood and when not functioning normally can lead to risk of cardiovascular diseases such as stroke, heart attack or other heart diseases) function and activity as well as lipid (cholesterol and other fats) profile and inflammation. The decrease in platelet activity and improved lipids and inflammation following the treatment leads to decreased thrombotic (narrowing and blocking of arteries responsible for heart attack and strokes) tendency and heart disease in diabetes patients.

 The project has been approved by the Griffith University and Gold Coast University Hospital Human Research Ethics Committee, but we still need your consent to be part of the research.

Why have you been approached?

 You have been selected for this research because you volunteered in response to our advertisement.

What is the project about? What are the questions being addressed?

 Diabetes results in an increase in the stickiness of platelets, which results in an increased risk of thrombosis and other cardiovascular diseases. This is usually prevented with conventional cardiovascular medication (most commonly used Aspirin).

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 Aspirin is a commonly used antiplatelet therapy for patients with cardiovascular disease. However, it has greatly reduced efficacy in diabetes.

 This project is being conducted to evaluate if an alternative antiplatelet agent such as natural antioxidants (we have previously shown QGP juice antioxidants act on platelet in similar manner as does the Aspirin) can result in decreased risk of thrombosis in diabetes population.

 The data from this study will be used to guide management of antiplatelet and anticoagulant therapy more effectively in this population.

 All your collected specimen and data will be stored in safe and locked laboratory space in the research laboratory of Griffith University Gold Coast campus for a period of 5 years and then it will be destroyed and discarded. Any papers will be shredded, blood specimen will be discarded under safe biological waste procedures and electronic data will be deleted. None of these stored data will be able to identify you at any time.

If I agree to participate, what will I be required to do?

 You will be required to complete a questionnaire and consume anthocyanin (antioxidant capsules) provided for 4 weeks and 75 mg Aspirin (1 tablet)/day for 4 weeks. There will be a rest period of four weeks between the two treatments.

 You will be required to keep a food diary for at least 2 days per week of intervention.

 You will also be required to give small amount of fasting blood sample from your arm vein before and after each treatment on day 1 and day 29 before and after first treatment) followed by day 57 and day 85 (before and after second treatment).

 You will not be on any treatment between day 29 and day 57.

What are the risks or disadvantages associated with participation?

 Blood will be collected from your arm vein using the same method as for routine medical blood tests by an experienced and qualified person. A sterile needle is passed into the vein and blood is drawn. The needle is then withdrawn, a cotton swab passed over the site and gentle pressure is applied for approximately two minutes. Adverse effects of taking blood are the minor discomfort associated with the needle passing through the skin and the possibility of minor painless bruising near the puncture site. The risk of infection is only minimal when venepuncture is performed under sterile conditions as described above. Some people may feel briefly dizzy after blood has been collected. Please let us know if this occurs.

 In the case of any adverse effects occurring, the personnel officially trained in first aid, will provide medical attention and first aid followed by check-up by the endocrinologist Dr Peter Davoren (one of the investigators).

 Any abnormal results will be noted and you will be referred to your general practitioner for further investigation.

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 Participation in this study is solely voluntary. You will be free to withdraw at any time for any reason. If you chose to withdraw all data and blood samples collected will be destroyed and disregarded from the study.

What are the benefits associated with participation?

 There are no direct benefits to you from your participation; however it will help the research in this area and may benefit the patients and doctors in future by making guided therapy in diabetes.

What are my rights as a participant?

 We want to draw your attention to your rights, which include:

 The right to withdraw their participation at any time, without prejudice.  The right to have any unprocessed data withdrawn and destroyed, provided it can be reliably identified, and provided that so doing does not increase the risk for the participant.  The right to have any questions answered at any time.

Whom should I contact if I have any questions?

 You should contact any of the researchers: Dr Indu Singh (07 55529821)

Yours sincerely,

Dr Indu Singh

Dr Peter Davoren

Dr Natalie Colson

Prof Alfred Lam

The conduct of this research involves the collection, access and / or use of your identified personal information. The information collected is confidential and will not be disclosed to third parties without your consent, except to meet government, legal or other regulatory authority requirements. A de-identified copy of this data may be used for other research purposes. However, your anonymity will at all times be safeguarded. For further information consult the University’s Privacy Plan at http://www.griffith.edu.au/about-griffith/plans- publications/griffith-university-privacy-plan or telephone (07) 3735 4375.”

Any concerns or complaints about the ethical conduct of the research are to be directed to the Manager, Research Ethics, Griffith University on 3735 4375 or research- [email protected] OR Gold Coast University Hospital the telephone number is (07) 56873879. Email [email protected]

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Appendix 6. Volunteers Initial Screening

VOLUNTEER SCREENING

Confidentiality: The information given on this form will be treated as confidential. The information will not be copied and will be destroyed if a subject is not selected for the study. Only the researchers involved will have access to this data.

Name : SUBJECT CODE M/F

Date :

Address :

Telephone : Home Work

Date of Birth : Age : Weight :______Height:______

What is the earliest time you would be available in the mornings?

VOLUNTEER INCLUSION CRITERIA: 1. Between 25-75 years of age. 2. Non-smoker 3. Healthy or Diabetic 4. No known problems with venepuncture

VOLUNTEER EXCLUSION CRITERIA: 1. Excessive bleeding tendency 2. Anti-Coagulant therapy 3. Recent GI bleed 4. Liver Disease 5. Anti-inflammatory Drugs affecting platelets 6. Plt <125 & >450

PLEASE COMPLETE THE FOLLOWING DETAILS:

Yes No Not sure Have you had, or do you have :

High blood pressure

Angina

Heart attack

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Stroke

High cholesterol (>6mmol/L)

High triglycerides (>2mmol/L)

Renal disease

Allergies (including asthma)

Bypass surgery or angiogram

Gestational Diabetes

Please provide details (and medication, if any):______

Are you undergoing treatment for : Angina

Lowering blood fats

Lowering blood pressure

Diabetes

Anti-platelet therapy

Do you take any other medication, including aspirin?

Yes No

If yes, please give details: ______

______

Would you consume more than two standard glasses of any alcoholic beverage :

Daily A few days a week

Once a week occasionally

Rarely or never

If yes, type of beverage: ______

Thank you for your co-operation. We will be selecting a study group of 100 volunteers (25 normal healthy subjects, 25 diabetics without a history of heart disease, 25 diabetics with a history of heart disease and 25 prediabetics). Four blood samples will be collected before and after each of the 4 weeks supplementation with anthocyanin capsules and 81 mg Aspirin with 4 weeks of no supplementation between 2 treatments. Please do not be offended if you are not chosen, it certainly does not mean that

194 you were not suitable, but rather, that we had too many applicants in your age range. We would however like to keep your name on our records for involvement with further studies if you agree.

I give my consent to be contacted to participate in future studies______(signature)

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Appendix 7. Consent Form

Gold Coast University Hospital

CONSENT FORM Anthocyanins: Possible Antiplatelet Alternative for Aspirin Resistant Diabetic Population

Prescribed Consent Form for Persons Participating in Research Projects Involving Interviews, Questionnaires, blood collection or Disclosure of Personal Information.

Institute Griffith Health Institute

School of Medical Science

Name of participant:

Project Title: Anthocyanins: Possible Antiplatelet Alternative for Aspirin Resistant Diabetic Population

Dr Indu Singh Phone: 07 55529821

Name(s) of investigators: Dr Peter Davoren

1. I have received a statement explaining the questionnaire involved in this project.

2. I consent to participate in the above project, the particulars of which - including details of the interviews or questionnaires, blood collection from me at 4 different times and oral intake of anthocyanin capsules and 75 mg aspirin for 4 weeks each with a break of 4 weeks between two treatments - have been explained to me.

3. I authorise the investigator or his or her assistant to interview me or administer a questionnaire, provide anthocyanin capsules and aspirin tablets for consumption by me and collect fasting blood from my arm at 4 different times.

4. I acknowledge that:

(a) Having read explanatory Statement, I agree to the general purpose, methods and demands of the study. (b) I have been informed that I am free to withdraw from the project at any time and to withdraw any unprocessed data previously supplied. (c) The project is for the purpose of research and/or teaching. It may not be of direct benefit to me. (d) The privacy of the personal information I provide will be safeguarded and only disclosed where I have consented to the disclosure or as required by law. (e) The security of the research data is assured during and after completion of the study. The data collected during the study may be published, and a report of the project outcomes will be provided to any participants who request it. Any information which will identify me will not be used.

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(f) The information gained from this research may result in improved methods for diagnosis or treatment, but as an individual I do not have ownership of these results, research records, or the sample that I give.

Participant’s Consent

Participant:

(Signature) (Name) (Date)

Witness:

(Signature) (Name) (Date)

Participants should be given a photocopy of this consent form after it has been signed.

Any concerns or complaints about the ethical conduct of the research are to be directed to the Manager, Research Ethics, Griffith University on 3735 4375 or [email protected] OR

Gold Coast University Hospital the telephone number is (07) 56873879. Email

[email protected]

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Appendix 8. Analysis of the Anthocyanin Capsule

Medox® Hemicellulose capsules containing powder of anthocyanins.

Source: Bilberries (Vaccinium myrtillus) and Black Currants (Ribes nigrum) Content: Purified alchohol extract from plant material: Minimum 80mg anthocyanincitrates pro capsule as measured by UV-Vis (30000). 40mg Citric acid as counter ion and 35mg Maltodextrin for stabilization. Pigments: The: 3-O-rutinosides of Cyanidin and Delphinidin

and the: 3-O-β-galactopyranosides, 3-O-β-glucopyranosides and 3-O--arabinopyranosides of Cyanidin, Peonidin, Delphinidin, Petunidin and Malvidin

HPLC-analysis Column: Hypersil ODS (200.0 x 4.6 mm, 5 m) Solvents: A, H2O (0.5% TFA); B, MeCN (0.5% TFA) Gradient: 0-10 min, 10-18% B in A (linear); 10-18 min, 18-28% B in A (linear); 18-19 min, 28-40% B in A (linear); 19-22 min, 40% B in A (isocratic); 22-23 min, 40-10% B in A (linear); 23-25 min, 10% B in A (isocratic) Flow: 1 mL/min Detection: 520 nm and 280 nm

198

M edox 0310024 4: Diode Array 13.34 520 100 14.87 2.46e5

15.24

% 17.77 12.67

18.90 21.87 0 0310024 4: Diode Array 14.87 280 100 13.34 1.54e5

15.24

% 17.77 12.67

18.90 21.87 3.54 8.97 0 Tim e 2.50 5.00 7.50 10.00 12.50 15.00 17.50 20.00 22.50 25.00

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Appendix 9. Food Questionnaire, Antioxidant Questionnaire, Food diary form and Instructions

Food Questionnaire, Antioxidant Questionnaire and Food Dairy

Anthocyanins: Possible Antiplatelet Alternative for Aspirin Resistant Diabetic Population

Subject ID: Date: Interviewer:

200

Demographic data describing the subject:

Project: Anthocyanins: Possible Antiplatelet Alternative for Aspirin Resistant Diabetic Population

Age: Gender: Male/Female Body weight: Any special dietary needs:

201

SELF ADMINISTERED FOOD QUESTIONNAIRE

This questionnaire looks at your usual eating habits. We would like to know as much as possible about the types of food you eat, how often you eat them, and approximate serving sizes.

We would like you to go through this booklet, thinking about your typical meal pattern and to record which of the foods listed, you usually eat at breakfast, lunch, dinner and between meals.

The information you give us is important, as this will be used to assess your usual intake of antioxidant as part of your normal regular diet.

Please read the instructions and look at the “Sample Page”, before completing the questionnaire. Description (Food Lists) and standard serving sizes are also provided to help you ascertain what are “small”, “medium”, and “large” serves.

Note: In the food list attached “Standard” serve is equivalent to a medium serve.

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FOOD

LISTS

203

STANDARD SERVE

BREAKFAST CEREALS

1 cup Cornflakes, ricebubbles 1 biscuit/ ½ cup Wholewheat, eg Vitabrite, Weetbix, Wheeties, Branflakes ½ cup Processes Bran, eg Allbran, Sanbran, Branbuds ½ cup Oatbran Cereal, eg Balance ½ cup Muesli - Toasted - Untoasted - Muesli Flakes 1 cup Fortified Cereals, eg Special K, Nutrigrain, Extra G, Vital 1 cup Sugar coated, eg Cocoa Pops, Strawberry Pops, Fruit Loops, Fruit & Honey, Just Right 1 cup Puffed Wheat 1 cup Porridge, cooked 1 tbspn Plain Bran 1 tbspn Other cereals, eg Wheatgerm, Oatbran

BREAD

1 med roll Roll - White - Wholemeal - Multigrain - Oatbran - Brown

1 med slice Sliced - White - Wholemeal - Multigrain - Oatbran - Brown

1 med pocket Pocket/Lebanese 1 med slice Fruit Bread, eg Raisin bread

OTHER BAKED PRODUCTS

1 Crumpets 1 Doughnuts 1 Muffins 1 Pancakes 1 Pikelets 1 Scones 1 Dumplings 1 Croissant 1 Fruit Bun eg, Coffee Scroll

204

STANDARD SERVE

BISCUITS

1 med biscuit Savoury 1 biscuit Crispbreads 1 biscuit Short bread style 1 biscuit Sweet Plain 1 biscuit Sweet Fancy, eg Cream filled, with nuts, fruit or icing 1 biscuit Chocolate coated

CAKES & PASTRIES

1 med slice cake Cake, eg Chocolate, Banana, Orange 1 med slice cake Cake (oil based), eg Carrot 1 med slice cake Fruit Cake 1 med slice cake Packet cakes 1 Lamington 1 Rock cakes 1 sml, 1/6 family pie Fruit pies 1 sml, 1/6 family pie other sweet pies 1 Pastries - Danish - Éclairs - Custards squares 1 Other pastries (please state type and usual serving size)

PUDDINGS & DESSERTS

½ cup Bread and Butter or Rice Pudding 1/6 family pudding Steamed Pudding ½ cup Trifle ½ cup Fruit Crumble/Sponges 1/8 of pie Cheesecake ½ cup Milk Puddings, eg Custard, Blancmange 2 scoops Ice cream ½ cup Jelly 1 Meringues/Pavlova Others (please state your serving size)

PASTA

1 cup Cooked pasta/noodles - White/Green/Wholemeal - Other eg Tortellini, Ravioli

RICE

1 cup (cooked) Boiled Rice - Brown 1 cup (cooked) - White

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STANDARD SERVE

MIXED DISHES

1 cup Macaroni Cheese 1/6 large pie, 1 small Quiche 1 cup Mornay (Fish or Seafood) Dishes 1 cup Fried Rice 1 cup Shepherds pie 1 cup Lasagne ½ cup Canned Spaghetti ½ cup Canned Baked Beans Other (please state type and usual serving size)

TAKEAWAYS

1 large, 2 small Sausage Rolls 1 pie, 1/6 family pie Meat pies 1 Pasties 1 Potato Cakes 1 roll Spring Rolls 1 dim sim Dim Sims 1 roll Chiko Rolls 1 Tacos 1 Souvlaki 1 Hamburger and Bun ¼ large, 2 slices Pizza 1 bucket Chips - French fries Chinese takeaways (please state type and usual serving size) Other takeaways (please state type and usual serving size)

BEEF & VEAL

1 slice Roast 1med Steak 1 large piece Schnitzel 1 slice Corned Beef 1 med Rissoles 1 cup Casserole, Stew or Curry 1 cup Stewed Mince ½ cup Bolognese Sauce for Pasta

LAMB

1 small Chops and Cutlets 1 slice Roast 1 med Steak 1 cup Mince 1 cup Casserole, Stew or Curry Other Lamb types (please state type and usual serving size).

206

STANDARD SERVE

PORK

1 med/2 ribs Chops and ribs 1 slice Roast 1 cup Casserole or Stew 1 med Steak 1 rasher Bacon 1 slice Ham 1 steak Ham steak Other pork (please state type and usual serving size).

CHICKEN & OTHER POULTRY

1 drumstick/2wings Chicken - Boiled or 2 slices of breast - Roast as above - Fried/BBQ (including takeaway) 1 cup -Other Other Poultry (please state type and usual serving size).

FISH, SEAFOOD AND PRODUCTS

1 med fillet Fish - Without batter, eg steamed or grilled 1 med fillet - Fried (including takeaway) 1/3 cup - Canned, Salted Fish, eg tuna, salmon, anchovy etc 1 fishcake/2 fingers - Fishcakes, Fish fingers ½ cup Seafood eg prawns, oysters, scallops etc

PREPARED MEATS

2 thick/3 thin Sausage - Beef 2 thick/3 thin - Pork 1 long thin, I thick - Frankfurts and Saveloys 1 med slice (10cm across) Luncheon, eg Devon, Fritz, Mortadella and similar 1 med slice (5cm across) Salami, Strasburg, Metwurst, Liverwurst 10cm length Kabana 1 med slice (10cm across) Chicken luncheon/Roll 1 tbspn Liver Pate Other (please state type and usual serving size).

OFFAL

½ liver Liver 1 kidney Kidney Other (please state type and usual serving size).

207

STANDARD SERVE

SALADS

½ cup Coleslaw ½ cup Lettuce salad ½ cup Potato salad ½ cup Rice salad ½ cup Waldorf salad Other (please state type and usual serving size).

POTATO

2/3 cup Potato - Mashed 1 med - Boiled 1 med - Roasted 17-18 chips - Chips, home cooked 1 med piece Sweet Potato, Yams

PEAS & BEANS

1/3 cup Green beans 1/3 cup Green peas

LEGUMES & PULSES

1/3 cup Haricot, Lima, Kidney beans, Lentils, Broad beans and similar types

TOMATO

½ med Tomato - Fried 2 slices - Raw ½ med - Baked

CARROTS, SWEDE & SIMILAR

2 slices Beetroot 1/3 cup Carrots 1 med Radishes 1/3 cup Turnip, Swede, Parsnip

LEAFY GREEN VEGETABLES

1/3 cup Bean shoots 1 stick, 15cm Celery 2 leaves Lettuce 1/3 cup Spinich/Silverbeet

CABBAGE & SIMILAR

5-6 sprouts Brussel Sprouts 1/3 cup Cabbage ½ cup Cauliflower ½ cup Broccoli

208

STANDARD SERVE

PUMPKIN, SQUASH & SIMILAR

2 strips (1/2cm thick) Capsicum 3 slices (1/2cm thick) Cucumber 2 slices (1/2cm thick) Eggplant 1/3 cup Pumpkin, Butternut Pumpkin 1/3 cup/1 med squash Zucchini/Squash

ONIONS, LEEKS & SIMILAR

½ cup Leeks ¼ cup Onion - Fried 2 rings/slices - Raw 1 med - Baked/boiled

OTHER VEGETABLES

3 spears Asparagus 2 Mushroom ½ cup/ ½ cob Sweetcorn 1/3 cup Frozen mixed vegetables Other (please state type and usual serving size).

209

STANDARD SERVE

CITRUS

1 med Orange 1med Mandarin ½ grapefruit Grapefruit 1 tdspn Lemon (as juice) 1 med Tangerine Others (please state type and usual serving size).

APPLES, PEARS & SIMILAR

1 med Apples/Pears Others (please state type and usual serving size).

BERRYFRUIT

¾ cup Raspberry 5.6 med Strawberry ¾ cup Boysenberry sm bunch Grapes 1 med Kiwifruit Other (please state type and usual serving size).

STONEFRUIT

1 med Apricots 8 –10 Cherries 1 med Nectarines/Peaches 1 med Plum Others (please state type and usual serving size).

TROPICAL

½ avocado Avocado 1 slice Melons 1 passionfruit Passionfruit 1 slice Pineapple 1 slice Pawpaw/Mango 1 med Banana Others (please state type and usual serving size).

FRUIT, CANNED OR STEWED

½ cup In syrup ½ cup In water &/or Juices

DRIED FRUIT 1/3 cup Raisins, Currants 4 - 5 pieces Dried Apricots, Peaches 4 - 5 pieces Dried Apples, Pears

210

STANDARD SERVE

MILK

1 med glass (200ml) Plain 1 med glass (200ml) Flavoured standard size Milkshake standard size Thickshake

CHEESE

1 med slice Cheddar 1 tbspn Cream ¼ cup Cottage ¼ cup Ricotta 1 med slice Edam/Gouda 1/6 round Soft cheeses eg Camembert, Brie 1 slice Processed 1 tbspn Parmesan Others (please state type and usual serving size).

YOGHURT

1 carton (200g) Plain 1 carton (200g) Fruit/Flavoured

CREAM

1 tbspn Sour Cream, Thickened Cream

EGGS

1 egg Boiled or Poached 1 egg Fried 2 eggs Omelette or Scrambled Others (please state type and usual serving size).

211

STANDARD SERVE

SNACK FOODS

1 sm bag (50g) Cheezels and Twisties 1 sm bag (50g) Corn chips 1 cup Popcorn 1 sm bag (50g) Potato chips 20 pretzels Pretzels Others (please state type and usual serving size).

NUTS

10 nuts Cashews and Peanuts 5 nuts Others - Almonds, Pistachio, Hazelnuts, Macadamias, etc 1 tbspn - Pumpkin, Sunflower, Sesame seeds

CONFECTIONERY

2 sq’s, pieces Chocolate - Filled 1 small bar - Bars 1 bar Carob 1 bar Muesli type bars 1 bar Novelty bars, eg Mars Bars, Cherry Ripe, etc 4 - 5 pieces Caramel Sweets, eg Caramels, Toffees, Butterscotch 4 - 5 pieces Other Sweets, eg Liquorice, Boiled Lollies, Jelly Beans etc

NON-ALCOHOLIC

1 med glass (200ml) Pure fruit juice, eg Orange, Apple, Pineapple “ “ Vegetable/Tomato juice “ “ Fruit juice drinks, eg Prima, Cordials, etc “ “ Soft drinks and flav mineral waters - Cola, eg Pepsi, Coke “ “ - Other Lemonades etc “ “ - Ciders and Grape juice “ “ Low energy soft drinks and Cordial - Lemonade and Cola “ “ - Mineral water, plain “ “ - Soda water, Tonic water “ “ - Cordial 1 cup Tea - Common, Herb 1 cup Coffee - Decaffeinated, Instant, Percolated 1 cup Substitutes eg Carob based 1 cup Chocolate Based - Cocoa, Drinking Chocolate - Other eg, Milo, Ovaltine Other eg Sustagen, Staminade etc (please state type and usual serving size).

ALCOHOLIC

1 pot (285ml) Beer - Common 1 pot (285ml) - Low alcohol 1 med glass (200ml) Cider, alcoholic 1 nip Liqueur 1 sherry glass Port 1 sherry glass Sherry 1 nip Spirits

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1 wine glass Wine 1 cocktail glass Others, eg cocktails etc (please state type and usual serving size).

213

Please answer the following questions:

1. How much milk do you usually add to your serving of breakfast cereal? (please tick one) a. None or I don’t eat breakfast cereal b. About half a cup c. About 1 cup d. About 1 and a half cups e. About 2 cups f. More than 2 cups

2. How many teaspoons of sugar do you usually add to breakfast cereal?

(one dessertspoon = two teaspoons)

Number of teaspoons

3. If you usually eat muesli, is it usually: (please tick one) a. Natural or untoasted (please specify brand) . b. Toasted (please specify brand) . c. Muesli flakes (please specify brand) . d. Don’t eat muesli

4. If you eat rice, is it usually: (please tick one) a. White rice b. Brown rice c. About half the time white and half the time brown d. I do not eat rice

5. State what type of milk you usually use:

(please tick) whole milk 2% fat skim milk soy milk none a. On cereal b. Glasses or drinks of milk c. In tea d. In coffee e. In coffee substitute f. In chocolate type drink [2% fat milk = Rev, Physical. Skim Milk = Skinny Milk, Powdered Skim Milk]

6. Do you usually make your cocoa / milo / chocolate based drink with: (please tick one) a. Mostly milk b. Mostly water c. About half & half d. I do not drink chocolate based drinks

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7. How many teaspoons of sugar do you usually have in each cup of: (circle a number for each teaspoon) a. Tea 0 1 2 3 4 b. Coffee 0 1 2 3 4 c. Coffee substitute 0 1 2 3 4 d. Chocolate based drinks 0 1 2 3 4

8. What type of bread do you usually eat? (please tick one) a. White b. Brown c. Wholemeal or mixed grain d. About half the time wholemeal and half the time white e. About half the time wholemeal and half the time brown f. About half the time brown and half the time white g. Other breads (please specify) . h. I do not eat bread

9. Which of the following do you usually spread on bread? (please tick one) a. Butter b. Margarine (please specify brand) . c. Blended spread (please specify brand) . d. Other fat type spread (please describe) . e. I don’t use anything

10. Which of the following do you usually spread on crackers? (please tick one) a. Butter b. Margarine (please specify brand) . c. Blended spread (please specify brand) . d. Other fat type spread (please describe) . e. I don’t use anything

11. If you use butter, margarine or a similar type of spread on bread or crackers, how much do you use? (please tick one) a. A thin spread b. A medium spread c. A thick spread d. I don’t use anything

12. If you eat the following, how are they usually cooked? (please tick) fried grilled don’t eat a. Sausages b. Chops c. Bacon

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13. When frying or roasting meat, which one of the following do you usually use? (please tick one) a. Butter b. Dripping or lard c. Margarine (please specify brand) . d. Vegetable oils (please specify type) . e. Cooked in own juices f. Something else (please describe) . g. I never eat fried or roasted meat

14. Do you usually make gravy? No Yes If YES, how do you make gravy?

15. Do you usually trim fat and skin from poultry before: (please tick) yes no don’t eat a. Frying b. Grilling c. Baking d. Casseroling

16. When you eat poultry do you usually eat: (please tick one) a. Meat with the skin on b. Meat only, no skin c. I do not eat poultry

17. Do you usually trim fat from meat before: (please tick) yes no don’t eat a. Frying b. Grilling c. Baking d. Casseroling

18. When you have meat with a moderate amount of fat on it, do you usually eat: (please tick one) a. Little or no fat b. About half of the fat c. Most of the fat d. All of the fat e. I do not eat meat

216

19. Do you usually skim fat off soups and casseroles? (please tick one) a. Yes b. No c. Don’t cook them

20. If you eat fried fish, in what is it usually coated? (please tick one) a. Batter b. Breadcrumbs c. Flour d. Other coatings e. Fried without coating f. I don’t eat fried fish

21. When you eat fresh fish (and it is not fried), how is it usually cooked? (please tick one) a. Baked (method) . b. Grilled (method) . c. Steamed (method) . d. Other (method) . e. I do not eat fresh fish

22. If you eat fried or scrambled eggs, which of the following do you usually cook with? (please tick one) a. Butter b. Dripping or lard c. Margarine (please specify brand) . d. Vegetable oils (please specify type) . e. Something else (please describe) . f. I never fry or scramble eggs

23. If you fry or roast vegetables more than once a week, which do you use most often? (please tick one) a. Butter b. Dripping or lard c. Margarine (please specify brand) . d. Vegetable oils (please specify type) . e. Something else (please describe) . f. I never fry or scramble eggs

217

24. When using fat or oil in general food preparation, what type do you usually use? (please tick one) a. Butter b. Dripping or lard c. Margarine (please specify brand) . d. Vegetable oils (please specify type) . e. Something else (please describe) .

25. Do you eat: (please tick) always sometimes rarely never a. Reduced fat cheeses? b. Reduced fat yoghurt? c. Reduced fat cream? d. Reduced fat mayonnaise?

26. Do you take any vitamin, mineral or other dietary supplements regularly? Yes No (please tick)

27. If you take any type of dietary supplement please fill in the table below:

Check the labels on the box or bottle if you are unsure of some of the answers.

Brand Name of product Size of dose Number of doses (eg. Nyal) (eg. Vitamin C pills) (eg. 250 mg) (eg. 2 per day)

28. Check list for completion: Have you? a. Checked the food lists for any other foods you eat? b. Checked the serve size for each food? c. Answered all questions on cooking and eating habits?

218

ADDITIONAL

QUESTIONS

219

WINTEC • NZ ACADEMY OF SPORT

Dietary Antioxidant Questionnaire

GENERAL INSTRUCTIONS

 Answer each question as best you can. Estimate if you are not sure. A guess is better than leaving a blank.  Use only a black or blue ball-point pen. Do not use a pencil or felt-tip pen. Do not

fold, staple, or tear the pages.

 Put an X in the box next to your answer.

 If you make any changes, cross out the incorrect answer and put an X in the box next

to the correct answer. Also draw a circle around the correct answer.  If you mark NEVER, NO, or DON’T KNOW for a question, please follow any arrows or instructions that direct you to the next question.

BEFORE TURNING THE PAGE, PLEASE COMPLETE THE FOLLOWING QUESTIONS.

Are you male or Today's date: In what month were female? you born? In what year were

MONTH DAY YEAR you born? a Male

01 Jan 2007 01 Jan b Female |___|___| 02 Feb 2008 02 Feb 0 0 0 0 03 Mar 2009 03 Mar 1 1 04 Apr 1 1 04 Apr 2 2 2 2 05 May 05 May 3 3 06 Jun 3 3 06 Jun 4 4 07 Jul 4 07 Jul 5 5 08 Aug 5 08 Aug 6 6 7 7 09 Sep 6 09 Sep 10 Oct 8 8 10 Oct 7 9 9 11 Nov 11 Nov 8 12 Dec 9 12 Dec

Are you taking oral contraceptive pills? 02 3-4.9 hours a week Oral contraceptive may act as an antioxidant 03 5-6.9 hours a week hence we need to ask this question. 04 7-9 hours a week

05 >9 hours a week 01 Yes 02 No How many hours do you spend doing resistance 03 Not applicable (Male) training (weights) a week?

What is your current weight? 01 <3 hours a week 02 3-4.9 hours a week 01 <50 kg 06 81-90 kg 03 5-6.9 hours a week 02 51-60 kg 07 91-100 kg 04 7-9 hours a week 03 61-70 kg 08 101-110 kg 05 >9 hours a week 04 71-80 kg 09 >110 kg How many hours do you spend doing additional How long have you been at your current weight? aerobic training a week?

01 <1 month 01 <3 hours a week 02 1-6 months 02 3-4.9 hours a week 03 7-12 months 03 5-6.9 hours a week 04 12 months or longer 04 7-9 hours a week 05 >9 hours a week What is your lightest adult weight? Do you typically eat breakfast before training? 01 <50 kg 05 81-90 kg 02 51-60 kg 06 91-100 kg a Yes 03 61-70 kg 07 101-110 kg b No 04 71-80 kg 08 >110 kg Have you been ill in the last week, to a point it How many years have you been involved in the has hindered your training? sport? a Yes 01 <2 years b No 02 2-3 years 03 >3-5 years Have you required on going consumption of anti- 04 >5-7 years inflammatory medication? 05 > 7 years a Yes How many hours do you spend doing on water b No training a week?

01 <3 hours a week

221

1. Over the past 1 month, how often did you drink Over the past 1 month…. tomato juice or vegetable juice? 4. How often did you drink other fruit drinks (such NEVER (GO TO QUESTION 2) as thriftee, Vitafresh, Kool-Aid, sports drink, diet or regular)? b 1 time per month or less g 1 time per day c h 2–3 times per month 2–3 times per day a NEVER (GO TO QUESTION 5) d 1–2 times per week i 4–5 times per day e 3–4 times per week j 6 or more times per day b 1 time per month or less g 1 time per day f 5–6 times per week c 2–3 times per month h 2–3 times per day d 1–2 times per week i 4–5 times per day 1a. Each time you drank tomato juice or e 3–4 times per week j 6 or more times per day vegetable juice, how much did you usually f 5–6 times per week drink? 4a. Each time you drank other fruit drinks, how a Less than ¾ cup (200mL) much did you usually drink? b ¾ to 1¼ cups (200 to 300mL)

c More than 1¼ cups (300mL) a Less than 1 cup (250mL) 2. Over the past 1 month, how often did you drink b 1 to 2 cups (250-500mL) orange juice, apple, pineapple, cranberry or c More than 2 cups (500mL) grape juice ? 4b. How often were your fruit drinks enriched a NEVER (GO TO QUESTION 3) (added) with vitamin-C?

b 1 time per month or less g 1 time per day a Almost never or never c 2–3 times per month h 2–3 times per day b About ¼ of the time d 1–2 times per week i 4–5 times per day c About ½ of the time e 3–4 times per week j 6 or more times per day d About ¾ of the time f 5–6 times per week e Almost always or always f Don’t know 2a. Each time you drank orange juice, pineapple, apple, cranberry or grape juice, 5. How often did you drink hot drinks such as how much did you usually drink? coffee, black, green or oolong tea ?

a Less than ¾ cup (200mL) a NEVER (GO TO QUESTION 6) b ¾ to 1¼ cups (200 to 300mL) c More than 1¼ cups (300mL) b 1 time per month or less g 1 time per day c 2–3 times per month h 2–3 times per day 3. Over the past 1 month, how often did you drink d 1–2 times per week i 4–5 times per day fruit drinks containing blackberry, strawberry, e 3–4 times per week j 6 or more times per day cranberry, raspberry, blackcurrent or f 5–6 times per week blueberry? 5a. Each time you drank coffee, black, green or a NEVER (GO TO QUESTION 4) oolong tea, how much did you usually drink?

b 1 time per month or less g 1 time per day a Less than 1 cup (150mL) c 2–3 times per month h 2–3 times per day b 1 to 2 cups (150-300mL) d 1–2 times per week i 4–5 times per day c 3 to 4 cups (450-600-mL) e 3–4 times per week j 6 or more times per day d More than 4 cups (600mL) f 5–6 times per week 6. How many glasses of ICED tea, caffeinated or 3a. Each time you drank fruit drinks containing decaffeinated, did you drink? blackberry, strawberry, cranberry, raspberry, blackcurrent or blueberry, how a NEVER (GO TO QUESTION 7) much did you usually drink? b Less than 1 cup per f 5–6 cups per week a Less than ¾ cup (200mL) month g 1 cup per day b ¾ to 1½ cups (200-300mL) c 1–3 cups per month h 2–3 cups per day c More than 1½ cups (300mL) d 1 cup per week i 4–5 cups per day e 2–4 cups per week j 6 or more cups per day

222

7. Over the past 1 month, did you drink red wine? a Less than 10 berries b Between 10 and 20 berries a NO (GO TO QUESTION 8) c More than 20 berries

b YES

10. How often did you eat dried fruit, such as 7a. How often did you drink red wine? prunes, raisins or dates ?

b 1 time per month or less g 1 time per day a NEVER (GO TO QUESTION 11) c 2–3 times per month h 2–3 times per day d 1–2 times per week i 4–5 times per day b 1–6 times per year g 2 times per week e 3–4 times per week j 6 or more times c 7–11 times per year h 3–4 times per week f 5–6 times per week per day d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day 7b. Each time you drank red wine, how much f 1 time per week k k 2 or more times per day did you usually drink? 10a. Each time you ate dried fruit, how much did a 1-2 glasses (110-220mL) you usually eat? b 3-4 glasses (330-440mL) c 5-6 glasses (550-660mL) a Less than 2 tablespoons d More than 7 glasses (770mL) b 2 to 5 tablespoons c More than 5 tablespoons Over the past 1 month…..

8. How often did you drink beer? 11. Over the past 1 month, did you eat strawberries, boysenberries, blueberries, or raspberries? a NEVER (GO TO QUESTION 9) a NO (GO TO QUESTION 12) b 1 time per month or less g 1 time per day c 2–3 times per month h 2–3 times per day b YES d 1–2 times per week i 4–5 times per day e 3–4 times per week j 6 or more times f 5–6 times per week per day 11a. How often did you eat strawberries, blueberries, boysenberries, or 8a. Each time you drank beer, how much did you raspberries? usually drink? b 1–6 times per year g 2 times per week a 1 glass to 1 can (110-375mL) c 7–11 times per year h 3–4 times per week b More than 1 to 2 cans (400ml-750mL) d 1 time per month i 5–6 times per week c More than 2 to 3 cans (800-1125mL) e 2–3 times per month j 1 time per day d More than 3 cans (1125mL) f 1 time per week k 2 or more times per day 9. How often did you eat blackberries or blackcurrents (fresh, canned, or frozen)? 11b. Each time you ate strawberries, blueberries, boysenberries or raspberries, a NEVER (GO TO QUESTION 10) how much did you usually eat?

b 1–6 times per year g 2 times per week a Less than ½ cup c 7–11 times per year h 3–4 times per week b ½ to ¾ cup d 1 time per month i 5–6 times per week c More than ¾ cup e 2–3 times per month j 1 time per day f 1 time per week k k 2 or more times per day

9a. Each time you ate blackberries or

blackcurrents, how many did you usually

eat?

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12. How often did you eat cranberries, or cherries? 14. How often did you eat pears?

a NEVER (GO TO QUESTION 13) a NEVER (GO TO QUESTION 15)

b 1–6 times per year g 2 times per week b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week c 7–11 times per year h 3–4 times per week d 1 time per month i 5–6 times per week d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day e 2–3 times per month j 1 time per day f 1 time per week k k 2 or more times per day f 1 time per week k k 2 or more times per day

12a. Each time you ate cranberries or cherries, 14a. Each time you ate pears, how much did you how much did you usually eat? usually eat?

a Less than ½ cup or less than 10 fruit a Less than 1 b ½ to 1 cup or 10 to 30 fruit b 1 to 2 pears c More than 1 cup or more than 30 fruit c More than 2 pears

13. Over the past 1 month, did you eat plums or Over the past 1 month….. pineapple? 15. How often did you eat oranges or kiwifruit? a NO (GO TO QUESTION 14) a NEVER (GO TO QUESTION 16) b YES

b 1–6 times per year g 2 times per week

c 7–11 times per year h 3–4 times per week 13a. How often did you eat plums or pineapple ( d 1 time per month i 5–6 times per week canned or fresh)? e 2–3 times per month j 1 time per day f 1 time per week k 2 or more times per day b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week 15a. Each time you ate oranges or kiwifruit, how d 1 time per month i 5–6 times per week much did you usually eat? e 2–3 times per month j 1 time per day f 1 time per week k 2 or more times a Less than 1 orange or 2 kiwifruit per day b 1-3 oranges or 2-4 kiwifruit

c More than 3 oranges or 4 kiwifruit 13b. Each time you ate either plums or pineapple, how much did you usually eat? Over the past 1 month…

a Less than 1 plum or less than ½ cup 16. How often did you eat bran flakes or whole b Between 2 – 4 plums or 1-2 cups grain (Weet-bix, VitaBrits) breakfast c More than 4 plums or more than 2 cups cereal?

a NEVER (GO TO QUESTION 17)

b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day f 1 time per week k 2 or more times per day

16a. Each time you ate bran flakes or whole grain breakfast cereal, how many did you usually eat?

a Less than 1 cup b 1-2 cups c More than 3 cups

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17. Over the past 1 month, did you eat All Bran, Over the past 1 month… Sultana Bran or Cocoa Krispies (or other chocolate cereal) breakfast cereal? 19. How often did you eat artichokes or artichoke hearts? a NO (GO TO QUESTION 18) a NEVER (GO TO QUESTION 20) b YES b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week 17a. How often did you eat All Bran, Sultana d 1 time per month i 5–6 times per week Bran or Cocoa Krispies? e 2–3 times per month j 1 time per day f 1 time per week k k 2 or more times per day a NEVER 19a. Each time you ate artichokes, how much did b 1–6 times per year g 2 times per week you usually eat? c 7–11 times per year h 3–4 times per week d 1 time per month i 5–6 times per week a Less than ½ cup e 2–3 times per month j 1 time per day b ½ to 1 cup f 1 time per week k 2 or more times c More than 1 cup per day 20. How often did you eat cabbage (red or white)? 17b. Each time you ate All Bran, Sultana Bran or Cocoa Krispies, how much did you usually a NEVER (GO TO QUESTION 21) eat? a Less than 1 cup b 1–6 times per year g 2 times per week b 1-2 cups c 7–11 times per year h 3–4 times per week c More than 2 cups d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day 18. How often did you eat Cornflakes, Rice crispies f 1 time per week k k 2 or more times per day or Grinners breakfast cereal?

a NEVER (GO TO QUESTION 19) 20a. Each time you ate cabbage (red or white), how much did you usually eat? b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week a Less than ½ cup d 1 time per month i 5–6 times per week b ½ to 1 cup e 2–3 times per month j 1 time per day c More than 1 cup f 1 time per week k 2 or more times per day Over the past 1 month… 18a. Each time you ate Cornflakes, Rice crispies or Grinners, how much did you 21. How often did you eat potatoes (such as red usually eat? (Maori) or orange kumera). regardless of cooking method? a Less than 1 cup b 1 to 2 cups a NEVER (GO TO QUESTION 22) c More than 2 cups

b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day f 1 time per week k k 2 or more times per day

21a. Each time you ate potatoes, how much did you usually eat?

a Less than 1 medium potato b 1-2 medium potatoes c More than 2 potatoes

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Over the past 1 month…. 22. How often did you eat spinach ? 25. How often did you eat pecans or walnuts ? a NEVER (GO TO QUESTION 23) a NEVER (GO TO QUESTION 26) b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week b 1–6 times per year g 2 times per week d 1 time per month i 5–6 times per week c 7–11 times per year h 3–4 times per week e 2–3 times per month j 1 time per day d 1 time per month i 5–6 times per week f 1 time per week k k 2 or more times per day e 2–3 times per month j 1 time per day f 1 time per week k k 2 or more times per day 22a. Each time you ate spinach, how much did you usually eat? 25a. Each time you ate pecans or walnuts, how much did you usually eat? a Less than ¼ cup b ¼ to 1 cup a Less than ¼ cup c More than 1 cup b ¼ to ¾ cup c More than ¾ cup 23. How often did you eat capsicums (red, green or yellow)? Over the past 1 month…

a NEVER (GO TO QUESTION 24) 26. Did you eat canned spaghetti, baked beans (navy beans) or kidney beans? b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week a NO (GO TO QUESTION 27) d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day b YES f 1 time per week k k 2 or more times per day

23a. Each time you ate capsicum, how much did 26a. How often did you eat canned spaghetti, you usually eat? baked beans or kidney beans?

a Less than ½ cup b 1–6 times per year g 2 times per week b ½ cup to 1 cup c 7–11 times per year h 3–4 times per week c More than 1 cup d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day 24. How often did you eat broccoli (fresh or frozen)? f 1 time per week k 2 or more times per day a NEVER (GO TO QUESTION 25)

b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week d 1 time per month i 5–6 times per week 26b. Each time you ate canned spaghetti, baked e 2–3 times per month j 1 time per day beans or kidney beans, how much did you f 1 time per week k k 2 or more times per day usually eat?

24a. Each time you ate broccoli, how much did a Less than ½ cup you usually eat? b ½ to 1 cup c More than 1 cup a Less than ½ cup b Less than ½ cup to 1 cup c More than 1 cup

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27. Over the past 1 month, how often did you eat 30. How often did you eat lasagna with meat (fresh unsweetened cooking chocolate or sugar free or frozen and cooked)? dark chocolate? a NEVER (GO TO QUESTION 32) a NEVER (GO TO QUESTION 28) b 1–6 times per year g 2 times per week b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week c 7–11 times per year h 3–4 times per week d 1 time per month i 5–6 times per week d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day e 2–3 times per month j 1 time per day f 1 time per week k k 2 or more times per day f 1 time per week k k 2 or more times per day 30a. Each time you ate lasagna with meat, how 27a. Each time you ate unsweetened cooking much did you usually eat? chocolate or sugar free dark chocolate, how much did you usually eat? a Less than 1 cup b 1 to 2 cups a Less than ¼ cup (fun size bar) c More than 2 cups b ¼ to 1 cup (standard size bar) c More than 1 cup (1/2 block of chocolate) Over the past 1 month…

28. Over the past 1 month, how often did you eat 31. How often did you eat condensed (tinned) milk chocolate or dark chocolate (not already tomato soup ? mentioned above)? a NEVER (GO TO QUESTION 33) a NEVER (GO TO QUESTION 29) b 1–6 times per year g 2 times per week b 1–6 times per year g 2 times per week c 7–11 times per year h 3–4 times per week c 7–11 times per year h 3–4 times per week d 1 time per month i 5–6 times per week d 1 time per month i 5–6 times per week e 2–3 times per month j 1 time per day e j f 1 time per week k k 2 or more times per day 2–3 times per month 1 time per day f 1 time per week k k 2 or more times per day 31a. Each time you ate condensed (tinned) 28a. Each time you ate milk or dark chocolate, tomato soup, how much did you usually how much did you usually eat? eat?

a Less than ¼ cup (fun size bar) a Less than 1 cup b ¼ to 1 cup (standard size bar) b 1 to 2 cups c More than 1 cup (1/2 block of chocolate) c More than 2 cups

29. How often did you eat chocolate cake or 32. How often did you eat chocolate ice-cream (full, chocolate chip cookies? reduced or low fat)?

a NEVER (GO TO QUESTION 30) a NEVER (GO TO QUESTION 33)

29a. Each time you ate chocolate cake or 32a. Each time you ate chocolate ice-cream, chocolate chip cookies, how much did you how much did you usually eat? usually eat? a Less than 1 cup a Less than 1 slice or 3 cookies b 1 to 2 cups b 1 slice or 3 cookies c More than 2 cups c More than 1 slice or 3 cookies

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33. How often did you consume milk (full, reduced or The next questions are about your use of low fat)? antioxidant or vitamin pills. a NEVER (GO TO QUESTION 34) 37. Over the past 1 month, did you take any b 1–6 times per year g 2 times per week multivitamins, such as One-a-Day-, Theragran-, c 7–11 times per year h 3–4 times per week or Centrum-type multivitamins (as pills, liquids, or d 1 time per month i 5–6 times per week packets)? e 2–3 times per month j 1 time per day f 1 time per week k k 2 or more times per day a NO (GO TO QUESTION 38c)

33a. Each time you consumed milk, how much b YES did you usually eat?

a Less than 1 cup 38. How often did you take One-a-day-, Theragran-, b 1 to 2 cups or Centrum-type multivitamins? c More than 2 cups a Less than 1 day per month 34. How often did you drink flavoured milk or eat b 1–3 days per month yoghurt (full or reduced fat)? c 1–3 days per week d 4–6 days per week a NEVER (GO TO QUESTION 35) e Every day

b 1–6 times per year g 2 times per week 38a. Does your multivitamin usually contain c 7–11 times per year h 3–4 times per week antioxidants (such as vitamin C, vitamin E d 1 time per month i 5–6 times per week or selenium.)? e 2–3 times per month j 1 time per day f 1 time per week k k 2 or more times per day a NO b YES 34a. Each time you drank flavoured milk or ate c Don't know yoghurt how much did you usually consume? 38b. For how many years have you taken multivitamins? a Less than 1 cup b 1 to 2 cups a Less than 1 year c More than 2 cups b 1–4 years c 5–9 years 35. Please mark any of the following single herbs d 10 or more years and spices you consumed more than once per week either fresh or dried:

a cinnamon d ginger 38c. Did you take any vitamins, minerals, or other b cloves e mustard seeds herbal supplements other than your c oregano leaf f tumeric multivitamin?

Over the past 1 month… a NO (GO TO QUESTION 44)

36. How many servings of fruit (not including juices) b YES (GO TO INTRODUCTION TO did you eat per week or per day? QUESTION 39.)

a Less than 1 per week f 2 per day b 1–2 per week g 3 per day c 3–4 per week h 4 per day d 5–6 per week i 5 or more per day e 1 per day

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40a. When you took Vitamin A, about how much These last questions are about the vitamins, did you take in one day? minerals, or herbal supplements you took that are NOT part of a One-a-day-, Theragran-, or a Less than 8,000 IU Centrum-type of multivitamin. b 8,000–9,999 IU c 10,000–14,999 IU

Please include vitamins taken as part of an d 15,000–24,999 IU e 25,000 IU or more antioxidant supplement. f Don't know

39. How often did you take Beta-carotene (NOT as 40b. For how many years have you taken part of a multivitamin in Question 38)? Vitamin A?

a NEVER (GO TO QUESTION 40) a Less than 1 year b b Less than 1 day per month 1–4 years c c 1–3 days per month 5–9 years d d 1–3 days per week 10 or more years

e 4–6 days per week f Every day 41. How often did you take Vitamin C (NOT as part of a multivitamin in Question 38)? 39a. When you took Beta-carotene, about how much did you take in one day? a NEVER (GO TO QUESTION 42)

b Less than 1 day per month a Less than 10,000 IU c 1–3 days per month b 10,000–14,999 IU d 1–3 days per week c 15,000–19,999 IU e 4–6 days per week d 20,000–24,999 IU f Every day e 25,000 IU or more

f Don't know 41a. When you took Vitamin C, about how much 39b. For how many years have you taken Beta- did you take in one day? carotene? a Less than 500 mg b 500–999 mg a Less than 1 year c 1,000–1,499 mg b 1–4 years d 1,500–1,999 mg c 5–9 years e 2,000 mg or more d 10 or more years f Don't know

41b. For how many years have you taken Vitamin 40. How often did you take Vitamin A (NOT as part C? of a multivitamin in Question 38)? a Less than 1 year b 1–4 years a NEVER (GO TO QUESTION 41) c 5–9 years

d b Less than 1 day per month 10 or more years

c 1–3 days per month d 1–3 days per week Over the past 1 month… e 4–6 days per week f Every day 42. How often did you take Vitamin E (NOT as part of a multivitamin in Question 38)?

a NEVER (GO TO QUESTION 43)

b Less than 1 day per month c 1–3 days per month d 1–3 days per week e 4–6 days per week f Every day

Question40 continues in the next 229 column

The last two questions ask about usual eating 42a. When you took Vitamin E, about how much patterns and meal quantity. did you take in one day? 44. Please mark which of the following best a Less than 400 IU describes your usual eating patterns: b 400–799 IU c 800–999 IU d 1,000 IU or more 1 3 meals and 3 snacks e Don't know 2 3 meals and 2 snacks 3 2 meals and 3 snacks 42b. For how many years have you taken 4 2 meals and 2 snacks Vitamin E? 5 5 snacks 6 No set meal pattern a Less than 1 year b 1–4 years 45. Please mark which of the following best c 5–9 years describes your usual meal size. d 10 or more years

43. How often did you take selenium (NOT as part 1 < 1 cup 2 >1 to 2 cups of a multivitamin in Question 38)? 3 >2 to 3 cups 4 >3 to 4 cups a NEVER (GO TO QUESTION 44) 5 >5 cups

b Less than 1 day per month c 1–3 days per month d 1–3 days per week

e 4–6 days per week

f Every day

43a. When you took selenium, about how much did you take in one day?

a Less than 20 μg Thank you very much for completing this b 21–100 μg questionnaire! Because we want to be able to use c 101–200 μg all the information you have provided, we would d 201 μg or more greatly appreciate it if you would please take a e Don't know moment to review each page making sure that you:

 Did not skip any pages and 43b. For how many years have you taken  Crossed out the incorrect answer and circled the selenium? correct answer if you made any change

a Less than 1 year b 1–4 years c 5–9 years d 10 or more years

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Dr Natalie Colson Telephone (07) 5552 9075 [email protected]

Food Diary Name: ______Sex: M / F Date: / / Day :(eg. Sun, Mon) ______Age: _____

Please Remain as Accurate As Possible!! Time Food Type / Brand (if known) Quantity (eg. grams or number of items).

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