Mechanisms of Action of the Duodenal-Jejunal Bypass Sleeve

Mechanisms of action of the duodenal-jejunal bypass sleeve

Jessica Jade McMaster

BSc, MDietSt

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2019

Faculty of Medicine

ABSTRACT

The prevalence of obesity and related comorbidities, such as type 2 diabetes mellitus (T2DM) have reached epidemic proportions globally. Weight loss improves morbidity and mortality in people with obesity and T2DM. However, effective long-term obesity treatments are limited. Lifestyle modification is the first line treatment, but long-term data suggest a return to baseline weight after successful weight loss. In contrast, bariatric surgery causes permanent anatomical changes in the gastrointestinal tract (GIT) to facilitate weight loss. This is the most effective long-term treatment for obesity and T2DM. However, compared to the large number of eligible individuals, very few patients undergo bariatric surgery.

Minimally invasive treatment options have been developed to fill the treatment gap between conservative and surgical measures. One treatment option is the duodenal-jejunal bypass sleeve (DJBS; EndoBarrier®; GI Dynamics, Lexington, MA, USA). This is a 60 cm impermeable sleeve device, which is placed endoscopically into the duodenal bulb, extending to the jejunum and left in place for up to 48 weeks. The device separates bile and pancreatic secretions from chyme, reducing the contact of ingested food with the small intestinal mucosa. Significant weight loss and improvements in glycaemic control have been reported after DJBS treatment, but little is known about the mechanisms of action of the device. The device dwells in the GIT, but the impact of implantation on gastrointestinal function has undergone limited investigation. Obesity and T2DM are associated with derangements in normal physiology. We currently have a poor understanding of the impact of DJBS treatment on these parameters, and how this might be linked to device mechanisms of action. The effect of DJBS implantation on the upper GIT microbiota or the local or systemic immune response is unknown. Therefore, this thesis addresses the mechanisms of action of the DJBS in terms of clinical response, gastrointestinal function and microbial and immune parameters.

The clinical effects of DJBS treatment, in addition to dietary intake, lifestyle factors and long-term outcomes after device removal were assessed. DJBS treatment for up to 48 weeks resulted in significant improvements in anthropometric and metabolic measures. In parallel, reductions in dietary intake were observed. One year after device removal, the majority of patients were unable to sustain clinically significant weight loss. Despite this, some beneficial metabolic effects remained.

Investigation of the effect of DJBS treatment on gastrointestinal function revealed no significant impact of DJBS treatment on gastric emptying or intraluminal lipolytic activity. However, gastrointestinal symptoms were significantly increased following DJBS implantation. In particular, there was a significant increase in epigastric pain symptoms after device placement. When considered in combination with the dietary intake results, a key mechanism of action of the

i device for inducing weight loss appears to be the induction of gastrointestinal symptoms. This occurs primarily in relation to meals and may facilitate weight loss by reducing dietary intake.

Changes to patients’ gastrointestinal microbiota during DJBS treatment was investigated using 16S rRNA gene sequencing technology. The gastric and duodenal mucosa-associated microbiota (MAM), stool microbiota and the device biofilm microbial community were characterised. DJBS treatment induced significant changes in the gastric and duodenal MAM, such as decreases in organisms typical of the proximal GIT and increases in organisms typical of the distal intestine. A distinct microbial community was formed on the device, characterised largely by organisms typical of the distal intestine. DJBS treatment induced transient changes in the stool microbiota. This was mirrored by changes in the predicted functional capacity of the microbiota, suggesting an alteration in energy metabolism, with a preference for carbohydrate metabolism.

Finally, the systemic and local immune response to DJBS treatment was assessed. There was no significant change in circulating pro-inflammatory cytokines following DJBS treatment. There were changes in T cell phenotype that were broadly suggestive of an increase in immune activation. In the duodenum, modest changes in morphology occurred following DJBS treatment, with villous blunting observed in a number of patients. In parallel, there was a significant increase in eosinophils and intraepithelial lymphocytes. This suggest the DJBS, a foreign body in the duodenum that is colonised by microbes typical of the distal intestine, induces a local immune response.

In summary, this thesis has demonstrated the DJBS was clinically effective in the study population, resulting in weight loss and broad metabolic improvements. The DJBS induces significant gastrointestinal symptoms (including meal-related symptoms) in parallel with a significant reduction in dietary intake that may facilitate weight loss. There was no evidence of change in gastric emptying or altered absorption of fat. There were significant changes in the gastrointestinal microbiota following DJBS treatment, with a proliferation of more distal bowel organisms compared to those typically observed in the duodenum. In concert, a local immune response was observed, with potential implications for device safety. These results may provide insight into DJBS efficacy and tolerability and provide avenues of future research.

DECLARATION BY AUTHOR This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis.

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PUBLICATIONS INCLUDED IN THIS THESIS No publications included.

SUBMITTED MANUSCRIPTS INCLUDED IN THIS THESIS No manuscripts submitted for publication.

OTHER PUBLICATIONS DURING CANDIDATURE

Peer-reviewed papers von Wulffen M, Talley NJ, Hammer J, McMaster J, Rich G, Shah A, Koloski N, Kendall BJ, Jones M, Holtmann G. Overlap of Irritable Bowel Syndrome and Functional Dyspepsia in the Clinical Setting: Prevalence and Risk Factors. Dig Dis Sci. 2018. 64:480-486 DOI: 10.1007/s10620-018- 5343-6

Conference Abstracts and Presentations

Oral Presentations

Chapter 1: McMaster JJ, Rich GG, Shanahan ER, Do AT, Fletcher LM, Kutyla MJ, Tallis C, Macdonald GA, Chachay VS, Morrison M, Holtmann GJ. Mechanisms of action for weight reduction in patients treated with a duodenal-jejunal bypass sleeve.

Asia Pacific Digestive Week, Seoul, South Korea, November 2018

Chapter 2: McMaster JJ, Rich GG, Shanahan ER, Do AT, Fletcher LM, Kutyla MJ, Tallis C, Macdonald GA, Chachay VS, Morrison M, Holtmann GJ. Perturbation of the gut-brain-axis as the mechanism of action for weight reduction in patients treated with a duodenal-jejunal bypass sleeve.

Australian Gastroenterology Week, Brisbane, Australia, September 2018

Chapter 3: McMaster J, Rich G, Shanahan E, Fletcher L, Macdonald G, Koloski N, Morrison M, Chachay V, Holtmann G. Metabolic improvements following treatment with a duodenal-jejunal bypass sleeve in patients with obesity and type 2 diabetes

Australia and New Zealand Obesity Society/ Obesity Surgery Society of Australia and New Zealand/Asia-Oceania Conference on Obesity Annual Scientific Meeting, Adelaide, Australia, October 2017

Chapter 4: McMaster J, Rich G, Fletcher L, Gandhi A, Macdonald G, Shanahan E, Koloski N, Morrison M, Chachay V, Holtmann G. Metabolic and nutrition-related effects of a duodenal- jejunal bypass sleeve in patients with obesity and type 2 diabetes: preliminary results of a pilot study

Australia and New Zealand Obesity Society Annual Scientific Meeting, Brisbane, Australia, October 2016

Poster Presentations

1. McMaster J.J, Shanahan E.R, Do A.T, Rich G.G, Chachay V.S, Macdonald G.A, Holtmann G.J. Weight loss and metabolic improvements one year after treatment with a duodenal- jejunal bypass sleeve (EndoBarrier®)

Accepted for Digestive Disease Week, San Diego, USA, May 2019

2. Ryder B, Lynne M, Frydenberg H, Fishman S, Cohen R, de Jonge C, Greve J, Bascomb J, Bessel J, McMaster J, Holtmann G, Rich G, Collins J, Kow L, Pferschy P, Sourij H, Byrne J, Mason J, Teare J, Benes M. Duodenal jejunal bypass liner for diabesity – risk versus benefit data from the Association of British Clinical Diabetologists (ABCD) worldwide EndoBarrier registry

Accepted for the 4th World Congress on Interventional Therapies for Type 2 Diabetes, New York, USA, April 2019

3. Ryder REJ., Munro L, McMaster JJ, Bessell J, Bascomb JM, Collins JE., Kow L, Chisholm J, Sourij H, Pferschy PN, Jr., Teare JP., Mason JC., Byrne JP., Wyres MC., Cull ML., Burbridge W, Irwin SP., Yadagiri M, Fogden E, Anderson M, Gupta PS., Benes M. First Risk:Benefit Data from the Worldwide Endobarrier Registry

American Diabetes Association 78th Scientific Sessions, Orlando, USA, June 2018

4. McMaster J, Shanahan E, Rich G, Chachay V, Macdonald G, Morrison M, Holtmann G. The duodenal-jejunal bypass sleeve (Endobarrier®) induces transient changes in the stool microbiota, Gastroenterology 154.6 (2018): S652

Poster of Distinction for Digestive Diseases Week, Washington DC, USA, June 2018

Translational Research Symposium, Translational Research Institute, Brisbane, July 2018

Princess Alexandra Hospital Health Symposium, August 2018

5. McMaster J, Shanahan E, Rich G, Chachay V, Macdonald G, Morrison M, Holtmann G. Characterization of the mucosal- and device-associated microbiota following 48 weeks treatment with a duodenal-jejunal bypass sleeve (Endobarrier®), Gastroenterology 154.6 (2018): S654

Digestive Diseases Week, Washington D.C, USA, June 2018

Translational Research Symposium, Translational Research Institute, Brisbane, July 2018

Princess Alexandra Hospital Health Symposium, August 2018

6. McMaster J, Rich G, Shanahan E, Fletcher L, Macdonald G, Koloski N, Morrison M, Chachay V, Holtmann G. Metabolic improvements following treatment with a duodenal- jejunal bypass sleeve in patients with obesity and type 2 diabetes

Translational Research Symposium, Translational Research Institute, Brisbane, July 2017

Princess Alexandra Hospital Health Symposium, August 2017

7. McMaster J, Rich G, Gandhi A, Kutyla M, Tallis C, Macdonald G, Fletcher L, Chachay V, Holtmann G. Augmentation of meal-related symptoms following placement of duodenal- jejunal bypass sleeve is a potential mechanism of action inducing weight loss, Gastroenterology 152.5 (2017): S638

Digestive Diseases Week, Chicago, USA, May 2017

8. Rich G, McMaster J, Gandhi A, Chachay V, Fletcher L, Tallis C, Macdonald G, Holtmann G. Improvements of liver and glycaemic parameters after Duodenal-Jejunal Bypass Sleeve (DJBS) insertion, Gastroenterology 152.5 (2017): S638

Digestive Diseases Week, Chicago, USA, May 2017

9. McMaster J, Shanahan E, Rich G, Chachay V, Morrison M, Holtmann G. Microbial colonisation of the duodenal-jejunal bypass sleeve used for treatment of obesity.

Falk Symposium 207 (Gut Microbiome and Mucosal or Systemic Dysfunction: Mechanisms, Clinical Manifestations and Interventions), Brisbane, Australia, May 2017

10. Rich G, McMaster J, von Wulffen MT, Gandhi A, Fletcher L, Gupta S, Russell A, Chikani V, Chachay V, Holtmann G. Endoscopic treatment of obesity with a duodenal-jejunal bypass sleeve: does impairment of fat absorption explain weight loss, Gastroenterology 150.4 (2016): S604

Digestive Diseases Week, San Diego, USA, May 2016

Invited presentations

“Clinical effects and mechanisms of action of an endoscopically-placed weight loss aid”

Queensland Hypertension Association, October 2018

“The Duodenal-Jejunal Bypass Sleeve as an Endoscopically-Placed Weight Loss Aid Device: Clinical Trial Protocol and Interim Results of the Pilot Study”

Brisbane Diamantina Health Partners Metabolic Research Collaborative, June 2016

University of Queensland School of Human Movement and Nutrition Sciences Seminar Series, June 2016

Princess Alexandra Hospital Department of Diabetes and Endocrinology Journal Club, June 2016

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CONTRIBUTIONS BY OTHERS TO THE THESIS Professor Gerald Holtmann, Associate Professor Graeme Macdonald, Dr Erin Shanahan, Dr Anh Do and Dr Veronique Chachay were my PhD advisors. They provided intellectual input to study design, data interpretation and writing assistance. Professor Holtmann was involved in study conception and design and secured funding for the project. Dr Graeme Rich was the clinical lead for the project and was also involved in study design. Professor Holtmann and Dr Rich performed all endoscopic procedures. Dr Shanahan performed bioinformatics analysis of sequencing data and provided laboratory training and supervision. Dr Do provided intellectual input into the interpretation of immunological data and provided supervision for the flow cytometry runs. Dr Chachay was a supervisor for one year and provided intellectual input into study design, preliminary data analyses and assisted with writing and presentation skill development.

Professor Mark Morrison provided laboratory space and equipment for the microbiota sequencing work. Dr Katie Togher and Nida Murtaza provided assistance with bioinformatics and data analysis for the work using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States software package. The Australian Centre for Ecogenomics performed the sequencing runs.

Medical reviews of patients were conducted by Dr Graeme Rich, Dr Arjun Gandhi and Dr Alex Huelsen-Katz. Blood collection was performed by junior doctors within the Department of Gastroenterology and Hepatology, Princess Alexandra Hospital. Peripheral blood mononuclear cell isolation was performed by Drs Shanahan and Do and Mr Thomas Fairlie. Mr Fairlie also provided laboratory training in sample preparation for flow cytometry and enzyme-linked immunosorbent assay, performed the flow cytometry runs and a subset of the enzyme-linked immunosorbent assays.

Associate Professor Linda Fletcher provided training in undertaking and analysing the clinical breath tests. Fibroscan procedures were performed by trained nurses (Marrianne Black, Kristel Vergara and Rhiannon Saltwell) within the Department of Gastroenterology and Hepatology. Dual-energy x-ray absorptiometry procedures were performed in the Department of Radiology, Princess Alexandra Hospital. Clinical blood specimens were collected and analysed by Pathology Queensland. Dr Andrina McGivern completed the histological analysis of duodenal specimens.

Statistical advice was provided by Dr Justin Scott from the Queensland Facility for Advanced Bioinformatics and Professor Mike Jones from Macquarie University.

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STATEMENT OF PARTS OF THE THESIS SUBMITTED TO QUALIFY FOR THE AWARD OF ANOTHER DEGREE No works submitted towards another degree have been included in this thesis.

RESEARCH INVOLVING HUMAN OR ANIMAL SUBJECTS The study was approved by the Human Research Ethics Committees of the Metro South Hospital and Health Service (HREC/15/QPAH/246) and is registered on the Australian New Zealand Clinical Trials Registry (ACTRN12615001229561).

ACKNOWLEDGEMENTS I am very grateful for all the people who have supported me on my PhD journey. Firstly, I give thanks to my supervisors. Gerald, for taking me on with enthusiasm and reminding me to think of the big picture. Graeme, for your clinical insight, thinking of the finer details and always encouraging me with a kind word when needed. To Erin and Anh, for being my friends first. I think this helped to build a great relationship for me to learn from two kind, compassionate and knowledgeable female scientists and to help me appreciate my forgotten joy of being in the lab. Erin, without your incredibly generous contributions in time, advice and training, there is absolutely no way I would have made it to the end. I am forever grateful. To Veronique, for finding a great opportunity, helping me to develop my writing and presentation skills and your attention to detail. To my milestone panels, particularly Mark, Simon and Brad, for providing a fresh perspective and some invaluable expert advice.

I was very fortunate to work with some amazing colleagues. Firstly, to Graeme Rich, for being the other half of the “army of two”. We made a great team and your absence was noted every day. To Linda, for being an invaluable mentor. Your kind words and faith in me really helped keep me going during times where I wasn’t sure this was the right path. To Heidi, for idea-bouncing, proof- reading and just generally being a kind, supportive, passionate woman in STEM – in addition to becoming one of my closest friends. To Tom, for being a patient teacher, ELISA pro and a great lunch buddy. To Andrina, for being a wonderful, patient and efficient collaborator for the histology works.

I was very fortunate to be welcomed into two other research groups during my candidature. To the Morrison Group, for providing such a friendly environment for a fish out of water to test her skills. To the MOBY research group, for allowing me to learn so much from you every week and being friendly and providing what was needed at the time – either some constructive feedback or reassurance from those much wiser than myself. In particular, thank you to Ingrid for providing valuable advice and being a great role model of what a research life can be for a dietitian.

To the funding bodies, thank you for supporting this research and to the Australian Government and University of Queensland for allowing me to be the one to do it via the Research Training Program scholarship.

To Mum, Rod and Jenna - for believing I can do it, whatever “it” may be at the time and for knowing when to snap me out of being a drama queen and put things into perspective. To mum for your wonderful proof-reading skills. To Daddy, I wish you were here to see this day – once this degree is conferred I’ll be Dr McMaster forever and I know how immensely proud that would make you.

To Nicky, Alex and Ash for being my second family and providing me with welcome relief from whatever was stressing me (and possibly wine).

To Sven, for being my best friend, sounding board and voice of reason (sometimes), for listening to every talk (many times), for every tear-filled cuddle and for helping me believe I could do this.

Finally to my patients, without whom these three and a bit years would have been both impossible and very dull. I thank you for letting me into your lives and I hope our work together has inspired you and given you some tools to help you on your journey towards a healthier life.

FINANCIAL SUPPORT This research was supported by an Australian Government Research Training Program Scholarship. Project funding was provided by Queensland Health (Health Technology Assessment and Evaluation Grant) and the Princess Alexandra Hospital Research Support Scheme.

KEYWORDS duodenal-jejunal bypass sleeve, obesity, endoscopy, bariatric, diabetes, weight loss, gastrointestinal function, gastrointestinal microbiota, immune function

AUSTRALIAN AND NEW ZEALAND STANDARD RESEARCH CLASSIFICATIONS (ANZSRC) ANZSRC code 110307, Gastroenterology and Hepatology, 50%

ANZSRC code: 060504 Microbial Ecology, 30%

ANZSRC code 110707, Innate Immunity, 20%

FIELDS OF RESEARCH (FoR) CLASSIFICATION 1103, Clinical Sciences, 50%

1108, Medical Microbiology, 30%

1107, Immunology, 20%

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Table of Contents

ABSTRACT ...... i

DECLARATION BY AUTHOR ...... iii

PUBLICATIONS INCLUDED IN THIS THESIS ...... iv

SUBMITTED MANUSCRIPTS INCLUDED IN THIS THESIS ...... iv

OTHER PUBLICATIONS DURING CANDIDATURE ...... iv

CONTRIBUTIONS BY OTHERS TO THE THESIS ...... viii

STATEMENT OF PARTS OF THE THESIS SUBMITTED TO QUALIFY FOR THE AWARD OF ANOTHER DEGREE ...... ix

RESEARCH INVOLVING HUMAN OR ANIMAL SUBJECTS ...... ix

ACKNOWLEDGEMENTS ...... x

FINANCIAL SUPPORT ...... xii

KEYWORDS ...... xii

AUSTRALIAN AND NEW ZEALAND STANDARD RESEARCH CLASSIFICATIONS (ANZSRC)...... xii

FIELDS OF RESEARCH (FoR) CLASSIFICATION ...... xii

LIST OF FIGURES ...... xix

LIST OF TABLES ...... xxii

LIST OF ABBREVIATIONS ...... xxiv

Chapter 1: Background ...... 1

1.1 OBESITY AND RELATED COMORBIDITIES ...... 2

1.1.1 Obesity – Definitions and Prevalence ...... 2

1.1.2 Obesity-related Comorbidities ...... 2

1.1.3 The Aetiology of Obesity ...... 3

1.2 OBESITY INDUCED PERTURBATIONS OF NORMAL PHYSIOLOGY ...... 6

1.2.1 Gastrointestinal Function ...... 6

1.2.2 The Gastrointestinal Microbiota ...... 12

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1.2.3 Inflammation ...... 17

1.3 OBESITY TREATMENT STRATEGIES ...... 21

1.3.1 Lifestyle Modification Intervention ...... 21

1.3.2 Pharmacotherapy ...... 22

1.3.3 Bariatric Surgery ...... 22

1.3.4 Endoscopic Bariatric Therapies ...... 31

1.4 SUMMARY AND AIMS ...... 47

1.4.1 Aims and Hypotheses...... 47

Chapter 2: Methods ...... 49

2.1 STUDY SUMMARY ...... 50

2.2 THE DJBS INTERVENTION ...... 52

2.2.1 Recruitment ...... 52

2.2.2 Inclusion and Exclusion Criteria ...... 52

2.2.3 Procedure Details ...... 53

2.2.4 Safety Monitoring ...... 54

2.3 CLINICAL INVESTIGATIONS ...... 54

2.3.1 Clinical Observations ...... 54

2.3.2 Dual Energy X-ray Absorptiometry ...... 55

2.3.3 Clinical Blood Investigations ...... 55

2.3.4 Transient Hepatic Elastography (Fibroscan®) ...... 56

2.3.5 Dietary Intake Assessment ...... 56

2.3.6 Six Minute Walk Test ...... 56

2.3.7 Health and Wellbeing Questionnaires...... 57

2.4 GASTROINTESTINAL FUNCTIONAL TESTING ...... 58

2.4.1 Gastric Emptying ...... 58

2.4.2 Intraluminal Lipolytic Activity ...... 58

2.4.3 Standardised Nutrient Challenge Test...... 59

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2.4.4 Structured Assessment of Gastrointestinal Symptoms ...... 59

2.5 GASTROINTESTINAL MICROBIOTA ...... 61

2.5.1 Sample collection ...... 61

2.5.2 Lysate preparation for genomic DNA extraction from gastric and duodenal biopsies 62

2.5.3 Lysate preparation for genomic DNA extraction from EndoBarrier device surfaces .. 62

2.5.4 Lysate preparation for genomic DNA extraction from stool samples ...... 62

2.5.5 Purification of genomic DNA ...... 63

2.5.6 High-throughput sequencing to assess microbial communities ...... 63

2.5.7 Functional metagenomics analysis of 16S rRNA gene sequencing data ...... 65

2.5.8 Bacterial load assessment...... 65

2.6 IMMUNOLOGY ASSESSMENT ...... 65

2.6.1 Histology ...... 65

2.6.2 PBMC Phenotyping and Cytokine Production Following PBMC Culture ...... 66

Chapter 3: Effect of the Duodenal-Jejunal Bypass Sleeve on Clinical Measures of Obesity and the Metabolic Syndrome, Dietary Intake and Lifestyle Factors ...... 69

3.1 INTRODUCTION ...... 70

3.1.1 Aims and Hypotheses...... 72

3.2 METHODS ...... 73

3.2.1 Safety Monitoring ...... 73

3.2.2 Anthropometric Measures and Body Composition ...... 73

3.2.3 Peripheral Blood Metabolic Profiles ...... 73

3.2.4 Markers of Hepatic Fibrosis and Steatosis ...... 73

3.2.5 Dietary Intake and Nutrition Status ...... 74

3.2.6 Health-Related Quality of Life ...... 74

3.2.7 Statistical Analyses ...... 74

3.3 RESULTS ...... 76

3.3.1 Safety ...... 76

3.3.2 Anthropometric Measures and Body Composition ...... 76

3.3.3 Peripheral Blood Metabolic Profiles ...... 81

3.3.4 Markers of Hepatic Fibrosis and Steatosis ...... 85

3.3.5 Dietary Intake and Nutrition Status ...... 87

3.3.6 Health-Related Quality of Life ...... 88

3.3.7 Durability of Improvements ...... 92

3.4 DISCUSSION ...... 103

Chapter 4: Effect of the Duodenal-Jejunal Bypass Sleeve on Gastrointestinal Function ...... 110

4.1 INTRODUCTION ...... 111

4.1 Aims and Hypotheses ...... 112

4.2 METHODS ...... 113

4.2.1 Gastric Emptying ...... 113

4.2.2 Intraluminal Lipolytic Activity ...... 113

4.2.3 Gastrointestinal Symptoms ...... 113

4.2.4 Statistical Analysis ...... 113

4.2.5 Schedule of Investigations ...... 114

4.3 RESULTS ...... 115

4.3.1 Gastric Emptying ...... 115

4.3.2 Intraluminal Lipolytic Activity ...... 116

4.3.3 Gastrointestinal Symptoms ...... 118

4.4 DISCUSSION ...... 125

Chapter 5: Effect of the Duodenal-Jejunal Bypass Sleeve on the Gastrointestinal Microbiota .. 128

5.1 INTRODUCTION ...... 129

5.1.1 Aims and Hypotheses...... 131

5.2 METHODS ...... 132

5.2.1 Sample collection ...... 132

5.2.2 16S rRNA sequencing for microbiota identification ...... 132

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5.2.3 Functional metagenomics analysis of 16S rRNA sequencing data ...... 132

5.2.4 Bacterial load assessment...... 132

5.2.5 Statistical analyses ...... 132

5.2.6 Schedule of investigations ...... 134

5.3 RESULTS ...... 135

5.3.1 Mucosa-associated microbiota ...... 135

5.3.2 Device-associated microbiota ...... 149

5.3.3 Stool microbiota ...... 159

5.3.4 Functional predictions from 16S sequencing ...... 163

5.3.5 Bacterial load ...... 166

5.3.6 Relationships between microbiota and device efficacy ...... 166

5.3.7 Relationships between microbiota and device tolerability ...... 167

5.3.8 Relationships between microbiota and dietary intake ...... 168

5.3.9 Results summary ...... 170

5.4 DISCUSSION ...... 171

Chapter 6: Effect of the Duodenal-Jejunal Bypass Sleeve on Histological and Immune Parameters ...... 179

6.1 INTRODUCTION ...... 180

6.1.1 Aims and Hypotheses...... 181

6.2 METHODS ...... 182

6.2.1 Histology ...... 182

6.2.2 PBMC Phenotyping and Cytokine Production Following PBMC Culture ...... 182

6.2.3 Statistical Analysis ...... 182

6.2.4 Schedule of Investigations ...... 184

6.3 RESULTS ...... 185

6.3.1 Histology ...... 185

6.3.2 PBMC Phenotyping ...... 187

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6.3.3 Cytokine Production Following PBMC Culture ...... 192

6.3.4 Correlation with the Gastrointestinal Microbiota ...... 194

6.4 DISCUSSION ...... 196

Chapter 7: General Discussion ...... 201

7.1 SUMMARY ...... 202

DJBS treatment increases meal-related GI symptoms, facilitating weight loss by reducing energy intake ...... 204

DJBS treatment induces changes in the GI microbiota, and these changes may be linked to a key adverse event of treatment...... 205

DJBS treatment induces a local immune response in the duodenum and does not improve systemic markers of inflammation ...... 207

7.2 STRENGTHS AND WEAKNESSES ...... 209

7.2.1 Strengths...... 209

7.2.2 Weaknesses ...... 209

7.3 FUTURE DIRECTIONS ...... 212

7.4 CONCLUSIONS ...... 213

REFERENCES ...... 214

APPENDIX ...... 245

1.1 Human Research Ethics Committee Approval and Site Specific Approval ...... 245

1.2 Participant Information and Consent Form ...... 248

1.3 Questionnaires ...... 259

1.4 Microbiology Primers and Reactions ...... 274

1.5 Contaminants Removed from Sequencing Results ...... 278

1.6 Full List of OTUs Recovered in Samples ...... 281

1.7 Full Statistical Analyses for Microbiota Data ...... 291

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LIST OF FIGURES Figure 2.1: Consort diagram of study recruitment...... 51 Figure 2.2: Device sections for genomic DNA extraction...... 61 Figure 2.3: Illumina MiSeq library preparation and analysis workflow...... 63 Figure 3.1: Body weight and BMI...... 76 Figure 3.2: Waist, hip and neck circumference...... 77 Figure 3.3: Waist circumference, stratified by gender...... 78 Figure 3.4: Body fat percentage, fat mass, lean mass and lean: fat mass ratio...... 79

Figure 3.5: Fasting glucose, fasting insulin and HbA1c...... 81 Figure 3.6: Total cholesterol, triglycerides, HDL-cholesterol and LDL-cholesterol...... 82 Figure 3.7: MetSSS...... 82 Figure 3.8: Ferritin, transferrin saturation, haemoglobin and CRP...... 83 Figure 3.9: Ferritin and haemoglobin, stratified by gender...... 84 Figure 3.10: ALT, stratified by gender...... 86 Figure 3.11: Hepatic stiffness and the CAP as indicators of hepatic fibrosis and steatosis...... 86 Figure 3.12: HRQoL as measured by the SF-36 questionnaire...... 90 Figure 3.13: Body weight and BMI at one year post-explant...... 92 Figure 3.14: Waist, hip and neck circumference at one year post-explant...... 93

Figure 3.15: Fasting glucose, fasting insulin and HbA1c at one year post-explant...... 95 Figure 3.16: Total cholesterol, triglycerides, HDL-cholesterol and LDL-cholesterol at one year post-explant...... 96 Figure 3.17: MetSSS at one year post-explant...... 97 Figure 3.18: Ferritin, transferrin saturation, haemoglobin and CRP at one year post-explant...... 97 Figure 3.19: ALT at one year post-explant...... 99 Figure 3.20: Hepatic stiffness and the CAP as measures of hepatic fibrosis and steatosis at one year post-explant...... 99 Figure 3.21: HRQoL at one year post-explant...... 102 Figure 4.1: Schedule of GI function investigations...... 114 Figure 4.2: Gastric emptying kinetics...... 115 Figure 4.3: Gastric emptying lag time and half-time at baseline, with the DJBS in situ and within three weeks after device explant...... 116 Figure 4.4: Intraluminal lipolytic activity...... 117 Figure 4.5: Intraluminal lipolytic activity at baseline, with the DJBS in situ and within three weeks after device explant...... 117 Figure 4.6: GI symptom response to a standardised nutrient challenge with the device in situ. .... 119 xix

Figure 4.7: GI symptom response to a standardised nutrient challenge within three weeks after device explant...... 120 Figure 4.8: GI symptom response to a standardised nutrient challenge test at baseline, with the DJBS in situ and within three weeks after device explant...... 121 Figure 4.9: GI symptom (SAGIS) domain scores during the peri-implant period and late treatment period...... 123 Figure 5.1: Schedule of gastrointestinal microbiota investigations...... 134 Figure 5.2: Alpha diversity analysis of gastric mucosal biopsy samples at genus level...... 139 Figure 5.3: Principal co-ordinate analysis of gastric biopsies using a weighted UniFrac distance matrix...... 140 Figure 5.4: Sparse partial least squares discriminant analysis of gastric biopsies...... 141 Figure 5.5: Principal co-ordinate analysis of gastric biopsies using a weighted UniFrac distance matrix, compared to a control group...... 142 Figure 5.6: Alpha diversity analysis of gastric mucosal biopsy samples (at genus level), compared to a control group...... 143 Figure 5.7: Alpha diversity analysis of duodenal mucosal biopsy samples at genus level...... 146 Figure 5.8: Principal co-ordinate analysis of duodenal biopsies using a weighted UniFrac distance matrix, compared to a control group...... 147 Figure 5.9: Alpha diversity analysis of duodenal mucosal biopsy samples (at genus level), compared to a control group...... 148 Figure 5.10: Principal co-ordinate analysis of duodenal biopsies using a weighted UniFrac distance matrix...... 149 Figure 5.11: Principal co-ordinate analysis of device samples compared to duodenal biopsy samples, using a weighted UniFrac distance matrix...... 153 Figure 5.12: Principal co-ordinate analysis of stool samples using a weighted UniFrac distance matrix...... 160 Figure 5.13: Alpha diversity analysis of stool samples at genus level...... 163 Figure 5.14: Firmicutes to Bacteroidetes ratio in stool samples...... 163 Figure 5.15: KEGG ortholog functional pathways that were significantly different from baseline to explant in gastric biopsies...... 165 Figure 5.16: KEGG ortholog functional pathways that were significantly different from baseline to explant in duodenal biopsies...... 166 Figure 6.1: Schedule of histological and immune parameter investigations...... 184 Figure 6.2: Histological analysis of immune cell populations...... 185 Figure 6.3: Histological analysis of immune cell populations compared to a control group...... 186 xx

Figure 6.4: T helper cells (CD4+) populations from baseline to explant and compared to a control group...... 187 Figure 6.5: Cytotoxic T cell (CD8+) populations from baseline to explant and compared to a control group...... 188 Figure 6.6: CD4+ T cell subtypes from baseline to explant...... 189 Figure 6.7: CD8+ T cell subtypes from baseline to explant...... 190 Figure 6.8: Gut homing T cells from baseline to explant...... 191 Figure 6.9: Cytokine production following LPS stimulation from baseline to explant...... 192 Figure 6.10: Cytokine production following LPS stimulation compared to a control group...... 193

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LIST OF TABLES Table 1.1: Safety, weight loss and glycaemic control outcomes following DJBS implantation in subjects with obesity...... 37 Table 3.1: Anthropometric, body composition and BMD measures following DJBS treatment. .... 80 Table 3.2: Glycaemic control, cardiovascular risk, haematological and inflammatory and iron status parameters following DJBS treatment...... 85 Table 3.3: Liver enzymes and indicators of hepatic fibrosis and steatosis following DJBS treatment...... 87 Table 3.4: Dietary energy, fibre and macronutrient intake following DJBS treatment...... 87 Table 3.5: Serum vitamin and mineral status following DJBS treatment...... 88 Table 3.6: HRQoL measures following DJBS treatment...... 91 Table 3.7: Medication usage...... 91 Table 3.8: Anthropometric, body composition and BMD measures one year after DJBS treatment...... 94 Table 3.9: Glycaemic control, cardiovascular risk, haematological and inflammatory and iron status parameters one year after DJBS treatment...... 98 Table 3.10: Liver enzymes and markers of hepatic fibrosis and steatosis one year after treatment with a DJBS...... 100 Table 3.11: Dietary energy, fibre and macronutrient intake following DJBS device removal...... 100 Table 3.12: Circulating vitamin and mineral status at one year post-explant...... 101 Table 3.13: HRQoL parameters one year post-explant...... 102 Table 4.1: Meal-related GI symptoms following a standardised nutrient challenge test...... 122 Table 4.2: Self-reported GI symptoms measured by the SAGIS instrument...... 124 Table 5.1: RA of the three most abundant taxa (at genus level) in biopsy specimens ...... 136 Table 5.2: Taxonomic changes in the MAM of the gastric antrum from baseline to explant...... 137 Table 5.3: Taxonomic changes in the MAM of the second part of the duodenum from baseline to explant...... 144 Table 5.4: Taxonomic difference between the mucosa-adjacent and luminal-adjacent device surfaces at explant...... 151 Table 5.5: Taxonomic differences between the mucosa-adjacent device surface collocated with the duodenal biopsy at device explant...... 155 Table 5.6: Taxonomic differences between the mucosa-adjacent device surface collocated with the duodenal biopsy at baseline...... 157 Table 5.7: Taxonomic changes in the stool microbiota from baseline to eight weeks post-implant...... 161 xxii

Table 5.8: KEGG ortholog functional pathways that were significantly different from baseline to explant in gastric biopsy samples...... 164 Table 5.9: Relationship between dietary intake and the RA of microbial taxa (at genus level) isolated from gastric and duodenal biopsy samples on univariate analysis...... 168 Table 5.10: Relationship between dietary intake and the RA of microbial taxa (at genus level) isolated from stool samples on univariate analysis...... 169 Table 6.1: The relationship between immunological parameters and the RA of microbial taxa or bacterial load isolated from gastric and duodenal biopsies...... 195 Table 1S: Full list of OTUs present in negative controls removed as contaminants prior to analysis (917 primer set)...... 278 Table 2S: Full list of OTUs present in negative controls removed as contaminants prior to analysis (926 primer set)...... 280 Table 3S: Full list of OTUs present in samples, sorted alphabetically...... 281 Table 4S: Full statistical analyses for gastric biopsies from baseline to explant (FDR q assessed by linear regression modelling)...... 291 Table 5S: Full statistical analyses for duodenal biopsies from baseline to explant (FDR q assessed by linear regression modelling)...... 295 Table 6S: Full statistical analyses for mucosal adjacent device surface compared to luminal adjacent device surface (FDR q assessed by linear regression modelling)...... 299 Table 7S: Full statistical analyses for top mucosal adjacent device surface compared to explant duodenal biopsy (FDR q assessed by linear regression modelling)...... 303 Table 8S: Full statistical analyses for top mucosal adjacent device surface compared to baseline duodenal biopsy (FDR q assessed by linear regression modelling)...... 307 Table 9S: Full statistical analyses for eight weeks post-implant stool samples compared to baseline (FDR q assessed by linear regression modelling)...... 311 Table 10S: Full statistical analyses for eight weeks post-implant stool samples compared to explant (FDR q assessed by linear regression modelling)...... 315 Table 11S: KEGG ortholog pathways that were significantly different between the gastric biopsies of DJBS patients at explant, compared to control patients...... 317 Table 12S: KEGG ortholog pathways that were significantly different between the duodenal biopsies of DJBS patients at explant, compared to control patients...... 319

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LIST OF ABBREVIATIONS 16S rRNA 16S ribosomal ribonucleic acid 6MWT Six minute walk test ALT Alanine aminotransferase ARTG Australian Register of Therapeutic Goods ASGE American Society for Gastrointestinal Endoscopy ASMBS American Society for Metabolic and Bariatric Surgery AST Aspartate aminotransferase BMD Bone mineral density BMI Body mass index CAP Controlled attenuation parameter CCK Cholecystokinin CCR C-C chemokine receptor CD Cluster of differentiation CRP C-reactive protein DEXA Dual energy x-ray absorptiometry DJBS Duodenal-jejunal bypass sleeve DMSO Dimethyl sulfoxide DNA Deoxyribonucleic acid EDTA Ethylenediaminetetraacetic acid ELISA Enzyme-linked immunosorbent assay EWL Excess weight loss FBS Foetal bovine serum FDR False discovery rate FGF Fibroblast growth factor FGID Functional gastrointestinal disorder FTO Fat mass and obesity-associated FXR Farnesoid X receptor gDNA Genomic deoxyribonucleic acid GGT Gamma-glutamyl transferase GI Gastrointestinal GIP Glucose-dependent insulinotropic peptide GIT Gastrointestinal tract GLP Glucagon-like peptide HADS Hospital anxiety and depression scale HbA1c Glycated haemoglobin HDL High-density lipoprotein HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HOMA-IR Homeostatic model of insulin resistance HRQoL Health-related quality of life IEL Intraepithelial lymphocyte IL Interleukin IRIS Isotope ratio infrared spectrometry KEGG Kyoto encyclopaedia of genes and genomes kPa Kilopascal xxiv

LAGB Laparoscopic adjustable gastric band LDL Low-density lipoprotein L-FABP Liver-type fatty acid binding protein LPS Lipopolysaccharide LSG Laparoscopic sleeve gastrectomy MAM Mucosa-associated microbiota MetSSS Metabolic syndrome severity score NAFLD Non-alcoholic fatty liver disease NASH Non-alcoholic steatohepatitis NF- κB Nuclear factor kappa B OAGB One-anastomosis gastric bypass OTU Operational taxonomic unit PAH Princess Alexandra Hospital PBMC Peripheral blood mononuclear cell PBS Phosphate-buffered saline PCR Polymerase chain reaction PEI Pancreatic exocrine insufficiency PICRUSt Phylogenetic investigation of communities by reconstruction of unobserved states PPARγ Peroxisome proliferator-activated receptor γ PPI Proton pump inhibitor PYY Peptide YY qPCR Quantitative polymerase chain reaction RA Relative abundance ROS Reactive oxygen species RPMI Roswell Park Memorial Institute RYGB Roux-en-Y gastric bypass SAE Serious adverse event SAGIS Structured Assessment of Gastrointestinal Symptoms SCFA Short-chain fatty acid T1DM Type 1 diabetes mellitus T2DM Type 2 diabetes mellitus TBWL Total body weight loss TGR5 Takeda G-protein coupled receptor 5 TLR-4 Toll-like receptor-4 TMAO Trimethylamine N-oxide TNF Tumour necrosis factor VLCD Very low calorie diet

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Chapter 1: Background

1.1 OBESITY AND RELATED COMORBIDITIES

1.1.1 Obesity – Definitions and Prevalence Obesity is defined as abnormal or excessive fat accumulation that impairs health1. The World Obesity Federation has defined obesity as a “chronic, relapsing, progressive disease process”2. In clinical practice, Body Mass Index (BMI), is most commonly used to assess weight status3. For Caucasian individuals, a BMI of ≥ 30 kg/m2 is the threshold for the diagnosis of obesity. This common definition of obesity makes no distinction in adipose tissue distribution. Central adiposity is the accumulation of adipose tissue in the abdominal region. As opposed to a gynoid obesity distribution, where adipose tissue accumulates on the lower body, central adiposity is more strongly associated with negative health outcomes4.

The prevalence of obesity has reached epidemic proportions in both developed and developing nations. The World Health Organization estimated that 13% of the global adult population suffered from obesity in 20161. The most recent Australian National Health Survey estimated that obesity impacted 28% of the Australian population5. Nearly 25% of the Australian adult population had a waist circumference that predisposed them to an increase risk of metabolic disease5. Recent estimates suggest future disease burden due to overweight and obesity could be reduced by 14% with a modest degree of weight loss (3kg)5. Socioeconomic status is a relevant co-factor in the development of obesity. In Australia, those in the lowest socioeconomic grouping were more likely to have obesity (34%), as compared to those in the highest socioeconomic group (22%)5.

The costs of obesity are significant. In Australia, the estimated total cost in 2011-12 was $8.6 billion, including $3.8 billion in direct costs (health-care related costs) and $4.8 billion in indirect costs (lost productivity of the individual and carers, welfare costs and reduced taxation revenue)6. Without targeted action to reduce the rise in obesity prevalence, these costs are predicted to contribute an additional $87.7 billion dollars over the ten year period up to 20256.

1.1.2 Obesity-related Comorbidities The comorbidities of obesity are extensive and carry additional health risks. Obesity impacts all organ systems in the body, with significant cardiovascular, endocrine, gastrointestinal (GI), hepatobiliary, haematological, musculoskeletal, psychological, pulmonary and reproductive outcomes7. Overweight and obesity have also been linked to the development of colon, kidney, liver, pancreatic, gallbladder, stomach, oesophageal, breast, prostate, endometrial and ovarian cancers8.

One of the most common comorbidities of obesity is type 2 diabetes mellitus (T2DM)9. T2DM results from the body’s inability to appropriately respond to insulin10. This is referred to as insulin resistance, and primarily impacts the liver, muscle and adipose tissue11. Whilst insulin

2 resistance is pronounced in T2DM, insulin secretion is also reduced due to islet cell dysfunction12. This leads to the requirement for exogenous insulin to adequately control blood glucose levels, similar to in type 1 diabetes mellitus (T1DM). In Australia, approximately one million adults with T2DM are registered on the National Diabetes Service Scheme13. This is likely an underrepresentation of true prevalence, as it is estimated that 50% of individuals with T2DM globally have not received a formal diagnosis14.

Impaired glucose control is a key component of the metabolic syndrome. In 2009, a consensus definition was released15. The diagnostic criteria for the metabolic syndrome are the presence and/or treatment of any three of five the following parameters: elevated waist circumference; elevated triglycerides of ≥ 1.7 mmol/L; reduced high-density lipoprotein (HDL) cholesterol below 1.3 mmol/L for females and 1.0 mmol/L for males; elevated blood pressure of ≥130 mmHg and/or ≥ 85 mmHg for systolic and diastolic pressure respectively; and elevated fasting blood glucose of 5.6 mmol/L or greater. All parameters except elevated waist circumference are applicable to all ethnic populations. Elevated waist circumference, an indicator of abdominal adiposity, has population specific targets. In Australia, current population guidelines classify a waist measurement >80 cm for Caucasian women and >94 cm for Caucasian men as increasing the risk of chronic disease, including the metabolic syndrome3. For those of Asian heritage, the thresholds are >80 cm and >90 cm for women and men respectively. These guidelines report that specific targets for Aboriginals, Pacific Islanders and African Americans are yet to be determined.

Non-Alcoholic Fatty Liver Disease (NAFLD) is a condition where fat accumulates in the liver in the absence of alcohol abuse16. NAFLD is strongly associated with obesity and insulin resistance17, and leads to changes in glucose, lipid and lipoprotein metabolism in the liver18, 19. NAFLD is the most common cause of chronic liver disease in Australia and refers to a spectrum of disorders that can be histologically classified20. Non-alcoholic fatty liver refers to hepatic steatosis, without evidence of hepatocellular injury. Non-alcoholic steatohepatitis (NASH) refers to the presence of hepatic steatosis with evidence of inflammation and hepatocellular injury16. The two disorders differ in their likelihood of progression to cirrhosis and hepatocellular carcinoma21, 22.

1.1.3 The Aetiology of Obesity Obesity develops due to a long-term imbalance between energy intake and expenditure. This positive energy balance leads to storage of excess energy as triglyceride, primary in the adipose tissue. The aetiology and pathophysiology of obesity are complex, with an interplay of genetic, environmental and behavioural factors that alter either energy intake or energy expenditure.

Monogenic defects leading to obesity are rare, and are more relevant in the aetiology of obesity in childhood. A number of these single gene defects act within the leptin-melanocortin pathway, a key 3 pathway in the regulation of energy balance23. Chromosomal abnormalities can also lead to conditions with an obese phenotype24.

In the majority of individuals, the genetic determinants of obesity are polygenic. Both genetic and epigenetic mechanisms have been identified25. Hundreds of genetic susceptibility loci have been linked to obesity through genome wide association studies26. The gene most strongly associated with obesity is the fat mass and obesity-associated (FTO) gene27. Overall, the genetic contribution to the development of obesity is estimated to be low with a conglomerate of 97 loci only representing 2.7% of variation in BMI, highlighting the multifactorial nature of obesity pathogenesis26.

The “set-point hypothesis” refers to a theory of homeostatic defence of a certain body weight that is regulated by neuronal and hormonal mechanisms28. After major weight loss, induced by lifestyle-based means29 or bariatric surgery30, weight recidivism is common. Metabolic adaptations such as a reduction in metabolic rate29 and an increase in appetite stimulating hormones31 observed after weight loss support this theory.

Environmental factors have contributed to both an increase in energy intake and a decrease in energy expenditure. There has been widespread change in the food supply over recent decades, to the point our food environment is now described as being “obesogenic”. This refers to the increase in availability (both convenience and quantity) and accessibility to food, in addition to the marketing environment surrounding food32. This large shift in our food supply has included massive increases in the availability of low cost, energy-dense, nutrient-poor food and drinks. Financial and living environments have also been linked with obesity, likely due to relationships with food accessibility and availability. In Australia, those in lower socioeconomic groups have a higher likelihood of developing obesity5. This may be associated with perceptions surrounding food availability and affordability, which may impact food consumption patterns33. Those in regional locations are also more likely to have obesity than those in metropolitan areas5. This may be associated with either restricted availability or higher cost of ‘healthy’ foods in rural and remote areas34, 35.

In parallel to changes in dietary intake, changes in the built environment have precipitated a reduction in physical activity. Environmental factors such as increased sedentary transport time due to increased travelling distance in urban areas, lack of physical activity infrastructure (such as walking paths or recreational facilities) and land use patterns may contribute to a reduction in physical activity36. Other environmental factors that have been linked to obesity and are under further investigation include the impact of exposure to environmental chemicals on the endocrine system37.

Behavioural factors, such as the choice of foods consumed, are also a strong determinant of obesity. Consumption of a “Western diet” is typified by an increased consumption of refined carbohydrates,

4 sugars and fats. Broadly, the majority of Australians consume a Western style diet, with up to 35% of energy intake estimated to be derived from high fat, high sugar discretionary foods and drinks38. Nearly 10% of adult Australians consume sugar sweetened beverages daily39. This shift in diet composition has occurred in parallel with increasing portion size, particularly in discretionary foods and drinks. Between 1995 and 2012, there was a 66% increase in the energy content (per portion) of discretionary food items5. This highlights how dietary choices, and therefore consumption patterns, are changing to facilitate weight gain.

Over recent decades, physical activity has also reduced. In 2017-18, it was estimated that only 15% of Australian adults met population guidelines for physical activity39. Behavioural determinants of physical activity include sedentary behaviours such as screen time, the use of energy-sparing equipment at home and work and the reduction in leisure time physical activity by choice, increased work hours or other factors40.

The contribution of neurobiological mechanisms to weight gain has also been investigated, primarily in animal models. Concepts such as environmental stimuli (such as food marketing images), hedonic hunger, and neurological changes in food reward systems linking addiction-like behaviours to food consumption have been explored41. Further work is required in humans but cumulatively, these suggest both conscious and subconscious neurological modulation of food intake.

Treatment of comorbid conditions can also have a negative impact on weight. Some anti-convulsant, anti-psychotic and anti-depressant medications are associated with weight gain42. In the context of T2DM, insulin and insulin secretagogue medications are also associated with weight gain42.

1.2 OBESITY INDUCED PERTURBATIONS OF NORMAL PHYSIOLOGY

1.2.1 Gastrointestinal Function The gastrointestinal tract (GIT) is a critical component in the aetiology of obesity, as it is the site where all ingested nutrients are absorbed. The enteric nervous system is involved in the regulation of GI motor function, regulation of fluid secretion (including exocrine secretion) and GI blood flow. Gastric emptying, pancreatic exocrine function and GI sensory function will be discussed below as relevant components of GI function that are altered in obesity and T2DM.

1.2.1.1 Gastric Emptying Functionally, the stomach can be divided into the proximal stomach, responsible for generating a phasic basal pressure; the distal stomach, responsible for generating a tonic pressure to grind ingested food; and the pylorus, responsible for emptying of gastric contents into the duodenum.

Gastric smooth muscle contractions are generated in the pacemaker region of the stomach, located between the fundus and the proximal body. These slow waves originate in the interstitial cells of Cajal. Slow waves are propagated both circumferentially and distally from the pacemaker region with a frequency of three cycles per minute.

When food is swallowed, the lower oesophageal sphincter and proximal stomach relax to allow entry of food into the stomach. This vagovagal reflex is called receptive relaxation. The result of this process is an increase in intragastric volume without an increase in intragastric pressure. In response to food contents within the stomach, a process of gastric accommodation occurs, whereby a sustained decrease in gastric tone allows sustained expansion of intragastric volume. This process is mediated by the vagal nerve.

Stomach contents are emptied into the duodenum in a pulsatile fashion43, 44. The regulation of gastric emptying is multifactorial with gastric neuromuscular coordination45, intragastric volume46, caloric content47, macronutrient composition48, viscosity49, particle size50, glycaemic control51, 52 and hormone-mediated feedback signalling from the small intestine53-58 all influencing the rate of gastric emptying. The pattern of gastric emptying also differs depending on the type of meal ingested.

Liquids Non-nutritive liquids, such as water, exit the stomach rapidly, initially in an exponential pattern followed by a slower linear pattern47. The liquids disperse in the stomach and are emptied without a lag phase47.

Gastric emptying of nutritive liquids is regulated by both intragastric volume59 and caloric content47. The emptying of nutritive liquids occurs in an approximately linear pattern60. Nutritive liquid gastric emptying is also mediated by duodenal feedback in response to nutrient stimulation60. 6

Solids Gastric emptying of solids is biphasic, with a lag phase followed by a linear emptying phase. When solids are ingested, they are initially stored in the proximal stomach. They are slowly moved to the antrum, where food particles are ground against the closed pylorus, reducing particle size to <2 mm61. This is reflective of the lag phase of gastric emptying. Following the lag phase, the pattern of gastric emptying becomes linear. Particles are propelled towards the pyloric sphincter by contractions in the proximal antrum. The pyloric sphincter will only allow particles of <1-2 mm to pass into the duodenum62. Particles that are too large are propelled to the proximal antrum for further grinding. Larger meal volumes and greater caloric density of meals delays gastric emptying63.

When a mixed meal of liquids and solids are consumed together, gastric emptying is delayed, compared to a solid meal consumed alone64. When a mixed meal is consumed, gastric emptying of solids is delayed until 80% of the liquid component has been emptied, and then is emptied in a linear fashion65.

Gastrointestinal Regulation of Gastric Emptying In the small intestine, mechanical66 or chemical stimulation67 has been demonstrated to delay gastric emptying via relaxation of the proximal stomach. GI peptide hormones produced by small intestinal mucosal cells in response to nutrient stimulation such as cholecystokinin (CCK)53, 54, peptide YY (PYY)55, 56 and glucagon-like peptide (GLP)-157, 58 have been demonstrated to delay gastric emptying. Conversely, ghrelin, which is secreted primarily in the stomach, has been demonstrated to accelerate gastric emptying68.

Assessment of Gastric Emptying Direct Assessment The “gold standard” for the assessment of gastric emptying is scintigraphy69. The test involves ingesting a meal that has been labelled with a radioisotope, the most common being 99mTc-sulfur colloid for solid meals. For mixed or liquid meals, the liquid component is most commonly labelled with 111In-diethylenetriaminepentaacetic acid. Images are then taken by a gamma camera at defined time-points for between one and four hours. It is recommended that scintigraphy should be performed for a minimum of four hours to adequately capture gastric emptying rate70. The key reporting outcomes are gastric emptying lag time (tlag), half time (t1/2) and percent of gastric retention.

Indirect Assessments A common indirect measure of gastric emptying utilises a stable, non-radioactive 13C isotope. 13C can be bound to either a medium chain triglyceride (octanoic acid for solids or acetate for liquids) or a protein-rich algae (spirulina) which is added to a test meal. The 13C substrate is absorbed in the

13 small intestine and carried to the liver by the vena portae, where it is oxidised to CO2. The ratio of 13 12 CO2 compared to atmospheric CO2 ( CO2) can then be detected in breath samples, which are 13 analysed via isotope ratio mass spectrometry or infrared spectroscopy. The kinetics of CO2 appearance in the breath is indicative of gastric emptying rate. The key reporting outcomes are tlag and t1/2. This test has been validated against scintigraphy for assessing the gastric emptying kinetics of solid71, 72 and liquid meals73.

The absorption kinetics of paracetamol have also been used to assess gastric emptying. Gastric emptying measured by paracetamol absorption correlates well with scintigraphic measures of liquid gastric emptying74.

Impact of Obesity and T2DM on Gastric Emptying Data on gastric emptying rates in obesity have largely been drawn from small studies, producing contradictory results. Normal75-77, accelerated78-81 and delayed82-84 gastric emptying of solids has been reported. In a large prospective study, obesity was significantly associated with accelerated gastric emptying of solids and liquids85. This is the largest documented cohort of patients with obesity (n=201) where gastric emptying has been assessed, using the gold standard gastric scintigraphy method.

Acutely, the relationship between gastric emptying and glycaemia is bidirectional and is mediated via small intestinal feedback mechanisms86. Acute hyperglycaemia has been demonstrated to delay gastric emptying in patients with T1DM52 and T2DM87. In a study of patients with T1DM and healthy controls, administration of insulin accelerated gastric emptying51. Blood glucose concentration at the time of a gastric emptying investigation has also been shown to be important in those with diabetes88. In patients with blood glucose concentrations of ≤ 15 mmol/L, retention at 10 minutes was significantly less than those with blood glucose concentrations > 15 mmol/L.

The prevalence of delayed gastric emptying in small studies of patients with long-standing T2DM has been reported to be between 30 and 56%87, 89, 90. The prevalence of diabetic gastroparesis, a clinical condition where delayed gastric emptying occurs in the absence of a mechanical obstruction, and in the presence of GI symptoms such as nausea, postprandial fullness and early satiety is low91. In the context of T2DM, a 1.0% incidence of developing diabetic gastroparesis over a 10 year period has been reported92.

1.2.1.2 Pancreatic Exocrine Function Exocrine function of the pancreas includes the secretion of pancreatic juice into the duodenum. Pancreatic juice contains acid-neutralising bicarbonate and digestive enzymes. Pancreatic secretion is modulated by three phases: the cephalic phase, the gastric phase and the intestinal phase.

The intestinal phase is responsible for 60-70% of secretion93. Secretion of bicarbonate and fluid is mediated by secretin, which is secreted by S cells in the duodenal mucosa in response to acidification. Bile acids and lipids also stimulate secretin secretion. Pancreatic acinar cells secrete proteases, amylases, lipases and nucleases to facilitate breakdown and absorption. Secretion of these enzymes in the postprandial state is stimulated by CCK, which in turn is stimulated by lipids and proteins in the duodenal lumen. The presence of carbohydrate or fat in the distal small intestine inhibits pancreatic secretion94. Pancreatic exocrine insufficiency (PEI) refers to the insufficient production of digestive enzymes following a meal to allow for the normal digestive process to occur93. Approximately 90% of exocrine function must be lost before severe PEI is evident95.

Assessment of Pancreatic Exocrine Function The “gold standard” for assessment of pancreatic exocrine function are direct tests that involve the measurement of luminal enzyme activity (adjusted by flow markers) such as the secretin-CCK test. This test involves the placement of a duodenal tube and assessment of enzymatic output following intravenous administration of CCK96. This test is infrequently performed as it is invasive and costly.

A number of indirect measures have been utilised to assess pancreatic exocrine function. The “gold standard” for the indirect assessment of steatorrhoea is the 72 hour faecal fat test. Stool is collected over a 72 hour period, while the patient consumes a specified diet with at least 100 g fat/day97. This test is also an indirect measure for the assessment of PEI. Sensitivity for PEI is high, but specificity is low98 since this test does not discriminate between pancreatic and non-pancreatic causes of fat malabsorption.

Faecal elastase-1 is a proteolytic enzyme produced by pancreatic acinar cells. Faecal elastase concentrations have been shown to correlate well with the output of other pancreatic enzymes99. This test is commonly used in clinical practice as it is non-invasive and cost effective. This test has also has a high sensitivity; however, sensitivity varies depending on the population measured, with a high false positive rate in low risk patients100.

Another indirect measure of pancreatic function uses a 13C labelled isotope administered in a test meal. This test relies on intraluminal metabolism of lipids in the small intestine. The most commonly used substrate is 13C mixed triglyceride. This molecule is hydrolysed by pancreatic lipase to a medium chain fatty acid (13C octanoic acid) which can be absorbed directly, and labelled monoglycerides which are incorporated into chylomicrons for absorption101. The labelled substrate is then transported to the liver and metabolised to carbon dioxide. This travels in the circulation and 13 can be detected as CO2 in the breath. This test is only appropriate in those without liver disease, and cannot discriminate between pancreatic and non-pancreatic origins of malabsorption.

Impact of Obesity and T2DM on Pancreatic Exocrine Function In pre-clinical models, the exocrine response to CCK administration is reduced in obese rats and mice102. In humans, there is limited evidence to suggest obesity is associated with alterations in pancreatic enzyme output103, 104. However, the endocrine and exocrine functions of the pancreas are intrinsically linked, implying metabolic dysfunction leads to alterations in pancreatic function. Endocrine products secreted in the islets of Langerhans, such as insulin, travel through the exocrine pancreas in the portal circulation. Insulin has been demonstrated to have a trophic effect on the exocrine pancreas, with a reduction in exocrine pancreas volume reported in diabetes105. PEI has been associated with both T1DM and T2DM106. The prevalence of PEI, as evidence by faecal elastase concentration, has been estimated to be 27% in T2DM107. Faecal elastase concentrations have also been shown to correlate with glycated haemoglobin (HbA1c) levels, as a marker of endocrine dysfunction in T2DM108.

1.2.1.3 Gastrointestinal Sensory Function Stimulation of GI mechano-and chemo-sensors (e.g. via ingestion of a meal or contractions) results in visceral sensation. The majority of afferent sensory information is perceived unconsciously and serves key homeostatic functions. However, sensations such as fullness, nausea, satiety and pain occur with conscious awareness.

In the stomach, sensory function is mediated by vagal and splanchnic afferent nerves. These nerves respond to both mechanical and chemical stimuli. Mechanoreceptors respond to distension and can signal satiety and fullness109. High threshold mechanoreceptors are typically involved in the pain response, and are thus termed mechanonociceptors110. Mucosal receptors (particularly in the oesophagus) respond to chemical stimuli and are involved in the sensations of nausea and vomiting, as well as satiety110, 111. Mucosal receptors can respond to local factors such as CCK112 and serotonin113, leading to sensitisation to perception. Afferent responses can also be stimulated by tissue damage and inflammation110.

Assessment of Gastrointestinal Sensory Function The “gold standard” for the assessment of GI sensory function uses a barostat to measure the mechanosensory response to stimulus (increasing pressure of the balloon dwelling in the stomach). Patients are asked to report upper GI symptoms on a visual analogue scale during stimulation114. Brain imaging techniques such as positron emission tomography, single photon emission computer tomography and functional magnetic resonance imaging have also been used to assess brain activity as a result of visceral stimulation115. However, these techniques are invasive, expensive and require specialised equipment and thus less invasive techniques have been developed.

A nutrient drink test can be used to assess gastric accommodation and sensation as components of GI sensory function. In the literature, a variety of protocols have been used, with different drinks, maximum volumes and speed of consumption116-123. These tests have been conducted in healthy controls and patients with functional GI disorders (FGIDs). A standardised nutrient challenge test using a commercially available nutrient drink (Fresubin; Fresenius, Germany) has been used to assess symptom response following administration of three 200 mL boluses across 15 minutes. This was validated in 48 blood donors with GI symptoms, 20 healthy controls, and 40 FGID patients124. In symptomatic blood donors and FGID patients, there was a significant difference in upper GI symptoms reported on a visual analogue scale, compared with healthy controls. This protocol has been adopted within the Princess Alexandra Hospital (PAH) Department of Gastroenterology and Hepatology using a locally available nutrient drink (Resource Plus; Nestlé, Vevey, Switzerland).

Gastrointestinal Sensory Function in Obesity and T2DM Kim et al. conducted a study of 13 individuals with obesity and 19 lean controls, finding no difference in fasting and postprandial total gastric volumes in those with obesity as compared controls125. The fasting volume of the distal stomach was greater in those with obesity. This difference did not impact the maximum tolerated volume or symptom response (nausea, pain, bloating and fullness) to a nutrient drink test, suggesting functionally relevant changes in gastric accommodation may not occur in obesity.

In a study by Acosta et al., obesity was associated with a higher volume to reach fullness in a nutrient drink test85. There was no correlation with maximal tolerated volume, corroborating the findings of Kim and colleagues. In this study, obesity was associated with a higher fasting gastric volume, conflicting the findings of Kim and colleagues85. Instead, acceleration of gastric emptying was observed, suggesting alterations in GI motor function may be relevant to changes in sensory function in obesity.

T2DM has been associated with an increased prevalence of upper GI symptoms compared to matched community controls126. In this study, self-reported poor glycaemic control was independently associated with upper GI dysmotility symptoms. In healthy adults, supraphysiological hyperglycaemia has also been associated with an increase in nausea and fullness symptoms during duodenal lipid infusion127. In healthy adults, glucose infusion reduced both the threshold for detection of pain and pain tolerance following electric stimulation of the hand128. Investigation into neuronal pathways that may be related have been limited to animal models129, 130. Together, these findings suggest a link between glycaemia and perception of upper GI symptoms.

1.2.2 The Gastrointestinal Microbiota The human GIT is colonised by a variety of bacteria, archaea, viruses and unicellular eukaryotes. The number of microbial cells dwelling within the human GIT has been estimated to be in the order of 1014 131. Despite the identification of over 500 species, the number of phyla within the GIT are relatively limited. The predominant phyla are Bacteroidetes and Firmicutes, but Synergistetes, Proteobacteria, TM7, Spirochaetes, Tenericutes, Lentisphaerae, Verrucomicrobia, Actinobacteria, Fusobacteria and Cyanobacteria may also be present132, 133.

The GI microbiota develops rapidly after birth and by one year of age resembles that of a mature adult microbiota134. The adult GI microbiota is relatively stable, and although diverse, a large portion of an individual’s microbiota tends to be represented by approximately 40 core species135, which are characteristic of each individual. Despite this relative stability, a number of factors can alter the microbiota, including static factors such as host genetics, and modifiable factors such as environment136. Recent evidence from a large human study has suggested environment plays the most critical role in shaping the microbiota. In this study, differences in diet and anthropometric measures were associated with the inter-individual variability in microbiota profiles137. This highlights the potential for modification of the microbiota through lifestyle-based approaches.

1.2.2.1 Assessment of the Gastrointestinal Microbiota Over the last two decades, there have been significant advances in the understanding of the composition of the human GI microbiota. This has been driven by advances in technology, with culture independent methods allowing us to harness the genetic content of the microbial community. This is preferable as culture-based techniques are impractical in this context, in part due to the high prevalence of anaerobic taxa. The explosion in knowledge of the microbiota, driven by the Human Microbiome Project132, has fostered the interest of clinicians, researchers and consumers alike.

To date, the most common molecular technique for the assessment of the microbiota uses a common marker gene, 16S ribosomal ribonucleic acid (16S rRNA), that is highly conserved in bacteria and archaea. The 16S rRNA gene contains nine hypervariable regions that can be selected to maximise resolution for taxonomic classification. Sample sequences are compared to reference genome databases such as Greengenes138 and SILVA139 based on similarity to the 16S rRNA marker gene. Often, this results in classification to the Operational Taxonomic Unit (OTU) level, which is usually determined on the basis of 97% similarity and is a proxy marker for species level for a taxon that appears in the database.

Metagenomic sequencing refers to the sequencing of the entire genome of a sample. This technique is more powerful as compared to amplicon based techniques, as it allows a more detailed characterisation at both a taxonomic and functional level. To date, the use of this technique has been 12 limited by cost, but is becoming more affordable and thus more common. Sample sequences are compared to databases such as MEGAN140 and MetaPhlAn141.

Sequencing data can also provide information about the functional profiles of the bacteria. This information is inherently valuable as ultimately it is the function of microbial communities, such as metabolic outputs, that drive impacts on health. In the case of 16S rRNA sequencing, the functional outputs of bacteria can be inferred using bioinformatics software that maps samples to existing reference genomes142. Metagenomic sequencing provides a more accurate representation, as the entirety of the microbial genome in each sample is sequenced, and thus the metabolic activity can be directly inferred based on the genes present.

1.2.2.2 Regional Variation in the Gastrointestinal Microbiota Colonisation across the length of the GIT varies, with the number of microbes and diversity of taxa present increasing from the proximal to the distal GIT. The biological niche that exists at each location varies, and this shapes the community formed. In the proximal GIT, transit is rapid and the environment is more acidic, has a higher presence of antimicrobial peptides and bile acids and has a higher presence of oxygen than in the distal GIT143. The functional profiles of bacteria in these regions are different as well, driven by their exposure to nutrients. For example, in the proximal GIT, genes involved in simple sugar metabolism are enriched144, while in the distal GIT, pathways for the fermentation of non-digestible carbohydrates are enriched145.

The composition of the GI microbiota also differs at the same anatomical site, with different communities of microorganisms colonising the mucosa as compared to the luminal contents. The mucosa-associated microbiota (MAM) refers to the microbial community adhered to the mucosa. This is an important population, as it represents the microbial community that is in direct contact with the host. The colonic MAM has been demonstrated to be different to that recovered in faeces, which is more representative of the luminal microbiota146-148. Significant differences in the MAM between the proximal and distal GIT have also been reported149. Due to the inherent difficulty of sampling the mucosa, the majority of reports on GI microbiota refer to stool microbiota.

1.2.2.3 Impact of Diet on the Gastrointestinal Microbiota Diet is a key modulator of the GI microbiota as it is a key source of nutrients, and the availability of these nutrients influences the community of GI microbes and the metabolites they produce. Cross-sectional data from two diverse communities has suggested that long-term dietary patterns modulate an individual’s microbiota profile150. In this study, children from Burkina Faso, who typically consume a diet high in fibre, had a high abundance of Bacteroidetes and a low abundance of Firmicutes. Conversely, Italian children, who typically consume a lower fibre diet, were enriched in Firmicutes and in particular members of the Enterobacteriaceae family. 13

Whilst long-term dietary patterns determine the microbiota profile, and this profile may be stable in an individual over a long period of time, major dietary change can rapidly alter the GI microbiota. In particular, a shift to an animal-based diet resulted in an increase in bile tolerant organisms such as Bacteroides and a decrease in the abundance of non-digestible carbohydrate fermenting taxa such as Roseburia151. When this profound dietary change was ceased, the microbiota profiles rapidly returned to their baseline state. This finding is supported by the seasonal cyclic changes in the microbiota in the Hadza hunter-gatherer population in Tanzania, associated with seasonal changes in the food environment across the year152.

Gut microbes produce beneficial metabolites that have relevant impacts on human health. Short-chain fatty acids (SCFAs) are a major product of microbial fermentation of indigestible carbohydrates in the colon153. They are also a minor product of protein fermentation154. The most common SCFAs produced in the human colon are acetate, propionate and butyrate155. Production of SCFAs is modulated by dietary fibre subtype intake and GI transit time156. SCFAs are an important energy source for colonocytes157. With the exception of acetate, which is found in the circulation, the majority of absorbed SCFAs are metabolised in the liver158. Acetate and butyrate are metabolised to acetyl-co enzyme A, whilst propionate enters the citric acid cycle and can be used to produce glucose via gluconeogenesis156. As such SCFAs are an additional energy source, accounting for 5-15% of total caloric intake159. In patients with T2DM, an increase is circulating acetate levels has 160 been reported . This was correlated with fasting glucose and HbA1c levels. The most well studied SCFA is butyrate, which has been demonstrated to be associated with maintaining colonic health156.

Non-dietary derived substrates are also important for microbial metabolism. Some microbes can metabolise host glycans, and cross-feeding of host glycans also occurs161. The mucus secreted in the GIT is glycan rich, providing an endogenous energy source162. Proximity to the glycan layer, which may include adhesion, is important for metabolism of host glycans. Microbes must also possess genes encoding the relevant enzymes for glycan degradation162. In particular, members of the genera Bacteroides and Bifidobacterium and Akkermansia muciniphilia have been demonstrated to be capable of metabolising mucus163. Fermentation products can also be used as substrates for other organisms. For example, hydrogen gas produced as part of carbohydrate fermentation, can be utilised by sulfate-reducing bacteria such as Desulfovibrio and methanogenic archaea such as Methanobrevibacter164. These relationships highlight the complexity of measuring the impact of diet on the GI microbiota.

Of particular relevance to this thesis is the response to a Western diet, typified by high fat and sugar intake and low fibre intake. In a murine model, a Western diet was associated with a significant increase in the relative abundance (RA) of Firmicutes and Verrucomicrobia and a significant decrease 14 in Bacteroidetes, compared to mice consuming a high fibre diet165. A Western diet was also associated with a bloom of bacteria that are capable of metabolising simple sugars in the distal GIT of mice166. In humans, long-term consumption of a high fat diet was positively associated with the RA of Bacteroidetes and Actinobacteria, and was negatively associated with the RA of Firmicutes and Proteobacteria167. The influence of fat on the microbiota appears to be dependent on the type of fat consumed. Saturated fat intake appears to be associated with deleterious impacts on the microbiota, in particular alterations in the ratio of Bacteroidetes to Firmicutes168.

1.2.2.4 Links between Obesity and the Gastrointestinal Microbiota The most pervasive evidence of the role of the GI microbiota in weight status comes from a study of germ-free mice. Germ-free mice had lower body fat than those colonised with a normal microbiota169. In these germ-free mice, subsequent re-colonisation with a normal microbiota resulted in an increase in body fat and insulin resistance despite an overall reduction in food intake. This was the first evidence suggesting a role of the microbiota in the regulation of body weight and metabolic health. In another study, germ-free mice colonised with faecal microbes from obese mice had significantly greater body fat than mice colonised with faecal microbes from lean littermates170. Metagenomic analysis revealed these animals to be enriched in Firmicutes, with an increased capacity for dietary energy harvest, evidenced by an increase in caecal SCFAs and a decrease in calories recovered in the stool.

In humans, obesity has been associated with an altered microbiota profile171 and alterations in colonic energy harvesting172. Broadly, it has been suggested there is an increase in the ratio of Firmicutes to Bacteroidetes in obesity171, 173; however, findings are inconsistent. Some studies have shown change in the Bacteroidetes to Firmicutes ratio174, 175. More specifically, members of the Clostridium cluster XIVa, Lactobacillus and Bacteroides have been reported to be enriched, whilst Bifidobacterium, Faecalibacterium and Oscillospira have been reported to be depleted176.

1.2.2.5 Links between T2DM and the Gastrointestinal Microbiota TD2M has been associated with distinct changes in the GI microbiota. In a study of 345 Chinese individuals, T2DM was characterised by alterations in taxonomic profile, but not alpha diversity. Specifically, there was an enrichment in opportunistic pathogens such as Escherichia coli and a reduction in butyrate-producing bacteria such as Roseburia intestinalis177. The key mucin degrading bacterium, Akkermansia muciniphilia, was also enriched. Functional analyses revealed an enrichment of membrane transport pathways, and specifically for transport of sugars and branched-chain amino acids. In the context of T2DM, these would be highly available substrates to GI microbes178. T2DM has previously been associated with oxidative stress179. This study also identified an enrichment of oxidative stress resistance genes, suggesting a stimulation of microbial 15 defence against TD2M-related oxidative stress. These functional findings have been confirmed in a study of European women180, where a significant increases in four Lactobacillus species and significant decreases in five Clostridium species was also reported in the TD2M cohort.

Changes in the GI microbiota also appear to be related to glucose control independently of markers of adiposity. In a study of 36 European males, 18 of whom had T2DM, the ratio of Bacteroidetes: Firmicutes and the ratio of Prevotella: Clostridia (C. coccoides-E. rectale) were significantly positively correlated with plasma glucose concentration, but not BMI181. There was also a higher abundance of Betaproteobacteria in T2DM subjects compared to controls, and this was positively correlated with plasma glucose.

The abundance of particular microbes at genus or species level has also been investigated. Both Bifidobacterium174 and Faecalibacterium prausnitzii177, 182 are reported to be reduced in T2DM. This is notable, as these bacteria are associated with anti-inflammatory properties183, 184. These data indicate possible changes in the microbiota associated with T2DM that may be distinct from the impacts of obesity; however, more research is required.

An important consideration in the effect of T2DM on the microbiota is the pharmacological treatment protocol. Metformin is the most commonly prescribed medication for T2DM, and has been associated with alterations in the microbiota. The relationship is unclear with metformin usage associated with both an enrichment185 and a depletion186 in butyrate-producing bacteria.

1.2.2.6 Impact of Weight Loss on the Gastrointestinal Microbiota Dietary weight loss interventions have been associated with variable changes in the stool microbiota in humans. Following hypercaloric dietary intervention, bacterial diversity was increased in two studies187, 188, with no significant change in two studies189, 190. The majority of studies have reported no significant impact of hypercaloric diet intervention on taxonomy at the phylum level175, 189-192. Two studies reported an increase in Bacteroidetes and a decrease in Firmicutes171, 193, whilst one study reported an increase in Firmicutes and a decrease in Bacteroidetes194. In studies using a hypercaloric, but also high protein and low carbohydrate diet, a number of consistent observations were reported at species level. Specifically, a reduction in butyrate producers such as Roseburia spp, Clostridium cluster XIVa, Eubacterium rectale and Bifidobacterium spp were observed175, 188, 190, 192, 194. There was a high level of inter-individual variation across these studies and considerable heterogeneity in microbiota assessment methodologies. Bariatric surgery has also been associated with changes in the stool microbiota in humans. Further details can be found in Chapter 1 Section 1.3.3.4.

1.2.3 Inflammation 1.2.3.1 Inflammation and Obesity Obesity is associated with inflammation in the skeletal muscle, liver, pancreas, brain, GIT and adipose tissue195. Systemic markers of inflammation have been positively associated with increased adiposity196. As such, obesity is considered a chronic, low grade inflammatory state. Systemic markers of inflammation have also been associated with the pathogenesis of obesity-related comorbidities such as T2DM197, 198 and cardiovascular disease199.

In obesity, the adipose tissue is a key site of the inflammatory response, with the accumulation of macrophages playing a key role in the secretion of pro-inflammatory cytokines200, 201. Activated macrophages exist as either “classically activated” M1 macrophages, or “alternatively activated” M2 macrophages. M1 macrophages are activated by interferon gamma and lipopolysaccharide (LPS). M2 macrophages are a heterogeneous population and are activated by interleukin (IL)-4, IL-13, LPS, apoptotic cells, IL-10, transforming growth factor-β and glucocorticoids202. M1 macrophages are considered pro-inflammatory and secrete tumour necrosis factor (TNF), IL-6 and IL-1β in addition to producing reactive oxygen (ROS) and nitrogen species. M2 macrophages produce anti-inflammatory cytokines such as IL-10 and transforming growth factor-β and produce arginase-1, preventing nitric oxide production. M2 macrophages are associated with tissue remodelling203. In addition to the increased infiltration of macrophages into the adipose tissue in obesity, a phenotypic shift from M2 to pro-inflammatory M1 macrophages is reported204.

In the inflammatory process, neutrophils and lymphocytes are recruited into the adipose tissue prior to macrophage infiltration205. Obesity has been linked to alterations in neutrophil activity and function in the adipose tissue and systemically206, 207. Neutrophil infiltration in the adipose tissue appears to drive the polarisation of M1 macrophages206. In the circulation, neutrophils from patients with severe obesity had higher levels of chemotactic and random migration207. They also released significantly more basal superoxides. Phagocytosis and adherence were not significantly different to controls. Cumulatively, these results suggest the neutrophils are primed, which increase the response of the neutrophils when exposed to an activating stimulus208. However, in the context of T2DM, deficits in all of these functions have been demonstrated209-211, suggesting mechanisms could be different in these two closely related conditions. Other components of the innate immune system such as natural killer cells and dendritic cells have also been shown to have altered function in obesity and T2DM212.

Adaptive immunity is also altered in patients with obesity and T2DM. Adipose tissue and systemic T cell populations display altered activation states213, 214. Both cluster of differentiation (CD) 4+ and

CD 8+ effector T cells have been implicated in the initiation of adipose tissue inflammation and the ongoing inflammatory response215, 216. A recent murine study has demonstrated CD4+ effector T cells are implicated in weight regain following successful weight loss217. Infiltration of CD4+ effector T cells in the adipose tissue preceded weight regain, suggesting an ‘obesity memory’ response after introduction of a high fat diet. Deficits in regulatory and γδ T cell populations and compromised function have been reported in obesity218.

Despite strong links between obesity and adipose tissue T cell populations, derangements in leukocyte cell phenotype or function in the circulation provide a more clinically accessible marker of systemic inflammation and cardiometabolic risk. Overall monocyte, neutrophil and lymphocyte counts have been reported to be higher in obesity than lean controls, have been associated with metabolic dysregulations such at the metabolic syndrome219-221 and can be altered by weight loss222. Similarly, circulating cytokines provide another clinically accessible measure of inflammation. A recent systematic review assessed the impact of lifestyle-based or surgical weight loss interventions on circulating markers of inflammation. In the majority of included studies, a significant reduction in pro-inflammatory biomarkers, and in particular IL-6 and TNF was reported223.

1.2.3.2 Inflammation and Insulin Resistance In obesity, the key sites of insulin resistance are the adipose tissue, skeletal muscle and liver. TNF has been strongly linked to skeletal muscle and adipocyte insulin resistance224. TNF stimulates phosphorylation of the insulin receptor substrate-1 protein, disturbing signalling224. TNF also reduces the expression of glucose transporter type 4, a key transporter for glucose uptake in the adipose tissue, muscle and liver. TNF can also activate nuclear factor kappa B (NF- κB) and c-Jun N-terminal kinase signalling pathways224, increasing the concentration of pro-inflammatory cytokines and perpetuating the immune response. In the adipose tissue, TNF also inhibits peroxisome proliferator-activated receptor γ (PPARγ), a key driver of lipid synthesis and storage225.

Weight loss has been demonstrated to improve insulin sensitivity226. A modest degree of weight loss (5%) has been associated with improvements in improved adipose tissue, muscle and hepatic insulin sensitivity227. However, in this study there was no improvement in adipose tissue or systemic immune markers. This highlights the complexity of the relationship between insulin resistance and inflammation.

1.2.3.3 Relationship between the Gastrointestinal Microbiota and Inflammation The GI microbiota is a relevant modulator of inflammation in the human body. The relevance of the interaction between the immune system and the microbiota is best illustrated by perturbations in the microbiota observed in a number of intestinal and extra-intestinal inflammatory disease states228-231.

To date, human studies have provided only limited evidence as to whether these perturbations are a cause or consequence of the disease state.

Commensal microbes in the GIT are in close contact with the gut epithelium. The mucus layer separating the epithelium and gut microbes acts as both a physical and immunological barrier232. When intestinal permeability is compromised, microbes and other particles can cross this epithelial layer into the lamina propria. In the lamina propria, antigen presenting cells such as macrophages and dendritic cells present antigens to T helper cells233. Antigen presentation activates the effector function of T lymphocytes in the lamina propria; however, effector T lymphocytes are also found within the healthy intestinal epithelium.

Intestinal permeability increases in parallel with BMI and is associated with insulin sensitivity234. Evidence from animal studies suggest intestinal permeability may be associated with changes in the microbiota induced by the consumption of a high-fat diet235. Other factors that may contribute to intestinal permeability include nutrient deficiencies, high fructose intake and systemic inflammation236. Regardless of the pathogenic process involved, increased intestinal permeability can lead to metabolic endotoxaemia. This refers to the translocation of bacterial LPS across the epithelial membrane, into the lamina propria and ultimately into the circulation. This is the key pathway by which the microbiota and inflammation are linked in obesity. LPS binds to an activation complex containing the Toll-like receptor-4 (TLR-4)237. These activation complexes are highly expressed on liver, adipose tissue and muscle cells and activate NF- κB238. NF- κB activation leads to the production of pro-inflammatory cytokines and ROS. The increase in pro-inflammatory cytokines, including TNF, can lead to a signalling cascade that blocks the activity of insulin on its receptor239. Thus, LPS can perpetuate insulin resistance in patients with obesity. LPS has been demonstrated to trigger adipose tissue macrophage recruitment240, facilitating the phenotypic shift from M2 to M1 macrophages observed in obesity.

1.2.3.4 Diet-Immune and Diet-Immune-Microbiota Interactions Dietary constituents are also associated with the immune response. In brief, saturated fat is associated with a pro-inflammatory response via activation of the TLR-4 containing activation complex241. Saturated fats can also increase the production of atherogenic lipids which trigger an inflammatory response. Conversely, polyunsaturated fatty acids have anti-inflammatory effects via inhibition of the pro-inflammatory TLR-4 and NF- κB pathways and activation of the anti-inflammatory nuclear receptor PPARγ242.

Cross-talk between the microbiota and the immune system can be partly mediated by dietary constituents. For example, diets high in animal protein result in an increase in production of

19 trimethylamine N-oxide (TMAO) by the microbiota243. Physiological concentrations of TMAO in vitro have been demonstrated to increase pro-inflammatory cytokine gene expression, in part via activation of the NF- κB pathway244. In a rodent model, a high milk protein diet was associated with a reduction in NF- κB expression245. Thus, the source of dietary protein appears critical in the inflammatory response.

The microbiota are key to the relationship between carbohydrate intake and inflammation. In particular, SCFAs, a product of microbial fermentation, have been implicated in a variety of immunomodulatory roles. Butyrate activates the inflammasome by binding to G-protein coupled receptors, helping to maintain the integrity of the epithelial barrier246. Butyrate also appears to regulate the expression of tight junction proteins, improving barrier function247. Acetate has also been suggested to modulate barrier function in a murine model248. In mice, lethal colitis developed following a low fibre diet249. This was attributed to an increase in taxa capable of mucus degradation secondary to dietary fibre restriction. The erosion of the mucus barrier was linked to reduced barrier function, thus permitting lethal infection.

SCFAs can modulate both innate and adaptive immune responses. Butyrate inhibits NF- κB signalling and histone deacetylase function250, 251. Through these pathways, the pro-inflammatory macrophage and neutrophil response is reduced252, 253. Butyrate reduces dendritic cell activity in the lamina propria promoting immunotolerance254, 255. SCFAs also modulate T cell populations, resulting in expansion of regulatory T cell populations and have also been implicated in T cell differentiation250, 256. SCFAs can also act as signalling molecules, acting on G-protein coupled receptors, with relevant impacts on metabolic health via modulation of hormonal secretion, energy expenditure and insulin sensitivity257.

1.3 OBESITY TREATMENT STRATEGIES Obesity management is complex and individualised; however, algorithm-based approaches to treatment have recently been developed to aid primary care physicians. These approaches escalate through various treatment strategies on the basis of BMI and comorbidities, with a BMI of ≥40 kg/m2 or a BMI of ≥35 kg/m2 with significant comorbidities typically resulting in a faster escalation to bariatric surgery. Lifestyle modification intervention and pharmacotherapy are first line treatments. These are generally delivered in the primary care setting; however, tertiary medical obesity management services may also utilise these methods. Clinically significant weight loss (≥ 5% of baseline weight)258, as a result of lifestyle modification intervention or bariatric surgery is beneficial, leading to improvements in obesity-related comorbidities and cancer risk259-261. Similarly, in patients with obesity-related metabolic complications, lifestyle and pharmacotherapeutic management of deranged metabolic parameters are first line therapies.

Failure of conservative treatment measures is generally required before escalation to bariatric surgery. Recently, a paradigm shift in treatment has suggested bariatric surgery be considered in patients with a BMI 35.0-39.9 kg/m2 with inadequate glycaemic control despite medical and lifestyle therapy optimisation and in those with BMI ≥40 kg/m2 regardless of medical optimisation of glycaemic control262. Bariatric surgery has been demonstrated to improve glycaemic outcomes in a manner that is distinct from the weight loss benefits of surgery263, 264.

1.3.1 Lifestyle Modification Intervention Lifestyle modification intervention is the cornerstone of obesity treatment, either as a primary therapy or as an adjunctive therapy to pharmacotherapy or bariatric surgery. Lifestyle modification intervention for obesity management induces weight loss via a reduction in overall energy intake. Strategies used may include the modification of dietary intake via calorie restriction, macronutrient manipulation or cognitive behavioural therapy. Exercise may also be utilised to increase energy expenditure. Very low calorie diets (VLCDs) which aim to reduce energy intake to <3300 kJ/day (<800 kcal/day) by replacement of meals with formulated shakes, soups and bars can also be used. Typically, these are recommended for short-term interventions of approximately 12 weeks, but can be continued for up to 12 months under medical or allied health supervision265, 266. VLCDs have also been demonstrated to improve metabolic outcomes in patients with impaired glucose tolerance or T2DM265, 266. Medical nutrition therapy for T2DM aims to reduce body weight and independently improve glycaemic control through modification of carbohydrate intake, with a particular focus on dietary fibre intake and glycaemic index/load267.

Lifestyle modification intervention to facilitate weight loss has been demonstrated to be efficacious in the short-term, with maximum efficacy at six to 12 months268, 269. In severe obesity (BMI > 35 kg/m2), lifestyle modification intervention induces modest weight loss; however, there is limited evidence of a concurrent reduction in cardiometabolic risk270. Moreover, there is a lack of long-term efficacy for lifestyle modification intervention with the majority of individuals regaining all weight lost within two to five years, regardless of the magnitude of weight loss initially achieved271-273.

1.3.2 Pharmacotherapy Pharmacological agents are often used as an adjunctive therapy to lifestyle modifications in the obesity treatment paradigm. Globally, the number of pharmacotherapeutic obesity treatments is limited. Approval and usage status varies by country. In Australia, phentermine (monotherapy only), a naltrexone/bupropion combination, orlistat and liraglutide are approved for obesity management. Phentermine and naltrexone/bupropion act on the central nervous system274, 275, whilst orlistat acts in the GIT276. Liraglutide has both local and central activity277. Topiramate, an anticonvulsant, and phentermine/topiramate combinations may also be prescribed off-label.

Key issues with pharmacological weight loss treatments are the duration of administration and the impact on long-term weight status. The majority of studies do not extend beyond approximately one year of follow-up and as such the long-term efficacy and safety implications are unknown. In long-term studies of orlistat, the weight loss benefits were relatively limited, with an approximately 3 kg greater weight loss than placebo at 2-4 years278, 279. Cost may also be a prohibitive factor. In Australia, these medications cost between AUD$100 and AUD$400 per month, and are not subsidised under the Pharmaceutical Benefits Scheme for obesity treatment.

1.3.3 Bariatric Surgery The American Society for Metabolic and Bariatric Surgery (ASMBS) define bariatric surgery as surgical procedures which “cause weight loss by restricting the amount of food the stomach can hold, causing malabsorption of nutrients, or by a combination of both gastric restriction and malabsorption”280. The history of bariatric surgery is extensive, with the first bariatric surgical procedure, the jejunoileal bypass, performed in the United States in 1953. In the following decades, numerous procedures were introduced and discontinued. Currently, there are four commonly performed bariatric surgical procedures, described below.

1.3.3.1 Procedures Laparoscopic Adjustable Gastric Band (LAGB; Lap-Band®) An inflatable gastric band is laparoscopically inserted below the level of the gastroesophageal junction to create a gastric pouch. The band is connected to a subcutaneous infusion port to allow for infusion of saline to tighten or loosen the band, regulating the flow of ingested foods from the gastric pouch into the remainder of the stomach. This is the only reversible bariatric surgical procedure. This procedure has progressively become less common, due to a poorer long-term efficacy profile281 and a higher rate of revisions281, compared to other surgical techniques. LAGB procedures now represent less than 20% of surgeries performed in Australia282.

Laparoscopic Sleeve Gastrectomy (LSG) A tubular stomach is created by resecting the greater curvature of the stomach, preserving the pylorus and part of the antrum. This resection removes the fundus, which is the primary location of ghrelin production, leading to a marked reduction in circulating plasma ghrelin concentrations283. Typically, the stomach volume is reduced to approximately 100-150 mL. This is now the most commonly performed bariatric surgical procedure in Australia282.

Gastric Bypass Procedures The Roux-en-Y gastric bypass (RYGB) creates a small pouch of stomach (~30 mL), which is anastomosed to the jejunum, referred to as the Roux or alimentary limb. The remaining portion of the stomach produces digestive juices, and bile and pancreatic juices are secreted from the ampulla of Vater into the second part of the duodenum. This biliopancreatic limb transports these secretions to the jejunojejunostomy, which is formed by anastomosing the biliopancreatic limb to a more distal part of the jejunum. At the jejunojejunostomy, the alimentary and biliopancreatic limbs form a common channel, allowing ingested nutrients to come into contact with the digestive juices to facilitate breakdown and absorption. If both limbs are at their maximal length, then approximately one third of the small intestine is bypassed. The length of jejunum in the biliopancreatic limb has been demonstrated to be associated with fat malabsorption in patients that have undergone RYGB, with a significant reduction in fat absorption occurring at a length of 70 cm284. The RYGB is considered to be the “gold standard” of bariatric surgery280. Globally, gastric bypass is the most commonly performed procedure285. In Australia, its utilisation is more limited, representing less than 10% of procedures reported in the voluntary Bariatric Surgery Registry in 2017-18286.

The one-anastomosis gastric bypass (OAGB) creates a tubular stomach pouch, similar to LSG, which is anastomosed to a jejunal loop. The length of the afferent limb is typically 150-200 cm from the ligament of Treitz287. Adjustment of the limb length dependent on baseline BMI has been shown to result in better weight loss outcomes, with afferent limbs of up to 350 cm used in those with a BMI 23

>50 kg/m2 288. Of the 82% of bypass procedures billed to Medicare captured in the Bariatric Surgery Registry in 2017-18, approximately 40% were OAGB286. This indicates the growing popularity of this procedure in Australia.

Biliopancreatic Diversion with Duodenal Switch The biliopancreatic diversion with duodenal switch (BPD-DS) involves a partial sleeve gastrectomy, with the pylorus retained to prevent dumping. Like in the RYGB, both an alimentary limb and a biliopancreatic limb are created. In this procedure, there is a shorter common channel. In the alimentary limb, the terminal ileum is anastomosed just below the level of the pylorus. In the biliopancreatic limb, the duodenum is anastomosed at the level of the terminal ileum. This procedure is associated with the greatest degree of weight loss289; however, is rarely performed in Australia (<0.5% of all primary procedures)282.

1.3.3.2 Safety of Bariatric Surgical Procedures Safety outcomes are typically reported as early (<30 days) or late (>30 days) complications and further stratified as major or minor. The ASMBS defines major complications as any complication leading to ≥7 day hospital stay, requirement for anticoagulant administration, reintervention or reoperation290. In 2014, a large systemic review and meta-analyses reported outcomes from 161,756 patients who underwent bariatric surgery between 2003 and 2012291. Early (≤30 days) mortality was estimated at 0.08% (n=1,803) in Randomised Controlled Trials (RCTs) and 0.22% (n=136,903) in observational studies. Late (>30 days) mortality was estimated at 0.31% (n=1,703) in RCTs and 0.35% (n=66,897) in observational studies. In RCTs, sleeve gastrectomy (including open procedures) was associated with the highest early and late mortality rate, whilst in observational studies, gastric bypass (including both open and laparoscopic RYGB, laparoscopic BPD-DS and BPD-RYGB) was associated with the highest mortality rate. Due to heterogeneity in outcome reporting, only an overall complication rate was reported. Complication rate was estimated at 17.0% (n=1,778) in RCTs and 9.8% (n=113,002) in observational studies. Gastric bypass procedures were associated with the highest rate of complications in both RCTs (21.0%) and observational studies (12.0%). Thus, these results suggest that while the mortality rate is appreciably low, the overall complication rate is significant.

1.3.3.3 Clinical Efficacy of Bariatric Surgical Procedures Weight loss outcome reporting is variable after bariatric surgery. The ASMBS recommend reporting of mean baseline BMI, change in BMI, total body weight loss (TBWL) and percent excess weight loss (EWL) and/or percent excess BMI loss290. EWL refers to any weight carried over a BMI of 25.0 kg/m2 (in a Caucasian population). Duration of clinical outcome reporting is specified as short-term (<3 years post-operative), medium-term (>3 but <5 years post-operative) and long-term 24

(>5 years post-operative)290. In a systematic review and meta-analyses, mean EWL at one year was 59.8% across nine RCTs and 46.2% across 39 observational studies291. Gastric bypass procedures were associated with the greatest magnitude of early weight loss.

Long-term maintenance of weight loss is variable, with weight regain typically beginning between one and two years after surgery292. A number of factors have been linked to this weight recidivism, broadly classified as patient-related factors and procedure-related factors. Patient-related factors include non-compliance with post-surgery nutrition and physical activity recommendations293, 294 and the presence of mental health comorbidities295-297. Negative metabolic adaptations such as an increase in appetite stimulating hormones such as ghrelin298, 299 and a reduction in resting energy expenditure have also been reported300, 301. Long-term weight maintenance also appears to be linked to the surgical procedure the patient underwent. At five years, EWL was greater after OAGB as compared to LSG and RYGB302, 303. At 10 years, weight loss has been reported to be greatest after BPD-DS (71.0% EWL) followed by RYGB (60.1% EWL) and LAGB (48.8% EWL), although LSG was not included in this meta-analysis304.

Bariatric surgery is also an effective treatment for T2DM. Outcome reporting for improvement in diabetes is recommended to be stratified as total remission, partial remission, improvement, unchanged or recurrence290. Complete remission refers to normal glycaemia

(HbA1c <6%; fasting glucose <5.6 mmol/L) in the absence of anti-hyperglycaemic agents. Partial remission refers to subdiabetic glycaemia (HbA1c 6.0-6.4%; fasting glucose 5.6-5.9 mmol/L) in the absence of anti-hyperglycaemic agents. Improvement refers to a statistically significant improvement in either HbA1c or fasting glucose without meeting remission criteria or a reduction in anti- hyperglycaemic agents (discontinuation or 50% dose reduction of insulin or one oral agent). Using these definitions, a 2006 systematic review and meta-analysis reported on diabetes outcomes from 621 studies289. Across these studies, complete remission was reported in 78.1% of patients. The proportion of patients who had complete remission was highest for BPD with/without duodenal switch followed by gastric bypass. Sleeve gastrectomy was not reported on. A more recent network meta-analysis of 25 RCTs reports at least partial remission in 94.6% of BPD patients, 86.1% of OAGB patients, 73.9% of BPD-DS patients, 64.1% of RYGB patients, 52.4% of LSG patients and 30.3% of LAGB patients305.

1.3.3.4 Mechanisms of Action of Bariatric Surgical Procedures Historically, bariatric surgery procedures have been described as either primarily restrictive or primarily malabsorptive procedures. LAGB and LSG have been classified as primarily restrictive, whilst RYGB and BPD have been classified as primarily malabsorptive. Whilst these classifications

25 are intuitive, evidence suggests a more complex regulation of the weight loss response following surgery. These will be discussed as individual themes below.

Energy Expenditure A number of studies have assessed resting energy expenditure after RYGB. The majority of studies report a reduction in resting energy expenditure300, 306-308. However, when adjusted for predicted metabolic rate, an increase in energy expenditure has been reported in some studies309, 310. Other studies have reported that energy expenditure did not differ from lean controls when adjusted for fat mass or loss of fat-free mass308.

The contribution of diet-induced thermogenesis to weight loss after RYGB has also been assessed. A significant increase in diet-induced thermogenesis has been observed311, 312. Weight regain after RYGB has also been associated with a reduction in both diet-induced thermogenesis and resting energy expenditure313, 314. The effects of bariatric surgery, and in particular LSG, on energy expenditure require further investigation.

Modulation of Food Intake Whilst both LSG and RYGB have a mechanically restrictive component, this is unlikely to be a major mechanism by which weight loss is induced. In a large review including 5,000 LSG patients, there was no significant difference in EWL on the basis of larger versus smaller bougie size315. A small prospective study has identified antral volume, as opposed to overall bougie size, as a possible contributor to weight loss efficacy after LSG316. Following RYGB, weight loss at one317 and three years318 post-procedure was not correlated with the size of the gastric pouch.

Behavioural change and nutrition counselling are a key component of post-bariatric surgery care recommendations for weight loss and improvements in glycaemic control319. Lifestyle modification intervention alone is ineffective in producing long-term weight loss due to the difficulty of sustained behaviour change271-273. Animal models have provided evidence for a biologically, rather than behaviourally-driven change in food preference after bariatric surgery. Following LSG or RYGB, there is a shift towards a preference for a lower energy carbohydrate rich diet, as opposed to a higher fat diet320, 321. After LSG, this was associated with an aversion to intragastric oil320. In humans, alterations in self-reported dietary intake support these findings with a reduction in high calorie foods or dietary fat after RYGB306, 322, 323. After LSG, changes in taste intensity and decreased enjoyment of high calorie foods have been reported324, 325. Changes in the smell of food have also been reported after both LSG and RYGB326, 327. Functional imaging studies have also reported a reduction in brain reward pathways to high calorie foods following RYGB328. Further work is needed to assess the neurobiological determinants of alterations in food preference after bariatric surgery in humans.

Bariatric surgery has been demonstrated to modulate visceral sensation in response to nutrient stimulation, with an increase in satiety scores following a standardised nutrient drink329. Further, a higher baseline maximum tolerated volume was found to be independently negatively predictive of weight loss at one year after bariatric surgery in a cohort of 14 females with severe obesity330.

Satiety responses may be associated with changes in the hormonal regulation of appetite after bariatric surgery. The incretin effect refers to the ability of oral glucose to stimulate a greater insulin response than intravenously administered glucose331. After both LSG and RYGB, the incretin effect is substantially increased. The evidence of the effect of bariatric surgery on glucose-dependent insulinotropic peptide (GIP) is conflicting. Both an increase332, 333 and a decrease334, 335 in GIP has been reported after RYGB. An increase in GLP-1 has been reported following both RYGB and LSG333, 336-339. Evidence from animal models suggests the mechanism via which this occurs is different between the two procedures. Following LSG, the density of GLP-1 producing L cells is increased, whilst after RYGB there is intestinal hyperplasia and an increase in the number of L cells340. The physiological relevance of the observed increase in GLP-1 is poorly defined. Data from knockout animal models suggest that GLP-1 acting on its receptor is not critical for either the weight loss response or global improvements in glycaemic control after both LSG and RYGB341, 342. Further research is required to determine the role of incretin peptides in the weight loss and glycaemic control response to bariatric surgery.

Alterations in other gut derived hormones have been reported after bariatric surgery. PYY is a central modulator of satiety, acting in the hypothalamus343. An increase in the post-prandial PYY response has been reported after both LSG and RYGB344-347. Higher PYY levels have been correlated with lower appetite scores346, 348. GLP-2, a gut-derived peptide implicated in in mucosal homeostasis and proliferation349, has also been reported to increase after RYGB in rodents and humans350, 351.

Some of these hormonal changes may be linked with intestinal proliferation and hypertrophy. Following RYGB, intestinal hypertrophy has been observed in animal models350, 352. These changes have not been reported following LSG353. Morphological changes such as increased villus height and crypt depth have also been reported after RYGB in animal models, which may provide a mechanism via which malabsorption is limited350, 354. These changes have been linked to increases in GLP-2350. Pancreatic hyperplasia has also been reported following RYGB in animal models355. It has been proposed that this may be mediated by the parallel increase in GLP-1 or GIP following RYGB356. This contributes to the improvement in glucose control seen after RYGB.

Gastrointestinal Function In addition to morphological changes in the GIT, changes in GI function have been reported following bariatric surgery. Bowel habits in patients with severe obesity are altered357. However, following RYGB, constipation is significantly increased358, suggesting malabsorption may be limited. A limited number of studies of RYGB have reported fat malabsorption following RYGB284, 359, with clinically significant malabsorption likely associated with the length of the alimentary limb284. This may be a minor mechanism via which weight loss is facilitated.

Gastric emptying has also been assessed following bariatric surgery. Aa significant acceleration of gastric emptying has been reported after both LSG360-362 and RYGB363-366. However, more rapid pouch emptying has been associated with both better and poorer weight loss outcomes364, 365. The relevance of changes in GI motor function for weight loss outcomes requires further investigation.

The Gastrointestinal Microbiota and Bile Acids A number of taxonomic changes in the GI microbiota have been reported after bariatric surgery. These may be associated with alterations in dietary intake and physiological impacts of the anatomical changes caused by surgery, such as alterations in gastric emptying, intestinal pH and exposure to bile. At the phylum level, a decrease in the ratio of Firmicutes: Bacteroidetes has been reported in a number of studies following RYGB367-371. Conversely, one study has reported an increase in the ratio of Firmicutes: Bacteroidetes372. An increase in Proteobacteria368, 370, 371, 373 and more specifically Gammaproteobacteria has also been reported374, 375. Other phyla reported to be increased following RYGB include Fusobacteria373, Actinobacteria372 and Bacteroidetes . At the genus level, a significant increase in common taxa such as Bacteroides, Streptococcus, Prevotella, Enterobacter, Citrobacter and Veillonella has been reported367, 368, 370, 371, 373, 376. Bifidobacterium has been reported both to increase377 and decrease367. Similarly, conflicting evidence also exists about change in abundance of Escherichia (including E.coli) and Faecalibacterium (including F.prausnitzii)367, 368, 377. Significant reductions in the RA of common taxa such as Lactobacillus, Coprococcus and Anaerostipes have been reported after RYGB367, 368.

Following LSG, a significant decrease in the ratio of Firmicutes: Bacteroidetes has been reported in four studies371, 378-380. One study has reported no significant change in the ratio of Firmicutes: Bacteroidetes370. Studies have also reported either an increase in Bacteroidetes372, 379 or a decrease in Bacteroidetes371. One study has reported a decrease in Firmicutes380. Similarly, both a significant increase371 and a significant decrease370 in Proteobacteria has been reported following LSG.

In humans, these results do not define whether weight loss is a cause or consequence of changes in the microbiota, as weight loss does not occur in isolation from other metabolic changes which may precipitate changes in the microbiota, such as reduced systemic inflammation. However, murine models have provided a more causative link, with faecal transplant from both RYGB-treated mice and humans resulting in a reduction in fat mass in inoculated germ-free mice375, 381.

Following RYGB, three studies have reported an increase in bacterial richness369, 372, 373, whilst one study has reported no significant change in diversity370. In two studies assessing outcomes following LSG or LAGB, no change in diversity was reported380, 382.

Bile acids are produced and conjugated in the liver for secretion in bile into the duodenum at the ampulla of Vater. Bile acids facilitate the digestion of fat and ultimately the absorption of triglycerides, cholesterol and fat soluble vitamins. The majority of bile acids are reabsorbed in the ileum via the enterohepatic circulation. Bile acid composition is modulated by the composition of the GI microbiota. Specifically, the microbiota are critical for the formation of secondary bile acids, which are the product of microbial transformation of primary bile acids. Total bile acid concentration has been shown to be increased after RYGB383-386. Conflicting data exist on the impact of LSG on circulating bile acid concentrations. Both significant increases384, 387 and no change388, 389 in total or conjugated bile acid concentration has been reported after LSG.

Bile acids also act as a signalling molecule and are an important modulator of energy and glucose metabolism. A larger increase in circulating bile acid concentration has been associated with T2DM remission after RYGB385, 390. This may be associated with bile acid activity on both the Takeda G-protein coupled receptor 5 (TGR5), which increases GLP-1 secretion391, or on the farnesoid X receptor (FXR), which increases insulin secretion by pancreatic β-cells392. Murine models have demonstrated a key role of FXR in improvement in glycaemia, as knockout mice showed a reduction in improvement in glycaemia after LSG, as well as impaired weight loss393. Bile acids may also act on the TGR5 receptor in adipose and skeletal muscle tissue, increasing energy expenditure by increasing thyroid hormone activation394.

Glycaemic Control In addition to the aforementioned effects of changes in incretin peptides and bile acids on glycaemic control, distinct improvements in insulin sensitivity have also been reported. In animal models, intraperitoneal administered glucose loads are cleared faster after both LSG and RYGB, compared to obese rats but not body weight matched rats395. As intraperitoneal administration bypasses the intestine, this is more reflective of improvements in overall insulin sensitivity that are weight loss dependent395. However, it appears that improvements in hepatic insulin sensitivity occur following

RYGB in a weight loss-independent manner395. Enhanced insulin signalling in adipose tissue and skeletal muscle has also been reported in humans396.

Inflammation Bariatric surgery is effective is reducing inflammation in the adipose tissue. This is driven by a reduction in macrophage infiltration in subcutaneous adipose tissue397. In turn, this reduced infiltration is likely driven by a reduction in chemoattractant proteins and an increase in anti-inflammatory IL-10 production397. Changes in adipose tissue macrophage polarisation have also been observed, with a return to a predominantly M2 macrophage phenotype204. A murine study has suggested this phenotypic shift is weight loss independent398. Cytokine expression in the liver and adipose tissue is also reduced, whilst adiponectin expression is increased399, 400. Successful weight loss (>20%) has also been associated with a reduction in pro-inflammatory cytokine, chemokine and macrophage cell surface marker expression in subcutaneous adipose tissue401. In the colon, a reduction in pro-inflammatory cytokine expression and macrophage and T cell populations have also been reported402.

Following bariatric surgery, there are also alterations in circulating markers of inflammation. A significant increase in adiponectin has been reported after both RYGB and LSG403-406. A significant decrease in circulating leptin concentrations has also been reported after RYGB and LSG407-410. Investigation into the impact of bariatric surgery on circulating cytokines has produced inconsistent findings. The majority of studies report no significant effect on TNF in over one to 24 months post-surgery406, 411, 412. In the short-term, the effects on IL-6 are variable with either a decrease or no change reported406, 411, 413. Long-term studies suggest a reduction in IL-6406, 411, 413. These results could be impacted by heterogeneity in comorbidity burden within study cohorts, as patients without diabetes appear to have a larger change in adipokines after surgery414.

1.3.3.5 Clinical Utilisation of Bariatric Surgery In Australia, the majority of surgeries are currently performed primarily in private patients or patients with private health insurance. In 2014-15, 79% of weight loss surgeries were principally funded by private health funds, whilst 12% were self-funded282. Publicly funded weight loss surgeries represented less than 10% of all surgeries performed.

Medicare, the universal health care system in Australia, subsidises a fixed fee for a particular service. If a practitioner charges more than the scheduled fee, the patient (or healthcare fund) is required to fund the remaining cost. In 2014-15, approximately 25,000 weight loss procedures were billed via the Medicare system5. The total cost of these procedures was estimated at $62.8 million, with Medicare funding $25.7 million of costs and patients or insurance companies paying $37.1 million

30 dollars282. Primary bariatric procedures were estimated to constitute 79.4% of all procedures. More recent data from the voluntary Bariatric Surgery Registry suggest similar trends. In the 2017-18 financial year, the registry captured 64.9% of all bariatric surgeries billed to Medicare. Of these procedures, 77% were primary procedures. However, less than 5% were primary procedures performed in a public hospital.

The number of procedures performed is clearly disproportionate to the number of eligible patients. Indeed, on the basis of T2DM as an indication for surgery, it is estimated that 1.3% of eligible patients are treated with bariatric surgery each year in Australia415. The reasons for the underutilisation of surgery are multifactorial but include financial limitations416, patient417 and physician418 attitudes and beliefs surrounding obesity and surgery and fear of complications or negative side effects417.

1.3.4 Endoscopic Bariatric Therapies Endoscopic bariatric therapies utilise endoscopic techniques to mimic bariatric surgery. Like bariatric surgery, these devices are used in conjunction with lifestyle modification therapy. The first of these techniques, the intragastric balloon, became commercially available in the 1980s419. Whilst the clinical effects of the more established techniques are well defined, the mechanisms of action are largely unknown.

The American Society for Gastrointestinal Endoscopy (ASGE) and ASMBS Joint Bariatric Endoscopy Task Force have defined targets for the determination of clinical efficacy and safety profiles for endoscopic bariatric therapies420. For the primary treatment of obesity, clinical efficacy is determined as a minimum of 25% of EWL after 12 months of treatment. If a control group is used, the difference in EWL between the treatment and control group should be ≥ 15% and be statistically significant. Additionally, the risk of a serious adverse event (SAE) must be ≤ 5% to be endorsed by the Task Force. Broadly, endoscopic bariatric therapies can be divided into gastric therapies and small intestinal therapies, and will be presented as such below.

1.3.4.1 Gastric Procedures Intragastric Balloons Intragastric balloons are filled with fluid or gas and may dwell for a period of six to12 months. A volume of 400 mL is needed to produce greater weight loss than a control group421 and thus the majority of balloons have an inflated volume of 400-900 mL. The most widely utilised intragastric balloon is the Orbera® balloon (Apollo Endosurgery, TX, USA), which was previously sold as the BioEnterics Intragastric Balloon (Allergan, CA, USA). In Australia, there are two commercially available options: the Orbera® balloon and the Spatz3® balloon (Spatz Medical, NY, USA). The Orbera® balloon is filled with up 400-700 mL of saline, is endoscopically implanted and

31 removed and is approved for placement for up to six months. The Spatz3® balloon has an adjustable volume, is endoscopically implanted and removed and is approved for placement for up to 12 months.

A recent consensus statement from Brazil reports a mean weight loss of 18.4±2.9% from a total of 41,863 implants422. Treatment with the Orbera® balloon dominated, representing 78.2% of these procedures. In this same cohort, the SAE rate was 2.5% overall and 2% for the Orbera® balloon. The ASGE has endorsed the use of the Orbera® balloon for primary weight loss therapy, following a systematic review and meta-analysis of 17 studies reporting mean EWL of 25.44% at 12 months423. This meta-analysis also reported an acceptable safety profile, with less than 5% SAEs. Despite these safety data, to date this is the only endoscopic bariatric therapy that has been associated with mortality424. In Australia, there have been three reports of death following intragastric balloon use425.

A small number of studies have assessed mechanisms of action of intragastric balloon devices. Delayed gastric emptying has been reported in a number of studies426-428, and has been both positively associated426 or not associated428 with weight loss. The impact of intragastric balloon placement on circulating appetite control biomarkers has been studied in a limited number of small studies with conflicting findings428-432.

Endoscopic Suturing/Plication Techniques A variety of endoscopic suturing or plication techniques exist, with the purpose of reducing intragastric volume. In Australia, the only available technique is endoscopic sleeve gastroplasty performed using the OverStitch platform (Apollo Endosurgery). Endoscopic sleeve gastroplasty creates a tubular stomach, similar to that created by LSG. This is performed by using full thickness sutures to stitch along the greater curvature from the distal stomach to the proximal stomach. This reduces both the diameter of the lumen, and also the length of the stomach. Unlike LSG the fundus is preserved.

The largest single centre cohort comes from 1,000 consecutive patients in Saudi Arabia433. In this cohort, mean weight loss at 12 months was 15.0±7.7% (n=216). At 18 months post procedure, weight loss had stabilised at 14.8±8.5% (n=85). A total of 24 patients were readmitted due to SAEs. In a large multicentre study, mean weight loss at 24 months post-procedure was 18.6% (95% confidence interval: 15.7-21.5)434. In this cohort, there were five (2%) SAEs. To date, there are no published reports of weight loss outcomes beyond 24 months post-procedure. One study has reported on mechanisms of action, with a reduction in calories required to reach fullness on a nutrient drink test and a delay in gastric emptying post-procedure435.

Other endoscopic gastroplasty techniques include Primary Obesity Surgery Endolumenal (POSE) which is performed using the Incisionless Operating Platform (USGI Medical, CA, USA), the 32

Transoral gastric volume Reduction as an Intervention for weight Management (TRIM) procedure which is performed using the RESTORe suturing system (C.R. Bard, NJ, USA) and Transoral Endscopic Vertical Gastroplasty (TOGa®) which is performed using the TOGa® Sleeve Stapler and the TOGa® Restrictor. In a meta-analysis of two studies using the POSE procedure, mean weight loss was 5.8±7.1% (n=376), with a total of five SAEs436.

Aspiration Therapy The AspireAssist® system (Aspire Bariatrics, PA, USA) uses a percutaneous endoscopic gastrostomy to facilitate the aspiration of approximately 30% of ingested gastric contents. The gastrostomy tube is attached to a skin port. The skin port attaches to a proprietary connector device, which has an inbuilt counter. This counter allows for 115 aspiration cycles before it locks, preventing access to the skin port for further aspiration. This is designed as a safety feature to prevent misuse of the technology. At the connecter, a siphon, 600 mL reservoir and drain tube are connected to allow for drainage and flushing of gastric contents directly into the toilet. This procedure is commercially available in Australia.

In a four year study, TBWL was 18.2±9.4% (n=155) after one year and 19.2±13.1% (n=12) after four 437 years . The mean reduction in HbA1c at one year was 1.0%. Seven cases of buried bumpers occurred and one case of peritonitis were reported. The pivotal USA-based trial reported similar results with a mean weight loss of 12.1±9.6% over one year and five SAEs438. There was one case each of peritonitis, pre-pyloric ulcer and skin-port malfunction (leading to A-tube replacement) and two cases of severe abdominal pain (in one patient). The mechanisms of action of this device have received limited investigation. However, weight loss results exceed what would be expected by maximal compliance with a 30% reduction in caloric intake, achieved by aspirating gastric contents 20 minutes after a meal439.

1.3.4.2 Small Intestinal Procedures Whilst a number of gastric procedures are commercially available, the majority of small intestinal procedures are only approved for investigational use and have undergone very limited human investigation to date.

Self-Assembling Magnets for Incisionless Anastomosis This procedure uses simultaneous gastroscopy and colonoscopy to deploy self-assembling magnets using the Incisionless Anastomosis System (GI Windows, MA, USA). Once connected, these magnets apply compressive pressure to the tissue trapped within the magnets. Over a number of days, this tissues undergoes necrosis, creating a fully patent anastomosis. The anastomosis creates a dual path for transport of nutrients. To date, one clinical study has been reported in a cohort of 10 patients

33 with obesity and T2DM440. In this cohort, a jejunoileostomy was performed. One year after the procedure, mean weight loss was 14.6% and the mean reduction in HbA1c was 1.9%.

Duodenal Mucosal Resurfacing Duodenal Mucosal Resurfacing is performed using the Revita™ device (Fractyl Laboratories Inc., MA, USA). This procedure hydrothermally ablates the superficial duodenal mucosa. It is hypothesised that this alters enteroendocrine signalling and increases incretin activity to improve T2DM parameters441. To date one clinical study has been published, but pivotal trials are underway in Europe and the United States. In this study of 44 patients with T2DM, HbA1c was reduced by 1.2±0.03% after six months442. Improvements in glycaemic control were greater in those that had a larger section (>9 cm) of mucosa ablated (mean reduction in HbA1c of 2.5%), as compared to those who had a shorter section (<6 cm) of mucosa ablated (mean reduction in HbA1c 1.2%). In a subgroup of patients with a larger mucosal ablation, screening baseline HbA1c between 7.5 and 10% and stable medication usage, there was a modest weight loss of 2.5±0.1 kg six months after the procedure.

Duodenal-Jejunal Bypass Sleeve (EndoBarrier®) The duodenal-jejunal bypass sleeve (DJBS; EndoBarrier®; GI Dynamics, MA, USA) is a 60 cm fluoropolymer sleeve that is endoscopically-placed and anchored in the proximal duodenum. The self-expanding metal ring at the base diverts ingested food contents down the internal surface of the sleeve, whilst bile and pancreatic secretions travel down the external surface of the sleeve. The nutrients and digestive juices meet in the mid jejunum for absorption. The DJBS was approved for use in the Australian market in 2011. In October 2016, the device was removed from the Australian Register of Therapeutic Goods (ARTG) and is no longer commercially available in Australia.

The safety profile of the DJBS has received significant attention recently. SAEs leading to early explant include device migration, upper GI bleeding, and sleeve obstruction443-464. Common mild-moderate adverse events include abdominal pain, nausea and vomiting. There have been a limited number of cases of hepatic abscess reported in the literature450, 452, 454, 458, 459, 461-465. There was a 3.2% prevalence of hepatic abscess in the United States-based pivotal ENDO Trial. This lead to the early termination of the study as it exceeded the predetermined safety threshold of 2%466. A number of other trials have also reported SAE rates that exceed the 5% threshold considered acceptable by the ASGE423. The adverse event rate between studies is variable, ranging between 0 and 69%449, 461. A 2018 systematic review of 1,056 patients reported a SAE rate of 3.7%. Further research is required to determine the modulators of device-related SAEs.

Since the emergence of the DJBS in 2007, numerous studies assessing clinical efficacy have been published. Early studies of the DJBS ran for a 12 week duration in small populations, without an emphasis on T2DM. More recently, studies have been conducted for 24-144 weeks, with variable post-explant follow-up, up to four years post-explant. Weight loss efficacy and improvements in glycaemic control are well established after DJBS treatment. The majority of studies do not include a control population, which may introduce bias and reduce the confidence of conclusion drawn from the data. The Joint Bariatric Taskforce guidelines define ≥25% EWL at 12 months as demonstrating efficacy420; however, EWL is not always reported. Differing study populations, durations and lack of reporting on impact of lifestyle modification intervention compliance further complicates interpretation of results. To date, no study has reported in detail on dietary compliance, despite the majority of studies prescribing a calorie-controlled diet that in itself would initiate significant weight loss.

The impact of DJBS implantation on body composition has been poorly studied. Four studies have found a decrease in fat mass. One study of 12 patients with obesity and T2DM reported a significant reduction in fat mass at 24 weeks467. In a 52 week study reporting outcomes on 16 patients, significant reductions in fat mass were observed, despite no change in body fat percentage, indicating a concurrent loss of lean body mass451. Conversely, in 30 patients with obesity and T2DM, a significant reduction in fat mass without a reduction in lean body mass was observed after 10 months of DJBS dwell. In a 26 week study of 11 patients with obesity and T2DM and 13 patients with obesity and normal glucose tolerance, no significant weight loss occurred in either group; however, there were significant losses in total body fat mass and visceral fat mass456. In these studies, there was no report on compliance with an exercise regimen, which may prevent lean body mass loss in the context of weight loss468. Further research is needed to clarify these findings.

A smaller number of studies have also assessed cardiovascular, hepatic and inflammation-related outcomes, and baseline predictors of intervention success. Improvements in blood pressure and either an improvement or no overall change in lipid profile have been reported449, 454, 457, 461, 464, 469. de Jonge and colleagues have assessed the impact of DJBS implantation on serum markers of NAFLD. Significant improvements (p<0.05) in alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), caspase-cleaved cytokeratin-18 and liver-type fatty acid binding protein (L-FABP) were reported within three months of DJBS implantation470. At six months, serum ALT and GGT continued to improve, with other parameters stabilising. At six months post-explant, ALT, GGT and caspase-cleaved cytokeratin-18 were still significantly improved (p<0.05) compared to baseline; however, AST and L-FABP had returned to near baseline levels. These findings have been supported by Stratmann et al., in a cohort of 18 patients with obesity and

T2DM, serum ALT, AST and GGT were significantly reduced451. In an Australian cohort of 114 individuals (38 with T2DM), a significant reduction in GGT was observed, whilst a significant increase in ALP occurred454. A trend toward a reduction in ALT (p=0.059) was also observed. In a small United Kingdom cohort (n=12), a significant reduction in ALT was observed over the 12 month implant duration, but this improvement was lost at six months post-explant464. To date one study has assessed markers of hepatic fibrosis and steatosis. This was performed using hepatic transient elastography in a cohort of 19 patients with obesity and T2DM455. Over a one year treatment period, there was a significant reduction in liver stiffness (as a marker of fibrosis) and the Controlled Attenuation Parameter (CAP; as a marker of steatosis). Despite the reduction in stiffness, the authors noted the majority of patients still had high grade steatosis (on the basis of CAP) at device explant. Overall, these results suggest the DJBS may be beneficial for improving NAFLD parameters. de Jonge and colleagues assessed the effect of DJBS treatment on serum markers of inflammation471. In this cohort, serum C-reactive protein (CRP), TNF-α, IL-6 and myeloperoxidase were not significantly different from baseline following six months of treatment with DJBS, despite significant weight loss. These data suggest implantation with DJBS may convey additional metabolic benefits; however, further research is needed, as to date, study numbers investigating metabolic parameters have been small.

Two studies have identified parameters that may predict intervention success. In a 52 week study of 79 patients with obesity (22 with T2DM), univariate analysis revealed EWL at 52 weeks was inversely associated with fasting glucose, homeostatic model of insulin resistance (HOMA-IR) score and HbA1c, whilst multivariate analysis found only baseline HbA1c to be significantly associated with EWL at 52 weeks450. In a large cohort of 185 patients with obesity and T2DM, c-peptide ≥ 1.0 mmol/L and body weight ≥ 107 kg at baseline were independent predictors of intervention success452.

The clinical benefit of the DJBS after device explant is poorly studied. However, significant weight regain has been reported in six studies448, 454, 461, 462, 469, 472 over six months to four years post-explant. Deterioration of metabolic improvements achieved in the intervention period have been reported in six studies461, 462, 464, 465, 469, 473. Combined, these data suggest the durability of the beneficial metabolic effects of DJBS implantation are likely to be lost post-explant, especially in the context of weight regain. Further research on the long-term efficacy and factors predicting long-term success is required. Table 1.1 summarises the safety, weight and diabetes-related outcomes for the DJBS.

Table 1.1: Safety, weight loss and glycaemic control outcomes following DJBS implantation in subjects with obesity.

Authors/year Design Population Safety Outcomes Efficacy Outcomes Gersin et al., 12 weeks, no controls 1 F, age 36 years, No SAEs Weight loss of 9.1 kg 2007474 BMI 45.2 kg/m2 Rodriguez- 12 weeks, no controls, 4 with 12 (7 F, 5 M, mean 71 adverse events total /55 device-related: 6 abdominal Mean weight loss 10.2 kg. No statistical Grunert et al., T2DM age 41, mean BMI pain, 18 nausea, 16 vomiting, 1 diarrhoea, 12 implant site analyses reported; 75% of patients with 475 2 2008 43 kg/m ) inflammation, 2 partial pharyngeal tears on explant; 2 T2DM had ≥ 0.5% reduction in HbA1c

patients had device removed after 9 days due to poor within 12 weeks placement Tarnoff et al., 12 week RCT: LFD + DJBS or Treatment: 15 F, 10 5 devices requiring early explant – 3 GI bleeding, 1 anchor Weight loss of 10.3 ±3.2 kg in device 2009444 LFD control, 25 treatment M, mean age 38 ± migration and 1 sleeve obstruction. group and 2.6±3.5 kg in the control group, 3 T2DM, 14 control, 1 10.1 years, mean group. No statistical analyses reported. T2DM BMI 42 ± 5.1 kg/m2 At 12 weeks, 20 patients in Control: 8 F, 6 M, treatment, 4 in control (10 lost to mean age 43± 10.6 follow-up) years, mean BMI 40 ± 3.5 kg/m2

Rodriguez et 24 – 52 weeks, randomised, 18 (11 F, 7 M, mean 3 devices requiring early explant – 3 device migration + 2 No significant weight loss; significant 443 al., 2009 sham-controlled, 12 treatment, 6 age 47 ± 10 years, migrations noted either at scheduled endoscopy or improvements in FPG and HbA1c at 12 control, all with T2DM mean BMI 38.9 ± scheduled explant and 24 weeks 6.1 kg/m2)

Schouten et al., 12- 24 week RCT, 30 treatment, Treatment: 30 (22 F, 4 devices requiring early explant – 1 migration, 1 Significant EWL at 12 weeks compared 446 2010 8 with T2DM, 11 control, 2 with 8 M, mean age 40.9 dislocation of anchor, 1 sleeve obstruction and 1 continuous to control; no change in FPG or HbA1c. T2DM years, mean BMI epigastric pain 48.9 kg/m2

Control: 11 (9 F, 2 M, mean age 41.2 years, mean BMI 47.4 kg/m2

Authors/year Design Population Safety Outcomes Efficacy Outcomes Gersin et al., 12 weeks, randomised, sham Treatment: 21 (15 F, 8 devices requiring early explant – 3 GI bleeding, 2 Significant weight loss at 12 weeks; nil 2010445 controlled, 21 treatment, 14 6 M, mean age 45 ± abdominal pain, 2 nausea and vomiting, 1 device-unrelated diabetes-related outcomes reported. T2DM, 26 control, 12 T2DM 7 years, mean BMI illness (breast cancer) 46 ± 5 kg/m2 Control: 26 (23 F, 3 M, mean age 43 ± 10 years, mean BMI 46 ± 6 kg/m2 de Moura et al., 52 weeks, no controls, all with 22 (19 F, 3 M, mean 9 early explants: 3 device migration, 1 GI bleed, 2 Significant weight loss at 52 weeks; 2012447 T2DM, 13 completed 52 weeks age 46.2 ± 10.5 abdominal pain, 2 principal investigator request, 1 unrelated significant reductions in fasting glucose, years, mean BMI malignancy insulin and HbA1c occurred 44.8 ± 7.4 kg/m2 Escalona et al., 52 weeks, no controls, 24 in 39 (31 F, 8 M, mean 15 adverse events requiring early explant: 8 anchor Significant weight loss at 52 weeks; 448 2012 completer population, 6 with age 36 ± 10 years, migration, 3 device obstruction, 2 abdominal pain, 1 acute significant reduction in HbA1c T2DM mean BMI 44.7 ± cholecystitis, 1 patient request 5.9 kg/m2 Cohen et al., 52 weeks, no controls, all with 20 (7 F, 13 M, mean 4 early explants: 2 device migration, 1 abdominal pain, 1 Significant weight loss at 52 weeks; 449 2013 T2DM, 16 completed 52 weeks age 49.8 ± 6.7, mean investigator request significant reduction in FPG and HbA1c BMI 30.0 ± 3.6 kg/m2) De Jonge et al., 24 weeks, no controls, all with 17 (3 F, 14 M, mean No early explants. 3 adverse events requiring Significant weight loss at 24 weeks; 2013476 T2DM age 51 ± 2 years, hospitalisation: 1 melaena, 1 severe constipation, 1 significant reduction in FPG , mean BMI 37.0 ± abdominal pain leading to dehydration postprandial glucose response and Patients 1.3 kg/m2) glucagon response common to470, 471, 477-479

Cohen et al., 52 weeks, no controls, all with 16 (6 F, 10 M, mean No SAEs Significant weight loss at 52 weeks; 2013473 T2DM age 49.8 ± 6.7, mean significant reduction in FPG, HOMA-IR BMI 30.0 ± 3.6 and HbA1c 26 week follow-up period kg/m2) 26 weeks post-explant, fasting glucose, HbA1c and insulin sensitivity deteriorated.

Authors/year Design Population Safety Outcomes Efficacy Outcomes Koehestanie et 24 weeks, no controls, all with 12 (5 F, 7 M, mean No SAEs Significant weight loss and reduction in al., 2014467 T2DM age 50.3 ± 1.9 years, fat mass at 24 weeks; reduced fasting mean BMI 33.5 ± insulin and HOMA-IR 0.8 kg/m2)

Muñoz et al., 52 weeks, no controls , 22 with 79 (44 F, 35 M, 18 early explants: 8 device migration, 5 device obstruction, Significant EWL; no significant 2014450 T2DM mean age 35.4 ± 9.7 2 abdominal pain, 1 liver abscess, 1 upper GI bleed, 1 difference in EWL between patients years, mean BMI cholangitis, 1 ulcerative colitis, 1 acute cholecystitis, 1 with and without T2DM. Data on 39 43± 5.6 kg/m2) patient request patients reported in448

Koehestanie et 52 weeks, RCT: 6 months of Treatment: 34 (13 8 adverse events requiring hospitalisation in device group– Significant weight loss at 24 weeks but al., 2014480 device + 6 months of post- F, 21 M, mean age 5 device-related, managed conservatively; 1 oesophageal no significant difference with control at explant lifestyle modification 49.5 years, mean perforation on removal requiring oesophageal tenting; 8 52 weeks; improved HbA1c at 24 weeks intervention vs. 52 weeks BMI 34.6 kg/m2) adverse events requiring hospitalisation in control group – but no significant difference with lifestyle modification 5 resolved without sequelae ; 2 early explants – 1 following control at 52 weeks Control: 39 (14 F, intervention only, 34 treatment, obstruction, 1 following abdominal pain 25 M, mean age 35 control, all with T2DM 49.0 years, mean BMI 36.8 kg/m2)

Koehestanie et 36 months, no controls, all with 5 (1 F, 4 M, mean No SAEs Significant weight loss at 24 weeks; al., 2015481 T2DM age 45 years, mean increased body weight between implant BMI 33 ± 1.3 kg/m2) periods; further significant weight loss Device inserted from 0-6 in second implant period; It is safe and months, patients followed-up for feasible to reimplant a DJBS 18 months and then device –re- implanted from 24-36 months

Authors/year Design Population Safety Outcomes Efficacy Outcomes Betzel et al., 12- 52 weeks, no controls 24 week group: 26 24 adverse events requiring early explant, 16 abdominal Significant weight loss in 24 week and 465 2015 (15 F, 11 M, mean pain, 5 GI bleeding, 2 pancreatitis, 1 hepatic abscess 52 week group; improved HbA1c in 24 12 week group: 8, 1 with T2DM age 50 ± 8.9 years, week group but no significant difference 24 week group: 26, all with mean BMI 35.6 ± in 52 week group T2DM 4.6 kg/m2) 52 week group: 118, 116 with 52 week group: 118 T2DM (52 F, 66 M, mean age 52 ± 7.5 years, mean BMI 35.1 ± 4.2 kg/m2)

Stratmann et al., 52 weeks, no controls, all with 18 (4 F, 14 M, mean 2 early explants: 1 abdominal pain and 2 device migration Significant EWL at 12 months without 2016451 T2DM age 50.1 ± 7.9 years, change in body fat percentage mean BMI 48.8 ± 8.5 kg/m2)

Kaválková et 13 months, no controls, all with 30 (10 F, 20 M, No SAEs. Unscheduled endoscopies performed to assess Significant decreases in weight, waist al., 2016469 T2DM mean age not acute complications as result of abdominal pain, dyspepsia and hip circumference; body fat reported, mean BMI and suspected upper GI bleeding. Nil bleeding or acute significantly reduced, with no change in 10 month device-dwell period 2 42.7 ± 1.2 kg/m ) complications found; No early explants lean body mass; fasting glucose, HbA1c, fasting insulin and HOMA-IR

Gollisch et al., 13 months, no control, all with 20 (14 F, 6 M, 1 early explant due to abdominal pain,1 early explant due to Significant reduction in body weight, 455 2017 T2DM median age 53.0 migration, 1 early explant due to requirement for BMI and HbA1c at 13 months (47.3-61.0) years, anticoagulants, 3 cases of obstruction resolved without early Trend toward reduction in daily insulin median BMI 41 (38- explantation units (p=0.05) 46) kg/m2)

Rohde et al., 26 weeks, 10 patients with 24 (gender not 3 early explants in NGT group, and 2 in T2DM group – due No significant weight loss but 2017456 obesity and T2DM and 9 reported, treatment to abdominal pain and participant request. For 1 early significant change in body fat mass and patients with obesity with NGT median age 50 (40- explant, sleeve obstruction due to sleeve invagination was visceral fat mass; in the T2DM group 67), BMI 38.6 noted at explant; Adverse events that did not require early (31.6-40.5) explant: 1 fever, 8 light-moderate abdominal pain, 5 light- moderate nausea, 4 single episodes of vomiting

Authors/year Design Population Safety Outcomes Efficacy Outcomes Betzel et al., 52 week post-explant follow-up Completer: 59 (27 Post-explant study only – no SAEs Significant decreases in body weight 2017453 study, 59 completers, 30 lost-to- F, 32 M, mean age and BMI remained; statistically follow-up 52 ± 8 years, mean significant increases in HbA1c and BMI 34.4 ± 3.5 fasting glucose kg/m2) Non-completer: 30 (12 F, 18 M mean age 50 ± 7 years, mean BMI 35.8 ± 5.6 kg/m2)

Vilarrasa et al., 96 weeks, no controls, HbA1c 21 (9 F, 12 M, mean 2 adverse events requiring early explant: 1 abdominal pain,1 Reduction in HbA1c was not correlated 482 2017 ≥8%, and on insulin age 54.1 ± 9.5 years, cholecystitis with weight loss; weight and HbA1c at 1 mean BMI 33.4 ± month were predictive of HbA1c at 12 48 week implant period, plus 48 2 1.9 kg/m ) months; HbA1c returned to baseline weeks of follow-up values 12 months after explant

Betzel et al., 52 weeks, no controls, all with 185 (90 F, 95 M, 57 early explants (30.8%). 34 were due to device Significant decreases in body weight 452 2017 T2DM mean age 52 ± 8 intolerance, 1 due to uterus carcinoma, 2 due to requirement and HbA1c years, mean BMI to start anticoagulants. 20 were due to adverse events; 39 35.1 ± 4.3 kg/m2) adverse events requiring hospitalisation including 4 perforations of the anchor into the stomach, 12 bleeds with device in situ, 2 bleeds following explant, 6 pancreatitis, 4 liver abscess, and 2 procedure related (oesophageal lesion/perforation)

Forner et al., 52 weeks, no controls, 38 with 114 (47 F. 67 M, 28 (24%) early explants: 18 due adverse events (1 nausea Significant decrease in body weight 2017454 T2DM mean age 51 ± 13 and vomiting, 6 severe pain, 4 obstruction, 3 GI No significant change in HbA1c, but years, mean BMI 39 haemorrhage, 1 diarrhoea, 2 liver abscess, 1 pancreatitis) reduction/cessation of insulin in 80% off ± 6 kg/m2) 74% experienced some adverse event – 46% of which were those on insulin. mild Post-explant weight regain in 72% of patients up to 6 months and 69% beyond 6 months

Authors/year Design Population Safety Outcomes Efficacy Outcomes van Rijn et al., 26 or 52 weeks of DJBS 28 (11 F, 17 M, 4 early explants in first 6 months – 1 obstruction, 3 Significant decrease in body weight, 457 2017 treatment, median age 50 (46- abdominal pain HbA1c and FPG at 6 months, stable at 12 56), median BMI months 26 weeks of dietary treatment 4 early explants between 6 and 12 months– 1 sleeve 37.0 (33.0-42.9) plus 26 weeks follow-up prior in invagination, 1 obstruction, 1 migration, 1 vomiting Significant increase in body weight and kg/m2) control arm fasting insulin 6 months post-explant Mild GI symptoms in 78.6% of patients. Hypoglycaemia in 10 completed 26 17.9% of patients Glucose lowering medication reduced in weeks of DJBS 54% of patients treatment, 18 completed 52 weeks of DJBS treatment

Quezada et al., 12- 144 weeks, no controls 80 (55 F, 25 M, 55 patients with SAEs, a total of 55 device-related SAEs. 2 Significant reduction in body weight 2018461 mean age 35.4 ± 10 upper oesophageal perforations during attempted device and waist circumference up to 96 weeks, Baseline: 80, 17 with T2DM years, mean BMI explant no significant difference at 144 weeks 52 weeks: 72, T2DM status not 42.2 ± 5 kg/m2) compared to baseline 23 early explants: 2 abdominal pain, 9 device migration, 4 specified upper GI bleeding, 3 liver abscess, 2 device obstruction, 1 Significant reduction in HbA1c , insulin 72 weeks: 56, T2DM status not cholangitis, 1 ulcerative colitis, 1 pancreatitis and HOMA-IR up to 96 weeks, no specified significant difference at 144 weeks Most SAEs occurred between 52 and 96 weeks compared to baseline 96 weeks: 43, T2DM status not 95% of patients reported mild adverse events (nausea, pain specified No change in glucose lowering and vomiting) medication in patients with T2DM 144 weeks: 11, 2 with T2DM (n=17)

Ryder et al., 52 weeks, no controls 12 (8 F. 4 M, mean 3 GI haemorrhages (2 devices explanted early), 1 hepatic Significant decrease in body weight, 464 2018 age 52.4 ± 9.3 years, abscess (device explanted early) HbA1c and daily insulin dose at 52 All with T2DM mean BMI 41.7 ± weeks. 33% of those on insulin ceased 9.8 kg/m2) insulin. Improvements maintained 6 months post-explant. 75% of participants on insulin

Authors/year Design Population Safety Outcomes Efficacy Outcomes Patel et al., 72 weeks, no controls 45 (23 F. 22 M, 6 early explants – 1 melaena, 1 migration, 4 medical Significant decrease in body weight, 460 2018 mean age 49.9 ± 7.9 conditions contraindicating continued implant HbA1c, fasting glucose and insulin at 52 All with T2DM years, mean BMI weeks. No significant change in fasting

40.0 ± 5.8 kg/m2) glucose or insulin between explant and 6 months post-explant. 28% reduced or discontinued metformin at 12 months. 31% reduced or discontinued sulphonylureas at 12 months. Riedel et al., Up to 46 months, no controls 77 (51 F, 26 M, Abdominal pain reported in 36.4% of patients. 5 cases of In entire cohort: 2018462 mean age 53.1 ± liner obstruction, 5 cases of device migration, 1 case of 75 with T2DM in full cohort, 31 Significant reduction in BMI was 10.4 years, mean hepatic abscess with T2DM in 1 year follow-up maintained post-explant, HbA1c was not BMI 42.0 ± 6.8 cohort significantly different to baseline post- kg/m2) explant 1 year follow-up: 32 In cohort with 1 year follow-up: (22 F, 10 M, mean age 51.1 ± 11.1 BMI and HbA1c still significantly years, mean BMI reduced compared to baseline. 43.2 ± 7.8 kg/m2)

Riedel et al., 52 weeks, no controls 66 (46 F, 20 M, At explant visit, 1 case of obstruction, 1 case of device Significant decrease in body weight, 463 2018 mean age 51.8 ± migration, 1 case of suspected hepatic abscess and 1 case of BMI, HbA1c, daily insulin units at 52 62 with T2DM 10.2 years, mean cardiac decompensation. weeks BMI: 43.4 ± 6.5 kg/m2)

Laubner et al., 48 weeks, no controls 111 (67 F, 44 M, Abdominal pain reported in 27.7% of patients. 2 cases of Significant decrease in body weight, 459 2018 mean age 51.9 ± 9.0 liner obstruction, 6 cases of device migration, 4 cases of waist circumference and HbA1c All with T2DM; 64% on insulin years, mean BMI: hepatic abscess,1 case of GI bleeding (with aspirin usage), 51% able to reduce usage of oral 42.6 ± 6.8 kg/m2) 1 case of biliary colic, 1 case of oesophageal lesions hypoglycaemics or GLP-1 receptor agonists. Significant reduction in insulin usage

Authors/year Design Population Safety Outcomes Efficacy Outcomes Younas et al., 52 weeks, bariatric surgery DJBS group:11 (4 F, No SAEs Significant reduction in body weight 2018483 group controls 7 M, median age 46 (median TBWL was 12%). Significant (34-68) years, reduction in HbA1c 7 with T2DM in DJBS group. 6 median BMI: 61 with T2DM in surgery group (50-70) kg/m2) 57% of patients with T2DM could Surgery group:11 (4 reduce/cease glucose lowering F, 7 M, median age medication after 12 months treatment 48 (36-66) years, with DJBS median BMI: 58 (51-85) kg/m2) Deutsch et al., 24 months, no controls 51 (22 F, 29 M, 6 early explants due to discomfort, 2 cases of hepatic Body weight and BMI were 2018458 mean age 57.8 ± 8.0 abscess, 2 cases of major bleeding, 1 case of obstruction, 1 significantly reduced, and remained All with T2DM, 40.5% on years, mean BMI case of gastric outlet obstruction significantly reduced compared to insulin 2 37.3 ± 4.9 kg/m ) baseline 1 year post-explant. HbA1c was significantly reduced at device explant, but not at 1 year post-explant 33% of patients on insulin were able to discontinue insulin during treatment (n=4) or follow-up (n=1) van Rijn et al., Four year post-explant follow- 15 (gender not Post-explant study only – no SAEs Five of 15 patients underwent bariatric 2019472 up after 6-12 months treatment reported, mean age surgery in follow-up (4 RYGB and 1 with DJBS not reported, LAGB) median BMI 33.1 In 10 remaining patients, there was a (32.3-38.5) kg/m2) trend toward reduced body weight at 4 years post-explant. No persistent improvement in metabolic parameters

Leventi et al., 28 months, no controls 5 (1F, 4 M, mean No SAEs Body weight, BMI and HbA1c were 2019484 age: 48.6±21.2 reduced in first treatment period. Two 12 month implantation Some patients has pseudo-polyps however; re-implantation years, mean BMI: Increase in all parameters 3 months periods separated by 4 months was safe 40.9±10.3 kg/m2) post-explant. Decreased again after reimplantation. No statistics due to small number.

Abbreviations: BMI; body mass index, DJBS; duodenal-jejunal bypass sleeve, EWL; excess weight loss, F; female, FPG; fasting plasma glucose, HbA1c; glycated haemoglobin, HOMA-IR; homeostatic model of assessment – insulin resistance, GI; gastrointestinal, M; male, NGT; normal glucose tolerance, LAGB: laparoscopic adjustable gastric banding, LFD; low fat diet, RCT; randomised controlled trial, RYGB; Roux en Y gastric bypass, SAE; serious adverse event, T2DM; Type 2 diabetes mellitus

A limited number of studies have evaluated possible mechanisms of action for the DJBS in inducing weight loss and metabolic improvements. Due to the design of the device and extrapolation from the bariatric surgery literature, it has been proposed that the DJBS causes weight loss via reduced absorption of calories, although this has not been investigated446. Two studies have evaluated device- related changes in gastric emptying kinetics. de Moura et al. conducted a small study (n=25), finding gastric emptying time increased while the device was in situ, and there was a trend toward a relationship between gastric emptying time and weight loss485. Conversely, Rohde et al. found no change in gastric emptying kinetics in a cohort of 19 patients with obesity (10 with normal glucose tolerance and nine with T2DM), with a median (range) weight loss of 6.8 (2.5-16.1) kg in the normal glucose tolerance group and 6.2 (0.7-11.0) kg in the T2DM group over six months456.

Rapid improvements in glycaemic control are observed following DJBS implantation. One study has assessed changes in hepatic insulin sensitivity following DJBS implantation in seven patients with obesity (six with T2DM) via the hyperinsulinaemic-euglycaemic clamp method486. Calorie intake was controlled via a VLCD throughout. A clamp was performed at baseline, after one week of the VLCD prior to device implantation and again one week after device implantation. There was a significant reduction in hepatic glucose production following one week of a VLCD, compared to baseline. There was no significant difference in pre-implantation (VLCD) and post-implant hepatic glucose production, despite similar weight loss occurring between time-points. These results suggest energy restriction may be the key driver of improvements in hepatic insulin sensitivity.

To date, seven studies have evaluated changes in appetite control biomarkers as a possible mechanism, with conflicting findings. GLP-1 has been suggested to significantly increase both in the fasting state451 and postprandially456, 476, which would increase satiety. Conversely, other studies have reported no significant change in the fasting state467 or postprandially469, 482 during DJBS treatment. A significant increase in fasting PYY, which would increase satiety, has been reported after six to 12 months of DJBS treatment477, 482. Two studies have reported postprandial changes in PYY with one study reporting a significant increase477 and one reporting no significant change456.

These studies also reported gut hormone profiles associated with increased hunger. A significant increase in fasting plasma ghrelin, leading to increased hunger, in patients with obesity and T2DM was reported after six months of DJBS dwell time467, 477and 12 months of DJBS dwell482. Conversely, studies have also reported a reduction in both fasting451 and postprandial ghrelin response477. GIP has been found to significantly decrease postprandially in one study476, which would increase hunger, whilst no change was reported in three other studies451, 456, 469. A trend towards a significant decrease in the fasting state467 after four weeks of DJBS treatment has also been reported. Another study reported no significant change in fasting GIP in a cohort of 18 patients with obesity 45

and T2DM451. A significant decrease in postprandial glucagon response, which would increase hunger, has been reported in three studies456, 469, 476. Additionally, a significant increase in adiponectin was reported 18 patients with obesity and T2DM451.

One study has reported on the impact of DJBS treatment on the stool microbiota in 14 patients479. Between baseline and six months post-implant, a trend towards an increase in alpha diversity was observed. A significant increase in Bacteroides uniformis et rel., two Lactobacillus species, three members of the Clostridium cluster IV, Veillonella and five members of the Proteobacteria phylum, including Klebsiella pneumoniae et rel., Serratia and Escherichia coli et rel. There was a significant reduction in Dialister, Megamonas hypermegale et rel. and five members of the Clostridium cluster XIVa. In this cohort, weight loss was negatively correlated with the RA of the Clostridium cluster XIVa and other members of the Firmicutes phylum on redundancy analysis. A positive correlation between EWL and the RA of the phyla Proteobacteria and Bacteroidetes and the Bacilli order was observed. Improvements in fasting glucose and HbA1c were correlated with increases in Ruminococcus callidus et rel. and Oscillospira guillermondii et rel., respectively.

Changes in the bile acid pool and signalling have also been observed following DJBS treatment. Kaválková et al. reported a significant increase in total plasma bile acids at 10 months post-DJBS implantation469. Recently, Samuel van Nierop et al. have reported a significant increase in fasting and postprandial total bile acids, after 24 weeks treatment with a DJBS in 17 patients478. They also assessed bile salt fraction, reporting this increase was driven by an increase in unconjugated bile acids, and specifically taurine conjugated bile acids. A significant increase in fasting and postprandial cholic, deoxycholic and chenodeoxycholic acids, but not ursodeoxycholic acid was also observed. The ratio of cholic to deoxycholic acid was also significantly increased, suggesting mediation by changes in the GI microbiota. There was no significant change in any of these parameters one week after device implantation, suggesting a gradual increase in the bile acid pool. Both studies also assessed the impact of DJBS treatment on fibroblast growth factor (FGF) 19, a molecule involved in suppression of hepatic bile acid synthesis via FXR signalling487, with conflicting results. Kaválková et al. reported a significant increase in fasting plasma FGF19, without a reduction in total bile acids469. At three months post-explant, there was no persistent increase in either fasting total bile acids or FGF-19. Samuel van Nierop et al. reported no significant change in FGF19 at one or 24 weeks post DJBS implantation478.

1.4 SUMMARY AND AIMS In summary, obesity and related comorbidities are a growing challenge to the health of Western societies. While primary prevention should have a high priority and long-term lifestyle interventions may be effective for a limited number of individuals, bariatric surgery is currently the most effective long-term treatment option. Despite this, surgical procedures are grossly underutilised, in the context of the number of eligible patients. Therefore, endoscopic bariatric therapies have been developed to provide additional, less invasive treatment options. The DJBS is a 60 cm endoluminal sleeve device, which is proposed to act as a RYGB mimetic. The mechanisms of RYGB for inducing weight loss are complex, with impacts on energy expenditure, food intake, glycaemic control, GI function, the GI microbiota and inflammation. Similar to the recent paradigm shift in our understanding of the these mechanisms away from simple malabsorption to more broad reaching effects, the DJBS deserves the same thorough investigation if it is to be clinically useful.

1.4.1 Aims and Hypotheses This thesis is divided into four experimental chapters which broadly aim to characterise potential mechanisms of action of the DJBS for inducing weight loss and improvements in glycaemic control, and how these might relate to device tolerability.

Specific aims were to:

• Characterise the clinical response to DJBS treatment in our centre, and to assess novel features of clinical efficacy such as dietary intake, health-related quality of life (HRQoL) and functional exercise capacity; • Determine the effect of DJBS implantation on gastric emptying and fat absorption and provide a detailed characterisation of GI symptoms in response to treatment; • Characterise changes in the luminal and MAM following DJBS treatment, characterise the growth of any microbes on the device surface and determine whether these findings relate to device efficacy and tolerability outcomes; and • Define changes in the local and systemic immune response to DJBS treatment and investigate whether these findings are linked to device tolerability.

The key hypotheses are as follows:

Treatment with a DJBS for up to 48 weeks will:

• Result in clinically relevant and statistically significant weight loss and improvements in metabolic parameters that capture the severity of the metabolic syndrome; • Delay gastric emptying and reduce intraluminal lipolytic activity;

• Alter the gastric and duodenal MAM and result in a distinct microbial community forming on the mucosa-adjacent device surface; • Impact the morphology of the villi in the duodenum; and reduce systemic inflammation.

Chapter 2: Methods

2.1 STUDY SUMMARY This study was approved by the Metro South Human Research Ethics Committee (HREC/15/QPAH/246; see Appendix 1.1) and was prospectively registered on the Australian New Zealand Clinical Trials Registry (ACTRN12615001229561).

At the time of study commencement, the DJBS was commercially available on the Australian market and appeared on the ATRG (ARTG ID: 186462). We designed a trial with the aim of recruiting 50 patients, with varying levels of intervention (dietary management) pre- and post-implant. However, in October 2016, the ARTG listing for the device was cancelled by the Therapeutic Goods Administration. After liaison with the institutional ethics board and key stakeholders, it was decided that no further devices would be implanted, limiting recruitment to 15. Those that were randomised to a six month delay period prior to implantation did not undergo treatment with the DJBS. Ethics approval was also gained to retrospectively include data from four patients who had undergone clinical treatment with the DJBS, and a limited number of investigations in a pilot study in 2015-16. As a result, post-explant dietetics follow-up was standardised to two sessions, at 26 and 52 weeks post-explant for all patients, to match standard post-bariatric surgery follow-up intervals and due to resource allocation.

Patients underwent investigation of clinical and metabolic parameters (including weight; anthropometry; body composition; peripheral blood markers of metabolic health and nutrition status; hepatic fibrosis and steatosis; dietary intake and HRQoL), GI function (gastric emptying, intraluminal lipolytic activity and GI symptoms), the GI microbiota (mucosa- and sleeve-associated and stool microbiota composition and functional profiles and bacterial density); and immunological parameters (duodenal immune cell infiltration and morphology and peripheral blood immune cell phenotype and activation).

Figure 2.1 details the consort flow diagram from receipt of referral to data analysis and study exit for the final cohort (n=19).

Figure 2.1: Consort diagram of study recruitment.

2.2 THE DJBS INTERVENTION

2.2.1 Recruitment Patients were referred from the Complex Obesity Service at the PAH, private obesity physicians and local general practitioners.

Upon receipt of referral, the hospital medical records of the patient were screened to determine initial eligibility. Suitable patients were then screened over the phone. For patients without a hospital medical record, phone screening was the first point of medical screening. During phone screening, patients were asked to report anthropometric data, current medications and medical history, with particular emphasis on the study exclusion criteria.

Patients were then invited to attend a small group information session detailing the device, procedure and safety and efficacy data from other studies. Informed consent was provided (see Appendix 1.2). Recruitment commenced in July 2015. The last patients were randomised to immediate treatment in July 2016 and recruitment was ceased in October 2016. The consort flow diagram (Figure 2.1) details the volume of patients screened and reasons for exclusion.

2.2.2 Inclusion and Exclusion Criteria The criteria for inclusion in the study were:

• age between 18 and 65 years; • BMI of ≥ 35 kg/m2; • T2DM managed without exogenous insulin; • functional level (as measured by Eastern Cooperative Oncology Group score) of equal or less than 2. The range of score for inclusion were 0 (“Fully active, able to carry on all pre-disease performance without restriction”) to 2 (“ambulatory and capable of all self-care but unable to carry out any work activities; up and about more than 50% of waking hours”); • English speaking with an ability to provide informed consent; and • ability to participate in a 3 year clinical trial.

The exclusion criteria were:

• previous GI surgery; • structural or mucosal GI abnormality that would impede device insertion; • planned abdominal surgery during the study period; • any known inflammatory disease of the GIT; • requirement to use anti-coagulant and/or anti-platelet therapy or non-steroidal anti-inflammatory drugs during the intervention period; • personal or family history of connective tissue disease; • inadequate cardiopulmonary reserve for anaesthesia; • any active malignancy; • significant coronary artery disease; • any medical condition with a likely survival of 5 years or less; • pregnancy or desire to conceive during the study period; • chronic pancreatitis; • antibiotic use at the time of initial endoscopy; and • psychiatric comorbidity likely to interfere with trial safety.

2.2.3 Procedure Details DJBS devices were endoscopically implanted under general anaesthetic by trained gastroenterologists (GH or GR) with the assistance of specially trained nursing staff. The device was deployed using a capsule delivered by a proprietary catheter system. In brief, the delivery capsule was positioned in the pylorus and the sleeve was deployed by releasing the tracking ball. Fluoroscopic guidance was used to ensure correct positioning prior to anchor deployment. Once correct positioning was confirmed, the anchor was deployed out of the capsule and fixed via a self-expanding stent.

To minimise the risk of bleeding following the procedure, patients were prescribed a proton pump inhibitor (PPI; 40 mg pantoprazole) to be taken daily for the 48 week implant period and for four weeks post-explant.

During the endoscopy procedure, prior to device placement, biopsies were taken from the gastric antrum for the detection of Helicobacter pylori infection. Samples were processed, stained with haematoxylin and eosin and read by anatomical pathologists from Queensland Pathology. Patients with a positive result underwent PPI-based triple therapy for eradication.

For device removal, endoscopic retrieval of the device was conducted by a trained gastroenterologist (GH or GR) under general anaesthetic using a proprietary retrieval device with the assistance of specially trained nursing staff. The anchor was collapsed using a grasping device and anchor barbs were encapsulated in the retrieval hood. Fluoroscopic guidance was used to ensure all barbs were encapsulated prior to removal.

2.2.4 Safety Monitoring All patients had monthly safety blood tests included fasting glucose, iron studies, full blood count, liver and kidney function and CRP. Blood test results were reviewed by a gastroenterologist. At scheduled clinical assessments, the gastroenterologist also screened for any medical symptoms requiring further investigation. SAEs were defined as any event resulting in hospital admission.

2.3 CLINICAL INVESTIGATIONS

2.3.1 Clinical Observations Height was measured at baseline on a stadiometer in metres and centimetres, rounded to the nearest centimetre. Weight was measured on the same set of calibrated digital scales (Model GL-6000; G-Tech International, Gyeonggi-do, Korea) on all occasions. Patients were weighed fully clothed without shoes, measured to the nearest 0.1 kg. Baseline weight was measured on the day of device insertion. All patients were weighed at each clinical review. Final implant period weight was measured on the day of device removal.

BMI was calculated as:

Weight (kg)/ (height (m))2

TBWL percentage was calculated at each time-point using the equation:

((Baseline weight (kg) – current weight (kg)) / baseline weight (kg) x 100)

EWL was calculated as:

((Baseline weight (kg) – current weight (kg) / (baseline weight (kg) – weight at BMI of 24.9 kg/m2)) x 100)

Waist circumference was measured at the level of the umbilicus with the tape kept horizontal, rounded to the nearest 0.5 cm. Hip circumference was measured at the level of the iliac crest with the tape kept horizontal, rounded to the nearest 0.5 cm. Neck circumference was measured at the midpoint of

the neck, between the mid-cervical spine and the mid-anterior neck, rounded to the nearest 0.5 cm. All measurements were conducted with a stretch resistant tape measure.

Systolic and diastolic blood pressure were measured in the seated position as part of routine clinical care during follow-up visits and in the supine position on the day of device insertion using the outpatient clinic dynamap (Welych Allyn, NY, USA).

2.3.2 Dual Energy X-ray Absorptiometry Bone mineral density (BMD) and body composition were measured at baseline, explant and one year post-explant at the PAH Department of Radiology using full body Dual Energy X-ray Absorptiometry (DEXA; GE Lunar Prodigy Advance, GE Healthcare, WI, USA). Total body fat percentage, lean mass and fat mass were reported. Total BMD (g/cm2) of the lumbar spine (L1-L4) and the proximal femur (left and right) was assessed. At these sites, both a T-score and a Z-score was calculated. A BMD T score compares the BMD result with the peak BMD for a healthy young adult. A BMD Z score compares the BMD result with that of age and gender matched individuals. The threshold for normal BMD is a T-score above -1.0488.

2.3.3 Clinical Blood Investigations As described above, a monthly ‘safety’ panel of fasting glucose, iron studies, CRP, liver and kidney function parameters and full blood count was assessed. Additionally, at baseline and every 16 weeks during the study, markers of glycaemic control (fasting insulin and HbA1c), vitamin and mineral status

(vitamin B12, folate, retinol, 25-hydroxyvitamin D, alpha-tocopherol and international normalised ratio, copper, selenium and zinc) and fasting lipid profile (total cholesterol, triglycerides, HDL- and low-density lipoprotein (LDL) cholesterol) were assessed. The full panel of biomarkers was repeated at one year post-explant. ALT results (from the liver enzyme panel) were compared to gender specific ‘healthy’ upper limit of <31 U/L for males and <20 U/L for females489. Clinical reference ranges derived from Pathology Queensland were used for all other parameters.

All blood test parameters were measured by Pathology Queensland using the following platforms: UniCel® DxC 800 Biochemical Analyser (Beckman Coulter, CA, USA) (CRP, transferrin, transferrin saturation, fasting lipids, fasting glucose, liver enzymes); AU480 Chemical Analyser (Beckman Coulter) (serum zinc), XE-5000 Automated Hematology System (Sysmex, Kobe, Japan) (full blood count); Variant™ II (Bio-Rad, CA, USA) (HbA1c), ACL TOP 700 (Werfen, Barcelona, Spain) (international normalised ratio), 280Z Atomic Absorption Spectrometer (Varian, CA, USA) (serum copper and selenium), UniCel® DxI 800 Immunoassay System (Beckman Coulter) (ferritin, fasting insulin, folate, vitamin B12). Retinol and alpha-tocopherol concentrations were measured by high

performance liquid chromatography. 25-hydroxyvitamin D concentrations were measured by mass spectrometry (Acquity Premier XE Mass Spectrometer, Waters, MA, USA).

2.3.4 Transient Hepatic Elastography (Fibroscan®) Hepatic fibrosis was estimated as liver stiffness (kilopascals; kPa) measured by transient hepatic elastography (Fibroscan®; Echosens, Paris, France) with the patient in a dorsal decubitus position and the probe placed along the right mid-axillary line. Scan results were considered valid where at least ten valid measures were obtained and the interquartile range was less than 30%. Measures were performed using the XL probe following a three hour fast at baseline; 24 weeks post-implant; following device explant and one year post-explant by trained operators. The XL probe has been validated for the determination of hepatic stiffness in obese individuals490. The CAP (decibels/min) was used as an indicator of intrahepatic fat.

2.3.5 Dietary Intake Assessment Dietary intake was assessed by 24 hour recall. Patients were interviewed either in person or via phone consult. Detailed records of food and beverage intake over the preceding 24 hours were obtained. 24 hour recalls were obtained at baseline, and thereafter monthly for the duration of the implantation period. Following device removal, an additional 24 hour recall was recorded at six months and one year post-explant. On each occasion, the 24 hour recall was used as a basis for individualised dietary counselling provided by an Accredited Practising Dietitian based on the area of greatest concern or possible improvement. In this scenario, a small number of practical alternatives were discussed and the patient was advised to focus on this for one month. Dietary energy and macronutrient composition was analysed using FoodWorks 8 (Xyris Software, Brisbane, Australia).

2.3.6 Six Minute Walk Test The six minute walk test (6MWT) is a validated and reproducible investigation to assess submaximal functional exercise capacity in patients with obesity491. All participants completed the 6WMT on the same 30 metre point-to-point track at baseline, 24 weeks post-implant, at explant and one year post-explant. A measure of the maximum distance covered in the six minutes was recorded at each time-point. If patients stopped mid-way between the markers at the end of the six minutes, the distance walked between the markers was measured.

Results were compared both to predictive equations previously utilised in a Caucasian Australian population492 and baseline results. The following predictive equations were used to estimate 6MWT distance:

Males: 867 – (5.71 x age in years) + (1.03 x height in centimetres)

Females: 525 – (2.86 x age in years) + (2.71 x height in centimetres) – (6.22 x BMI).

2.3.7 Health and Wellbeing Questionnaires All health questionnaires are included in the Appendix 1.3. All questionnaires were completed at baseline, 24 weeks post-implant, at explant and one year post-explant.

2.3.7.1 SF-36 The SF-36 is a 36 item self-administered questionnaire that assesses patient-reported health within eight domains (physical functioning, role limitations due to physical health, pain, general health, role limitations due to emotional problems, energy/fatigue, emotional wellbeing and social functioning)493. Each domain was reported on a 0-100 scale. A lower score indicates poorer quality of life for that domain. Results were compared to gender-specific population norms for the Australian general population494 and an Australian population with obesity and T2DM495.

2.3.7.2 Epworth Sleepiness Scale The Epworth Sleepiness Scale is a short self-administered questionnaire used to assess daytime sleepiness. There are a total of eight questions, with a maximum score of 24. Scores of 10 or more are considered to be indicative of likely sleep apnoea496.

2.3.7.3 Active Australia Survey The Active Australia Survey was used to assess physical activity497. Traditionally, this survey is administered as a computer assisted telephone interview. For this study, it was conducted as a face-to-face interview with the patient. A composite score (sufftime) was used to assess physical activity in minutes over a week. The proportion of participants meeting national physical activity guidelines (150 minutes of moderate intensity activity per week)498 was also determined. The formula for the composite score was:

Sufftime = walking time (in minutes) + moderate activity time (in minutes) + (2 x vigorous activity time; in minutes)

2.3.7.4 Hospital Anxiety and Depression Scale The Hospital Anxiety and Depression Scale (HADS) is a self-administered 14 item ordinal scale. Seven of the items are associated with anxiety and seven to depression, with a maximum possible score of three for each item. The HADS has been demonstrated to be effective in assessing symptom

severity and for use as a diagnostic tool for depression and anxiety in the general population499. It has also been used to assess improvements in anxiety and depression after bariatric surgery500, 501.

2.4 GASTROINTESTINAL FUNCTIONAL TESTING

2.4.1 Gastric Emptying The test was performed at baseline, eight weeks post-implant and within three weeks after device explant. The gastric emptying breath test utilises a stable, non-radioactive 13C isotope. Whilst naturally occurring, this isotope represents 1.11% of all atmospheric carbon and all measurements are associated with the baseline 13C enrichment in the breath, making this isotope appropriate for use in physiological testing502. The isotope used in this test, 13C octanoic acid (Eurisotop, Saint-Aubin, France), is a medium chain triglyceride. After ingestion, octanoic acid passes through the stomach and is rapidly absorbed in the small intestine. The absorbed octanoic acid 13 is oxidised and enters the body pool of CO2-HCO3. Thus, CO2 is exhaled and can be quantified in the breath.

Patients fasted overnight (from midnight) before the test. The test meal consisted of a scrambled egg where the yolk has been injected with 91.4 µL 13C octanoic acid. This was served with two slices of white bread, 12 g of butter (Western Star, Richmond, Australia) and 150 mL of water. This meal contained approximately 300 calories and 11.4 g of protein, 16.4 g of fat and 28.8 g of carbohydrate. A breath sample was taken for baseline measurements before the meal. After the test meal, breath samples were taken every 15 minutes during the first two hours and every 30 minutes for the last two hours of the test.

13C enrichment in the breath was determined by Isotope Ratio Infrared Spectrometry (IRIS; Wagner 13 Analysen Technik GmbH, Bremen, Germany). The kinetics of appearance of C in breath CO2 reflects the rate of gastric emptying of the solid phase of a meal503. The results were reported as gastric emptying lag time (tlag) and half time (t1/2). The normal reference range for gastric emptying lag time was 118±73 minutes.

2.4.2 Intraluminal Lipolytic Activity The test was performed at baseline, eight weeks post-implant and within three weeks after device explant. To ensure complete isotope clearance, this test and the gastric emptying breath test were not performed within seven days of each other.

This non-invasive assessment of intraluminal lipolytic activity uses a 13C-labelled mixed triglyceride substrate, 1, 3-distearoyl-2-13C-carboxy-octanoyl glycerol (Eurisotop, Saint-Aubin, France). The test

was undertaken following an overnight fast. The test meal consisted of 200 mg of the 13C labelled substrate mixed into 16 g of butter. This was served with two pieces of white bread and 150 mL of water. This meal contained approximately 260 calories and 5.1 grams of protein, 14.1 grams of fat and 28.2 grams of carbohydrate. The substrate contains a 13C labelled medium chain fatty acid molecule, octanoic acid, at the sn-2 position101. A long chain fatty acid, stearic acid, is located at the sn-1 and sn-3 position. The two stearoyl chains must be hydrolysed for the labelled 13C to be absorbed. The rate limiting enzyme for this reaction is pancreatic lipase. The absorbed substrate is 13 oxidised and enters the body pool of CO2-HCO3. Thus, CO2 is exhaled and can be quantified in the breath. A breath sample was taken before the test meal for baseline measurements and in the following six hours after the test meal, breath samples were taken every 30 minutes. 13C enrichment in the breath was determined by IRIS. The cumulative percentage of 13C recovered in breath during the six hour collection period was used as the reporting parameter.

To exclude concomitant PEI, faecal elastase (an enzyme produced by the exocrine pancreas) was assessed at baseline, eight weeks post-implant and within three weeks after device explant by Pathology Queensland using a commercial enzyme-linked immunosorbent assay (ELISA) kit (ScheBo Biotech AG, Giessen, Germany). The sensitivity and specificity of this test for the diagnosis of PEI is >90%504.

2.4.3 Standardised Nutrient Challenge Test This tool was used to assess meal-related symptoms in a standardised fashion. Patients reported their baseline severity of five meal-related symptoms (fullness, abdominal pain, nausea, retrosternal/abdominal burning and acid regurgitation) on a visual analogue scale121, 124. Patients were then asked to drink 200 mL of a liquid meal (Resource Plus, Nestlé) consisting of 5.5 g protein, 22.4 g carbohydrate and 4.5 g fat/100 mL every five minutes and report their symptoms on a visual analogue scale after each five minute interval, up to a maximum consumed volume of 600 mL. If patients could not consume the full volume, the test was ceased and a maximal score was applied for each symptom domain for all remaining time-points. The baseline value for each symptom was subtracted from the cumulative score for each symptom across the three post-ingestion time-points. A cumulative score of all symptoms was also calculated.

2.4.4 Structured Assessment of Gastrointestinal Symptoms The Structured Assessment of Gastrointestinal Symptoms (SAGIS) instrument assesses the severity of 22 distinct GI symptoms across five symptom domains. Individual symptoms are rated on a five point Likert scale - (0 = no problem, 1 = mild (“can be ignored”), 2 = moderate (“cannot be ignored but does not affect daily activities”), 3 = severe (“influencing your concentration on daily activities”) 59

and 4 = very severe problem (“markedly influences your daily activities and/or requires rest) over the last week. The questionnaire has been previously validated in the clinical setting505. The questionnaire was completed by each patient upon arrival for clinical assessment at baseline, four, eight, 16, 24 and 36 weeks post-implant and at device explant. At four (early post-implant) and 36 (late post-implant) weeks post-implant, symptoms were compared to baseline both individually and in clustered symptom domains. The five domains were epigastric pain (postprandial pain, epigastric pain, bloating, fullness, early satiety, retrosternal discomfort and abdominal cramps), diarrhoea and discomfort (diarrhoea, loose stools, urgency to defecate, pain prior to defecation, excessive gas flatulence and faecal incontinence), reflux (dysphagia, excessive belching and acid eructation), nausea/vomiting (sickness, nausea, vomiting, loss of appetite) and constipation (constipation and difficulty defecating).

2.5 GASTROINTESTINAL MICROBIOTA

2.5.1 Sample collection At the time of endoscopy at baseline (device implant) and at device explant, biopsy samples were taken from the gastric antrum mucosa and the second part of the duodenum for assessment of the MAM. Specifically, the duodenal biopsy taken at explant was taken from mucosa that had been covered by the device during the treatment period. Samples were stored in RNAlater® (Qiagen, Venlo, Netherlands), incubated at room temperature for 30 minutes and stored at -80°C.

Upon removal, DJBS devices were transferred in sterile surgical specimen containers from the endoscopy suite to the laboratory laminar flow biosafety cabinet for processing. Six specimens were collected from each patient’s device (see Figure 2.2 below). The device was separated into three segments of four cm in length: upper (just below the anchor), mid and lower. Each section was cut lengthways to create a flat surface from which to remove material. A sterile inoculation loop was used to scrape 50% of the material present on the mucosa-associated device surface into an Eppendorf tube containing 500 µL of sterile anaerobic phosphate-buffered saline (PBS). This procedure was repeated for the luminal surface. The Eppendorf tubes were centrifuged at room temperature for 10 minutes at 16,000 rpm to form a pellet. The supernatant was discarded and the pellet stored at -80°C.

Stool samples were collected using screw cap containers with an inbuilt sampling spoon. Samples were collected at baseline, eight weeks post-implant and within three weeks after device explant. Samples were frozen at -20°C and transferred to -80°C for long-term storage.

Figure 2.2: Device sections for genomic DNA extraction.

2.5.2 Lysate preparation for genomic DNA extraction from gastric and duodenal biopsies Genomic deoxyribonucleic acid (gDNA) was isolated using a modified repeated bead beating protocol initially detailed by Morrison and Yu506. Specimens were thawed on ice for 15 minutes and the biopsy tissue was transferred into 2 mL screw cap tubes with an O-ring (Quality Scientific Plastics, ThermoFisher Scientific, CA, USA) containing 0.4 g sterile zirconia beads (1:1 ratio of 0.1 mm and 1 mm). 300 µL of lysis buffer (500 mM sodium chloride, 50 mM Tris-hydrochloric acid pH 8.0, 50 mM ethylenediaminetetraacetic acid (EDTA), 4% w/v sodium dodecyl sulphate) was added and the specimen was homogenised at 5,000 rpm for 3x60 second intervals, with 15 second pauses between intervals (Precelleys 24; Bertin Technologies, Berlin, Germany). Samples were incubated at 70°C for 10 minutes, then centrifuged at 10°C for five minutes at 16,000 g (Centrifuge 5424 R, Eppendorf, Hamburg, Germany). Supernatant was removed and added to a fresh 1.5 mL Eppendorf tube. The lysis procedure was repeated with an additional 200 µL of lysis buffer, with a reduced incubation period of five minutes. The supernatant was then pooled for automated gDNA purification. Tissue-free (reagent only) control samples were processed in an identical manner, commencing from the homogenization step. All steps were performed in a laminar flow biosafety cabinet, utilising aseptic techniques. Samples were processed in a random order to reduce processing bias.

2.5.3 Lysate preparation for genomic DNA extraction from EndoBarrier device surfaces For lysate preparation, each pellet was resuspended in 300 µL of lysis buffer, which was transferred into a 2 mL screw cap tube with an O-ring (Quality Scientific Plastics) containing 0.4 g sterile zirconia beads (1:1 ratio of 0.1 mm and 1 mm). The lysis/gDNA extraction procedure was then performed in an identical manner to the biopsy samples (Section 2.5.2).

2.5.4 Lysate preparation for genomic DNA extraction from stool samples Stool specimens were thawed on ice for 25 minutes and 0.15-0.2 g of stool was transferred into 2 mL screw cap tubes with an O-ring (Quality Scientific Plastics) containing 0.4 g sterile zirconia beads (1:1 ratio of 0.1 mm and 1 mm). 400 µL of lysis buffer was added and the specimen was homogenised at 5,000 rpm for 3x60 second intervals, with 15 second pauses between intervals (Precelleys 24). Samples were incubated at 70°C for 10 minutes, then centrifuged at 10°C for five minutes at 16,000 g (Centrifuge 5424 R, Eppendorf). Supernatant was removed and added to a fresh 1.5 mL Eppendorf tube. The lysis procedure was repeated with an additional 300 µL of lysis buffer and 30 µL of Proteinase K (Qiagen) and incubated for 20 minutes at 56°C. The supernatant was pooled for automated purification. Tissue-free (reagent only) control samples were processed in an identical manner. Where possible, all steps were performed in a laminar flow biosafety cabinet, utilising aseptic techniques.

2.5.5 Purification of genomic DNA For all samples, gDNA was purified from lysates prepared as described above, using a Maxwell MDx AS2000 (Promega, WI, USA) and Maxwell Tissue DNA Purification Kits (AS1030, Promega). In brief, 400 µL of each lysate sample was added to the first well of a tissue purification cartridge. For each sample, a separate elution tube was prepared with 300 µL of elution buffer. Tissue purification cartridges and elution tubes were loaded into the MDx and the Standard Elution Volume tissue DNA protocol was applied. gDNA was eluted in approximately 200 µL of elution buffer, which was transferred to a 1.5 mL Eppendorf tube. This was placed into a magnetic stand for removal of any remaining beads from the isolation process. The supernatant was aliquotted into 50 µL volumes for storage at -80°C. gDNA was quantified using spectrophotometry (NanoDrop Lite®, ThermoFisher Scientific).

2.5.6 High-throughput sequencing to assess microbial communities Amplicon sequencing of the 16S rRNA gene was performed on gDNA extracted from biopsy, device and stool samples in order to profile microbial communities present. Library preparation for sequencing on the Illumina MiSeq platform (Illumina Inc., CA, USA) is detailed below (Figure 2.3).

Amplicon PCR PCR Clean-Up Barcoding PCR

Library DNA Normalising PCR Clean-Up Quantification and Pooling

Sequencing (Illumina Bioinformatics MiSeq)

Figure 2.3: Illumina MiSeq library preparation and analysis workflow.

Amplicon polymerase chain reaction (PCR) was conducted to selectively amplify the hypervariable regions (V6-V8) of the bacterial 16S rRNA gene from total gDNA, using primers tagged with Illumina adapters. The 917F and 1392R bacterial domain specific primers were used for biopsy and device samples. For stool samples, amplification was performed with the 926F and 1392R universal primers to enhance detection of archaea in addition to bacteria (primer details in Appendix 1.4). PCR template amounts varied with respect to the proportion of bacterial DNA present in the specimen. 100 ng and 20 ng of gDNA was added for biopsy samples, and stool/device samples, respectively. The PCR reaction and amplification protocol followed Illumina library preparation specifications (full details in Appendix 1.4). A 96-well plate format was used, with each plate including tissue-free reagent controls, a positive control (purified Escherichia coli gDNA) and a template-free negative control. PCR products were visualised on a 1% agarose gel using a 1 kb ladder (HyperLadder® 1 kb, Bioline, TN, USA). PCR amplicons were purified using a paramagnetic bead system (AMPure XP® beads, Beckman Coulter). In brief, PCR amplicons were bound to magnetic beads, washed in 80% ethanol and purified DNA was eluted in 10 mM Tris pH 8.5.

Barcoding of amplicons for each sample was performed using the Nextera XT v2 dual-index system (Kits A and B; Illumina) as per the manufacturer’s instructions. In brief, a PCR was performed in which 5 µL of purified amplicon PCR product was used as the template, with Nextera XT v2 primers used to barcode each sample. The final library was visualised on a 1% agarose gel using a 1 kb ladder (HyperLadder® 1 kb, Bioline) and purified using AMPure XP beads. Purified samples were quantified using the Quantifluor® dsDNA fluorimetry system (Promega). The concentration of DNA was normalised to 10 ng per sample and samples pooled, with 10 mM Tris pH 8.5 used to dilute DNA to a total volume of 480 µL (per 96 well plate). The pooled library was transferred to the Australian Centre for Ecogenomics (University of Queensland) for sequencing on the Illumina MiSeq Platform using the MiSeq Reagent Kit v3 (2 x 300 bp).

Bioinformatics analysis was performed by Dr Erin Shanahan using the Quantitative Insights into Microbial Ecology (QIIME) pipeline version 1.9.1, via the Virtual Box package507. Specifically, fastq files for the forward and reverse reads were first joined using the join_paired_ends.py command through the fastq-join method. The split_libraries_fastq.py command was then applied using a Phred quality threshold of Q20 (to remove low quality sequence reads). OTUs were assigned using the pick_open_reference_otus.py command508. The Greengenes database (version 13.8) was used as the reference database and a sequence similarity of 97% applied138. The resulting OTU table was chimera-checked using ChimeraSlayer and subsequently filtered to remove sequences with a RA of less than 0.05%509. The reagent control samples were concurrently processed to generate a list of specific “contaminant” OTUs. These specific OTUs were then filtered

from the patient samples to ensure OTUs present for each sample were representative of mucosal, device or stool microbial communities. All samples with a final read count of less than 500 sequence reads were also excluded from the OTU table due to insufficient sequence coverage.

2.5.7 Functional metagenomics analysis of 16S rRNA gene sequencing data Functional metagenomics data was predicted from the 16S rRNA marker gene using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software package142, accessed via the Galaxy web application. A compatible OTU table was created by closed- reference OTU picking at 97% alignment to the Greengenes database and conversion of the hdf5 biom file to a json biom file in QIIME. The compatible table was normalised by 16S rRNA gene copy number. A predicted metagenome was derived using the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database and summarised into functional pathways510.

2.5.8 Bacterial load assessment Bacterial load was assessed by quantitative PCR (qPCR) using gDNA from mucosal biopsy specimens. Both the human β-actin gene and the bacterial 16S rRNA gene were amplified using optimised primer sets (β-actin: forward: TCCGCAAAGACCTGTACGC; reverse: CAGTGAGGACCCTGGATGTG, 16S rRNA: 1114-forward: CGGCAACGAGCGCAACCC; 1221-reverse: CCATTGTAGCACGTGTGTAGCC). The qPCR reactions used 15 ng/µL of template DNA and Power SYBR Green Master Mix (ThermoFisher Scientific) and samples were analysed using the ViiA 7 Real-Time PCR system. Samples were compared to standards of known concentration (plasmids containing either the β-actin or 16S rRNA PCR product). Template free controls were used as negative controls. The standard curve was used for the quantification of gene copy number for β-actin and 16S rRNA. The ratio of 16S rRNA to human β-actin was used as a measure of bacterial density as described previously511.

2.6 IMMUNOLOGY ASSESSMENT

2.6.1 Histology At baseline and explant, duodenal mucosal biopsies were taken for routine histology, from sites adjacent to, and immediately following, those collected for characterisation of the GI microbiota. These biopsies were stored in formalin and sent to the PAH Department of Pathology, where specimen processing, staining and mounting was performed. Specifically, tissue was dehydrated in increasing concentrations of ethanol, washed in xylene and embedded in paraffin wax. Blocks were cut into 4 µm sections and mounted on glass slides. Tissue sections were stained with haematoxylin and eosin using a standard protocol. Assessment of villous architecture was conducted by anatomical pathologists from Queensland Pathology as part of routine clinical care. Approval to gain access to

these stored diagnostic slides of gastric and duodenal biopsy specimens was obtained from the Metro South Human Research Ethics Committee and Queensland Pathology. Slides were scanned using an Olympus Slidescanner (VS120; Olympus, Tokyo, Japan) to high resolution digital images at 40x magnification at the Translational Research Institute Microscopy Core Facility. Immune cell populations were then manually counted using cellSens Standard (version 1.8, Olympus) by a consultant anatomical pathologist (AM). Specifically, intraepithelial lymphocytes (IELs) were quantified per 500 enterocytes in one tissue section. Duodenal eosinophils were quantified in the same tissue section. Only eosinophils with an intact nucleus and granular structure in the mucosa were counted. Tissue section areas were measured using Visiopharm (magnification 20x, sensitivity 72%; Visiopharm, Hoersholm, Denmark) and eosinophil counts were expressed per mm2 of tissue.

2.6.2 PBMC Phenotyping and Cytokine Production Following PBMC Culture 2.6.2.1 PBMC Sample Preparation At baseline and explant, 12 mL of venous blood was collected in Vacutainer™ blood collection tubes with EDTA (BD Biosciences, NJ, USA). Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood diluted 1:1 in sterile PBS containing 2mM EDTA, using Ficoll-Paque Plus® (GE Healthcare; IL, USA). Samples then underwent centrifugation at 4°C at 400 x g for 30 minutes (without acceleration and brake). PBMCs were carefully obtained from the interface between the Ficoll and plasma layer. Isolated PBMCs were washed in PBS and resuspended in freezing media (40% foetal bovine serum (FBS), 10% dimethyl sulfoxide (DMSO), Roswell Park Memorial Institute (RPMI) media). The samples were stored in liquid nitrogen for long-term storage.

For PBMC assessment, cryopreserved cells were thawed rapidly in a water bath at 37°C and resuspended in a dropwise manner in 14 mL of complete media (L-glutamine free RPMI media 1640; ThermoFisher Scientific) supplemented with 10% FBS, 100 U/mL penicillin streptomycin, 2 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) and 2 mM GlutaMAX (ThermoFisher Scientific). Cells were centrifuged at 400 x g for seven minutes to form a pellet. The supernatant was discarded and the pellet resuspended with 14 mL PBS and centrifuged at 450 x g for five minutes. The supernatant was discarded and the pellet was resuspended in 3 mL complete media. Cells were counted at 10 x magnification using a haemocytometer. Cells suspended in complete media were centrifuged at 400 x g for five minutes, the supernatant discarded and the pellet resuspended in cold PBS to a concentration of one million cells per mL for flow cytometric analysis.

2.6.2.2 Flow Cytometry The PBMCs were prepared for flow cytometric analysis to phenotype T lymphocytes. A fixable viability stain (FSV700, BD Biosciences) was used to discriminate dead cells. Samples were then

washed with FACS buffer (3% FBS in PBS) and centrifuged at 2,500 x g for one minute. Supernatant was removed and discarded. Cells were resuspended in 100 µL FACS buffer and incubated with human Fc Block (BD Biosciences) for 10 minutes to prevent non-specific binding of antibodies to Fc.

A panel of 13 fluorophore conjugated anti-human antibodies were used to assess T-cell phenotype. Cells were incubated with anti-human CD45RO for 10 minutes, followed by anti-human C-C Chemokine Receptor (CCR) type 7. They were then incubated with a cocktail of 11 antibodies (CD3, CD4, CD8, CD45RA, α4, CCR4, Chemokine Receptor CXCR3, β7, CCR6, CD62L, CCR9) for 15 minutes. These were sourced from Becton Dickson Biosciences (CD3 – BV510, CD4 – FITC, CD8 – APC-H7, CD45RO – PE-Cy7, CD45RA – BV711, α4 – PE/CF594, CCR4 – BV421, CCR6 – BV785) and from BioLegend (CXCR3 – APC, CCR7 – BV605, PE-CD62L – PE, β7 – PE, CCR9 – PerCP/Cy5.5; BioLegend; CA, USA). Data was collected using a BD LSRFortessa (BD Biosciences) flow cytometer. Data was visualised and analysed using Kaluza Analysis Software (Beckman Coulter).

2.6.2.3 Cell Culture Stimulation of PBMCs with LPS was undertaken to assess cytokine production. After thawing, PBMCs suspended in complete media were incubated at 37°C for 30 minutes. After incubation, samples were centrifuged at 400 x g for six minutes and resuspended in culture media without FBS (L-glutamine free RPMI media 1640; ThermoFisher Scientific) supplemented with 100 U/mL penicillin streptomycin, 2 mM HEPES and 2 mM GlutaMAX (ThermoFisher Scientific). PBMCs were cultured for 24 hours at concentration of 1x106 cells/mL with or without stimulation by LPS (2 ng/mL). Following this incubation, the culture supernatant was collected and stored in 140 µL aliquots at -80°C for subsequent cytokine analysis.

2.6.2.4 Cytokine Analysis The concentrations of four cytokines (TNF-α, IL-6, IL-8 and IL-10) from cultured PBMC supernatants were measured by commercial ELISAs (BD Biosciences). For each patient sample (baseline and explant), both unstimulated and LPS-stimulated PBMC supernatant was assayed in duplicate. Samples, standards and controls were prepared in accordance with manufacturer specifications.

Flat bottom 96 well plates were coated with capture antibody diluted in coating buffer (0.1 M sodium carbonate, pH 9.5), sealed and incubated at 4°C overnight. Plates were washed in triplicate using wash buffer (PBS with 0.05% Tween-20; Sigma Aldrich, MI, USA) and blotted to remove excess. Plates were blocked with assay diluent (PBS with 10% FBS), covered and incubated at room

temperature for one hour. Lyophilised standards were reconstituted in deionised water. Samples were diluted in assay diluent (IL-8: 1:800; IL-6 1:200, IL-10: 1:5, TNF: 1:10). Assay diluent was used as the negative control. Standards, samples and controls (100 µL) were added into individual wells (in duplicate), covered and incubated at room temperature for two hours. Plates were washed five times with wash buffer and blotted to remove excess. 100 µL of working detector of diluted biotinylated antibody and streptavidin-horseradish peroxidase conjugate was added to each well, covered and incubated at room temperature for one hour. Plates were washed seven times with wash buffer, with a 30 second soak between washes and blotted to remove excess. A 1:1 solution of tetramethylbenzidine and hydrogen peroxide was added to each well (100 µL) and incubated uncovered for six minutes, resulting in a blue colour change for positive samples. A stop solution of sulphuric acid was added to each well (50 µL), stopping the reaction and resulting in a colour change from blue to yellow.

The absorbance of samples was immediately read at 450 nm on a spectrophotometer (Multiskan Go, ThermoFisher Scientific). A seven point standard curve was created using the mean value of the replicates for each standard, minus the mean absorbance of the replicates of the blank control. The concentration of the samples was derived from the standard curve, with the limit of detection set to the mean value of the lowest standard. Samples were multiplied by their dilution factor to give the final value and duplicates averaged to provide the final cytokine concentration per patient sample in pg/mL.

Chapter 3: Effect of the Duodenal-Jejunal Bypass Sleeve on Clinical Measures of Obesity and the Metabolic Syndrome, Dietary Intake and Lifestyle Factors

3.1 INTRODUCTION

In Australia, it is estimated that 28% of the adult population suffer from obesity512. It is also estimated that 18% of the population have some form of cardiovascular disease and 4.3% have T2DM5. The burden of these chronic diseases on the healthcare system is immense. In 2005, the estimated direct costs of obesity in Australia were AUD 8.3 billion513.

Intentional weight loss, achieved via lifestyle modification intervention, was associated with a 28% reduction in cardiovascular disease- and diabetes-related mortality in a large cohort of 5,000 individuals514. However, lifestyle modification intervention has been demonstrated to result in poor long-term outcomes, with approximately 97% of patients regaining all weight initially lost within 2-5 years271-273. Similarly, the long-term efficacy profile for pharmacotherapeutic treatments is poor278, 279, in part due to the limited number of treatments approved for long-term use.

Bariatric surgery is the most effective long-term solution for the treatment of obesity and is associated with significant improvements in obesity-related comorbidities. In a large systematic review and meta-analysis of 135,246 bariatric surgery patients, total remission of T2DM was achieved in 78% of patients515. In 2014-15, a total of 18,036 bariatric surgery procedures were performed in Australia282. Of these, 16.7% of primary surgeries listed T2DM as an additional diagnosis. However, despite the demonstrated benefits of surgery, very few individuals with obesity undergo bariatric surgery. In a prospective, population-based cohort study of 49,364 Australian adults with obesity, 312 underwent surgery, with 307 of these procedures occurring in the private setting416.

Thus, a treatment continuum exists, with escalation from lifestyle modification intervention and pharmacotherapy, which is readily available and is relatively cost effective but has low long-term efficacy, to bariatric surgery, which has a more limited availability and significant cost burden for the patient, but a more durable effect. To combat this treatment gap, new minimally invasive technologies have been developed such as endoscopic techniques, which can be used to mimic the physiological changes induced by bariatric surgery in the stomach or small intestine.

The DJBS is an endoscopically-placed weight loss aid implanted in the duodenum. Over the past 10 years, a number of studies have investigated the safety profile and clinical efficacy of the device, mostly in small cohorts of 20-30 participants. In a recent systematic review and meta-analysis including 352 patients with obesity and T2DM that underwent treatment with a DJBS for 12-52 weeks, DJBS treatment (mean duration 9.2 ± 3.1 months) was associated with a mean TBWL 516 of 18.9% . A 1.3% reduction in HbA1c was also reported in 388 patients (mean duration 8.4 ± 4.0 months)516. Other cohort studies have also demonstrated beneficial effects of DJBS treatment on cardiovascular disease risk454, 457, 459, 461-463, 469 and markers of NAFLD451, 455, 464, 470.

Despite these beneficial effects, significant weight regain has been reported during six to 12 months of post-explant follow-up454, 457, 461, 481 and deterioration in improvements in markers of glycaemic control have also been observed457, 460, 461, 473, 482. Sustained improvements in cardiovascular disease risk over three months post-explant have been observed in some462, but not other studies464, 469. Notably, one study has reported sustained improvements in body weight and glycaemic control at six months post-explant464, whilst three other studies have reported sustained improvements in body weight but not glycaemic control one year post-explant453, 458, 462. Two studies have reported improvements in plasma markers of NAFLD remain six to 12 months post-explant458, 470. Thus, the DJBS appears to be effective in the short-term, with metabolic effects greatly diminished once the device is removed.

3.1.1 Aims and Hypotheses

The overall aim, addressed in this chapter, was to characterise the clinical response of patients with obesity and T2DM to 48 weeks of treatment with a DJBS in our centre. Additionally, the study aimed to assess the durability of improvements in clinical or metabolic parameters for one year following device explant.

Specifically, anthropometric and metabolic markers, markers of hepatic fibrosis and steatosis, dietary intake, circulating micronutrient status and HRQoL were assessed.

It was hypothesised that 48 weeks of treatment with a DJBS would:

• Reduce body weight, BMI and waist, hip and neck circumference; • Reduce body fat percentage, measured by DEXA;

• Improve glycaemic control, measured by fasting plasma glucose and HbA1c; • Improve markers of NAFLD, measured by hepatic enzymes and transient elastography; • Reduced self-reported energy intake, measured by monthly 24 hour recalls; • Reduce circulating micronutrient concentrations, measured by plasma or serum concentrations of relevant micronutrients; and • Improve HRQoL, measured by survey data.

During the one year post-explant follow-up period, it was hypothesised:

• Body weight, BMI and anthropometric measures would increase, but would remain lower than baseline values; and • Improvements in glycaemic control would deteriorate, as compared to baseline.

3.2 METHODS

3.2.1 Safety Monitoring

Monthly safety blood tests were conducted and patients were screened for symptoms of infection or other conditions warranting further investigation at all clinical reviews as described in Chapter 2 Section 2.2.4. SAEs were defined as any event resulting in hospital admission. 3.2.2 Anthropometric Measures and Body Composition

Body weight, height, waist, hip and neck circumference were measured. BMI, TBWL and EWL were calculated as described in Chapter 2 Section 2.3.1. Blood pressure was measured at clinical reviews.

BMD and body composition was measured at baseline, explant and one year post-explant by the PAH Department of Radiology by full body DEXA (GE Lunar Prodigy Advance, GE Healthcare, Madison, WI, USA) as described in Chapter 2 Section 2.3.2. 3.2.3 Peripheral Blood Metabolic Profiles

Full metabolic biochemical screening was performed at 16-week intervals throughout the intervention period and at one year post-explant. Briefly, these included measures of glycaemic control, vitamin and mineral status and lipid profile. These were assessed in addition to a ‘safety’ panel of fasting glucose, iron studies, CRP, liver and kidney function parameters and full blood count that were measured monthly during the intervention period and at one year post-explant. All parameters were measured by Pathology Queensland. Further details are described in Chapter 2 Section 2.3.3.

Metabolic syndrome severity score (MetSSS) was calculated at baseline, explant and one year post-explant using an algorithm derived by Wiley and Carrington517. This algorithm requires the input of gender, waist circumference, systolic and diastolic blood pressure, HDL-cholesterol, serum triglycerides and fasting blood glucose. A z score of zero indicates average severity compared to an Australian cohort from the Healthy Hearts study, with a higher score (above 0) indicating greater severity. 3.2.4 Markers of Hepatic Fibrosis and Steatosis

Liver enzymes were measured monthly during the intervention period and at one year post-explant. ALT levels were compared to gender specific ‘healthy’ upper limits. Further details are described in Chapter 2 Section 2.3.3.

Hepatic fibrosis and steatosis were assessed by hepatic transient elastography (Fibroscan®) at baseline, 24 weeks, device explant and one year post-explant using the XL probe. Further details are described in Chapter 2 Section 2.3.4.

3.2.5 Dietary Intake and Nutrition Status

Dietary intake was measured by 24 hour recall on a monthly basis during the intervention period and at one year post-explant. Raw data was input into FoodWorks 8 (Xyris Software, Brisbane, Australia) for the assessment of dietary energy, fibre and macronutrient intake.

Serum markers of nutrition status (vitamins and minerals) were assessed at 16 week intervals throughout the intervention period and at one year post-explant. See Chapter 2 Sections 2.3.5 (dietary intake) and Section 2.3.3 (serum measures) for more detail. 3.2.6 Health-Related Quality of Life

At baseline, 24 weeks, device explant and one year post-explant, patients completed the SF-36 questionnaire, HADS, Epworth Sleepiness Scale and Active Australia Survey. At the same intervals, patients also completed a 6MWT. SF-36 results were compared to gender-specific population norms for the Australian general population494 and an Australian cohort with obesity and T2DM495. Further details can be found in Chapter 2 Section 2.3.6 and Section 2.3.7. 3.2.7 Statistical Analyses 3.2.7.1 Power Calculation

In the study, the sample size of 19 yielded a statistical power of 0.8 at the 0.05 (two-tailed) level of significance for a Cohen’s d effect size 0.75 or greater. 3.2.7.2 Data Analysis

All continuous variables were described as mean and standard deviation with missing data not imputed. All analyses were presented as pre- to post-intervention measurements due to missing data at intermediate time-points. Normality of distribution was assessed using the Shapiro-Wilk normality test. Normally distributed data was analysed using paired t-tests. Non-normally distributed data was assessed by the Wilcoxon signed rank test. Dependent on normality of distribution, Pearson or Spearman bivariate correlations were used to screen for baseline variables correlating with weight loss response at 48 weeks (TBWL percentage).

Medication usage at baseline and explant were converted into binary categories and change in usage was assessed using the McNemar’s test. For the SF-36 survey, differences between patient data and population norms were compared using a one-sample t-test.

The primary outcome was change in body weight (TBWL). Secondary outcomes included changes in anthropometrics and body composition, cardiovascular disease risk parameters (including blood pressure and lipid profile), metabolic parameters (including glycaemic control and liver enzymes),

liver stiffness, haematological parameters, nutrition status (including dietary intake and circulating vitamins and minerals) and HRQoL.

Significance of results was determined by a two-tailed p value of less than 0.05.

All analyses were performed using IBM Statistical Package for Social Sciences Version 24 (IBM Corp., NY, USA), and figures were generated using GraphPad Prism Version 7 (GraphPad Software Inc, CA, USA). Statistical analysis was performed with the guidance of Justin Scott at QFAB and Professor Mike Jones from Macquarie University.

3.3 RESULTS

3.3.1 Safety

There were eight SAEs - four instances of GI bleeding (evidenced by hematemesis or melena), three instances of severe abdominal pain and one device-unrelated admission (urinary tract infection). All complications were conservatively managed. Patients underwent repeat endoscopy and/or imaging to confirm appropriate device positioning and patency. One patient underwent early explant at 29 weeks post-implant secondary to GI bleeding and device migration.

Three additional patients underwent elective early explant (at 17, 30 and 36 weeks post-implant) due to persistent abdominal pain.

There were no cases of hepatic abscess in this cohort. 3.3.2 Anthropometric Measures and Body Composition

Body weight was significantly reduced following DJBS treatment (Figure 3.1A; baseline 122.7±17.0 kg vs. explant 100.7±15.6 kg). The weight loss trajectory was more rapid in the first half of the intervention, with a mean TBWL of 11.0±4.1% and 17.0±6.5%, at 24 and 48 weeks respectively. This corresponded with an EWL of 19.2±7.1% and 29.5±11.4%, at 24 and 48 weeks respectively.

Figure 3.1: Body weight and BMI.

Both body weight (A; p<0.001) and BMI (B; p<0.001) were significantly decreased from baseline to explant. Dotted line indicates obese threshold for BMI. Each patient is displayed as an individual line. Pre- and post- intervention compared by paired t-test.

There was a significant reduction in BMI (Figure 3.1B), with a mean reduction of 6.9±3.0 kg/m2. At device explant, two patients were no longer classified as obese (e.g. BMI <30.0 kg/m2). There was a significant reduction in waist, hip and neck circumference (Figure 3.2); however, all patients still exceeded gender specific waist circumference targets at explant (Figure 3.3). There was no significant change in systolic or diastolic blood pressure. However, bivariate correlations revealed TBWL to be positively correlated with baseline diastolic blood pressure (r=0.57; p=0.03).

Figure 3.2: Waist, hip and neck circumference.

There was a significant reduction in waist (A; p<0.01), hip (B; p<0.001) and neck (C; p<0.01) circumferences from baseline to explant. Each patient is displayed as an individual line. Pre- and post-intervention compared by paired t-test. 77

Figure 3.3: Waist circumference, stratified by gender.

Gender specific upper limits of <80 cm for females (A) and <94 cm for males (B) displayed as a dotted line on graph. Each patient is displayed as an individual line.

There was a significant reduction in body fat percentage (Figure 3.4A; baseline 47.1±6.7% vs. explant 43.5±7.2%; n=14; p<0.01). A concurrent loss in absolute fat and lean mass (in kg; Figure 3.4B and C) was observed; however, there was a significant increase in lean: fat mass ratio (Figure 3.4D), indicating a beneficial effect on body composition.

There was no significant change in total BMD or Z score at any site; however, there was a significant reduction in T score at the right femur (p=0.04). Improvements in anthropometric measures, body composition and BMD are summarised below (Table 3.1).

Figure 3.4: Body fat percentage, fat mass, lean mass and lean: fat mass ratio.

There was a significant reduction in body fat percentage (A; p<0.001), fat mass (B; p<0.001), lean mass (C; p<0.001) and lean: fat mass ratio (D; p<0.05) from baseline to explant. Each patient is displayed as an individual line. Pre- and post-intervention compared by paired t-test.

Table 3.1: Anthropometric, body composition and BMD measures following DJBS treatment.

Parameter (unit) Baseline 24 weeks 48 weeks P value at 24 P value at weeks 48 weeks Anthropometric and body composition measures Body Weight (kg) 122.2 ± 17.0 109.6 ± 17.9 100.7 ± 15.6 <0.0001^ <0.0001^^ TBWL (%) N/a 11.0 ± 4.1 17.0 ± 6.5 <0.0001^ <0.0001^^ BMI (kg/m2) 43.7 ± 5.3 39.1 ± 5.7 36.3 ± 6.0 <0.0001^ <0.0001^^ Waist circumference (cm) 132.2 ± 18.0 122.9 ± 16.0 118.8 ± 14.3 <0.0001^^^^ 0.001^^^^^ Hip circumference (cm) 133.7 ± 16.7 122.6 ± 17.2 121.1 ± 15.8 <0.0001^^^^ <0.0001^^^^^ Neck circumference (cm) 44.3 ± 5.7 42.7 ± 5.0 40.6 ± 3.9 <0.0001^^^^ 0.002^^^^^ Systolic blood pressure 139 ± 19 134 ± 16 131 ± 15 0.540^^^ 0.231^^ (mmHG) Diastolic blood pressure 80 ± 7 81 ± 8 81 ± 7 0.876^^^ 0.454^^ (mmHG) Body fat percentage (%) 47.1 ± 6.7 NM 43.5 ± 7.2 NM 0.003^^^ Fat Mass (kg) 56.1 ± 9.5 NM 45.2 ± 10.5 NM <0.0001^^^ Lean Mass (kg) 63.9 ± 13.9 NM 57.8 ± 10.2 NM <0.0001^^^ Lean:Fat Mass Ratio 1.18 ± 0.35 NM 1.36 ± 0.45 NM 0.013^^^ Bone Mineral Density Lumbar BMD (g/cm2) 1.337 ± NM 1.353 ± 0.175 NM 0.974^^^ 0.183 Lumbar T-Score 1.0 ± 1.5 NM 1.1 ± 1.4 NM 0.317^^^ Lumbar Z-Score 0.5 ± 1.5 NM 0.6 ± 1.4 NM 0.803^^^ Left Femur BMD (g/cm2) 1.173 ± NM 1.192 ± 0.124 NM 0.862^^^ 0.133 Left Femur T-Score 0.9 ± 1.0 NM 1.0 ± 0.9 NM 0.287^^^ Left Femur Z-Score 0.4 ± 1.2 NM 0.4 ± 1.0 NM 0.714^^^ Right Femur BMD (g/cm2) 1.203 ± NM 1.207 ± 0.137 NM 0.118^^^ 0.154 Right Femur T-Score 1.1 ± 1.2 NM 1.1 ± 1.0 NM 0.042^^^ Right Femur Z-Score 0.5 ± 1.4 NM 0.5 ± 1.1 NM 0.454^^^ ^ n=17 ^^n=15 ^^^ n=14 ^^^^ n=13 ^^^^^ n=11. Data measured by paired t-test and displayed as mean ± SD. Results deemed significant if p<0.05. BMD; Bone Mineral Density, BMI; Body Mass Index, NM; not measured, TBWL; total body weight loss.

3.3.3 Peripheral Blood Metabolic Profiles

There was a significant reduction in fasting glucose, fasting insulin and HbA1c over the intervention period (Figure 3.5). There was a significant reduction in total cholesterol (Figure 3.6A; p<0.05) and serum triglycerides (Figure 3.6B; p<0.01). No significant change in HDL- or LDL-cholesterol was observed (Figure 3.6C and D). There was a significant improvement in MetSSS (Figure 3.7; n=15; p=0.001) between baseline and explant.

Figure 3.5: Fasting glucose, fasting insulin and HbA1c.

There was a significant reduction in fasting glucose (A; p<0.01), fasting insulin (B; p<0.05) and HbA1c (C; p<0.01) from baseline to explant. Upper limit of clinical reference range (A: 6.0 mmol/L, B: 2.0-23.0 mU/L and C: 7.0% diabetic target) shown as a dotted line on graph. Each patient is displayed as an individual line. Pre- and post-intervention compared by Wilcoxon signed rank test. 81

Figure 3.6: Total cholesterol, triglycerides, HDL-cholesterol and LDL-cholesterol.

There was a significant reduction in total cholesterol (A; p<0.05) and serum triglycerides (B; p<0.01) but not HDL-cholesterol (C; p=0.22) or LDL-cholesterol (D; p=0.31) from baseline to explant. Upper (A: 4.0 mmol/L, B: 1.5 mmol/L and D: 2.5 mmol/L) and lower (C: 1.0 mmol/L) limit of clinical reference range shown as a dotted line on graph. Each patient is displayed as an individual line. Pre- and post-intervention compared by paired t-test.

Figure 3.7: MetSSS.

There was a significant reduction in MetSSS from baseline to explant (p=0.001). Each patient is displayed as an individual line. Results compared by Wilcoxon signed rank test.

There was also a significant decrease in ferritin, transferrin saturation and haemoglobin and a reduction in global inflammation, as indicated by the significant decrease in CRP (Figure 3.8). All female patients remained within the normal reference range for ferritin, whilst 60% of males (n=3) fell below the normal gender-specific reference range (Figure 3.9). One male and one female patient fell below the normal reference range for haemoglobin (Figure 3.9). Improvements in metabolic parameters are summarised below (Table 3.2).

Figure 3.8: Ferritin, transferrin saturation, haemoglobin and CRP.

There was a significant reduction in ferritin (A; p<0.01), transferrin saturation (B; p<0.01), haemoglobin (C; p<0.01) and CRP (D; p<0.05) from baseline to explant. Upper and lower limit of clinical reference range shown as a dotted line on graph (B: 15-45 %; D: <5.0 mg/L). Each patient is displayed as an individual line. Pre- and post-intervention compared by paired t-test (B and C) or Wilcoxon signed rank test (A and D).

Figure 3.9: Ferritin and haemoglobin, stratified by gender.

Gender specific reference ranges for ferritin of 5-150 ug/L for females (A) and 45-715 for males (B) and haemoglobin 115-160 g/L for females (C) and 135-180 g/L for males (D) displayed as a dotted line on graph. Each patient is displayed as an individual line.

Table 3.2: Glycaemic control, cardiovascular risk, haematological and inflammatory and iron status parameters following DJBS treatment.

Parameter (unit) Baseline 24 weeks 48 weeks P value at 24 P value at 48 weeks weeks Glycaemic control Fasting glucose (mmol/L) 8.7 ± 3.7 7.1 ± 2.2 6.5 ± 1.4 0.022^ 0.0012^^ Fasting insulin (mU/L) 21 ± 18 NM 10 ± 7 NM 0.012^^

2^^ HbA1c (%) 7.0 ± 1.4 NM 6.2 ± 0.8 NM 0.003 Cardiovascular risk parameters Total cholesterol (mmol/L) 4.9 ± 1.3 NM 4.2 ± 1.2 NM 0.0451^^ Triglyceride (mmol/L) 2.1 ± 0.7 NM 1.6 ± 0.5 NM 0.0041^^ HDL-cholesterol (mmol/L) 1.1 ± 0.3 NM 1.1 ± 0.2 NM 0.2191^^ LDL-cholesterol (mmol/L) 2.8± 1.1 NM 2.4 ± 0.9 NM 0.3081^^ Haematological, inflammatory and iron status parameters Ferritin (µg/L) 119 ± 94 86 ± 81 70 ± 66 0.012^ 0.0012^^ Transferrin Saturation (%) 21 ± 4 16 ± 6 16 ± 4 0.0021^^^ 0.0011^^ Haemoglobin (g/L) 140 ± 13 136 ± 12 135 ± 14 0.051^ 0.0061^^ CRP (mg/L) 10.7 ± 9.4 11.4 ± 13.3 7.4 ± 6.7 0.532^^^ 0.012^^ ^ n=17, ^^ n=15 ^^^ n=16. Data displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05.

CRP; C-reactive protein, HbA1c; glycated haemoglobin, HDL; high-density lipoprotein, LDL; low-density lipoprotein, NM; parameter not measured at this time-point.

3.3.4 Markers of Hepatic Fibrosis and Steatosis

There was a significant reduction in ALT over the intervention period. At baseline, 17 of 19 patients had ALT values outside of healthy clinical targets. By device explant, 13 of these 17 patients had reached gender specific targets (Figure 3.10: 9 females and 4 males). There was a statistically significant reduction in AST; however, the majority of patients were within the normal reference range at baseline. There was a trend toward a reduction in hepatic stiffness, as measured by transient elastography (Figure 3.11). There was a significant reduction in the CAP, which may be used as a surrogate marker of intrahepatic fat. Improvements in liver-related parameters are summarised below (Table 3.3).

Figure 3.10: ALT, stratified by gender.

Gender specific upper limit of <20 U/L for females (A) and <31 U/L for males (B) displayed as a dotted line on graph. Each patient is displayed as an individual line. For full cohort, pre- and post-intervention compared by Wilcoxon signed rank test.

Figure 3.11: Hepatic stiffness and the CAP as indicators of hepatic fibrosis and steatosis.

There was a trend toward a reduction in hepatic stiffness (A; p=0.07) and a significant reduction in the CAP (B; p<0.05) from baseline to explant. Each patient is displayed as an individual line. Pre- and post-intervention compared by Wilcoxon signed rank test (A) or paired t-test (B).

Table 3.3: Liver enzymes and indicators of hepatic fibrosis and steatosis following DJBS treatment.

Parameter (unit) Baseline 24 weeks 48 weeks P value at 24 weeks P value at 48 weeks ALT (U/L) 37 ± 16 23 ± 11 18 ± 9 0.0021^ 0.0032^ AST (U/L) 25 ± 15 18 ± 8 16 ± 6 0.022^ 0.012^ Hepatic stiffness (kPa) 10.4 ± 7.3 6.4 ± 2.6 6.6± 2.0 0.172^^ 0.072^^ CAP (decibels/min) 339 ± 48 318 ± 60 302 ± 60 0.171^^ 0.021^ ^ n=15 ^^ n=13. Data displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05. ALT, alanine transaminase; AST, aspartate transaminase, CAP; controlled attenuation parameter

3.3.5 Dietary Intake and Nutrition Status

At 24 weeks, there was a significant reduction in dietary energy intake, absolute (in grams) intake of protein, fat and carbohydrate and a reduction in the proportion of total energy derived from fat, compared to baseline. At 48 weeks, dietary energy and fibre intake, absolute fat and carbohydrate intake and the proportion of total energy intake derived from fat were reduced. Dietary intake is summarised in Table 3.4.

Table 3.4: Dietary energy, fibre and macronutrient intake following DJBS treatment.

Parameter (unit) Baseline 24 weeks 48 weeks P value at 24 P value at 48 weeks weeks Energy intake (kJ) 7703 ± 2978 4824 ± 2259 4474 ± 1468 0.0011 0.011 Dietary fibre (g) 21.8 ± 9.1 14.7 ± 7.5 16.2 ± 7.5 0.062 0.021 Protein intake (g) 95.7 ± 32.9 69.1 ± 29.9 67.4 ± 37.7 0.011 0.121 TEI derived from protein (%) 22 ± 6 26 ± 7 25 ± 11 0.281 0.271 Fat intake (g) 73.3 ± 32.0 36.6 ± 19.1 31.5 ± 13.7 0.0021 0.0021 TEI derived from fat (%) 35 ± 6 28 ± 6 26 ± 9 0.021 0.031 Carbohydrate intake (g) 185 ± 94 129 ± 77 118 ± 54 0.022 0.042 TEI derived from carbohydrate (%) 39 ± 9 44 ± 7 44 ± 12 0.061 0.381 Results displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05. TEI; total energy intake

There was a significant increase in vitamin D and a statistically significant reduction in vitamin E. The increase in vitamin D is suggested to be as a result of increased outdoor time and seasonal variation. Despite the statistically significant reduction in vitamin E, all patients remained within the normal clinical reference range and no patient exhibited any symptoms of deficiency. Serum vitamin and mineral status is summarised in the table below (Table 3.5).

Table 3.5: Serum vitamin and mineral status following DJBS treatment.

Parameter (unit) Baseline 48 weeks P value Fat soluble vitamins Vitamin A (µmol/L) 1.9 ± 0.3 1.7 ± 0.4 0.061^ Vitamin D (nmol/L) 72 ± 17 88 ± 29 0.021^ Vitamin E (µmol/L) 31 ± 8 27 ± 8 0.0041^^ INR 1.0 ± 0.1 1.1 ± 0.1 0.182^ Water Soluble Vitamins Vitamin B12 (pmol/L)^ 267 ± 102 280 ± 136 0.472^ Folate (nmol/L) 36.9 ± 11.8 41.0 ± 11.4 0.241^ Minerals Copper (µmol/L) 19 ± 3 19 ± 2 0.891^^^ Selenium (µmol/L) 1.1 ± 0.2 1.1 ± 0.1 0.341^^^ Zinc (µmol/L) 14 ± 1 13 ± 2 0.341^^^ ^ n= 15 ^^ n=14 ^^^ n=11. Data displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05. INR; international normalized ratio.

3.3.6 Health-Related Quality of Life

There was a significant improvement in daytime sleepiness and depression as measured by the Epworth Sleepiness Scale and HADS, respectively. There was a trend toward a reduction in anxiety as measured by the HADS (Table 3.6). Self-reported physical activity was significantly increased from a mean of 425±516 minutes/week at baseline to 874±767 minutes/week at explant. This represents an increase in the number of patients meeting national physical activity guidelines from 58% at baseline to 81% at explant. This was also matched by a significant increase in functional exercise capacity, with 6MWT distance increasing from 500±84 m at baseline to 607±92 m at 48 weeks. However, at baseline, only two patients could achieve the distance derived from the predictive equations in the six minute period. Of these two patients, neither had valid repeat measures at explant (one due to protocol non-compliance (running) and one due to knee injury).

There was a significant improvement in the physical functioning, role limitations due to physical health, general health, energy/fatigue, emotional wellbeing and social functioning domains of the SF-36 measure of HRQoL. Bivariate correlations revealed TBWL to be negatively correlated with baseline physical function domain score (r=-0.65; p=0.01), indicating a lower reported degree of physical function at baseline was associated with poorer weight loss outcomes.

Compared to Australian general population norms, the female cohort reported a significantly lower score on each of the eight domains at baseline (Figure 3.12A). However, scores were representative of norms derived in a study of females with obesity and T2DM, who also reported lower HRQoL scores than the general population. At 48 weeks, scores for all domains except pain were not significantly different from the general population norms (Figure 3.12B). Four of the eight domains (physical functioning, energy/fatigue, role limitations due to emotional problems, social functioning and emotional wellbeing) were also significantly higher than the female cohort with obesity and T2DM. For male participants, mental health and general health domains were significantly lower than the Australian general population and for all eight domains were not significantly different from a cohort of males with obesity and T2DM (Figure 3.12C). In males, at 48 weeks, scores for all domains were not significantly different from the general population norms, but all were significantly higher than the male cohort with obesity and T2DM (Figure 3.12D). HRQoL outcomes are summarised below (Table 3.6). The burden of polypharmacy was not significantly impacted by DJBS treatment, with no significant change in usage of any of the medication categories (Table 3.7).

Figure 3.12: HRQoL as measured by the SF-36 questionnaire.

Australian norm data from Australian Bureau of Statistics data494. AusDiab norm data from the Australian Diabetes, Obesity and Lifestyle study495.

Table 3.6: HRQoL measures following DJBS treatment.

Parameter (unit) Baseline 24 weeks 48 weeks P value at P value at (n=19) (n=12) (n=16) 24 weeks^ 48 weeks^^ Epworth Sleepiness Scale 7.6 ± 3.4 6.75 ± 4.0 4.6 ± 2.2 0.201 0.011 HADS-Anxiety 8 ± 4 5 ± 4 6 ± 5 0.082 0.061 HADS-Depression 6 ± 4 2 ± 3 3 ± 4 0.202 0.012 Active Australia – sufficient time (mins) 425 ± 516 910 ± 1418 874 ± 767 0.352 0.012 6MWT (m)* 500 ± 84 587 ± 107 607 ± 92 0.023^^^ 0.042^^^^ SF-36 – Physical functioning 52 ± 25 80 ± 17 73 ± 27 0.0021 0.012 SF-36 – Role Limitations due to Physical 46 ± 44 71 ± 45 77 ± 35 0.202 0.022 Health SF-36 – Pain 54 ± 26 58 ± 27 60 ± 33 0.631 0.601 SF-36 – General Health 44 ± 22 65 ± 23 64 ± 26 0.031 0.012 SF-36 – Role Limitations due to Emotional 51 ± 44 83 ± 33 67 ± 44 0.262 0.182 Problems SF-36 – Energy/Fatigue 44 ± 20 55 ± 27 64 ± 23 0.611 0.0031 SF-36 – Emotional Wellbeing 61 ± 24 73 ± 18 72 ± 19 0.511 0.0031 SF-36 – Social Functioning 57 ± 29 75 ± 19 80 ± 21 0.101 0.0022 ^ n= 12 ^^ n=16 ^^^ n=8 ^^^^ n=7. * per protocol analysis due to protocol non-compliance. Data displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05. 6MWT; six minute walk test, HADS; Hospital Anxiety and Depression Scale.

Table 3.7: Medication usage.

Baseline (n=19) % Explant (n=18) % P value (n=18) (number) (number) Metformin 67 (13) 50 (9) 0.45 Other oral agents 21 (4) 17 (3) 1.00 GLP-1 analogues 0 (0) 11 (2) .50 Hypertension 53 (10) 44 (8) .50 Dyslipidaemia 32 (6) 28 (5) 1.00 Mental Health 42 (8) 39 (7) 1.00 Pain 16 (3) 17 (3) 1.00 Reflux 21 (4) 22 (4) 1.00 Other 37 (7) 33 (6) 1.00

3.3.7 Durability of Improvements 3.3.7.1 Anthropometric Measures and Body Composition

Following device explant, patients began to gain weight. However, one year after device explant body weight (Figure 3.13A; n=14; p=0.02) and BMI (Figure 3.13B; n=14; p=0.02) were still significantly lower, compared to baseline. At one year post-explant, the mean TBWL from baseline was 5.31±6.91% (n=14; p=0.01), representing approximately 25% of maximal weight loss at device explant. Significant reductions in waist (Figure 3.14A; n=9; p=0.04), hip (Figure 3.14B; n=9; p=0.03) and neck circumferences (Figure 3.14C; n=9; p=0.04) were also maintained.

There was no significant difference in systolic (n=9; p=0.62) or diastolic (n=9; p=0.18) blood pressure, compared to baseline values. However, this was not significantly changed during the intervention period. There was no significant difference in body fat percentage or lean:fat mass; however, both absolute fat mass and absolute lean mass were significantly reduced at one year post- explant, compared to baseline. BMD was not significantly different, compared to baseline. Anthropometric measures, body composition and BMD measures one year after device explant are summarised below (Table 3.8).

Figure 3.13: Body weight and BMI at one year post-explant.

At one year post-explant, both body weight (A; n=14; p<0.05) and BMI (B; n=14; p<0.05) remained significantly reduced, as compared to baseline. Dotted line indicates obese threshold for BMI. Each patient is displayed as an individual line. Results compared by paired t-test.

Figure 3.14: Waist, hip and neck circumference at one year post-explant.

At one year post-explant, waist (A; p<0.05), hip (B; p<0.05) and neck (C; p<0.05) circumference remained significantly reduced, as compared to baseline (n=9 for all). Each patient is displayed as an individual line. Results compared by paired t-test.

Table 3.8: Anthropometric, body composition and BMD measures one year after DJBS treatment.

Parameter (unit) Baseline One year post- Mean reduction from P value explant baseline Anthropometric and body composition measures Body Weight (kg) 122.2 ± 17.0 117.7 ± 18.0 6.8 ± 9.4 0.018^ TBWL (%) N/a 5.3 ± 6.9 5.3 ± 6.9 N/a BMI (kg/m2) 43.7 ± 5.3 41.7 ± 6.8 2.3 ± 3.0 0.015^ Waist circumference (cm) 132.2 ± 18.0 122.8 ± 17.4 8.5 ± 10.4 0.039^^ Hip circumference (cm) 133.7 ± 16.7 124.9 ± 15.7 6.1 ± 6.8 0.028^^ Neck circumference (cm) 44.3 ± 5.7 43.7 ± 5.5 1.6 ± 1.9 0.028^^ Systolic blood pressure (mmHG) 139 ± 19 144 ± 12 -3 ± 15 0.618^^ Diastolic blood pressure (mmHG) 80 ± 7 82 ± 6 -3 ± 7 0.184^^ Body fat percentage (%) 47.1 ± 6.7 47.0 ± 7.5 0.7 ± 2.7 0.511^^^ Fat Mass (kg) 56.1 ± 9.5 50.7 ± 11.1 6.7 ± 4.8 0.011^^^ Lean Mass (kg) 63.9 ± 13.9 59.0 ± 12.9 5.0 ± 5.2 0.045^^^ Lean:Fat Mass Ratio 1.18 ± 0.35 1.21 ± 0.40 0.09 ± 0.53 0.163^^^ Bone Mineral Density Lumbar BMD (g/cm2) 1.337 ± 0.183 1.327 ± 0.226 -0.006 ± 0.031 0.650^^^ Lumbar T-Score 1.0 ± 1.5 0.96 ± 1.8 -0.03 ± 0.26 0.765^^^^ Lumbar Z-Score 0.5 ± 1.5 0.4 ± 1.7 0.0 ± 0.6 0.950^^^ Left Femur BMD (g/cm2) 1.173 ± 0.133 1.142 ± 0.147 -0.011 ± 0.074 0.703^^^ Left Femur T-Score 0.9 ± 1.0 0.6 ± 1.1 0.2 ± 0.4 0.320^^^^ Left Femur Z-Score 0.4 ± 1.2 -0.1 ± 1.1 0.3 ± 0.7 0.318^^^ Right Femur BMD (g/cm2) 1.203 ± 0.154 1.130 ± 0.123 0.020 ± 0.065 0.453^^^ Right Femur T-Score 1.1 ± 1.2 0.5 ± 0.9 0.2 ± 0.5 0.363^^^^ Right Femur Z-Score 0.5 ± 1.4 -0.2 ± 1.0 0.4 ± 0.8 0.287^^^ ^ n=14 ^^n=9, ^^^ n=7, ^^^^ n=6. Data measured by paired t-test and displayed as mean ± SD. Results deemed significant if p<0.05. BMD; Bone Mineral Density, BMI; Body Mass Index, TBWL; total body weight loss.

3.3.7.2 Peripheral Blood Metabolic Profiles

At one year post-explant, there was no sustained improvement in fasting glucose, fasting insulin or

HbA1c, compared to baseline (Figure 3.15). There was no persistent improvement in total cholesterol or serum triglycerides. LDL-cholesterol were not significantly different, compared baseline (Figure 3.16); however, HDL-cholesterol was significantly increased (Figure 3.16C).

Figure 3.15: Fasting glucose, fasting insulin and HbA1c at one year post-explant.

At one year post-explant, there was no persistent improvement in fasting glucose (A; p=0.24), fasting insulin (B; p=0.86) or HbA1c (C; p=0.11) compared to baseline. Upper limit of clinical reference range (A: 6.0 mmol/L, B: 2.0-23.0 mU/L and C: 7.0% diabetic target) shown as a dotted line on graph. Each patient is displayed as an individual line. Results compared by Wilcoxon signed rank test.

Figure 3.16: Total cholesterol, triglycerides, HDL-cholesterol and LDL-cholesterol at one year post- explant.

At one year post-explant there was no persistent improvement in total cholesterol (A; p=0.31), serum triglycerides (B; p=0.11) or LDL-cholesterol (D; p=0.25). However, HDL-cholesterol was significantly increased (C; p<0.05), compared to baseline. Upper (A: 4.0 mmol/L, B: 1.5 mmol/L and D: 2.5 mmol/L) and lower (C: 1.0 mmol/L) limit of clinical reference range shown as a dotted line on graph. Each patient is displayed as an individual line. Results compared by paired t-test. Despite the lack of clinically significant weight loss (>5% TBWL) maintenance in five of nine patients, at one year post-explant there was a trend toward a significant improvement in MetSSS (n=9; p=0.07). Overall, seven of nine patients had a MetSSS that was lower than baseline, indicating an improved risk profile (Figure 3.17).

One year post-explant, there was no significant difference in haemoglobin, transferrin or transferrin saturation, compared to baseline (Figure 3.18). However, one year post-explant, ferritin was significantly lower than baseline (n=9; p<0.01). There was no sustained improvement in global inflammation, as measured by CRP, compared with baseline. Data on metabolic parameters one year post-explant, compared with baseline are summarised below (Table 3.9).

Figure 3.17: MetSSS at one year post-explant.

There was a trend (p=0.07) toward a maintained improvement in MetSSS at one year post-explant, compared to baseline. Each patient is displayed as an individual line. Results compared by Wilcoxon signed rank test.

Figure 3.18: Ferritin, transferrin saturation, haemoglobin and CRP at one year post-explant.

At one year post-explant, ferritin was significantly reduced (A; p<0.01), compared to baseline. There was no significant difference in transferrin saturation (B; p=0.88), haemoglobin (C; p=0.27) or CRP (D; p=0.74). Upper and lower limit of clinical reference range shown as a dotted line on graph (B: 15-45 %; D: <5.0 mg/L). Each patient is displayed as an individual line. Results compared by paired t-test (B and C) or Wilcoxon signed rank test (A and D).

Table 3.9: Glycaemic control, cardiovascular risk, haematological and inflammatory and iron status parameters one year after DJBS treatment.

Parameter (unit) Baseline One year post- Mean reduction from P value (n=9) explant baseline Glycaemic control Fasting glucose (mmol/L) 8.7 ± 3.7 7.7 ± 1.8 1.8 ± 3.4 0.2362 Fasting insulin (mU/L) 21 ± 18 22 ± 25 2 ± 21 0.8592

2 HbA1c (%) 7.0 ± 1.4 6.5 ± 1.1 0.7 ± 1.3 0.109 Cardiovascular risk parameters Total cholesterol (mmol/L) 4.9 ± 1.3 4.8 ± 1.3 -0.4 ± 1.0 0.3101 Triglyceride (mmol/L) 2.1 ± 0.7 1.6 ± 0.7 0.4 ± 0.6 0.1141 HDL-cholesterol (mmol/L) 1.1 ± 0.3 1.1 ± 0.2 -0.1 ± 0.2 0.0291 LDL-cholesterol (mmol/L) 2.8 ± 1.1 2.9 ± 1.2 -0.4 ± 0.9 0.2481 Haematological, inflammatory and iron status parameters Ferritin (µg/L) 119 ± 94 47 ± 44 41 ± 31 0.0082 Transferrin Saturation (%) 21 ± 4 20 ± 10 -0.0 ± 9 0.8831 Haemoglobin (g/L) 140 ± 13 140 ± 16 1 ± 4 0.2681 CRP (mg/L) 10.7 ± 9.4 8.0 ± 11.8 1.0 ± 5.6 0.7352 Data displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05.

CRP; C-reactive protein, HbA1c; glycated haemoglobin, HDL; high-density lipoprotein, LDL; low-density lipoprotein

3.3.7.3 Markers of Hepatic Fibrosis and Steatosis

One year after device explant, ALT levels remained significantly reduced (Figure 3.19; p<0.05). Of the nine patients who completed the one year post-explant blood test, six remained within the gender specific targets (3 females and 3 males). The majority of patients were in the normal range at baseline and all patients who completed follow-up were within the normal range at one year post-explant. Despite the maintained improvements in hepatic enzymes, there was no significant difference in hepatic stiffness between baseline and one year post-explant (Figure 3.20A; n=7; p=0.35) nor the CAP (Figure 3.20B; n=7; p=0.69). Outcome data associated with liver enzymes and hepatic stiffness and steatosis are summarised below (Table 3.10).

Figure 3.19: ALT at one year post-explant.

Patient’s gender indicated by colour of baseline value (pink for female, blue for male). Gender specific upper limit of <20 U/L for females (marked with pink dotted line) and <31 U/L for males (marked with blue dotted line). Each patient is displayed as an individual line. For the full cohort (n=9; p<0.05), results compared by Wilcoxon signed rank test.

Figure 3.20: Hepatic stiffness and the CAP as measures of hepatic fibrosis and steatosis at one year post- explant.

There was no sustained improvement in hepatic stiffness (A; p=0.35) or the CAP (B; p=0.69) as indicators of hepatic fibrosis and steatosis at one year post-explant, compared to baseline (n=7). Each patient is displayed as an individual line. Results compared by Wilcoxon signed rank test.

Table 3.10: Liver enzymes and markers of hepatic fibrosis and steatosis one year after treatment with a DJBS.

Parameter (unit) Baseline One year Mean P value post-explant reduction from baseline ALT (U/L) 37 ± 16 26 ± 14 11 ± 9 0.0172^ AST (U/L) 25 ± 15 17 ± 7 4 ± 6 0.0502^ Hepatic stiffness (kPa) 10.5 ± 7.1 5.5 ± 1.8 1.6 ± 3.1 0.3522^^ CAP (decibels/min) 342 ± 49 329 ± 47 11 ± 68 0.6891^^ ^ n=9 ^^ n=7. Data displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05. ALT, alanine transaminase; AST, aspartate transaminase, CAP, controlled attenuation parameter

3.3.7.4 Dietary Intake and Nutrition Status

At six months post-explant, there was no significant difference in any measured dietary parameter, compared to baseline. However, at one year post-explant, there was a significant reduction in dietary energy intake and absolute (in grams) intake of fat and carbohydrates, compared to baseline. There was also a trend toward a reduction in dietary energy intake from protein. The differences between the two post-explant time-points are likely to reflect the static nature of 24 hour recall assessment. Post-explant dietary intake is summarised in Table 3.11.

Table 3.11: Dietary energy, fibre and macronutrient intake following DJBS device removal.

Parameter (unit) Baseline Six months post One year post- P value: six P value: One explant explant months post- year post- explant vs explant vs baseline^ baseline^ Energy intake (kJ) 7703 ± 2978 5810 ± 2071 6422 ± 4034 0.161 0.042 Dietary fibre (g) 21.8 ± 9.1 18.9 ± 8.3 21.9 ± 14.8 0.161 0.652 Protein intake (g) 95.7 ± 32.9 78.6 ± 16.8 88.4 ± 34.0 0.081 0.461 TEI derived from 22 ± 6 25 ± 8 26 ± 6 0.501 0.071 protein (%) Fat intake (g) 73.3 ± 32.0 51.5 ± 21.5 53.3 ± 27.9 0.181 0.031 TEI derived from fat 35 ± 6 33 ± 6 31 ± 5 0.721 0.091 (%) Carbohydrate intake 185.3 ± 94.5 142.4 ± 77.6 142.4 ± 105.7 0.262 0.022 (g) TEI derived from 39 ± 9 39 ± 11 37 ± 6 0.791 0.221 carbohydrate (%) ^ n = 9. Results displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05. TEI; total energy intake

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At one year post-explant, there was no significant difference in vitamins A, E, INR (as a proxy marker for vitamin K) or vitamin B12; however, vitamin D levels were significantly lower than baseline and folate levels were significantly higher than baseline. Copper levels were not significantly different compared to baseline; however, selenium was significantly higher than baseline (n=7; p<0.001). Due to a laboratory ordering error, blood zinc was measured instead of serum zinc for 66% of patients at one year post-explant, precluding analysis of serum zinc at this time-point. Post-explant circulating nutrient status is summarised below (Table 3.12).

Table 3.12: Circulating vitamin and mineral status at one year post-explant.

Parameter (unit) Baseline One year post-explant Mean reduction from baseline P value Fat soluble vitamins Vitamin A (µmol/L) 1.9 ± 0.3 1.8 ± 0.4 0.3 ± 1.0 0.8051^ Vitamin D (nmol/L) 72 ± 17 52 ± 9 20 ± 9 <0.00011^^ Vitamin E (µmol/L) 31 ± 8 28 ± 9 1 ± 4 0.6741^ INR 1.0 ± 0.1 1.0 ± 0.2 -0.1 ± 0.3 0.3802^^ Water Soluble Vitamins Vitamin B12 (pmol/L)^ 267 ± 102 268 ± 84 15 ± 42 0.3421^^^ Folate (nmol/L) 36.9 ± 11.8 41.3 ± 7.1 -7.4 ± 6.8 0.0121^^ Minerals Copper (µmol/L) 19 ± 3 20 ± 5 -2 ± 3 0.0842^ Selenium (µmol/L) 1.1 ± 0.2 1.7 ± 0.3 -0.7 ± 0.2 <0.00011^ ^ n= 7 ^^ n=9 ^^^ n=8. Data displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05. INR; international normalized ratio, N/A not applicable

3.3.7.5 Health-Related Quality of Life

With the exception of the 6MWT distance, which remained increased as compared to baseline, there was no significant persistent difference in any other HRQoL parameter. At this time-point, only one patient exceeded the predicted 6MWT distance. When separated by gender, female participants reported a significantly reduced “role limitations due to emotional problems” score than both the Australian general population norms and the female cohort with obesity and T2DM. Overall, our patient cohort were most closely matched with the female cohort with obesity and T2DM (Figure 3.21). For male participants, no statistical differences between the cohort and either the Australian general population norms or the male cohort with obesity and T2DM were observed. Post-explant HRQoL outcomes are summarised below (Table 3.13).

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Figure 3.21: HRQoL at one year post-explant.

Australian norm data from Australian Bureau of Statistics data494. AusDiab norm data from the Australian Diabetes, Obesity and Lifestyle study495.

Table 3.13: HRQoL parameters one year post-explant.

Parameter (unit) Baseline One year Mean P value (n=9) post- reduction from explant baseline Epworth Sleepiness Scale 7.6 ± 3.4 5.7 ± 3.9 2.4 ± 4.1 0.111 HADS-Anxiety 8 ± 4 7 ± 3 0 ± 4 0.8701 HADS-Depression 6 ± 4 4 ± 3 0 ± 4 0.372 Active Australia – sufficient time (mins) 425 ± 604 ± 316 -209 ± 616 0.172 516 6MWT (m)* 500 ± 84 588 ± 58 -3 ± 190 0.041^ SF-36 – Physical functioning 52 ± 25 61 ± 26 -5 ± 29 0.621 SF-36 – Role Limitations due to Physical Health 46 ± 44 56 ± 41 0 ± 47 0.732 SF-36 – Pain 54 ± 26 61 ± 26 8 ± 34 0.521 SF-36 – General Health 44 ± 22 63 ± 23 -15 ± 22 0.071 SF-36 – Role Limitations due to Emotional 51 ± 44 52 ± 38 7 ± 66 0.672 Problems SF-36 – Energy/Fatigue 44 ± 20 62 ± 16 -1 ± 14 0.911 SF-36 – Emotional Wellbeing 61 ± 24 65 ± 12 6 ± 19 0.242 SF-36 – Social Functioning 57 ± 29 65 ± 29 1 ± 36 0.921 ^ n= 8. Data displayed as mean ± SD. Normally distributed data analysed by 1. Paired t-test. Data not normally distributed analysed by 2.Wilcoxon signed rank test. Results deemed significant if p<0.05. 6MWT; six minute walk test, HADS; Hospital Anxiety and Depression Scale.

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3.4 DISCUSSION

In our cohort, treatment with a DJBS was effective, resulting in significant improvements in body weight and markers of metabolic health. Mean weight loss was 19.5±8.7 kg over the 48 week intervention period. This represents a mean TBWL of 17.0% and a mean EWL of 29.5%. This exceeds the threshold for clinical significance of 5.0% total weight loss reported in a 2013 consensus guideline258, as well as the EWL threshold for treatment success with an endoscopic bariatric therapy outlined in the 2011 ASGE guidelines420. Our findings are similar to those reported in a 2018 systematic review and meta-analysis, which assessed 352 patients across 10 studies516. The mean weight loss was 11.3 kg across a mean duration of 9.2±3.1 months. In a subset analysis (n=305 from four studies), this corresponded to a TBWL of 18.9% or a mean EWL of 36.9% (n=301 from four studies). However, relative weight loss is infrequently reported across studies of the DJBS, leading to only four studies being included in the subset analyses of the meta-analysis. This is despite the Joint Bariatric Taskforce guidelines using EWL at 12 months as the parameter for defining treatment success420.

We also assessed changes in body composition (by DEXA) following DJBS treatment. There was a significant reduction in body fat percentage and both lean and fat mass (in kg) following DJBS treatment. Other studies have also reported reductions in fat mass over six to 12 months post-implantation451, 456, 467, 469, 518. However, the concurrent loss of lean body mass observed in our cohort are in contrast to what has previously been reported451, 469, 518. This predisposes patients to weight regain (due to a reduction in basal metabolic rate29, 519) and poorer metabolic outcomes (due to the role of skeletal muscle mass in glucose metabolism) when the DJBS is removed.

At baseline, 9 of our 19 patients had HbA1c levels above 7%, exceeding the target for good control of diabetes520. After DJBS treatment, four of these nine patients met the above threshold for good control of diabetes. Overall, we observed a mean reduction in HbA1c of 0.9± 1.0%, reflecting a 520 clinically significant (>0.5% ) reduction in HbA1c in 10 of 19 patients. It is important to note that of the nine patients who did not achieve at least a 0.5% reduction in HbA1c, eight had good control at baseline (<7%). Our results are consistent with other published observations. A 2018 systematic review and meta-analysis assessed glycaemic control outcomes in 388 patients with obesity and

T2DM. The mean reduction in HbA1c was 1.3 % across 14 studies with a mean study duration of 9.2 ± 3.1 months516. Most studies to date have included patients receiving insulin444, 446, 447, 451-454, 457- 460, 462-465, 467, 469, 476, 480-482, 518 . The difference in baseline HbA1c, and reduction in HbA1c with treatment may be because we excluded these patients in our study. These results add to the body of literature, with approximately the same magnitude of improvements in weight and glycaemic control parameters as previously reported. 103

Whilst clinical efficacy has been demonstrated, concerns surrounding the safety profile of the DJBS have been voiced in recent years521. The major issue identified with the DJBS was the 3% incidence of hepatic abscess reported in one US trial. This lead to the early termination of the study. While we observed no cases of hepatic abscess in our cohort, four devices were removed early, accounting for 21% of our study cohort. Three of these cases were due to persistent abdominal pain and one early explant was secondary to GI bleeding and device migration when the patient did not adhere to the prescribed diet. This early explant rate is consistent with existing literature, which has ranged from 0-69%449, 461. In a 2018 systematic review and meta-analysis of nine studies including 350 patients who underwent DJBS treatment, the overall SAE rate was 15.7%516. In this report, nearly a third (16) of the reported SAEs were episodes of GI bleeding requiring hospital admission leading to early removal in 15 of 16 cases. Bleeding and migration may occur secondary to patient non-compliance with medication or dietary prescriptions464 and thus can be considered preventable with appropriate patient selection, education and close monitoring. However, the need for early device removal occurred despite strict patient selection, proper device positioning and close clinical monitoring and education of patients, indicating the need to develop strict inclusion and exclusion criteria for patients that ideally should incorporate criteria that should predict patient compliance.

Besides the early removal due to GI bleeding and migration, we had seven other SAEs resulting in hospital admission. There were three cases of GI bleeding, three instances of severe abdominal pain and one urinary tract infection. This represents a 42% SAE rate. In all cases, patients were conservatively managed and underwent repeat endoscopy to confirm device positioning and patency. All events resolved without further sequelae. Despite this, the safety data from our cohort does not meet the 5% threshold of SAEs considered acceptable by the ASGE423.

Our study also aimed to assess other measures of metabolic health, with a focus on components of the metabolic syndrome. The effect on waist circumference in the current study is consistent with existing literature459, 461, 469, with a statistically significant reduction observed (132.2±18.0 cm vs 118.8±14.3 cm). However, waist circumference at explant exceeded population guidelines for risk of chronic disease in all patients (>80 cm for females and >90 cm for males)15. Over the 48 week intervention period, a significant reduction in total cholesterol and triglycerides was reported. There was no significant change in LDL- or HDL-cholesterol. The evidence for the DJBS leading to improvement in cardiovascular disease risk is mixed. A significant increase in HDL-cholesterol has been reported in one study469, whilst other studies did not find a significant change in HDL- cholesterol449, 451, 454, 459, 461, 463, 480, 482. A significant improvement in total cholesterol447, 448, 452, 454, 459, 465, 469, 482 and LDL-cholesterol447-449, 451, 452, 459, 465, 469, 480, 482 has been observed in a number of studies. Unfavourable changes in cholesterol have also been reported443, 446. Significant reductions in serum

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triglycerides have also been reported447-449, 452, 457, 465, whilst other studies have reported no significant change451, 454, 459, 461, 463, 469, 480. Improvements in blood pressure have been reported in eight studies448, 449, 452, 454, 457, 461, 464, 469. In this cohort, no statistically significant impact on blood pressure was observed following DJBS treatment. However, at baseline 13 of 19 patients had systolic blood pressures meeting metabolic syndrome criteria (baseline mean: 139±19). At device explant, this was reduced to six of 19 (explant mean: 131±15), suggesting clinical benefits occur despite the lack of statistical significance.

Besides weight loss, one of the key objectives of this project was to assess the impact of treatment on the metabolic syndrome. At device explant, all patients still met diagnostic criteria for metabolic syndrome using a widely accepted consensus definition15. However, when a novel scoring system (MetSSS) was used to determine metabolic syndrome severity517, there was a significant reduction in MetSSS from baseline to explant, indicating a significant improvement of the metabolic syndrome over the 48 week treatment period.

More broadly, obesity and T2DM impact liver function, with NAFLD being a common obesity-related comorbidity. In out cohort, significant improvements in ALT and AST were observed following DJBS treatment. These improvements in liver enzymes following DJBS treatment have previously been reported451, 454, 464, 470. However, we also assessed hepatic elastography measures. To date, only one study has reported on hepatic stiffness (an indirect measure of hepatic fibrosis) and the CAP (an indicator of hepatic steatosis) using vibration controlled transient elastography in a cohort of 20 patients455. Hepatic stiffness is increased by a variety of factors including hepatic fibrosis, steatosis and necro-inflammation, with histologically more severe NAFLD being associated with higher transient elastography scores522. The authors reported a trend toward a reduction in liver stiffness after 52 weeks of DJBS treatment. In a subgroup of 11 patients where the standard probe (M probe) could be used, they observed a significant reduction in the CAP, which implies an improvement in hepatic steatosis. These results are similar to what was observed in the current cohort; however, we used the XL probe, which was designed for use in patients with obesity for the assessment of fibrosis490 and steatosis523.

The DJBS is purported to act as a RYGB mimetic, interfering with absorption of both macro- and micronutrients in the proximal GIT. Following bariatric surgery, changes in BMD and vitamin and mineral deficiencies have been reported524, 525, secondary to changes in vitamin and mineral absorption. In the current study, there was no significant change in BMD or Z score at any site. There was a significant reduction in the right femur T score, which is reflective of a comparison to a young healthy female. In all patients, this change in T score was clinically significant (defined as >5.6% change526) despite all patients remaining within the normal range as per WHO criteria527. 105

At one year post-explant, there was no statistically significant difference in BMD compared to baseline. To date, only one other study has evaluated the impact of DJBS treatment on BMD518. In a 52 week study of 19 patients with obesity and T2DM, a significant reduction (4.1±4.0%) in BMD at the femoral neck was observed. The authors observed no significant change in osteocalcin and a significant increase in Insulin-like Growth Factor-1, concluding the changes in BMD observed were not clinically significant. These results suggest DJBS treatment does not have a negative long-term impact on BMD.

In our cohort, a statistically significant reduction in vitamin E was observed over the 48 week intervention period. No significant change in any other fat soluble vitamin was observed and despite the statistically significant difference in vitamin E, values remained within the normal clinical reference range. This is consistent with other data469 and suggests DJBS treatment does not have a clinically significant effect on fat soluble vitamin status.

A significant reduction in serum ferritin was also observed. Three other studies have reported a significant decrease in serum ferritin, a marker of iron storage, following DJBS treatment452, 469, 518. This could imply that the DJBS reduces iron absorption in the duodenum or that low volume GI bleeding at the anchor site may occur. However, ferritin may not be an appropriate marker of iron status in our cohort. Iron is an acute phase reactant, and therefore the reductions observed may be reflective of an improvement in diabetes528 and NAFLD-related529 inflammation.

A significant increase in vitamin D was observed across the intervention period. This may reflect increased outdoor time associated with increased physical activity (as reported in the Active Australia Survey). Two previous studies have reported a significant increase in vitamin D following DJBS treatment463, 518, whist two have reported no change452, 469. In one study, this increase in vitamin D was attributed to the study supplementation protocol; however, the second study used data from a national registry so information about supplementation protocol or seasonal variation was not provided. In those who completed follow-up at one year post-explant, vitamin D was significantly lower than baseline. This is possibly associated with seasonal variation secondary to a delay in blood test sampling for this marker in some patients, with testing occurring at the end of winter.

A number of centres have prescribed a daily multivitamin and iron supplement as part of their treatment protocol; however, circulating micronutrient status was not reported in these studies447, 448, 450, 461. The prescription of micronutrient supplementation aligns with recommendations for nutrition care following bariatric surgery530. In our study, prophylactic supplementation was not prescribed. Our results show that, with the exception of iron status, no clinically significant negative effect on vitamin and mineral status was observed. Overall, the results from our cohort do not suggest that blanket supplementation is warranted. It is possible the frequent dietetics input, which focussed 106

on improving diet quality to facilitate weight loss, may contribute to the lack of deficiencies observed in our cohort. Unlike surgical procedures, which result in permanent anatomical changes, the DJBS is used as a transient measure to facilitate weight loss. This may reduce the long-term impacts on nutrition status; however, this is the first study to assess long-term post-explant markers of nutrition status. At one year post-explant, ferritin remained significantly lower than baseline. This warrants further investigation to determine if duodenal morphology is impacted by DJBS treatment, as villous atrophy could reduce iron absorption, as observed in coeliac disease531.

A number of novel investigations were conducted as part of this study. This is the first study to report on diet following DJBS implantation, either in the form of dietary intake data or detail on dietary compliance, despite the majority of studies prescribing a calorie-controlled diet that in itself would initiate weight loss. The magnitude of weight loss observed after DJBS treatment is greater than that typically achieved by dietary modification alone, which is most efficacious in the first six months of treatment, with weight loss plateauing at 12 months269. The majority of studies define dietary prescriptions or recommendations poorly, particularly beyond the first two weeks post-procedure where a texture modified diet is recommended by the device manufacturer. Further, the heterogeneous nature of advice given may impact study results. In the present study, diet was measured monthly using a 24 hour recall and individualised dietary advice was provided to facilitate weight loss by improving diet quality. Broadly, favourable changes in dietary intake, such as a significant reduction in energy intake (7,703±2978 kJ vs 4474±1468) and the contribution of fat to overall intake (35±6% vs 26±9%) were observed during the treatment period. At one year post- explant, self-reported energy intake remained significantly reduced, as compared to baseline. In parallel, there was a significant reduction in fat and carbohydrate intake. The impact on overall dietary macronutrient distribution was limited. A 24 hour recall is not an indicator of long-term intake, and the weight regain that occurred in our cohort suggests an increase in energy intake has occurred.

This is the first study to assess functional exercise capacity and HRQoL following DJBS treatment. There was a significant improvement in exercise tolerance and self-reported physical activity. At one year post-explant, functional exercise capacity remained significantly improved, with patients able to walk significantly further (500±84 m vs 607±92 m) during the 6MWT, despite no maintained increase in self-reported physical activity. There was also a significant improvement in six of eight HRQoL domains during the intervention period. Improvements in HRQoL have been observed in other weight loss interventions. In a systematic review of 53 weight loss intervention studies, there was a significant improvement in HRQoL in 17 studies; but that was not significantly associated with weight loss532. This suggests that weight loss itself is not the key driver for improvement in HRQoL.

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In our cohort, there was no persistent improvement in HRQoL at one year post-explant, despite maintenance of significant weight loss. This may be associated with patient expectations, as whilst a significant amount of weight loss was maintained, a large magnitude of weight regain occurred. This phenomenon warrants further investigation to determine the modulators of quality of life following treatment with a removable endoscopic weight loss aid.

There have been limited reports of data from long-term follow-up of patients after device explantation. These data have mostly been limited to weight and metabolic outcomes. The majority of studies with follow up to one year post-explant have reported a statistically significant reduction in body weight, compared to baseline453, 458, 462. Conversely, the majority of studies have reported no persistent improvement in glycaemic control at one year post-explant453, 458, 482; although, one study 462 has reported a sustained reduction in HbA1c at one year post-explant . The results of our study mirror the literature, with a statistically significant reduction in body weight at one year post-explant compared to baseline, but no sustained improvement in glycaemic control. Whilst this could be interpreted as a negative finding, there was a trend toward a significant reduction in MetSSS. This suggests that despite deteriorations in metabolic parameters that were improved through treatment, there may be some persistent metabolic benefit following DJBS treatment. Similarly, there was no sustained improvement in markers of hepatic fibrosis and steatosis, although ALT remained significantly reduced as compared to baseline. Of the nine patients that completed follow-up at one year post-explant, seven underwent repeat transient elastography studies. Of these, only three had been able to maintain clinically significant ( ≥7% in the context of NASH) weight loss, which has previously been demonstrated to improve hepatic fibrosis and steatosis533, 534 and this may explain the amelioration of beneficial effects.

In any weight loss intervention, establishing baseline predictors of success is helpful for triaging patients based on their unique physiology to maximise positive weight loss outcomes. One study has evaluated baseline predictors of success in a cohort of 185 individuals with severe obesity and T2DM452. In this cohort, regression modelling revealed high fasting c-peptide level (a marker of insulin production) and higher body weight (>107 kg) were independent predictors of treatment success, which was defined as TBWL of ≥5% (with separate targets for improvements in T2DM). In the present cohort, bivariate correlations were used to screen for baseline predictors of weight loss. Two predictors were found - baseline diastolic blood pressure, which was positively correlated with weight loss and baseline self-reported physical function score, with poorer physical function being negatively correlated with weight loss. Clinically, these are not useful for triaging patients for weight loss interventions, and statistically the study is underpowered to accurately capture baseline predictors

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of treatment efficacy. Further studies are required to determine clinically meaningful baseline predictors of intervention success.

The overarching limitations of the study include the small sample size and lack of a randomised control group (either diet only or sham endoscopy). Further, the dietary collection methodology, a monthly 24 hour recall, provides an isolated snapshot of dietary intake and cannot be representative of an entire month. Additionally, like all dietary intake assessment methodologies, this method requires accurate self-report. This method was selected to reduce participant burden.

The study also has several strengths. This is the first study to report on dietary intake, functional exercise capacity and HRQoL during the treatment period. However, the key strength of the study is the breadth of post-explant follow-up investigations. This is the first study to assess body composition, BMD, dietary intake, functional exercise capacity and HRQoL following device explant. We also assessed patients using a number of “gold standard” or clinically practical techniques. To date, there have been limited reports of body composition and BMD following DJBS treatment using the “gold standard” DEXA methodology. This is only the second study to use transient elastography to assess hepatic stiffness and the CAP (as a marker of steatosis) following DJBS treatment. Whilst this is not the “gold standard” for assessment of NAFLD pathology, this tool is a simple, non-invasive measure that is commonly used in clinical practice. Overall, the breadth of these investigations provide new clinical information on the impact of DJBS treatment on obesity-related comorbidities.

In summary, significant improvements in body weight and markers of the metabolic syndrome were observed at the end of DJBS treatment. While some patients were able to maintain clinically significant improvements in body weight, the majority of patients regained a significant amount of weight after removal of the DJBS. Despite this, there was a trend towards an improvement in metabolic syndrome severity. Little is known about the mechanisms via which the DJBS leads to significant weight loss. Subsequent chapters of this thesis will assess the impact of DJBS treatment on GI function, the GI microbiota and the immune system. These investigations may identify critical factors for a clinical response to treatment.

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Chapter 4: Effect of the Duodenal-Jejunal Bypass Sleeve on Gastrointestinal Function

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4.1 INTRODUCTION Obesity and related comorbidities such as T2DM have been associated with a number of perturbations of normal GI function. In particular, obesity and T2DM have been associated with delayed gastric emptying535-538. However, the largest study of patients with obesity reported significantly accelerated gastric emptying of solids in a cohort of 323 patients85. Gastric emptying rate is a relevant determinant of food intake, associated with feelings of fullness and satiation539. Therefore, in an obese cohort, delaying gastric emptying to enhance satiety would likely facilitate weight loss. T2DM has also been associated with an increased prevalence of PEI106, 540, 541, which results in a decrease in pancreatic enzymes and therefore changes to nutrient digestion and absorption. Many treatments for obesity and T2DM, including pharmacotherapy, endoscopic techniques and surgery, target the GIT to facilitate weight loss and metabolic improvements. As such, measures of GI function, and measurements of the impact of individual treatments on satiety and digestion are critical, as they have implications for treatment outcomes and tolerability.

The DJBS dwells in the proximal duodenum, yet there have been a limited number of studies assessing the impact of DJBS implantation on GI function. Two studies have assessed gastric emptying kinetics following DJBS implantation. In a cohort of 25 patients with obesity and T2DM, de Moura et al. used gastric scintigraphy to assess gastric emptying kinetics over four hours485. At baseline, 88% of patients had normal gastric emptying kinetics. The in situ presence of the DJBS significantly delayed gastric emptying at all time-points over the four hour test. There was also a trend towards a relationship between gastric retention at four hours and weight loss. Four weeks after device explant, there was a significant persistent delay of gastric emptying kinetics in the first two hours of the test. In a second study of 19 patients with obesity (10 with normal glucose tolerance and nine with T2DM), gastric emptying was assessed by paracetamol absorption kinetics over four hours456. In this study, no significant change in gastric emptying following DJBS implantation was reported. However, there was no description of whether patients had normal gastric emptying at baseline, making interpretation of these results difficult.

There are no published reports on the impact of DJBS implantation on fat absorption, despite suggestion that fat absorption may be impaired by the device separating chyme from bile and pancreatic secretions to the mid-jejunum, where the two streams meet446. Further, whilst GI symptoms have been reported as adverse events of treatment in the majority of published studies445, 447-452, 454-459, 461, 462, 465, 469, 475, 482, they have not been investigated thoroughly, particularly as a potential mechanism of action for inducing weight loss. Therefore, to more fully understand the mechanisms of action of the DJBS, more detailed characterisation of the impact of the device on GI function is required. 111

4.1 Aims and Hypotheses The overall aim of this chapter was to characterise the effect of the in situ presence of the DJBS on GI function in patients with obesity and T2DM.

Specifically, gastric emptying, intraluminal lipolytic activity, self-reported GI symptoms and symptom response to a meal challenge were assessed, along with any links to magnitude of weight loss achieved.

It was hypothesised the in situ presence of the DJBS would:

• Delay gastric emptying, measured by the 13C octanoic acid breath test; • Reduce intraluminal lipolytic activity, measured by the 13C mixed triglyceride breath test; • Increase self-reported GI symptoms, measured using the SAGIS instrument; and • Increase meal-related GI symptoms, measured by a standardised nutrient challenge.

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4.2 METHODS

4.2.1 Gastric Emptying Gastric emptying was assessed at baseline, eight weeks post-implant and within three weeks after device explant using the 13C octanoic acid breath test. Patient classification was based on gastric emptying lag time, with a normal reference range of 118±73 minutes. Further details can be found in Chapter 2 Section 2.4.1.

4.2.2 Intraluminal Lipolytic Activity Intraluminal lipolytic activity was assessed at baseline, eight weeks post-implant and within three weeks after device explant using the 13C mixed triglyceride breath test. The key reporting outcome 13 was cumulative CO2 recovered after six hours. Faecal elastase was also assessed by Pathology Queensland to exclude concomitant PEI. Further details can be found in Chapter 2 Section 2.4.2.

4.2.3 Gastrointestinal Symptoms Meal-related GI symptoms were assessed at baseline, four weeks post-implant and within three weeks after device explant using a standardised nutrient challenge test. GI symptoms were also evaluated at each clinical review using the SAGIS instrument. Further details can be found in Chapter 2 Section 2.4.3 and Section 2.4.4.

4.2.4 Statistical Analysis All quantitative measures were described as mean and standard deviation and were assessed for normality of distribution using the Shapiro-Wilk normality test. Normally distributed data was analysed using paired t-tests, which compared pre- to DJBS in situ measurements or pre- to post- explant measurements. Non-normal data was assessed by the Wilcoxon signed rank test. Dependent on normality of distribution, Pearson or Spearman bivariate correlations were used to screen for variables (baseline or change from baseline) correlating with weight loss response at device explant (TBWL percentage) and to assess the relationship between change in energy intake and GI symptoms. Where a significant correlation was observed, linear regression was performed and reported.

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4.2.5 Schedule of Investigations

• Gastric emptying assessment • Intraluminal lipolytic activity assessment • Standardised nutrient challenge test Baseline • SAGIS questionnaire

• Standardised nutrient challenge test 4 weeks • SAGIS questionnaire post-implant

• Gastric emptying assessment 8 weeks • Intraluminal lipolytic activity assessment post-implant

• SAGIS questionnaire 36 weeks post-implant

• Gastric emptying assessment • Intraluminal lipolytic activity assessment • Standardised nutrient challenge test Explant • SAGIS questionnaire

Figure 4.1: Schedule of GI function investigations.

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4.3 RESULTS

4.3.1 Gastric Emptying At baseline, gastric emptying lag time was normal in all patients. DJBS implantation did not significantly impact gastric emptying lag time (Figure 4.2A, n=14; p=0.35) or half time (Figure 4.2C, n=14; p=0.45) measured after eight weeks of device dwell. At device explant, there was no significant difference in gastric emptying lag time (Figure 4.2B, n=14; p=0.93) or half time (Figure 4.2D, n=14; p=0.57), compared to baseline. There was no significant correlation between baseline gastric emptying rates, or change in gastric emptying rates from baseline to eight weeks, and weight loss at device explant. Gastric emptying lag time and half time at all time-points are presented in Figure 4.3.

Figure 4.2: Gastric emptying kinetics.

The in-situ presence of the device (measured after eight weeks of dwell time) did not delay gastric emptying lag time (A; p=0.35) or half time (C; p=0.45) as measured by the 13C octanoic acid breath test. There was no significant difference between baseline and post-explant gastric emptying lag time (B; p=0.93) or half time (D; p=0.57). Normal reference range for lag time of 118±73 minutes (A and B) shown as a dotted line on chart. Each patient is displayed as an individual line. One outlier at 8 weeks dwell time removed for graphical purposes, but included in statistical analysis. Baseline vs 8 weeks dwell time (A) and baseline vs. explant (B) gastric emptying lag time compared by paired t-test. Baseline vs 8 weeks dwell time (C) and baseline vs. explant (D) gastric emptying half time compared by Wilcoxon signed rank test.

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Figure 4.3: Gastric emptying lag time and half-time at baseline, with the DJBS in situ and within three weeks after device explant.

There was no significant difference in gastric emptying lag time (A) or half-time (B) across the three time- points as measured by the 13C octanoic acid breath test. Data are presented grouped by time-point. One outlier excluded for graphical purposes.

4.3.2 Intraluminal Lipolytic Activity With the exception of one patient with a low faecal elastase throughout, all other patients had normal faecal elastase measures at all time-points.

There was no significant impact of DJBS implantation on intraluminal lipolytic activity compared to baseline at any time-point (Figure 4.4A; n=18; p=0.72 and Figure 4.4B; n=11; p=0.06). In addition, there was no significant correlation between baseline intraluminal lipolytic activity, or change in lipolytic activity (baseline to eight weeks) and weight loss at device explant. Indeed, those patients with the greatest magnitude of weight loss both after eight weeks of dwell time and at device explant also had the highest levels of intraluminal lipolytic activity with the device in situ (r=0.49; p= 0.03 at eight weeks and r=0.53; p=0.04 at explant). This further suggests that reduced intraluminal lipolytic activity is not a contributing mechanism to DJBS induced weight loss. Intraluminal lipolytic activity at all time-points is summarised in Figure 4.5.

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Figure 4.4: Intraluminal lipolytic activity.

The in-situ presence of the device (measured after eight weeks of device dwell time) did not impact the cumulative dose of 13C recovered in breath samples (A; n=18; p=0.72) as measured by the 13C mixed triglyceride breath test. There was no significant change in post-explant cumulative recovery of 13C in breath samples, compared to baseline (B; n=11; p=0.06). Each patient is displayed as an individual line. Results assessed by paired t-test.

Figure 4.5: Intraluminal lipolytic activity at baseline, with the DJBS in situ and within three weeks after device explant.

There was no significant difference in the cumulative dose of 13C recovered in breath samples across the three time-points as measured by the 13C mixed triglyceride breath test. Data are presented grouped by time-point.

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4.3.3 Gastrointestinal Symptoms A standardised nutrient challenge test was performed to assess meal-related GI sensory function. There was a significant increase in postprandial pain, fullness, retrosternal burning and total symptom score four weeks after device implant (Figure 4.6, Table 4.1). After device explant, symptoms returned to baseline levels in all but two patients, who still had a significant symptom burden (Figure 4.7, Table 4.1). Although meal-related symptoms increased with the device in situ, there was no direct correlation between baseline symptom scores (individual or cumulative), or change in symptom scores (individual or cumulative) from baseline to four weeks and weight loss at device explant. Similarly, there was no significant relationship between change in dietary energy intake from baseline and change in symptom scores (individual or cumulative) from baseline to four weeks. GI symptom responses to a standardised nutrient challenge at all time-points are summarised in Figure 4.8.

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Figure 4.6: GI symptom response to a standardised nutrient challenge with the device in situ.

There was a significant increase in postprandial pain (A), fullness (B), retrosternal burning (D) and total symptom score (F) four weeks after device implantation. There was no significant change in nausea (C) or acid regurgitation (E). Each patient is displayed as an individual line. Results analysed by Wilcoxon signed rank test.

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Figure 4.7: GI symptom response to a standardised nutrient challenge within three weeks after device explant.

There was no significant difference in scores for any symptom within three weeks after device explant, compared to baseline. Each patient is displayed as an individual line. Results analysed by Wilcoxon signed rank test.

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Figure 4.8: GI symptom response to a standardised nutrient challenge test at baseline, with the DJBS in situ and within three weeks after device explant.

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There was a significant increase in postprandial pain (A), fullness (B), retrosternal burning (D) and total symptom score (F) four weeks after device implantation, but this did not persist within three weeks after device explant Data are presented grouped by time-point.

Table 4.1: Meal-related GI symptoms following a standardised nutrient challenge test. Parameter (unit) Baseline 4 weeks Explant (48 weeks) P value at 4 weeks^ P value at explant^^ Nutrient Challenge Test Pain 9 ± 10 48 ± 58 34 ± 66 0.01 0.24 Fullness 84 ± 57 148 ± 101 126 ± 75 0.02 0.17 Nausea 30 ± 51 49 ± 65 44 ± 71 0.37 0.51 Retrosternal Burning 7 ± 12 50 ± 58 29 ± 63 0.02 0.67 Acid Regurgitation 11 ± 17 39 ± 47 34 ± 62 0.07 0.37 Total Score 141 ± 100 334 ± 269 267 ± 307 0.003 0.16 ^ n=15 ^^ n=13 ^^^ n=8. Results analysed by Wilcoxon signed rank test as compared to baseline, and displayed as mean ± SD.

The intensity of 22 upper and lower GI symptoms was assessed using the SAGIS questionnaire. Four weeks after device implant, there was a significant increase in 14 individual GI symptoms and the total symptom score (Table 4.2). There was also a significant increase in symptoms from the epigastric pain, reflux, nausea/vomiting and constipation domains, but not the diarrhoea and discomfort domain (Figure 4.9). At 36 weeks post-implant, there was a significant increase in 12 individual GI symptoms and the total symptom score (Figure 4.9; Table 4.2). At the domain level, there was a significant increase in symptoms from all domains, including the diarrhoea and discomfort domain. Although symptoms increased with the device in situ, there was no correlation between baseline symptom domain scores, or change in domain scores from baseline at four or 36 weeks and weight loss at device explant.

Linear regression was used to assess the relationship between SAGIS scores, domain scores, or change in these score from baseline and dietary energy intake. There was no significant relationship between symptom intensity and change in dietary energy intake. It should be noted that the study was not powered for assessment of these relationships. However, these results suggest GI symptoms are persistent, and whilst not directly correlated with weight loss or changes in dietary intake, are likely to contribute to the reduction in energy intake reported in Chapter 3 Section 3.3.5.

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Figure 4.9: GI symptom (SAGIS) domain scores during the peri-implant period and late treatment period.

At four weeks (blue), there was a significant increase in all symptom domains, with the exception of diarrhoea and discomfort. At 36 weeks (red), there was a significant increase in all symptom domains. Mean of patient scores displayed on graph. Results assessed by Wilcoxon signed rank test.

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Table 4.2: Self-reported GI symptoms measured by the SAGIS instrument. Parameter Baseline 4 weeks 36 weeks P value at 4 P value at 36 weeks^ weeks^^ Individual Symptom Score Acid eructation 0.4 ± 0.8 0.7 ± 0.9 0.6 ± 1.0 0.10 0.32 Dysphagia 0.2 ± 0.4 0.3 ± 0.5 0.1 ± 0.3 0.08 0.32 Fullness 0.2 ± 0.5 1.2 ± 1.0 1.1 ± 1.1 0.003 0.01 Early satiety 0.1 ± 0.3 1.2 ± 1.0 1.3 ± 1.0 0.002 0.002 Postprandial pain 0.2 ± 0.5 1.3 ± 1.2 0.8 ± 1.0 0.001 0.01 Epigastric pain 0.2 ± 0.4 1.3 ± 1.2 0.9 ± 1.1 0.003 0.02 Retrosternal discomfort 0.2 ± 0.4 1.1 ± 1.1 0.7 ± 0.9 0.003 0.03 Pain/discomfort prior to 0.4 ± 0.8 0.5 ± 0.8 0.5 ± 0.8 0.43 0.10 defecation Difficulty defecating 0.5 ± 0.8 1.4 ± 1.4 0.8 ± 1.2 0.01 0.08 Constipation 0.4 ± 0.7 1.7 ± 1.3 0.9 ± 1.0 0.001 0.01 Loose stools 0.5 ± 0.8 0.7 ± 0.9 0.6 ± 0.8 0.21 0.41 Faecal incontinence 0.3 ± 0.6 0.2 ± 0.4 0.1 ± 0.3 0.66 0.16 Urgency to defecate 0.6 ± 0.8 0.5 ± 0.8 0.5 ± 0.8 0.53 0.41 Diarrhoea 0.5 ± 0.8 0.4 ± 0.8 0.1 ± 0.3 0.56 0.08 Loss of appetite 0.3 ± 0.6 1.0 ± 0.9 0.9 ± 0.7 0.01 0.01 Abdominal cramps 0.3 ± 0.6 1.2 ± 1.2 1.0 ± 1.1 0.01 0.01 Sickness 0.1 ± 0.2 0.4 ± 0.8 0.7 ± 0.9 0.03 0.02 Nausea 0.1 ± 0.2 0.5 ± 0.8 0.6 ± 1.0 0.02 0.03 Vomiting 0.1 ± 0.2 0.1 ± 0.2 0.4 ± 0.5 1.00 0.01 Bloating 0.7 ± 0.9 1.6 ± 1.3 1.2 ± 1.0 0.01 0.07 Excessive gas flatulence 0.6 ± 1.0 1.4 ± 1.2 1.4 ± 0.8 0.01 0.01 Belching 0.3 ± 0.6 0.9 ± 1.2 1.1 ± 1.2 0.02 0.01 Cumulative GI symptoms 6.8 ± 9.1 19.5 ± 15.7 ± <0.001 0.001 14.2 13.2 Domain Score Epigastric pain 1.8 ± 2.6 8.8 ± 6.8 6.8 ± 5.4 <0.001 0.001 Diarrhoea and discomfort 2.8 ± 3.7 3.7 ± 3.1 3.1 ± 2.4 0.11 0.04 Reflux 0.9 ± 1.5 1.9 ± 2.3 1.7 ± 2.2 0.01 0.02 Nausea/vomiting 0.4 ± 0.8 2.0 ± 2.2 2.6 ± 2.8 0.002 0.004 Constipation 0.9 ± 1.5 3.1 ± 2.6 1.8 ± 2.1 0.002 0.02 ^ n=19 ^^ n=16 ^^^ n=12. Results displayed as mean ± SD. Data analysed by Wilcoxon signed rank test. Results deemed significant if p<0.05. GI; gastrointestinal

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4.4 DISCUSSION Over the past ten years, the DJBS has been investigated for clinical efficacy and safety; however, little is known about the potential mechanisms of action in inducing weight loss. The key finding of our study was, contrary to our expectations, there was no evidence that DJBS treatment altered intraluminal lipolytic activity, as measured by the absorption of a 13C labelled mixed triglyceride. This finding is important as the device has previously been proposed to alter fat absorption446.

In patients with obesity, a delay in gastric emptying could enhance satiety and thus may facilitate weight loss. However, gastric emptying has been investigated in two studies after DJBS implantation, with conflicting results456, 485. In our cohort, we found no evidence of alteration in gastric emptying following DJBS treatment.

This is the first study to determine the prevalence of GI symptoms following placement of the DJBS. Epigastric pain symptoms have been widely reported as adverse events of DJBS treatment in the literature445, 447-452, 454-459, 461, 462, 465, 469, 475, 482 (see Chapter 1 Section 1.3.4.2). However, we believe they are key to the weight loss response to treatment. Assessment of GI symptoms with the SAGIS instrument showed there was a significant increase in GI symptoms from the early (four weeks post- procedure) to the late (36 weeks post-procedure) intervention period. In particular, epigastric pain domain symptoms (such as pain, fullness and early satiety) were increased nearly five-fold in the early intervention period, when weight loss is most rapid. Similarly, there was a significant increase in meal-related symptom response to a nutrient challenge test (self-reported abdominal pain, fullness and retrosternal burning). This suggests the generation of these epigastric symptoms are a relevant meal-related response. An alternate explanation for the relationship between the meal challenge and symptoms is that the study meal was a liquid meal, which does not reflect a typical meal for the patients. Therefore, it is possible that the liquid meal triggered an increase in symptoms for reasons other than DJBS placement, such as taste aversion. However, the finding of increased GI symptoms with the DJBS in situ from survey data measured over the preceding week supports a relationship between the DJBS and GI symptoms. Therefore, in concert with the reduction in dietary energy intake observed (Chapter 3 Section 3.3.5), these data suggest that an increase in meal-related symptoms may precipitate a reduction in dietary intake, facilitating weight loss.

In this study, the 13C mixed triglyceride breath test was used to assess intraluminal lipolytic activity as a measure of lipid absorption. At baseline, a detailed clinical assessment of pancreatic exocrine function was undertaken, with no clinically significant abnormalities detected. We hypothesised that DJBS implantation would reduce intraluminal lipolytic activity. However, we did not observe a significant reduction in intraluminal lipolytic activity following device implantation. This suggests that fat malabsorption is not a key mechanism of device action. Further supporting this is the lack of 125

clinical evidence of fat malabsorption (such as diarrhoea) reported on the SAGIS questionnaire. Data from a study of the RYGB has suggested at least 70 cm of jejunum must be included in the biliopancreatic limb to induce significant fat malabsorption284. At a total length of 60 cm, the DJBS device has only a limited projection into the jejunum. Taken together, these data suggest the DJBS does not induce significant fat malabsorption.

In this study, we utilised non-invasive breath tests using 13C labelled substrates to measure gastric emptying kinetics and intraluminal lipolytic activity. These tests were selected as they are well validated, safe and can be repeated frequently without exposing the patient to ionising radiation. The “gold standard” for assessment of gastric emptying is scintigraphy, a measure which involves exposing patients to ionising radiation. Although the breath test has been validated against scintigraphy, the method relies on uptake of the 13C labelled isotope in the duodenum. In the current study, the duodenum is excluded by the DJBS device, which theoretically reduces the accuracy of the in situ measure. However, little is known about the amount of chyme that is able to travel down the mucosa-adjacent surface of the sleeve for absorption. If truly completely excluded, the labelled substrate would not be absorbed until it reached the mid-jejunum. Despite this, small intestinal transit time is rapid and therefore a significant difference in gastric emptying induced by increased transit to the jejunum may not be detectable using this test methodology. Indeed, we observed no significant reduction in 13C labelled substrate absorption with the device in situ suggesting the test is appropriate for use in this population.

In relation to fat malabsorption, the “gold standard” of indirect measurement of steatorrhoea is measuring 72 hour faecal fat on a standardised relatively high fat diet. The measurement of faecal fat is burdensome for the patient and requires compliance with a diet of known fat quantity (100 g) for the test duration. It also provides a significant calorie load (2,700 calories from fat), which is counterintuitive in a weight loss intervention. Therefore the mixed triglyceride breath test was considered suitable for use in our patient group.

A limitation of the study is the relatively small sample size. Indeed, the associations between any of the measured variables and weight loss did not reach statistical significance, and this might be due to the small sample size. Another limitation of the study is that we did not measure appetite-related changes. This study was focused on the thorough investigation of GI function, not changes in GI appetite regulatory hormones and subjective changes in appetite. These are likely to be relevant for inducing weight loss and would provide further insight into the links between device mechanisms of action, GI function and symptoms, and magnitude of weight loss.

Based on the results of this study, the significant increase in GI symptoms following DJBS treatment appears relevant to the weight loss response to treatment. Contrary to our expectations, and what has 126

been suggested previously in the literature, no significant change in gastric emptying or intraluminal lipolytic activity was observed. This suggests changes to these aspects of GI function are of limited relevance to the weight loss response. Whilst the study is small, future studies should aim to assess the contribution of GI symptoms, and in particular meal-related symptoms, to the weight loss response observed after deployment of the DJBS, as well as the precise mechanisms behind symptom generation.

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Chapter 5: Effect of the Duodenal-Jejunal Bypass Sleeve on the Gastrointestinal Microbiota

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5.1 INTRODUCTION Alterations in the GI microbiota have been implicated in the pathogenesis of both obesity171, 173 and T2DM181, 542. In humans, a key reporting outcome has been the ratio of Firmicutes to Bacteroidetes, with patients with obesity having a higher ratio than lean controls. Murine models have elegantly suggested a more causative role in obesity pathogenesis, with germ free mice having less body fat, despite a greater feed intake169. By allowing these mice to become colonised with caecal microbes, body fat was increased, despite a reduction in feed intake. A number of factors have been implicated linking changes in the microbiota with obesity, and perhaps the most noteworthy is diet166, 167.

Both the influence of diet on the human gut microbiota and the rapid impact of changes in diet on the microbiota have been clearly demonstrated151, 377, 543. However, there is conflicting evidence on the impact of dietary weight loss intervention trials on the microbiota (at phylum level), with some studies showing no change175, 189-192, two studies showing increases in Bacteroidetes and decreases in Firmicutes171, 193 and one study reporting increases in Firmicutes and decreases in Bacteroidetes194.

The majority of studies assessing the impact of weight loss on the GI microbiota come from bariatric surgery, which causes permanent anatomical changes to the GIT. Following LSG, the most commonly performed bariatric surgical procedure in Australia, four studies have reported increases in Bacteroidetes and/or decreases in Firmicutes372, 378-380, one study has reported a decrease in Bacteroidetes 371, whilst two others report no change in either phylum370, 382. Following RYGB, three studies have reported a decrease in Firmicutes to Bacteroidetes367-369, one has reported a significant increase in Firmicutes to Bacteroidetes371 and one has reported no change370. Broadly, following either LSG or RYGB, increases in Bacteroidetes, Fusobacteria, Proteobacteria (in particular members of the Gammaproteobacteria class and Enterobacteriaceae family) and Verrucomicrobia (specifically Akkermansia) have been reported, whilst a reduction in Firmicutes (in particular members of the Lactobacillaceae and Lachnospiraceae families) have been reported.

To date, there is one published report on the impact of DJBS treatment on the GI microbiota479. In this study, transient taxonomic changes in the stool microbiota were reported after DJBS implantation. Specifically, significant increases in Gammaproteobacteria (including members of the Enterobacteriaceae family) and the Clostridiaceae family were observed. In parallel, reductions in butyrate-producers, such as Ruminococcus were observed. However, due to the paucity of investigations, changes in the microbiota following bariatric surgery are the closest thoroughly investigated comparator, as the transient changes in GI physiology induced by the device most closely mimic those induced by RYGB. The evidence for changes in the GI microbiota following bariatric

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surgery is derived from reports of changes in the stool microbiota. We hypothesize that the DJBS will result in changes to the stool microbiota. However, given the observed impacts of the DJBS on upper GI symptoms, such as epigastric pain (Chapter 1 Section 1.3.4.2), the MAM in the upper GIT, which is not reflected by the stool544, may also play an important role.

The microbial community in the gut adapts to the host environment. The proximal small intestine is usually an area of high nutrient availability and rapid transit. Nutrient digestion is facilitated by bile and digestive enzymes, while bile itself exerts a selective pressure on the microbiota545. The DJBS excludes nutrients from the duodenal mucosa and separates bile and digestive enzymes from the chyme. The impacts of this on the MAM have not been studied. Further, the acid environment is altered by mandatory PPI treatment during DJBS implantation to reduce the risk of upper GI bleeding. As the device dwells in the small intestine, characterisation of microbial community formation on the sleeve itself may provide a link between microbial communities and treatment efficacy, as well as the effect the presence of the device exerts on the MAM in the duodenum. In addition to the potential to understand the mechanisms of action of the device, profiling of the microbiota following treatment with the DJBS may also be important for understanding the drivers of complications. In particular, this may be relevant for understanding the pathogenesis of hepatic abscess, a rare complication of DJBS treatment proposed to occur as a result of ascending cholangitis or secondary to perforations induced by the anchor barbs.

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5.1.1 Aims and Hypotheses The aims of this chapter were to assess the changes in mucosal-, stool-, and sleeve-associated microbial communities following DJBS treatment, and to correlate microbiota changes with clinical characteristics.

It was hypothesised that implantation of a DJBS for 48 weeks would:

• Alter the gastric and duodenal MAM, measured by community profiling of gastric and duodenal mucosa at baseline and explant; • Alter the stool microbiota, measured by community profiling of stool at baseline, 8 weeks and explant; • Result in a microbial community forming on the mucosa-adjacent surface of the DJBS, measured by community profiling of mucosa-adjacent and luminal-adjacent biofilm microbiota at explant; • Result in an increase in bacterial load in the duodenum, measured by qPCR of gDNA isolated from mucosal biopsies; and • Result in a change in microbiota profile that will correlate with either the presence of constipation and/or with weight loss observed.

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5.2 METHODS

5.2.1 Sample collection At the time of endoscopy at baseline and explant, biopsy samples were taken from the mucosa of the gastric antrum and the second part of the duodenum. At explant, the duodenal biopsy was taken from mucosa that had been covered by the device during the treatment period. Device specimens were taken from six points along the device (see Figure 2.2) to represent the mucosa-adjacent and luminal- adjacent microbiota at the upper, mid and lower sections of the device. Stool samples were taken at baseline, eight weeks post-implant and within three weeks after device explant. Mucosal samples were stored in RNAlater® (Qiagen), incubated at room temperature for 30 minutes, snap-frozen, and stored at -80°C. Stool and device samples were snap-frozen and stored at -80°C. Full details are outlined in Chapter 2 Section 2.5.1.

5.2.2 16S rRNA sequencing for microbiota identification In brief, gDNA was isolated using a modified repeated bead beating method506 and purified using an automated system. Purified gDNA was amplified using primers targeting the hypervariable region (V6-V8) of the 16S rRNA gene using either bacterial specific primers (for biopsy and device samples), or universal primers (for stool and repeated device samples). PCR amplicons were barcoded using the Nextera XT v2 dual-index system and sequenced using the Illumina MiSeq platform. Raw data underwent bioinformatics analysis using the QIIME pipeline. OTUs were assigned using Greengenes version 13.8 as the reference database. Full details are outlined in Chapter 2 Sections 2.5.2-2.5.6.

5.2.3 Functional metagenomics analysis of 16S rRNA sequencing data Functional metagenomics data was predicted from the 16S rRNA marker gene using the PICRUSt software package. Data were summarised as functional pathways for analysis. Further details can be found in Chapter 2 Section 2.5.7.

5.2.4 Bacterial load assessment Bacterial load was assessed by qPCR using gDNA from mucosal biopsy specimens. Both the human β-actin gene and the bacterial 16S rRNA gene were amplified using optimised primer sets. The ratio of 16S rRNA to human β-actin was used as a measure of bacterial load. Further details can be found in Chapter 2 Section 2.5.8.

5.2.5 Statistical analyses Prior to analysis, a total of 81 contaminant OTUs were removed (55 for 917 primer; 26 for 926 primer; see Chapter 2 Section 2.5.6 and Appendix 1.5) and microbial community profile data (OTU table) was normalised using total sum scaling with square root transformation, accounting for RA of taxa.

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A full list of OTUs present in samples can be found in Appendix 1.6. Data was analysed using the web-based program Calypso v8.48 (for phylogenetic analysis) and v8.68 (for functional predictions)546 and GraphPad Prism Version 7 (GraphPad Software Inc., CA, USA).

Alpha diversity (abundance and evenness of taxa) was assessed by the Shannon Index, with paired t-tests or Wilcoxon matched pairs signed rank tests used to test for significance, dependent on normality of distribution. Beta-diversity (inter-individual microbiota profile similarity) was visualised using principal co-ordinate analysis using a weighted UniFrac distance matrix. Differences in community profiles between groups was assessed using the Adonis metric, a multivariate analysis of variance technique that accounts for phylogenetic distance. A constrained analysis (sparse partial least squares discriminant analysis) was applied to discriminate the relative importance of taxa based on sample grouping (for example, baseline vs explant)547. Data on longitudinal change in abundance of individual taxa and functional pathways were assessed in Calypso by the inbuilt repeated measures analysis (mixed effects linear regression modelling), and corrected for repeated measures using the False Discovery Rate (FDR). Taxonomic differences between those who had their devices explanted early secondary to abdominal pain, compared to the rest of the cohort, were assessed by the Mann Whitney U test. Differences between DJBS patients and controls were assessed by the Mann Whitney U test. Bacterial load results were assessed for significance using a Wilcoxon signed rank test.

To assess the relationship between the RA of taxa and clinical measures, bivariate correlations were performed using IBM Statistical Package for Social Sciences Version 24 (IBM Corp., NY, USA) to screen for candidate variables for univariate linear regression analyses. Taxa were only screened where the minimum mean RA of that taxon was 1%. Non-normally distributed data was square root transformed. Any variables that were significant on univariate analysis were added to a multiple linear regression model with backward elimination. Significance of results was determined by a two-tailed p value of less than 0.05.

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5.2.6 Schedule of investigations

• Community profile assessment: • Mucosal biospy: gastric antrum and second part of the duodenum • Stool sample • Bacterial load assessment: Baseline • Mucosal biospy: gastric antrum and second part of the duodenum

• Community profile assessment: 8 weeks • Stool sample post-implant

• Community profile assessment: • Mucosal biospy: gastric antrum and second part of the duodenum • Stool sample • Device sampling • Bacterial load assessment: Explant • Mucosal biospy: gastric antrum and second part of the duodenum

Figure 5.1: Schedule of gastrointestinal microbiota investigations.

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5.3 RESULTS

5.3.1 Mucosa-associated microbiota We assessed microbial profiles of the MAM in the antrum and second part of the duodenum at baseline and device explant using high-throughput sequencing methods. After data quality control and filtering, a sequencing profile was successfully obtained for 11 of 19 baseline gastric biopsies and eight of 19 baseline duodenal biopsies. At explant, a sequencing profile was successfully obtained from all 19 patients at both sites, allowing for longitudinal comparison for 11 patients (gastric) and eight patients (duodenum). A control group of 23 patients referred for investigation of iron-deficiency anaemia (n=10) or positive faecal occult blood test (n=13) who underwent identical investigation of the gastric and duodenal MAM were recruited from a separate study at the same site (HREC/13/QPAH/690). From these patients, a total of 15 (five with BMI <25 kg/m2, five with BMI 25-29.9 kg/m2, five with BMI > 30 kg/m2) gastric mucosal biopsies and 20 (six with BMI <25 kg/m2, seven with BMI 25-29.9 kg/m2, seven with BMI > 30 kg/m2) duodenal mucosal biopsies were included for analysis.

A total of 765,812 reads were generated from mucosal samples, representing 248 OTUs. Specifically, sequences were identified from eight phyla, 13 classes, 17 orders, 29 families and 44 genera. At baseline, the mean number of reads following bioinformatics processing was 7,573 and 6,675 from gastric and duodenal biopsies, respectively. At explant, the mean number of reads was 14,212 and 9,941 from gastric and duodenal biopsies, respectively. In the control cohort, the mean number of reads following bioinformatics processing was 6,348 and 3,750 from gastric and duodenal biopsies, respectively. More specifically, the mean number of reads from gastric biopsies was 4,473, 6,209 and 8,363 for controls with a BMI < 25 kg/m2, with a BMI of 25-29.9 kg/m2 and with a BMI of > 30 kg/m2, respectively. The mean number of reads from duodenal biopsies was 3,147, 4,873 and 3,413 for control with a BMI < 25 kg/m2, with a BMI of 25-29.9 kg/m2 and with a BMI of > 30 kg/m2, respectively.

At baseline, both the duodenal and gastric biopsies were similar to the control biopsies. In the stomach, Streptococcus and Prevotella were the predominant genera. In the DJBS cohort, four patients were infected with H.pylori, leading to this being the third most abundant organism (at genus level). In the control cohort, the third most abundant genus was Veillonella. Similarly, in the duodenum, Streptococcus, Prevotella and Veillonella were the most abundant genera in both the DJBS and control cohorts. However, at device explant, the stomach was primarily colonised by Prevotella, Lactobacillus and members of the Enterobacteriaceae family. The duodenum was primarily colonised by Lactobacillus, Prevotella, and Streptococcus. Table 5.1 summarises the most abundant taxa at each sample site across the three comparison groups. 135

Table 5.1: RA of the three most abundant taxa (at genus level) in biopsy specimens

Baseline Explant Control Gastric Streptococcus (mean RA 35%) Prevotella (mean RA 17%) Streptococcus (mean RA 43%) Prevotella (mean RA 21%) Lactobacillus (mean RA 12%) Prevotella (mean RA 16%) Helicobacter (mean RA 20%) Enterobacteriaceae (mean RA 10%) Veillonella (mean RA 9%) Duodenum Streptococcus (mean RA 43%) Lactobacillus (mean RA 19%) Streptococcus (mean RA 50%) Prevotella (mean RA 24%) Prevotella (mean RA 14%) Prevotella (mean RA 15%) Veillonella (mean RA 8%) Streptococcus (mean RA 9%) Veillonella (mean RA 7%)

5.3.1.1 Gastric biopsies We assessed the MAM at baseline and explant in the gastric antrum. Full statistical analyses can be found in Appendix 1.7. In the overall cohort, between baseline and explant, there was a significant increase in the RA of two phyla - Fusobacteria and Synergistetes (Table 5.2). At the genus level, there was a significant increase in the RA of 11 taxa (Table 5.2). All of these taxa had a low baseline RA (mean RA <1%). Conversely, there was a significant reduction in seven genera, including Streptococcus and Helicobacter which were the most and third most abundant genera at baseline, respectively. The reduction in Helicobacter is as a direct result of antibiotic triple therapy H.pylori eradication in four patients. This occurred within the first eight weeks post-implant, with no further antibiotic treatment provided in the remaining 40 weeks until post-explant measures. The remaining seven patients with baseline gastric biopsy data did not have a significant presence of Helicobacter (<0.01% RA). When the antibiotic treatment patients were removed, a significant increase in the RA of four of the initial 11 taxa (at genus level) remained, whilst a significant decrease in the RA of six of the initial seven genera remained. In the full cohort, at OTU level, there was a significant increase in 28 OTUs, including 10 Enterobacteriaceae OTUs, 10 Lactobacillus OTUs, three Klebsiella OTUs, two Selenomonas OTUs and one each of Gammaproteobacteria, Bifidobacterium and Fusobacterium. A significant decrease in five Streptococcus OTUs, two Actinomyces OTUs and one Prevotella melaninogenica OTU was also observed. Broadly, the overall results suggest the DJBS causes significant changes in the gastric microbiota and promotes proliferation of taxa that are otherwise low abundance in this region. In addition to these broad taxonomic changes, there was a trend towards a significant increase in alpha diversity in gastric biopsies from baseline to explant (Figure 5.2; Shannon Index; n=11; 1.50±0.51 vs. 2.00±0.39; p=0.0529).

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Table 5.2: Taxonomic changes in the MAM of the gastric antrum from baseline to explant.

FDR q Change Baseline Explant from RA (%) RA (%) Taxon Baseline

Phylum Level

Fusobacteria 0.0048 Increased 1.21±1.28 8.78±10.28

Synergistetes 0.04 Increased 0.00±0.01 0.09±0.12

Genus Level

Rothia 0.00024 Decreased 1.47±1.46 0.16±0.23

Streptococcus 0.00035 Decreased 35.02±19.97 8.37±11.09

Dialister 0.0039 Increased 0.05±0.09 1.48±1.83

Fusobacterium 0.0039 Increased 0.88±1.14 6.89±9.42

Unclassified Enterobacteriaceae 0.0039 Increased 0.90±2.45 10.30±10.79

Unclassified Gammaproteobacteria 0.0039 Increased 0.02±0.02 6.30±7.62

Lactobacillus 0.0041 Increased 0.53±0.89 12.30±14.67

Klebsiella 0.0041 Increased 0.02±0.05 3.55±4.45

Actinomyces 0.0048 Decreased 2.76±2.89 0.50±0.64

Granulicatella 0.0048 Decreased 0.88±0.81 0.20±0.38

Bifidobacterium 0.0072 Increased 0.00±0.00 1.51±2.34

Helicobacter 0.012 Decreased 19.86±33.91 0.00±0.00

Selenomonas 0.013 Increased 0.20±0.22 2.83±4.76

Neisseria 0.021 Decreased 3.37±4.44 0.93±2.49

Bulleidia 0.021 Decreased 0.73±1.18 0.09±0.12

Erwinia 0.024 Increased 0.00±0.00 0.02±0.03

Megasphaera 0.024 Increased 0.66±0.88 1.75±1.70

TG5 0.024 Increased 0.00±0.01 0.09±1.70

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FDR q Change Baseline Explant from RA (%) RA (%) OTU Baseline p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 0.00006 Decreased 15.28±10.85 2.27±3.52 p__Fusobacteria__g__Fusobacterium_34791 0.000078 Increased 0.31±0.71 3.23±4.01 p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU379777 0.00098 Decreased 9.97±6.77 2.52±4.16 p__Firmicutes__g__Streptococcus__s__infantis_517754 0.00098 Decreased 2.59±2.15 0.44±0.76 p__Proteobacteria__f__Enterobacteriaceae_823118 0.00098 Increased 0.04±0.06 2.81±3.23 p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0.002 Decreased 1.37±1.39 0.26±0.50 p__Actinobacteria__g__Actinomyces_4350499 0.002 Decreased 1.53±1.66 0.20±0.24 p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 0.0022 Decreased 15.95±12.80 3.67±6.13 p__Proteobacteria__c__Gammaproteobacteria_808486 0.0028 Increased 0.02±0.02 6.30±7.62 p__Proteobacteria__f__Enterobacteriaceae_822899 0.0028 Increased 0.02±0.04 0.31±0.35 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.003 Increased 0.00±0.00 0.86±1.15 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.0037 Increased 0.01±0.02 1.85±2.52 p__Proteobacteria__f__Enterobacteriaceae_819636 0.0037 Increased 0.02±0.06 0.86±1.11 p__Firmicutes__g__Lactobacillus_563654 0.0037 Increased 0.00±0.01 0.30±0.49 p__Actinobacteria__g__Actinomyces_565136 0.0037 Decreased 1.15±1.25 0.21±0.33 p__Proteobacteria__f__Enterobacteriaceae_332958 0.0066 Increased 0.00±0.00 0.43±0.63 p__Firmicutes__g__Lactobacillus_574021 0.0074 Increased 0.02±0.05 0.11±0.19 p__Proteobacteria__f__Enterobacteriaceae_228556 0.0076 Increased 0.00±0.00 0.46±0.82 p__Proteobacteria__f__Enterobacteriaceae_2016095 0.0076 Increased 0.00±0.00 0.27±0.45 p__Actinobacteria__g__Bifidobacterium_4426298 0.0076 Increased 0.00±0.00 1.26±1.96 p__Firmicutes__g__Lactobacillus_586093 0.0076 Increased 0.00±0.00 0.23±0.40 p__Firmicutes__g__Lactobacillus_592160 0.011 Increased 0.01±0.02 3.88±6.80 p__Proteobacteria__g__Klebsiella_786708 0.012 Increased 0.01±0.02 0.30±0.42 p__Firmicutes__g__Lactobacillus_539647 0.015 Increased 0.00±0.00 0.14±0.30 p__Proteobacteria__f__Enterobacteriaceae_510870 0.02 Increased 0.00±0.00 0.14±0.29 p__Firmicutes__g__Lactobacillus_533133 0.02 Increased 0.00±0.00 0.05±0.10 p__Firmicutes__g__Lactobacillus_331248 0.02 Increased 0.01±0.02 0.05±0.08 p__Firmicutes__g__Lactobacillus_582884 0.021 Increased 0.00±0.01 0.43±0.83 p__Proteobacteria__f__Enterobacteriaceae_588216 0.021 Increased 0.05±0.13 0.22±0.33 p__Firmicutes__g__Streptococcus_349024 0.025 Decreased 3.00±3.14 1.06±2.10 p__Proteobacteria__f__Enterobacteriaceae_797229 0.027 Increased 0.32±0.89 1.59±2.46 p__Firmicutes__g__Selenomonas_4297955 0.027 Increased 0.02±0.05 0.92±2.00 p__Proteobacteria__f__Enterobacteriaceae_1109844 0.029 Increased 0.44±1.35 1.75±2.71 p__Firmicutes__g__Selenomonas_295019 0.029 Increased 0.17±0.20 1.43±2.84 p__Firmicutes__g__Lactobacillus_590168 0.029 Increased 0.01±0.02 0.08±0.14 p__Firmicutes__g__Lactobacillus_573010 0.036 Increased 0.05±0.07 2.18±4.31

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Figure 5.2: Alpha diversity analysis of gastric mucosal biopsy samples at genus level.

There was a trend toward a significant increase in alpha diversity (p=0.0529) from baseline to explant. Each patient is displayed as an individual line. Pre- and post-intervention compared by paired t-test. During the intervention period, three patients underwent elective early device explant secondary to persistent abdominal pain. When early explant patients were removed from the analysis, the pattern of taxonomic change was similar. A significant increase in the RA of nine of the initial 11 genera remained (Erwinia and TG5 lost significance), whilst a significant decrease in the RA of three genera remained (Actinomyces, Granulicatella, Neisseria and Bulleidia lost significance). When comparing the early explant group to those who completed the full intervention period, there was a significantly greater reduction in Actinomyces and Granulicatella (both FDR q<0.05) from baseline to explant in those who underwent early explant.

The beta-diversity of gastric biopsy samples was assessed using principal coordinate analysis. Although some separation of the baseline and explant samples, based on overall community profile, is visible on principal coordinate 1, this was not significant (Figure 5.3A; Adonis UniFrac p=0.56). This remained non-significant if the Helicobacter positive patients were removed (Figure 5.3B; Adonis UniFrac p=0.57). Sparse partial least squares discriminant analysis was performed to discriminate samples based on collection time-point (Figure 5.4). The genera most strongly associated with baseline biopsies were Streptococcus, Neisseria, Prevotella, Helicobacter and Rothia. Conversely, the genera most strongly associated with explant biopsies were Bifidobacterium, Dialister, Klebsiella and members of the Coriobacteriaceae family.

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Figure 5.3: Principal co-ordinate analysis of gastric biopsies using a weighted UniFrac distance matrix.

Helicobacter positive patients are showed in the red grouped rectangle. Overall there was no significant difference in community profiles observed between time-points (A; Adonis UniFrac p=0.56), which remains non-significant if the Helicobacter positive patients are removed (B; Adonis UniFrac p=0.57).

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Figure 5.4: Sparse partial least squares discriminant analysis of gastric biopsies.

The taxa most associated with baseline biopsies (red) were Streptococcus, Neisseria, Prevotella, Helicobacter, Rothia and Moryella. The taxa most associated with explant biopsies (blue) were Bifidobacterium, Dialister, members of the Coriobacteriaceae family and Klebsiella. The x-axis shows the strength of association with a particular group. The gastric biopsies of the DJBS cohort at baseline were most similar to the control biopsies. Indeed, there were no significant differences in overall community composition (beta-diversity) (Figure 5.5; Adonis UniFrac p=0.99), taxonomic or alpha diversity metrics (Figure 5.6; Shannon Index, baseline 1.50±0.51 vs. control 1.56±0.51; p=0.79) between the gastric biopsies at baseline and the control group. The overlap between baseline and control samples was retained when the controls were stratified by BMI class.

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Figure 5.5: Principal co-ordinate analysis of gastric biopsies using a weighted UniFrac distance matrix, compared to a control group.

There was no significant difference between the microbiota profiles of DJBS patients compared to control patients (grey diamonds) at baseline (red circles; Adonis UniFrac p=0.99) or explant (blue squares; Adonis UniFrac p=0.36). The explant biopsy samples appear separated on the principal coordinate analysis plot as compared to the baseline and control samples. Due to large inter-individual variation, there was no significant difference in overall community profile between explant and control biopsies (Figure 5.5, Adonis UniFrac p=0.36) although alpha diversity was significantly higher in the explant biopsies (Figure 5.6; Shannon Index, explant 1.96±0.36 vs. control 1.56±0.51; p=0.01). Specifically, in the explant biopsies, there was a significantly higher RA of members of the Gammaproteobacteria class and the Enterobacteriaceae (both FDR q<0.001) and Coriobacteriaceae (FDR q<0.01) families, as well as the genera Klebsiella (FDR q<0.001), Dialister, Lactobacillus, Bifidobacterium, Selenomonas, Morganella, TG5, Erwinia, Collinsella, Fusobacterium (all FDR q<0.01), Megasphaera and Parvimonas (both FDR q<0.05), compared to controls. Conversely, there was a significantly lower RA of members of the S24-7 family (FDR q<0.05) and the genera Streptococcus (FDR q<0.001), Actinomyces, Gemella and Granulicatella (all FDR q<0.01) in the explant biopsies, compared to controls. Interestingly, these taxonomic differences largely overlap with the taxa that were changed from baseline to explant. Specifically, there were only four taxa that were significantly higher in the explant biopsies, compared to controls that were not also significantly increased from baseline to explant. Similarly, with the exception of the S24-7 family, the other taxa that were significantly lower in explant biopsies were also significantly reduced from baseline to explant. Overall, these results suggest the gastric MAM has shifted away from a “typical” profile following treatment with a DJBS.

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Figure 5.6: Alpha diversity analysis of gastric mucosal biopsy samples (at genus level), compared to a control group.

There was no significant difference in alpha diversity between baseline mucosal biopsies in the DJBS cohort and the control cohort (p=0.79). Alpha diversity was significantly higher in the explant biopsies, compared to the control group (p=0.01). Between groups comparisons by unpaired t-test. 5.3.1.2 Duodenal biopsies We assessed the MAM at baseline and explant in the second part of the duodenum. Full statistical analyses can be found in Appendix 1.7. Between baseline and explant, there was a significant increase in the RA of members of the Fusobacteria phylum (Table 5.3). At the genus level, there was a significant increase in 10 taxa. Similar to what was observed in the gastric biopsies, these increases were all in rare taxa with a maximum mean RA of <3% at baseline. Conversely, there was a significant reduction in seven taxa. In particular, Streptococcus, which at baseline was the most abundant organism in the duodenal MAM, was reduced from a mean RA of 43% at baseline to 9% at explant. In the full cohort, at OTU level, there was a significant increase in 24 OTUs (Table 5.3), including eight Lactobacillus OTUs, seven Enterobacteriaceae OTUs, three Klebsiella OTUs, two Selenomonas OTUs and one each of Gammaproteobacteria, Bifidobacterium, Fusobacterium and Prevotella tannerae. A significant decrease in five Streptococcus OTUs, one Haemophilus parainfluenzae OTU and one Prevotella melaninogenica OTU was also observed. Despite the taxonomic changes observed, there was no significant change in alpha diversity from baseline to explant (Figure 5.7: Shannon Index; n=8, 1.68±0.31 vs. 2.02±0.38, p=0.24). Three patients underwent early explant due to persistent abdominal pain, and it would be ideal to capture any taxonomic changes in this group. Unfortunately, we are not able to further discriminate changes in the early explant group, as only one baseline duodenal sample in this subset of patients passed quality control.

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Table 5.3: Taxonomic changes in the MAM of the second part of the duodenum from baseline to explant.

FDR q Change Baseline RA (%) Explant RA (%) from Taxa Baseline

Phylum Level

Fusobacteria 0.031 Increased 12.78±8.54 19.28±16.46

Genus Level

Streptococcus 2.9E-07 Decreased 43.22±10.26 9.27±11.23

Dialister 0.000031 Increased 0.09±0.16 1.12±0.62

Haemophilus 0.0013 Decreased 2.86±1.76 0.51±0.60

Helicobacter 0.0043 Decreased 0.60±1.12 0.00±0.00

Lactobacillus 0.0056 Increased 0.57±0.70 19.34±20.52

Klebsiella 0.0062 Increased 0.01±0.02 2.78±3.38

Unclassified Gammaproteobacteria 0.0075 Increased 0.05±0.10 5.24±6.52

Gemella 0.0099 Decreased 1.19±0.84 0.91±2.24

Bifidobacterium 0.011 Increased 0.00±0.01 1.13±1.56

Selenomonas 0.011 Increased 0.23±0.20 2.76±3.26

Neisseria 0.013 Decreased 4.57±6.67 0.55±1.32

Megasphaera 0.019 Increased 0.59±0.57 2.03±1.78

Unclassified Enterobacteriaceae 0.026 Increased 2.83±6.72 8.89±8.54

Fusobacterium 0.026 Increased 1.49±2.05 8.56±11.34

Granulicatella 0.026 Decreased 1.05±1.13 0.25±0.46

Bulleidia 0.033 Decreased 0.73±1.22 0.10±0.10

Slackia 0.044 Increased 0.00±0.00 0.08±0.16

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FDR q Change from Baseline Explant OTU Baseline RA (%) RA (%) p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 2.8E-08 Decreased 19.32±7.07 2.96±4.85 p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU3797 11.81±4.24 2.71±3.61 77 2.2E-06 Decreased p__Firmicutes__g__Streptococcus__s__infantis_517754 9.5E-06 Decreased 3.01±1.48 0.43±0.55 p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 0.00014 Decreased 17.66±10.69 3.33±5.44 p__Fusobacteria__g__Fusobacterium_34791 0.00061 Increased 0.52±0.69 3.69±3.76 p__Firmicutes__g__Streptococcus_349024 0.00076 Decreased 4.94±4.02 0.77±1.36 p__Proteobacteria__g__Haemophilus__s__parainfluenzae_ 1.49±1.08 0.29±0.38 New.ReferenceOTU100511 0.0023 Decreased

0.04±0.08 6.36±10.7 p__Firmicutes__g__Lactobacillus_592160 0.0023 Increased 1 p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0.0033 Decreased 1.03±0.44 0.33±0.64 p__Proteobacteria__g__Klebsiella_786708 0.0035 Increased 0.00±0.01 0.20±0.37 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.0035 Increased 0.00±0.00 0.60±0.82 p__Proteobacteria__f__Enterobacteriaceae_819636 0.0035 Increased 0.00±0.01 0.65±0.78 p__Proteobacteria__f__Enterobacteriaceae_822899 0.0042 Increased 0.03±0.05 0.22±0.27 p__Proteobacteria__c__Gammaproteobacteria_808486 0.0043 Increased 0.05±0.10 5.24±6.52 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.0047 Increased 0.00±0.00 1.35±1.75 p__Firmicutes__g__Lactobacillus_331248 0.0075 Increased 0.00±0.00 0.09±0.13 p__Firmicutes__g__Lactobacillus_574021 0.0076 Increased 0.01±0.02 0.23±0.27 p__Proteobacteria__f__Enterobacteriaceae_823118 0.0079 Increased 0.22±0.47 3.03±4.13 p__Proteobacteria__f__Enterobacteriaceae_332958 0.0079 Increased 0.00±0.00 0.27±0.36 p__Firmicutes__g__Lactobacillus_582884 0.012 Increased 0.01±0.03 0.74±1.32 p__Proteobacteria__f__Enterobacteriaceae_228556 0.012 Increased 0.02±0.04 0.29±0.40 p__Firmicutes__g__Lactobacillus_590168 0.014 Increased 0.00±0.00 0.13±0.17 p__Firmicutes__g__Lactobacillus_573010 0.016 Increased 0.03±0.08 2.97±4.70 p__Firmicutes__g__Selenomonas_295019 0.017 Increased 0.14±0.16 1.08±1.43 p__Actinobacteria__g__Bifidobacterium_4426298 0.019 Increased 0.00±0.01 0.83±1.39 p__Proteobacteria__f__Enterobacteriaceae_510870 0.019 Increased 0.00±0.01 0.11±0.20 p__Firmicutes__g__Selenomonas_4297955 0.022 Increased 0.01±0.01 1.15±2.27 p__Firmicutes__g__Lactobacillus_563654 0.023 Increased 0.03±0.09 0.41±0.58 p__Proteobacteria__f__Enterobacteriaceae_2016095 0.024 Increased 0.00±0.00 0.18±0.29 p__Firmicutes__g__Lactobacillus_533133 0.037 Increased 0.00±0.00 0.07±0.12 p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.042 Increased 0.14±0.17 2.46±4.31

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Figure 5.7: Alpha diversity analysis of duodenal mucosal biopsy samples at genus level.

There was no significant change in alpha diversity (p=0.24) from baseline to explant. Each patient is displayed as an individual line. Pre- and post-intervention compared by paired t-test. There was considerable overlap between the changes seen from baseline to explant in the gastric and duodenal MAM. At the genus level, there were 13 common taxonomic changes (Table 5.2 and Table 5.3). Of these, eight were taxa that were commonly increased from baseline to explant, whilst five were commonly decreased. Furthermore, despite the patients having obesity and T2DM, the biopsies of the DJBS cohort at baseline were similar to the control biopsies. Indeed, there were no significant differences in community profile (Figure 5.8, Adonis UniFrac p=0.22) or alpha diversity metrics (Figure 5.9; Shannon Index, baseline 1.68±0.31 vs. control 1.43±0.56; p=0.24) between the duodenal biopsies of DJBS patients at baseline and the control group. With the exception of Helicobacter, which was higher in the DJBS patients at baseline, there were no taxonomic differences between DJBS patients and controls. Similarly, there were no observable differences when the controls were stratified by BMI class. Cumulatively, these data suggest DJBS treatment is inducing similar shifts in the gastric and duodenal MAM away from the profile observed in control patients.

There was also a significant overlap in the taxonomic difference between the control group and those that were increased from baseline to explant in the DJBS group, with 10 common taxonomic groups. However, the explant biopsy samples appear separated on the principal co-ordinate analysis plot, compared to the baseline and control samples. Indeed, there was a significant difference in overall community profile when comparing the explant biopsies and the control group (Figure 5.8; Adonis UniFrac p<0.001). Further, alpha diversity was significantly higher in the explant biopsies, compared to controls (Figure 5.9; Shannon Index, explant 2.07±0.36 vs. control 1.43±0.56; p<0.001). At the phylum level, there was a significantly higher RA of Fusobacteria and Synergistetes (both FDR q<0.001) in the explant biopsies, compared to controls. More specifically, there was a higher RA of members of the Gammaproteobacteria class (FDR q<0.00001), members of the Enterobacteriaceae,

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Coriobacteriaceae (both FDR q<0.001), Aeromonadaceae and Veillonellaceae (both FDR q<0.05) families, and the genera Klebsiella, Dialister, Lactobacillus, Fusobacterium, Selenomonas, TG5 (all FDR q<0.001), Slackia, Morganella, Collinsella, Parvimonas, Bifidobacterium (all FDR q<0.01), Megasphaera, Erwinia, Leptotrichia and Bacteroides (all FDR q<0.05). Conversely, there was a significantly lower RA of six typical upper GIT genera548 - Streptococcus, Actinomyces (both FDR q<0.001), Rothia, Granulicatella (both FDR q<0.01), Gemella and Neisseria (both FDR q<0.05). Overall, these results show a shift in the overall community profile of the duodenal MAM following treatment with a DJBS away from the taxa more typically observed in the duodenum.

Figure 5.8: Principal co-ordinate analysis of duodenal biopsies using a weighted UniFrac distance matrix, compared to a control group.

There was no significant difference between the microbiota profiles of DJBS patients compared to controls (grey diamonds) at baseline (red circles; Adonis UniFrac p=0.221). However, there was a significant difference between the control group and explant biopsies (blue squares; Adonis UniFrac p=0.000666).

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Figure 5.9: Alpha diversity analysis of duodenal mucosal biopsy samples (at genus level), compared to a control group.

There was no significant difference in alpha diversity between baseline mucosal biopsies in the DJBS cohort and the control cohort (p=0.24). Alpha diversity was significantly higher in the explant biopsies, compared to the control group (p<0.001). Within group comparisons by paired t-test. Between groups comparisons by unpaired t-test. Beta-diversity was assessed for duodenal biopsy samples using principal co-ordinate analysis. There was moderate clustering of baseline duodenal biopsies; however, in the full cohort, there was no significant difference between community profiles by time-point (Figure 5.10A; Adonis UniFrac p=0.90), which remained non-significant if Helicobacter positive patients (n=2) were removed (Adonis UniFrac p=0.40). When only patients with paired baseline and explant data were assessed, there was a significant difference in community profile by time-point (Figure 5.10B, Adonis UniFrac p<0.001). Sparse partial least squares discriminant analysis was performed to discriminate between collection time-points. The genera most strongly associated with baseline biopsies were Streptococcus, Helicobacter and Prevotella. The genera mostly strongly associated with explant biopsies were Lactobacillus, Leptotrichia, Klebsiella and Collinsella.

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Figure 5.10: Principal co-ordinate analysis of duodenal biopsies using a weighted UniFrac distance matrix.

Overall, there was no significant difference between community profiles based on time-point (A; including two Helicobacter positive patients (red oval); Adonis UniFrac p=0.90), which remains non-significant if the Helicobacter positive patients are removed (Adonis UniFrac p=0.40). However, when only patients with both baseline and explant data are compared, there is a significant difference between baseline and explant (B; Adonis UniFrac p=0.0006).

5.3.2 Device-associated microbiota Upon explant, the device was kept for assessment of device-associated microbiota via high throughout sequencing methods. A biofilm had formed on the device, and particularly on the mucosal-adjacent device surface. Three samples were taken from the luminal-adjacent device surface and three samples were taken from the mucosa-adjacent device surface. After filtering, a sequencing profile using the bacterial domain specific primers (917F/1392R) was successfully obtained for 150 of 152 device samples. Two luminal-adjacent device samples did not pass quality control (read depth <500). A total of 3,281,576 reads were generated from 245 OTUs. Specifically, sequences were identified from eight phyla, 13 classes, 17 orders, 28 families and 43 genera. A mean of 19,733 and 24,080 reads were generated from the mucosa-associated and luminal device surfaces, respectively.

Overall, at explant, the device was predominantly colonised by the genus Lactobacillus (mean RA 42%; mucosa-adjacent surface 37%, luminal surface 47%), members of the Enterobacteriaceae family (mean RA 12%; mucosa-adjacent surface 14%, luminal surface 12%) and Bacteroides (mean RA 9%; mucosa-adjacent surface 11%, luminal surface 5%). Overall, the RA of the predominant members of the duodenal community, as observed in the control duodenal biopsies above, were low; such as Streptococcus (mean RA 1%; mucosa-adjacent surface 0.3%, luminal surface 2%) and Prevotella (mean RA 1%; mucosa-adjacent surface 0.2%, luminal surface 3%).

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Broadly, whilst there was no significant difference in overall community profile (Adonis UniFrac p=0.61) or diversity (p=0.68) between the luminal-adjacent and mucosa-adjacent device surfaces, there were significant taxonomic differences observed between the surfaces when all device samples (upper, mid and lower) were included. Specifically, seven genera were significantly higher in RA on the mucosa-adjacent device surface, whilst 13 were significantly lower in RA (Table 5.4). At OTU level, five Enterobacteriaceae OTUs, three Klebsiella OTUs, two Veillonella OTUs, and one each of Gammaproteobacteria, Bacteroides and Lactobacillus were significantly higher on the mucosa-adjacent device surface, compared to the luminal-adjacent device surface. Conversely, 10 Lactobacillus OTUs, five Streptococcus OTUs, three Prevotella OTUs, one Actinomyces OTU and one Fusobacterium OTU were significantly lower in RA on the mucosa- adjacent device surface, compared to the luminal-adjacent device surface. The majority of these differences reflect the taxonomic shifts that were also observed in the duodenal biopsy samples from baseline to explant, with the mucosa-adjacent device surface being more similar to explant biopsies. When the mucosa-adjacent device surface samples were compared along the length of the device (e.g. upper vs mid or lower), there were no significant overall community profile or taxonomic differences. This suggests colonisation is consistent along the length of the mucosa-adjacent surface of the device.

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Table 5.4: Taxonomic difference between the mucosa-adjacent and luminal-adjacent device surfaces at explant.

FDR q Mucosa- Luminal-adjacent RA adjacent RA (%) Taxa (%)

Phylum Level

Proteobacteria 0.00031 28.06±22.36 20.23±22.38

Synergistetes 0.028 0.00±0.01 0.38±1.91

Genus Level

Klebsiella 0.00009 5.07±5.52 2.86±4.31

Streptococcus 0.00009 0.25±0.74 1.58±2.58

Atopobium 0.000097 0.02±0.05 0.20±0.44

Unclassified Gammaproteobacteria 0.00028 7.92±10.43 4.99±8.42

Parvimonas 0.00031 0.01±0.02 0.10±0.24

Unclassified Coriobacteriaceae 0.00052 0.24±0.95 1.58±3.99

Prevotella 0.0011 0.15±0.43 2.94±8.35

Porphyromonas 0.0011 0.00±0.01 0.12±0.44

Bacteroides 0.0032 11.24±21.12 4.73±18.29

Slackia 0.0032 0.01±0.02 0.29±1.24

Megasphaera 0.0047 1.88±2.91 1.02±2.27

Veillonella 0.0047 2.21±3.32 1.19±1.76

Bulleidia 0.0053 0.00±0.01 0.02±0.07

Rothia 0.0071 0.00±0.01 0.02±0.07

Gemella 0.0075 0.00±0.01 0.01±0.01

Unclassified Enterobacteriaceae 0.012 13.92±11.74 11.73±12.64

Actinomyces 0.014 0.15±0.56 0.71±2.37

TG5 0.016 0.00±0.01 0.38±1.91

Peptostreptococcus 0.032 0.00±0.01 0.04±0.13

Morganella 0.037 0.14±0.50 0.10±0.45

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FDR q Mucosa- Luminal-adjacent RA adjacent RA (%) OTU (%) p__Proteobacteria__g__Klebsiella_786708 4.1E-06 0.68±0.88 0.33±0.62 p__Proteobacteria__f__Enterobacteriaceae_819636 0.000048 1.68±2.07 1.06±1.70 p__Firmicutes__g__Lactobacillus_331248 0.00017 0.22±0.32 0.90±1.41 p__Proteobacteria__c__Gammaproteobacteria_808486 0.00032 7.92±10.43 4.99±8.42 p__Proteobacteria__f__Enterobacteriaceae_2016095 0.00037 0.38±0.42 0.25±0.48 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.00037 1.80±2.52 0.94±1.61 p__Proteobacteria__f__Enterobacteriaceae_228556 0.00037 0.82±1.14 0.59±1.11 p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.00037 0.03±0.11 0.48±1.24 p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 0.00089 0.02±0.06 0.13±0.35 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.0018 1.03±1.31 0.68±1.24 p__Proteobacteria__f__Enterobacteriaceae_332958 0.0023 0.64±0.69 0.45±0.72 p__Firmicutes__g__Streptococcus_349024 0.0032 0.02±0.05 0.06±0.15 p__Fusobacteria__g__Fusobacterium_545299 0.0032 0.05±0.16 0.27±0.68 p__Firmicutes__g__Streptococcus_New.ReferenceOTU13477 0.0032 0.06±0.24 0.25±0.55 p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0.0043 0.00±0.01 0.01±0.04 p__Firmicutes__g__Lactobacillus_582884 0.0066 1.10±3.96 1.62±2.55 p__Firmicutes__g__Lactobacillus_354225 0.0066 6.53±17.36 9.97±16.77 p__Bacteroidetes__g__Prevotella__s__nigrescens_2195 0.0066 0.03±0.10 1.48±5.90 p__Proteobacteria__f__Enterobacteriaceae_822899 0.0066 0.39±0.35 0.29±0.37 p__Firmicutes__g__Veillonella_561537 0.008 0.89±1.24 0.57±0.89 p__Firmicutes__g__Lactobacillus_533133 0.012 0.40±0.72 0.75±0.91 p__Firmicutes__g__Lactobacillus_539647 0.013 1.27±2.18 2.18±2.37 p__Firmicutes__g__Lactobacillus_573010 0.013 4.31±8.48 7.27±10.84 p__Firmicutes__g__Lactobacillus_586093 0.013 1.86±3.93 2.84±3.29 p__Firmicutes__g__Veillonella_518743 0.013 0.96±1.50 0.60±1.11 p__Bacteroidetes__g__Bacteroides_568118 0.013 4.25±16.63 0.17±0.67 p__Firmicutes__g__Lactobacillus_574021 0.014 0.71±1.04 1.28±1.53 p__Firmicutes__g__Lactobacillus_340960 0.021 1.89±3.21 2.91±3.12 p__Firmicutes__g__Lactobacillus_590168 0.022 0.48±1.09 0.67±0.79 p__Actinobacteria__g__Actinomyces_4350499 0.03 0.03±0.12 0.08±0.22 p__Firmicutes__g__Streptococcus__s__infantis_517754 0.034 0.01±0.02 0.01±0.03 p__Firmicutes__g__Lactobacillus_823803 0.039 2.07±6.48 0.78±4.77 p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 0.039 0.01±0.05 0.17±0.99

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5.3.2.1 Relationship between biopsies and device At explant, the duodenal MAM was distinct from the mucosa-adjacent device sections (Figure 5.11; explant biopsy vs. device p=0.029). Visually, on principal component analysis, the baseline biopsies were more distinct than the explant biopsies. However, there was no statistically significantly difference between the baseline biopsies and the mucosa-adjacent device sections (baseline biopsy vs. device p=0.11). This is likely to reflect the low sample number at baseline (n=8).

Figure 5.11: Principal co-ordinate analysis of device samples compared to duodenal biopsy samples, using a weighted UniFrac distance matrix.

At explant, the mucosal biopsy samples (blue circles) visually appear closer to the mucosa-adjacent device samples (grey circles; explant biopsy vs. device Adonis UniFrac p=0.0029) than the baseline biopsies (red circles; baseline biopsy vs. device Adonis UniFrac p=0.11). In the duodenum, the mucosa-adjacent device surface and the mucosal biopsy collocated with the device formed two distinct communities. Full statistical analyses can be found in Appendix 1.7. At genus level, there was a significantly higher RA of 22 taxa, and lower abundance of four, in the explant duodenal biopsies, compared to the collocated mucosa-adjacent device surface (Table 5.5). At OTU level, there was a significantly higher abundance of six Streptococcus OTUs, three Prevotella OTUs, two Actinomyces OTUs, two Veillonella OTUs, two Fusobacterium OTUs, two Selenomonas OTUs and one Haemophilus parainfluenzae OTU in the explant duodenal biopsies, compared to the collocated mucosa-adjacent device surface. There was a significantly lower RA of

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six Enterobacteriaceae OTUs, one Bifidobacterium OTU, and one Klebsiella OTU in the explant duodenal biopsies, compared to the collocated mucosa-adjacent device surface.

Similarly, there was a significantly higher RA of 18 taxa in the baseline duodenal biopsies, with six taxa lower, compared to the mucosa adjacent device surface (Table 5.6). At OTU level, there was a significantly higher abundance of six Streptococcus OTUs, three Prevotella OTUs, two Actinomyces OTUs, two Veillonella OTUs, one Fusobacterium OTU and one Haemophilus parainfluenzae OTU in the baseline duodenal biopsies, compared to the collocated mucosa-adjacent device surface. There was a significantly lower RA of eight Enterobacteriaceae OTUs, eight Lactobacillus OTUs, three Klebsiella OTUs, one Gammaproteobacteria OTU and one Bifidobacterium OTU, in the baseline duodenal biopsies, compared to the collocated mucosa-adjacent device surface.

There was a significantly higher alpha diversity in the explant biopsies, compared to the mucosa- adjacent device surface (biopsy: 2.02±0.38 vs device: 1.43±0.37; p<0.001). Broadly, a number of taxa that were significantly higher in the mucosal samples as compared to the device are those considered typical of the upper GIT microbiota. Specifically, Streptococcus and Veillonella were the dominant genera in the duodenum. This indicates there is a difference in the growth environment on the device as compared to the mucosa, facilitating the formation of these distinct communities.

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Table 5.5: Taxonomic differences between the mucosa-adjacent device surface collocated with the duodenal biopsy at device explant.

FDR q Mucosa- Explant adjacent RA Biopsy RA Taxa (%) (%)

Phylum Level

Bacteroidetes 0.00025 11.10±24.88 20.57±16.04

Fusobacteria 0.00025 3.11±4.50 10.49±12.50

Synergistetes 0.0026 0.01±0.01 0.36±0.91

Genus Level

Prevotella 4.4E-09 0.18±0.50 14.03±11.07

Parvimonas 3.1E-08 0.01±0.02 0.44±0.38

Streptococcus 1.4E-06 0.22±0.44 9.27±11.23

Bulleidia 0.00002 0.00±0.01 0.10±0.10

Porphyromonas 0.000038 0.00±0.02 0.66±0.92

Veillonella 0.000043 1.95±2.64 5.77±4.79

Dialister 0.000057 0.34±0.52 1.12±0.62

Atopobium 0.00006 0.01±0.02 0.36±0.49

Selenomonas 0.00011 0.90±1.43 2.76±3.26

Actinomyces 0.00016 0.09±0.30 0.64±0.71

Granulicatella 0.00024 0.00±0.01 0.25±0.46

Fusobacterium 0.00032 2.15±3.53 8.56±11.34

Peptostreptococcus 0.00081 0.00±0.01 0.23±0.37

Haemophilus 0.0022 0.19±0.45 0.51±0.60

TG5 0.0029 0.01±0.01 0.36±0.91

Rothia 0.0061 0.00±0.00 0.55±1.66

Gemella 0.0067 0.00±0.01 0.91±2.24

Erwinia 0.0083 0.07±0.12 0.02±0.04

Slackia 0.016 0.01±0.02 0.08±0.16

Leptotrichia 0.017 0.26±0.71 1.01±1.95

Neisseria 0.021 0.00±0.01 0.55±1.32

Unclassified Aeromonadaceae 0.024 0.27±0.55 0.71±1.22

Unclassified Enterobacteriaceae 0.024 13.40±11.11 8.89±8.54

Moryella 0.024 0.00±0.01 0.33±0.97

Klebsiella 0.026 4.67±4.97 2.78±3.38

Bifidobacterium 0.037 6.69±10.68 1.13±1.56

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FDR q Mucosa- Explant adjacent Biopsy OTU Level RA (%) RA (%) p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 0.00006 0.01±0.02 2.96±4.85 p__Actinobacteria__g__Actinomyces_4350499 0.00006 0.01±0.02 0.26±0.28 p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU379777 0.00008 0.06±0.21 2.71±3.61 p__Bacteroidetes__g__Prevotella__s__nigrescens_2195 0.00017 0.05±0.16 1.42±2.81 p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 0.00017 0.01±0.03 3.33±5.44 p__Firmicutes__g__Veillonella_561537 0.00017 0.90±1.45 1.99±1.59 p__Firmicutes__g__Streptococcus_349024 0.00017 0.01±0.01 0.77±1.36 p__Firmicutes__g__Streptococcus__s__infantis_517754 0.00017 0.00±0.01 0.43±0.55 p__Actinobacteria__g__Actinomyces_565136 0.0002 0.01±0.02 0.30±0.42 p__Firmicutes__g__Veillonella_518743 0.00024 0.81±1.15 2.82±2.70 p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.00034 0.04±0.15 2.46±4.31 p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0.00071 0.00±0.00 0.33±0.64 p__Proteobacteria__g__Haemophilus__s__parainfluenzae_New.ReferenceOTU100511 0.00081 0.07±0.17 0.29±0.38 p__Fusobacteria__g__Fusobacterium_34791 0.001 1.64±2.20 3.69±3.76 p__Fusobacteria__g__Fusobacterium_545299 0.001 0.07±0.22 2.38±6.16 p__Firmicutes__g__Streptococcus_New.ReferenceOTU13477 0.001 0.06±0.18 0.52±0.80 p__Firmicutes__g__Selenomonas_295019 0.0013 0.37±0.74 1.08±1.43 p__Firmicutes__g__Selenomonas_4297955 0.0047 0.46±0.92 1.15±2.27 p__Proteobacteria__g__Klebsiella_786708 0.0068 0.55±0.64 0.20±0.37 p__Proteobacteria__f__Enterobacteriaceae_588216 0.011 0.39±0.57 0.12±0.17 p__Proteobacteria__f__Enterobacteriaceae_819636 0.011 1.60±2.11 0.65±0.78 p__Proteobacteria__f__Enterobacteriaceae_228556 0.014 0.84±1.39 0.29±0.40 p__Actinobacteria__g__Bifidobacterium_4426298 0.022 5.66±9.15 0.83±1.39 p__Proteobacteria__f__Enterobacteriaceae_2016095 0.023 0.37±0.38 0.18±0.29 p__Proteobacteria__f__Enterobacteriaceae_332958 0.026 0.60±0.61 0.27±0.36 p__Proteobacteria__f__Enterobacteriaceae_822899 0.029 0.38±0.32 0.22±0.27

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Table 5.6: Taxonomic differences between the mucosa-adjacent device surface collocated with the duodenal biopsy at baseline.

FDR q Mucosa- Baseline adjacent RA Biopsy RA Taxa (%) (%)

Phylum Level

Bacteroidetes 0.035 11.10±24.88 25.43±12.42

Genus Level

Streptococcus 0 0.22±0.44 43.22±10.26

Prevotella 1.7E-13 0.18±0.50 23.85±12.73

Gemella 4.7E-11 0.00±0.01 1.19±0.84

Haemophilus 0.0000035 0.19±0.45 2.86±1.76

Rothia 0.0000035 0.00±0.00 1.69±2.00

Granulicatella 0.0000035 0.00±0.01 1.05±1.13

Atopobium 0.0000098 0.01±0.02 0.37±0.33

Actinomyces 0.000011 0.09±0.30 1.31±1.06

Neisseria 0.00012 0.00±0.01 4.57±6.67

Porphyromonas 0.00023 0.00±0.02 1.48±1.74

Bulleidia 0.00023 0.00±0.01 0.73±1.22

Peptostreptococcus 0.0003 0.00±0.01 0.26±0.34

Veillonella 0.00043 1.95±2.64 7.87±8.95

Unclassified Enterobacteriaceae 0.00043 13.40±11.11 2.83±6.72

Moryella 0.00086 0.00±0.01 0.64±1.10

Klebsiella 0.00089 4.67±4.97 0.01±0.02

Helicobacter 0.0014 0.00±0.01 0.60±1.12

Parvimonas 0.005 0.01±0.02 0.26±0.50

Unclassified Gammaproteobacteria 0.005 7.61±10.74 0.05±0.10

Lactobacillus 0.01 37.07±37.07 0.57±0.70

Erwinia 0.033 0.07±0.12 0.00±0.00

Actinobacillus 0.035 0.00±0.00 0.49±1.20

Bifidobacterium 0.045 6.69±10.68 0.00±0.01

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FDR q Mucosa- Baseline adjacent Biopsy RA OTU Level RA (%) (%) p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 0 0.01±0.02 19.32±7.07 p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU379777 0 0.06±0.21 11.81±4.24 p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0 0.00±0.00 1.03±0.44 p__Firmicutes__g__Streptococcus__s__infantis_517754 6.2E-14 0.00±0.01 3.01±1.48 p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 2.3E-12 0.01±0.03 17.67±10.69 p__Firmicutes__g__Streptococcus_349024 8.1E-09 0.01±0.01 4.94±4.02 p__Actinobacteria__g__Actinomyces_565136 1E-07 0.01±0.02 0.50±0.39 p__Actinobacteria__g__Actinomyces_4350499 1.1E-06 0.01±0.02 0.67±0.65 p__Proteobacteria__g__Haemophilus__s__parainfluenzae_New.ReferenceOTU100511 1.6E-0.6 0.07±0.17 1.49±1.08 p__Proteobacteria__f__Enterobacteriaceae_823118 0.000035 2.78±2.61 0.22±0.47 p__Bacteroidetes__g__Prevotella__s__nigrescens_2195 0.000095 0.05±0.16 0.97±1.27 p__Proteobacteria__f__Enterobacteriaceae_819636 0.00022 1.60±2.11 0.00±0.01 p__Firmicutes__g__Veillonella_518743 0.00046 0.81±1.15 5.11±4.98 p__Firmicutes__g__Streptococcus_New.ReferenceOTU13477 0.00075 0.06±0.18 0.38±0.33 p__Proteobacteria__f__Enterobacteriaceae_228556 0.00097 0.84±1.39 0.02±0.04 p__Proteobacteria__f__Enterobacteriaceae_822899 0.00097 0.38±0.32 0.03±0.05 p__Proteobacteria__f__Enterobacteriaceae_2016095 0.001 0.37±0.38 0.00±0.00 p__Proteobacteria__f__Enterobacteriaceae_332958 0.0011 0.60±0.61 0.00±0.00 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.0013 0.95±1.08 0.00±0.00 p__Fusobacteria__g__Fusobacterium_545299 0.0018 0.07±0.22 0.96±1.67 p__Proteobacteria__g__Klebsiella_786708 0.0029 0.55±0.64 0.00±0.01 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.0036 1.76±2.48 0.00±0.00 p__Proteobacteria__c__Gammaproteobacteria_808486 0.0048 7.61±10.74 0.05±0.10 p__Firmicutes__g__Lactobacillus_592160 0.013 5.61±10.17 0.04±0.08 p__Firmicutes__g__Lactobacillus_331248 0.024 0.21±0.30 0.00±0.00 p__Proteobacteria__f__Enterobacteriaceae_510870 0.032 0.52±0.76 0.00±0.01 p__Firmicutes__g__Lactobacillus_590168 0.032 0.48±1.12 0.00±0.00 p__Firmicutes__g__Lactobacillus_533133 0.032 0.39±0.74 0.00±0.00 p__Firmicutes__g__Lactobacillus_539647 0.033 1.23±2.16 0.01±0.02 p__Firmicutes__g__Veillonella_561537 0.033 0.90±1.45 2.13±3.54 p__Firmicutes__g__Lactobacillus_574021 0.034 0.73±1.08 0.01±0.02 p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.038 0.04±0.15 0.14±0.17 p__Firmicutes__g__Lactobacillus_563654 0.038 1.17±1.88 0.03±0.09 p__Proteobacteria__f__Enterobacteriaceae_588216 0.038 0.39±0.57 0.10±0.25 p__Firmicutes__g__Lactobacillus_340960 0.043 1.86±3.17 0.01±0.02 p__Actinobacteria__g__Bifidobacterium_4426298 0.044 5.66±9.15 0.00±0.01

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5.3.3 Stool microbiota Stool samples were collected at baseline, 8 weeks post-implant and within three weeks after device explant for the assessment of the microbiota via high-throughput sequencing methods. After filtering, a sequencing profile was successfully obtained for all stool samples collected (n=37). A total of 631,267 reads were generated from stool samples, representing 257 OTUs. Specifically, sequences were identified from 9 phyla, 13 classes, 16 orders, 28 families and 48 genera. At baseline, the mean number of reads was 16,879 (n=14). At eight weeks, the mean number of reads was 19,665 (n=12). At explant, the mean number of reads was 14,453 (n=11).

At baseline, the stool microbiota were dominated by the members of the Lachnospiraceae family (mean RA 18%) and Bacteroides (mean RA 11%). At 8 weeks, the stool microbiota were dominated by the genus Lactobacillus (mean RA 17%) and members of the Lachnospiraceae family (mean RA 10%). At explant, the stool microbiota were dominated by members of the Lachnospiraceae (mean RA 15%) and Ruminococcaceae families (mean RA 14%). Beta-diversity was measured in the stool using principal coordinate analysis. Overall, the stool microbiota profile was not significantly different between time-points based on principal coordinate analysis (Figure 5.12; Adonis UniFrac: baseline vs. 8 weeks p=0.25, baseline vs. explant p=0.69, 8 weeks vs explant p=0.80).

However, at eight weeks post-implant, there was a significant increase in 15 taxa in the stool at the genus level, compared to baseline (Table 5.7). All of these were rare taxa at baseline, with a maximum mean RA of <2%. Conversely, there was a significant reduction in members of the Lachnospiraceae family, the most abundant genus at baseline. Notably, other stool microbes such as Ruminococcus had a low baseline RA (mean: 3.36±3.43%). Indeed, Bifidobacterium was only detected in one baseline stool sample, at a very low abundance (0.01%). These findings are mirrored at OTU level, with a significant increase in the RA of 13 Lactobacillus OTUs, four Enterobacteriaceae OTUs, two Veillonella parvula OTUs and one each of Citrobacter, Bifidobacterium, Fusobacterium, Klebsiella and Streptococcus. A significant decrease in four Lachnospiraceae OTUs and one Clostridiales OTU (to which the Lachnospiraceae family belong) was observed from baseline to eight weeks post-implant. A full statistical analysis can be found in Appendix 1.7.

For the eight week time-point, three of eight patients had recently completed antibiotic triple therapy eradication for H.pylori infection. When these patients were removed, the taxonomic changes from baseline remained, with the exception of the increase in members of the Enterobacteriaceae family, which was no longer significant. In addition to a significant reduction in members of the Lachnospiraceae family seen in the full cohort, there was a significant reduction in Blautia and members of the Clostridiales order (both FDR q<0.05) when H.pylori eradication patients were removed. 159

Broadly, the changes observed in the stool have a moderate amount of overlap with what was seen in the gastric and duodenal mucosa. Of the 15 genera that were significantly increased in the stool at eight weeks post-implant, six were also increased at explant in both the gastric and duodenal biopsies. These include taxa with high RA in the stool, such as Lactobacillus, Klebsiella and members of the Enterobacteriaceae family.

Overall, the taxonomic changes observed in the stool were reversed rapidly following device removal. Indeed, there was no significant difference in the RA of any taxa between stool samples collected at baseline and within 3 weeks of device explant (n=9). Further, there was no significant change in alpha diversity at any time-point (Figure 5.13: Shannon Index; baseline: 2.02±0.32, 8 weeks: 2.19±0.35, explant: 1.90±0.36). There was no significant change in the ratio of Firmicutes to Bacteroidetes at any time-point (Figure 5.14; baseline: 543±1530 vs. 8 weeks: 463±987 vs. explant: 394±680).

Figure 5.12: Principal co-ordinate analysis of stool samples using a weighted UniFrac distance matrix.

There is no significant difference in overall microbiota profiles at baseline (blue squares) compared to eight weeks (red circles; Adonis UniFrac p=0.25) or explant (grey diamonds; Adonis UniFrac p=0.69). There was no significant difference in overall community profile between eight weeks and explant (Adonis UniFrac p=0.80).

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Table 5.7: Taxonomic changes in the stool microbiota from baseline to eight weeks post-implant.

FDR q Compared Baseline 8 week RA to RA (%) (%) Taxa Baseline

Phylum Level

Fusobacteria 0.0041 Increased 0.00±0.01 0.08±0.08

Proteobacteria 0.0041 Increased 4.19±8.69 13.58±9.70

Genus Level

Citrobacter 0.000024 Increased 0.00±0.00 0.07±0.07

Enterococcus 0.000024 Increased 0.00±0.00 0.93±1.21

Bifidobacterium 0.0025 Increased 0.00±0.00 3.25±6.44

Unclassified Clostridiaceae 0.0025 Increased 0.00±0.00 0.17±0.24

Fusobacterium 0.0025 Increased 0.00±0.01 0.08±0.08

Lactobacillus 0.0025 Increased 1.67±5.85 16.86±19.75

Clostridium 0.0025 Increased 0.00±0.01 0.15±0.20

Serratia 0.0037 Increased 0.00±0.00 0.10±0.16

Acinetobacter 0.0059 Increased 0.00±0.00 0.45±0.85

Unclassified Lachnospiraceae 0.0086 Decreased 17.54±9.92 9.55±7.20

Unclassified Enterobacteriaceae 0.013 Increased 1.73±3.00 6.98±7.37

Klebsiella 0.013 Increased 1.93±6.25 5.88±6.14

Veillonella 0.019 Increased 0.04±0.07 1.11±1.57

Unclassified Gammaproteobacteria 0.025 Increased 0.00±0.00 0.03±0.05

Unclassified Bacteria 0.048 Increased 0.00±0.00 0.07±0.16

Erwinia 0.048 Increased 0.00±0.00 0.01±0.16

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FDR q Compared Baseline 8 week to RA (%) RA (%) OTU Level Baseline p__Proteobacteria__g__Citrobacter_786708 0.00005 Increased 0.00±0.00 0.07±0.07 p__Proteobacteria__f__Enterobacteriaceae_819636 0.0006 Increased 0.00±0.01 0.23±0.29 p__Actinobacteria__g__Bifidobacterium_4426298 0.003 Increased 0.00±0.00 3.19±6.37 p__Firmicutes__g__Lactobacillus_563654 0.0032 Increased 0.03±0.03 2.35±2.94 p__Fusobacteria__g__Fusobacterium_545299 0.0033 Increased 0.00±0.01 0.08±0.08 p__Firmicutes__g__Lactobacillus_582884 0.0036 Increased 0.01±0.01 3.34±5.30 p__Firmicutes__g__Lactobacillus_329402 0.0036 Increased 0.01±0.01 5.49±8.77 p__Firmicutes__g__Lactobacillus_549756 0.0036 Increased 0.00±0.01 1.05±1.61 p__Firmicutes__f__Lachnospiraceae_New.CleanUp.ReferenceOTU93368 0.0041 Decreased 4.00±4.49 1.18±1.60 p__Firmicutes__f__Lachnospiraceae_New.ReferenceOTU100192 0.0041 Decreased 2.57±2.90 0.86±1.19 p__Proteobacteria__f__Enterobacteriaceae_New.ReferenceOTU100973 0.0041 Increased 0.00±0.00 0.20±0.30 p__Firmicutes__g__Lactobacillus_New.CleanUp.ReferenceOTU388917 0.0079 Increased 0.00±0.00 0.04±0.06 p__Firmicutes__g__Lactobacillus_586093 0.0088 Increased 0.01±0.01 0.55±0.83 p__Proteobacteria__f__Enterobacteriaceae_823118 0.0093 Increased 0.03±0.09 2.99±6.50 p__Firmicutes__g__Veillonella__s__parvula_518743 0.0093 Increased 0.01±0.02 0.47±0.55 p__Proteobacteria__g__Klebsiella_822899 0.011 Increased 1.93±6.25 5.74±6.06 p__Firmicutes__g__Lactobacillus_539647 0.011 Increased 0.00±0.01 0.25±0.40 p__Firmicutes__g__Lactobacillus_590168 0.014 Increased 0.00±0.00 0.11±0.18 p__Firmicutes__f__Lachnospiraceae_369709 0.014 Decreased 1.92±2.38 0.62±1.11 p__Firmicutes__o__Clostridiales_470382 0.016 Decreased 2.72±1.92 1.01±0.86 p__Firmicutes__g__Lactobacillus_725198 0.017 Increased 0.00±0.01 0.46±0.79 p__Proteobacteria__f__Enterobacteriaceae_812379 0.02 Increased 0.00±0.00 0.57±1.56 p__Firmicutes__g__Streptococcus_516966 0.02 Increased 0.17±0.23 1.89±2.50 p__Firmicutes__g__Lactobacillus_340960 0.02 Increased 0.00±0.00 0.07±0.14 p__Firmicutes__g__Lactobacillus_New.ReferenceOTU100589 0.02 Increased 0.00±0.00 0.03±0.06 p__Firmicutes__g__Veillonella__s__parvula_4352537 0.021 Increased 0.01±0.02 0.26±0.52 p__Firmicutes__f__Lachnospiraceae_New.CleanUp.ReferenceOTU1164950 0.028 Decreased 1.18±0.95 1.86±0.98 p__Firmicutes__g__Lactobacillus_823803 0.043 Increased 0.00±0.00 0.12±0.32 p__Firmicutes__g__Lactobacillus_331248 0.043 Increased 0.00±0.00 0.03±0.07

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Figure 5.13: Alpha diversity analysis of stool samples at genus level.

There was no significant change in alpha diversity (p=0.12; n=8) from baseline to eight weeks (A), eight weeks to explant (p=0.25; n=9; data not shown) or baseline to explant (B; p=0.77; n=10). Each patient is displayed as an individual line. Results compared by Wilcoxon signed rank test.

Figure 5.14: Firmicutes to Bacteroidetes ratio in stool samples.

There is no significant difference in Firmicutes to Bacteroidetes ratio between baseline and eight weeks (A: p=0.28) or baseline and explant (B: p=0.70). Each patient is displayed as an individual line. Results compared by Wilcoxon signed rank test.

5.3.4 Functional predictions from 16S sequencing Sequencing data was used to predict functional pathways using PICRUSt. In the gastric biopsies, there was a significant increase in six functional pathways, and a significant decrease in 10 functional pathways from baseline to explant (Table 5.8; Figure 5.15). Broadly, these can be summarised as a significant increase in cellular processes and signalling, particularly membrane transport, and genetic information processing, in particular transcription factors. There was a significant reduction in energy metabolism, amino acid metabolism, translation, replication and repair and genetic information processing. At baseline, there was no significant difference with the control group for any of the pathways. At explant, the functional profiles were markedly different from controls, with 72 pathways being significantly higher than baseline biopsies and 54 being significantly lower. 163

A full list of these can be seen in Appendix 1.7. Of the 16 pathways that were significantly different from baseline to explant, 11 were also significantly different at explant, compared to the control group. Interestingly, a number of the pathways that were significantly higher in the explant biopsies were for pathways associated with nutrient metabolism, in particular fat and protein metabolism pathways. Additionally, there was a significantly higher relative gene count for bile secretion and bile acid biosynthesis in the explant gastric biopsies.

Table 5.8: KEGG ortholog functional pathways that were significantly different from baseline to explant in gastric biopsy samples.

KEGG Ortholog Direction of change ABC transporters Increased Amino acid related enzymes Decreased Aminoacyl-tRNA biosynthesis Decreased Carbon fixation pathways in prokaryotes Decreased DNA replication Decreased DNA replication proteins Decreased Function unknown Increased Lysine biosynthesis Decreased Mismatch repair Decreased Other ion-coupled transporters Increased Oxidative phosphorylation Decreased Phosphotransferase system (PTS) Increased Protein folding and associated processing Decreased Ribosome Decreased Transcription factors Increased Transporters Increased

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Figure 5.15: KEGG ortholog functional pathways that were significantly different from baseline to explant in gastric biopsies.

Data analysed by using mixed effects linear regression modelling using paired data for each patient. For graphical purposes, data is presented as grouped data. Significance indicated by *. In the duodenal biopsies, there was a significant increase in carbohydrate metabolism (starch and sucrose metabolism) and cellular processes and signalling (transporters), particularly membrane transport (the phosphotransferase system) at explant, compared to baseline. There was a significant decrease in amino acid metabolism (glycine, serine and threonine metabolism; Figure 5.16). At baseline, there was no significant difference with the control group for any of the pathways. At explant, the functional profiles were markedly different from controls, with 75 pathways being significantly higher than baseline biopsies and 75 being significantly lower. A full list of these can be seen in Appendix 1.7. Similar to the gastric biopsies, the relative gene count of bile secretion, bile acid biosynthesis and fat and protein metabolism pathways were significantly higher in the explant biopsies than controls.

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Figure 5.16: KEGG ortholog functional pathways that were significantly different from baseline to explant in duodenal biopsies.

Data analysed by using mixed effects linear regression modelling using paired data for each patient. For graphical purposes, data is presented as grouped data. Significance indicated by *. In the stool samples, there were no significant differences in functional pathways between baseline and explant or 8 weeks and explant. However, between baseline and 8 weeks, there was a significant increase in transcription machinery. There was a significant decrease in other components of genetic information processing (replication and recombination and repair) and signalling and cellular processes (secretion system, two component system and bacterial motility proteins).

5.3.5 Bacterial load qPCR was used to assess the bacterial load of mucosal biopsy specimens. There was a trend toward a significant increase in bacterial load in the stomach (baseline: 0.02±0.02 vs. explant 0.07±0.07; n=11; p=0.05). There was no significant change in bacterial load in the duodenum as a result of DJBS treatment (baseline: 0.02±0.01 vs. explant 0.04±0.04; n=11; p=0.21). However, compared to a control group (control: 0.16±0.18), bacterial load in the duodenum was significantly lower at both baseline (p<0.0001) and explant (p=0.002).

5.3.6 Relationships between microbiota and device efficacy Linear regression analysis was performed to assess the relationship between weight loss and the RA of microbial taxa with a mean RA of at least 1%. There was a significant positive relationship between TBWL at device explant and the RA Streptococcus (r=0.73; p=0.04) in the baseline gastric biopsy. There was a significant negative relationship between TBWL at explant and the RA of 166

Helicobacter in the baseline gastric biopsy (r=-0.53; p=0.03). On multivariate analysis, Helicobacter was retained (r=-0.75, r2=0.57, p=0.03). There was a significant negative relationship between TBWL at device explant and the RA of members of the Gammaproteobacteria class (r=-0.52; p=0.05) in the explant gastric biopsy. There were no significant relationships between weight loss and any taxa in the duodenal biopsies with a mean RA of >1%. Significance of the above analyses was not altered by the removal of early explant patients from the analyses.

In the full cohort, linear regression revealed a significant positive relationship between weight loss and the mean RA of Megasphaera (r=0.55; p=0.02) and Klebsiella (r=0.46; p=0.05) on the device. On multivariate analysis, only Megasphaera was retained (r=0.55, r2=0.30, p=0.02). When all early explants were removed (n=4), no significant relationship between weight loss and the mean RA of any taxon with a minimum RA of 1% remained.

On linear regression, there was a significant positive relationship between TBWL at device explant and the RA of Dorea (r=0.55; p=0.04) in the stool at baseline. There was a significant positive relationship between TBWL at device explant and the RA of Lactobacillus (r=0.74; p=0.002) and Klebsiella (r=0.56; p=0.03) in the stool at eight weeks post-implant. On multiple linear regression analysis, only Lactobacillus was retained in the model (r=0.74, r2=0.55, p=0.002). There were no significant relationships between TBWL at device explant and the RA of any taxa in the stool within three weeks after device explant.

5.3.7 Relationships between microbiota and device tolerability Three patients had devices explanted early due to persistent GI symptoms. Compared to patients that completed the full intervention, there was a significantly higher mean RA of members of the Gammaproteobacteria class (p=0.02) and the genera Klebsiella (p=0.01) and Veillonella (p=0.01) identified on these devices. There was also a significantly lower RA of Megasphaera (p=0.01) present on these devices. At OTU level, this was driven by significantly higher RA of four Klebsiella OTUs (p<0.05), two Veillonella OTUs (p<0.05) and one Gammaproteobacteria OTU (p=0.02). In addition, the mean RA of three Enterobacteriaceae OTUs (p<0.05) and one Streptococcus OTU (p<0.05) was significantly higher in those that underwent early explant.

Following the observation of constipation after device placement, linear regression analysis was performed to assess the relationship between constipation and the RA of microbial taxa in the stool with a mean RA of at least 1%. At baseline and explant, there were no significant relationships between constipation (as measured by the SAGIS) and the RA of any taxon in the stool with a minimum mean RA of 1%. At eight weeks, the RA of Klebsiella was positively associated with the severity of constipation reported (r=0.77; p=0.04).

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5.3.8 Relationships between microbiota and dietary intake An exploratory analysis of the relationship between dietary variables and the RA of taxa at the genus level was conducted. It should be noted 24 hour recall data was not collected on the same day as biological samples for microbiota analyses and this limits the strength of conclusions that can be drawn from this analyses. At baseline, there was no relationship between the RA of any microbial taxon with at least 1% RA and energy, fibre, protein, fat or carbohydrate intake.

At explant, a number of taxa were associated with energy, fibre and macronutrient intake. Univariate linear regression analyses of dietary intake and microbial abundances in MAM are summarised below in Table 5.9. On multivariate analysis, the RA of Collinsella in the duodenal biopsies was positively associated with fibre intake (r=0.67, r2=0.45, p=0.009) and negatively associated with energy intake (r=-0.68, r2=0.47, p=0.007) and carbohydrate intake (r=0.60, r2=0.35, p=0.03). Klebsiella in the duodenal biopsies was positively associated with protein intake (r=0.61, r2=0.37, p=0.02) whilst Bifidobacterium in the gastric biopsies was positively associated with fat intake (r=0.62, r2=0.39, p=0.018).

Table 5.9: Relationship between dietary intake and the RA of microbial taxa (at genus level) isolated from gastric and duodenal biopsy samples on univariate analysis.

Variable Baseline Explant Positive Negative Positive Associations Negative Associations Associations Associations Energy Nil Nil Gastric: Lactobacillus Gastric: nil (r=0.57; p=0.04) Duodenum: Collinsella Duodenum: nil (r=-0.68; p=0.01) Fibre Nil Nil Gastric: Lactobacillus Gastric: Collinsella (r=0.54; p=0.05) (r=-0.58; p=0.03)

Duodenum: nil Duodenum: Collinsella (r=-0.67; p=0.01) Protein intake Nil Nil Gastric: nil Nil

Duodenum: Klebsiella (r=0.61; p=0.02) and Gammaproteobacteria (r=0.55; p=0.04) Fat intake Nil Nil Gastric: Bifidobacterium Nil (r=0.62; p=0.02)

Duodenum: Klebsiella (r=0.55; p=0.04) and Bifidobacterium (r=0.54; p=0.05) Carbohydrate Nil Nil Gastric: Lactobacillus Gastric: nil intake (r=0.54; p=0.05) Duodenum: Collinsella Duodenum: nil (r=-0.60; p=0.03)

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We also investigated the relationship between dietary constituents and the stool microbiota. At baseline, there was no relationship between dietary variables and any taxa with a minimum mean RA of 1% in the stool. After eight weeks of DJBS dwell time, both energy and fibre intake were negatively associated with the RA of members of the Enterobacteriaceae family in the stool. Within three weeks after device explant, a positive relationship between fat intake and the RA of Klebsiella in the stool was observed. A negative relationship between fibre intake and the RA of members of the Clostridiales order and carbohydrate intake and the RA of Collinsella and members of the Lachnospiraceae family was also observed. Linear regression analyses of dietary intake and microbial abundances in the stool are summarised below in Table 5.10.

Table 5.10: Relationship between dietary intake and the RA of microbial taxa (at genus level) isolated from stool samples on univariate analysis.

Variable Baseline 8 weeks dwell time Explant Positive Negative Positive Negative Positive Negative Association Association Association Association Association Association Energy Nil Nil Nil Enterobacteriaceae Nil Nil (r=-0.53; p=0.04) Fibre Nil Nil Nil Enterobacteriaceae Nil Clostridiales (r=-0.55; p=0.03) (r=-0.70; p=0.02) Protein Nil Nil Nil Nil Nil Nil intake Fat intake Nil Nil Nil Nil Klebsiella Nil (r=0.77; p=0.01) Carbohydrate Nil Nil Nil Nil Collinsella Lachnospiraceae intake (r=-0.70; (r=-0.66; p=0.04) p=0.02)

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5.3.9 Results summary High-throughput sequencing techniques were used to characterise the gastric and duodenal MAM and the stool microbiota, in addition to the microbial community on the device itself. Bacterial load of mucosal biopsies was assessed using qPCR. Following DJBS treatment there was a shift in both the gastric and duodenal MAM away from the profile observed in controls. Common upper GIT microbes, such as Streptococcus were significantly reduced following DJBS treatment. There was a concurrent increase in rare microbes that were of low abundance at baseline, such as Lactobacillus. Neither alpha diversity nor bacterial load were changed in the stomach or the duodenum following DJBS treatment. However, alpha diversity was significantly higher at both sites at explant, compared to controls. Bacterial load was significantly lower in the duodenum at baseline and explant, compared to controls.

The mucosa-adjacent device surface formed a distinct microbial community. On the device, there was a low RA of organisms typically seen in the duodenum such as Prevotella and Streptococcus. Indeed, the device was primarily colonised by microbes more common to the distal GIT such as Lactobacillus and members of the Enterobacteriaceae family. However, overall, the MAM in the duodenum at explant was distinct from the collocated mucosa-adjacent device community.

Treatment with a DJBS resulted in distinct changes in the stool microbiota during the device in situ phase. Similar to the MAM, a shift away from a “typical” stool microbiota profile was observed with a reduction in members of the Lachnospiraceae family and a proliferation of rare taxa. However, these taxonomic changes did not persist after device removal.

Functional gene predictions from the sequencing data suggest alterations in nutrient metabolism pathways from baseline to explant. In the stomach, this was reflected by a reduction in energy metabolism and protein metabolism pathways. In the duodenum, this was reflected by an increase in carbohydrate metabolism pathways and a reduction in protein metabolism pathways. In comparison to controls, there was an enrichment in pathways associated with bile secretion and bile acid synthesis in both the gastric and duodenal biopsies at device explant.

Interestingly, it was possible to correlate weight loss and device tolerability outcomes with the microbes colonising either the MAM or the device itself. Further, a number of taxa in the MAM at explant could be associated with dietary macronutrient intake data.

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5.4 DISCUSSION Obesity and T2DM have been demonstrated to alter the GI microbiota, with weight loss partially reversing these changes. The DJBS is a foreign body that resides in the upper GIT for up to 48 weeks, and is in contact with the duodenal mucosa. This potentially provides an ideal growth environment for microorganisms on the device surface. It is possible that changes in the local environment induced by the DJBS may lead to alterations in the duodenal MAM community. This may have important implications for device efficacy, tolerability and safety. Despite this, this is the first study to thoroughly assess the GI microbiota following DJBS treatment.

The MAM of the stomach and the duodenum were characterised at baseline and explant using high- throughput sequencing techniques. The overall community profiles (beta-diversity) observed in the stomach and the duodenum were not significantly different from a control population of patients with a positive faecal occult blood test or iron deficiency anaemia. Indeed, there were no significant differences between the baseline biopsies and the controls of similar BMI (≥35 kg/m2). However, there were significant taxonomic shifts as a result of DJBS treatment and considerable overlap in the taxonomic changes observed in the stomach and the duodenum, with eight taxa increased at both sites. Broadly, there was a significant increase in a number of lactate producing and lactate utilising taxa, including Lactobacillus, Dialister and members of the Enterobacteriaceae family. This is possibly reflective of changes in the environment induced by the presence of the device which may allow migration of aerotolerant distal bowel bacteria in addition to providing a surface for adhesion and growth, which may then seed an alteration the MAM. Once colonisation with lactate producing bacteria had occurred this will then define the succession of lactate utilising bacteria.

In addition to characterising the MAM, another aim of this chapter was to characterise any microbial community that formed on the device itself. A distinct community formed on the mucosa-adjacent device surface, with a preference for colonisation by more typically distal microorganisms such as Lactobacillus and Bacteroides than the most abundant core duodenal community members such as Streptococcus and Prevotella544, 548. Indeed, the mucosa-adjacent device surface that was collocated with the mucosal biopsy at explant was distinct, suggesting a difference in growth requirements at two geographically collocated sites.

We also characterised changes in the stool microbiota following treatment with the DJBS. This is particularly useful as the literature surrounding changes in the GI microbiota following bariatric surgery all use stool sampling, allowing for a more direct comparison of the results obtained. In the stool samples, treatment with a DJBS leads to distinct phylogenetic changes, without impacting the

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overall community profile or alpha diversity. This was characterised by an increase in taxa that were at very low abundance at baseline and a decrease in members of the dominant Lachnospiraceae family. This is particularly interesting as colonisation with Lachnospiraceae has been associated with increased fasting glucose in a rodent model of obesity549. To date, one other study of the DJBS has assessed the stool microbiota at baseline and after six months of treatment in 14 patients479. There was considerable overlap between the results observed in their cohort and ours. The RA of Lactobacillus, Veillonella, Klebsiella and Serratia increased in both study cohorts. In the de Jonge cohort, increases in Clostridium cluster IV, Enterobacter, Escherichia coli and Yersinia were also reported. These taxa belong to the Clostridiaceae family (Clostridium cluster IV), the Enterobacteriaceae family (Enterobacter and Escherichia) and Gammaproteobacteria class (Yersinia), which were all increased in our cohort. Therefore it is possible that further analysis to increase granularity may have also allowed us to discriminate to these lower taxonomic levels. In contrast to our results, they observed a decrease in Ruminococcus and Megamonas, where we observed a reduction in members of the Lachnospiraceae family. Broadly, all of these taxon are butyrate producers, suggesting the changes are functionally similar despite taxonomic differences. The taxonomic changes observed in our study are also broadly similar to those observed following bariatric surgery. One key difference between DJBS treatment and bariatric surgery is the transient nature of DJBS treatment. Indeed, transient phylogenetic changes were observed in the stool, with a return to baseline profile being observed within three weeks after device explant. Whilst we do not have any measures of the MAM after explant, it is possible that these changes are also reversed after device removal. Therefore, unlike surgery, the long-term implications of these changes may be of limited relevance in long-term weight and metabolic outcomes post-explant.

Functional predictions constructed from the sequencing data were used to assimilate taxonomic changes with changes in the genetically encoded functional potential of the microbial communities (for example, the presence of genes encoding particular enzymatic/metabolic pathways). An increase in membrane transport was observed after device dwell, as was an increase in carbohydrate metabolism. A reduction in overall energy metabolism, and more specifically amino acid metabolism, was also observed. These changes may reflect a shift towards more efficient utilisation of carbohydrate in the context of reduced consumption (Chapter 3 Section 3.3.5). They also mirror the taxonomic changes seen on the mucosa, with an increase in taxa that ferment carbohydrates, such as Lactobacillus550 and Bifidobacterium551. In particular, the increase in the phosphotransferase system (a major mechanism of carbohydrate uptake by bacteria) observed is likely to reflect the dominant colonisation by Lactobacillus552. A number of these functional changes have also been reported previously in the stool following bariatric surgery. Specifically, three studies that have

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broadly assessed changes in KEGG orthologs have reported an increase in the phosphotransferase system pathway after RYGB368, 372, 375. Graessler et al. proposed that these changes occur as a compensatory effect to enhance substrate assimilation to meet energy demands in the context of reduced substrate availability368. Carbon fixation by prokaryotes and glycine, serine and threonine metabolism pathways have also been reported to be increased following LSG, as observed in our cohort372. Tremaroli et al. observed an increase in the two component system following RYGB, whereas our results indicate a reduction in this pathway375. The other changes in functional pathways from the stool observed in this cohort have not previously been reported.

As part of the treatment protocol, patients were prescribed a PPI for the duration of the intervention, and for four weeks after device removal. This will impact all follow-up data points, but not the baseline data, and exerts an additional confounder into the analysis. PPI use has been associated with reduced alpha diversity and distinct phylogenetic changes in the stomach553. However, the majority of work regarding the impact of PPI use on the GI microbiota has assessed changes in the stool microbiota. In our study, three of 14 taxonomic groups that were increased (Enterococcus, Lactobacillus and Veillonella) after DJBS treatment have also been shown to be increased in the stool after chronic PPI treatment554. In our cohort, there was a significant reduction in members of the Lachnospiraceae family. PPI use has not been demonstrated to have a consistent effect, with both an increased555 and a reduced556 RA of this family reported with PPI use. Further, in our cohort, in the stomach, Rothia and Actinomyces were significantly decreased, whilst in the duodenum Haemophilus and Gemella were decreased. Broadly, these organisms are typical of the oral microbiota and are acidogenic genera. These organisms have been reported to be enriched with PPI use554, which suggests DJBS treatment is exerting a stronger effect on the GI microbiota than PPI use in our cohort. Whilst significant taxonomic changes were observed, there was no significant change in alpha diversity or bacterial density in the stomach or duodenum following DJBS treatment.

There was a trend toward an increase in bacterial load in the stomach following DJBS treatment. This is more likely to be reflective of chronic PPI use than a device-related effect. Studies have demonstrated an increase in the number of colony forming units in gastric juice aspirate following chronic PPI use, indicating the possibility for enhanced proliferation of the microbial community557, 558. Contrary to our expectations, there was no significant effect of DJBS treatment on bacterial load in the duodenum. This is likely to reflect the small sample size for paired comparisons (n=8), as bacterial load at explant was significantly higher than observed in a control group. However, the physical presence of the device and the microbial community on the mucosa-adjacent surface leads to an increase in bacteria overall in the region. Whilst PPI use was mandatory for all patients to reduce the risk of upper GI bleeding, the use of PPIs may also modulate the weight loss 173

response. In a study of RYGB patients, PPI use at six months post-surgery was associated with both a higher RA of Firmicutes and a lower EWL559. This evidence highlights the complexity of understanding the modulators of clinical response to DJBS treatment.

Antibiotic therapy has widespread and persistent impacts on the GI microbiota560, 561. In our cohort, antibiotic use was only precluded in the four weeks prior to recruitment and indeed four patients were treated with antibiotics for eradication of H.pylori following device implantation. Antibiotic therapy is likely to impact other taxonomic groups besides Helicobacter. As this treatment occurred soon after device implantation, this is most likely to impact the eight weeks post-implant stool sample, as this was the only device in situ measure of the GI microbiota. Despite this, the taxonomic differences between baseline and eight weeks post-implant time-points were not profound, with all but one taxonomic group remaining significantly increased when antibiotic-treated patients were removed. In the stool, the majority of the taxa increased after DJBS implantation are facultative anaerobes, even when H.pylori eradication treatment patients are removed. This is noteworthy as recent Helicobacter eradication therapy has been shown to increase the presence of facultative anaerobes in the stool562. However, with this trend persisting when these patients are removed, it suggests the device environment supports the growth of facultative anaerobes (such as the dominant Lactobacillus and Enterobacteriaceae), which are shed, thereby influencing the stool profile. This is strengthened by the observation of a reduction in obligate anaerobic taxa with a high baseline RA such as members of the Clostridiales order, Lachnospiraceae family and Blautia even when H.pylori eradicated patients were removed. These results suggest the DJBS is exerting a stronger and/or independent effect on the microbial community than antibiotic use.

A rare complication of DJBS treatment is the formation of pyogenic hepatic abscess. It has been postulated that this occurs either as a result of ascending cholangitis or secondary to perforations induced by the barbs563. Another potential mechanism is translocation of bacteria across the mucosal membrane. Obesity has been associated with an increase in small intestinal permeability564 and our results indicate a distinct microbial community is formed on the mucosa-adjacent device surface. Overall, this leads to more bacteria being in close contact with the duodenal mucosa, and may provide an opportunity for bacterial translocation and seeding into the portal vein. This is a possible explanation for the development of pyogenic hepatic abscess that has been observed as a complication of DJBS treatment. Indeed, while there were no cases in our cohort, some of the taxonomic changes observed are congruent with reports of sequencing data obtained from aspirated pyogenic liver abscess material. In particular, between baseline and explant there was a significant increase in the RA of Klebsiella, Fusobacterium, Dialister and members of the Enterobacteriaceae family in the duodenal biopsy samples, all of which have been reported in liver abscess fluid565. In the case of 174

Klebsiella, which has been frequently reported in hepatic abscess fluid in patients with diabetes mellitus566, there was a nearly 200-fold increase in the RA in the duodenum (baseline mean: 0.01% vs. explant mean: 2.78%). Moreover, the RA of Klebsiella was significantly higher on the collocated mucosa-adjacent device surface (mean: 5.07%) than in the explant biopsy. Furthermore, members of the Enterobacteriaceae family were one of the most dominant taxa to colonise the mucosa-adjacent device surface. Therefore, the changes induced by DJBS treatment both in the MAM and the proliferation of a bacterial community on the mucosa-adjacent device surface may be relevant for the manifestation of one of the key SAEs of device treatment.

A number of relationships between the RA of microbial taxa and weight loss were observed across the various sites of measurement. In the mucosa and on the device surfaces, these relationships suggested that a lesser degree of disturbance from a taxonomic profile that is characteristic of the upper GIT microbiota observed in controls was associated with better weight loss outcomes. For example, the RA Streptococcus in the stomach at baseline, a site typically high in RA of this taxon567, was associated with weight loss. Conversely, the RA of Helicobacter, a common pathogen, in the stomach at baseline, was negatively associated with weight loss. Similarly, the RA of members of the Gammaproteobacteria class in the stomach at device explant was negatively associated with weight loss. These organisms would typically dwell in the distal GIT. On the device surface, weight loss was positively associated with the RA of Megasphaera. This genus has been reported to increase after bilio-intestinal bypass568, but no direct correlation with weight loss was reported. In the stool at baseline, the RA of Dorea, a normal luminal microbe, was positively associated with weight loss. However, as this is only one of many core luminal taxa, this is insufficient evidence to suggest a more “typical” stool profile at baseline may be associated with weight loss. At the eight week post-implant time-point, the only device in situ measure of the GI microbiota, the RA of Lactobacillus was associated with weight loss. This is likely to be reflective of a flow through effect, as Lactobacillus is the predominant taxa colonising the device itself. Although the evidence is conflicting, a similar relationship between Lactobacillus and weight loss response has been reported in one dietary weight loss intervention study193. Further investigation is required to determine the relationship between microbial taxa and weight loss outcomes.

Changes in the GI microbiota induced by DJBS treatment may also be relevant for device tolerability. Indeed, it was possible to identify microbes that were associated with early explant in this group. In these patients, a significantly higher RA of members of the Gammaproteobacteria class and the genera Klebsiella and Veillonella were observed, whilst a significantly lower RA of Megasphaera was observed. Some of these taxa have been linked to either organic or functional bowel disorders. For example, the RA of Veillonella in the stool has been reported to be higher in IBS patients than 175

controls569. Similarly, an increase RA of members of the Gammaproteobacteria class has been reported in both IBS570 and active inflammatory bowel disease571. However, these taxa have not specifically been linked to abdominal pain. Finally, Megasphaera and Klebsiella have not previously been linked to GI symptoms. One limitation of these links between GI disorders and the abundance of bacterial taxa is that they are often performed using a stool sample. Given the location of the symptoms (primarily epigastric), further studies in a larger cohort should aim to assess taxonomic differences in the proximal small intestine in those who undergo elective early removal due to GI symptoms. This could provide insight into any microbial regulation of upper GI symptoms.

Due to the observation of constipation following DJBS treatment, we aimed to assess taxa that may be related. Members of the Gammaproteobacteria class and Enterobacteriaceae family, Klebsiella and Bifidobacterium were all negatively associated with constipation at device explant. Bifidobacterium administration as a probiotic supplement has been demonstrated to relieve constipation in humans and mice572, 573. The other three taxonomic groups have not specifically been associated with constipation; however, they could be considered “pro-inflammatory” taxa574. Inflammation has been associated with diarrhoea in patients with IBS575, and therefore the abundance of these taxa could perhaps be linked with more rapid motility. Alternatively, recent data suggest that methane, produced by methanogenic archaea and in particular Methanobrevibacter smithii576, 577, can cause constipation. These archaea were not revealed in our sequencing data. Future studies could assess methane production by using a targeted quantitative assessment of methanogenic archaea via qPCR or a qualitative assessment by using a glucose hydrogen breath test578. This could improve conclusions drawn about the relevance of archaeal changes in the pathogenesis of constipation following DJBS treatment.

Very little is known about the impact of diet on the MAM of the proximal GIT. However, this is the site at which exposure to simple carbohydrates and amino acids is maximal, and therefore dietary intake is likely to have a profound effect on the composition of the microbiota. Due to the lack of literature, our results will be compared to findings in the stool microbiota. At device explant, energy, carbohydrate and fibre intake were negatively associated with the RA of Collinsella, a typical upper GI microbe. In the literature, the RA of Collinsella (aerofaciens) in stool samples has been reported to decrease with a low carbohydrate weight loss diet192. Conversely, low fibre intake has been associated with an enrichment of Collinsella579, 580. Therefore it may be the fibre content rather than overall carbohydrate intake that is of importance. Macronutrient intake was also positively associated with the RA of Lactobacillus, Bifidobacterium and Gammaproteobacteria in the MAM. These organisms are typically higher in abundance in the distal bowel. Calorie restriction has been shown to increase Lactobacillus in a limited number of human studies581. However, calorie restriction in 176

humans is an imprecise assessment as it leads to weight loss, which may induce confounding effects. Dietary fat and protein intake were positively associated with the RA of Klebsiella. During the intervention, higher fat intake was discouraged due to the high contribution to energy intake. Klebsiella is a bile tolerant bacterial group582. Five day administration of an animal-based diet (30% of energy from protein with high saturated fat) has been associated with an increased abundance of bile tolerant bacteria151. Klebsiella has also been associated with pyogenic hepatic abscess566. Therefore, future studies should aim to assess dietary intake and the GI microbiota to validate the potential links between dietary intake and unfavourable changes in the microbiota that could relate to safety outcomes.

A major strength of the study is the breadth of sampling sites, as this allows for a more detailed characterisation of the impacts of the DJBS on the GI microbiota across the length of the GIT. In parallel, the dataset allowed for matched comparisons, which helps to overcome the large inter-individual variation inherent in studies of the GI microbiota, increasing the confidence the changes observed are as a result of the treatment protocol. The V6-V8 hypervariable region of the 16S rRNA gene was selected to provide the most accurate representation of communities present at genus and OTU level583; however, certain taxa such as Bacteroides may be underrepresented584. To enhance the accuracy of the data set, the post-sequencing data processing protocol removed any environmental contaminants, ensuring the data was representative of the samples themselves. Another strength is the inclusion and exclusion criteria for the DJBS group. Despite their status as patients with obesity and T2DM, which are both know modulators of the GI microbiota, the presence of any inflammatory disease of the GIT was an exclusion criteria. Further to this, the control group selected included patients with overweight and obesity and patients with metabolic comorbidities such as T2DM. This provided a useful control group to compare the impact of the DJBS, compared to a “healthy” control population.

The primary weakness of the study is the small sample size, which was further hampered by a number of baseline mucosal samples with reads that did not meet quality control standards, likely due to relatively low bacterial density. However, other limitations exist that may confound the conclusions drawn from the study. In this study, the ability to integrate dietary data into the analysis is suboptimal. Firstly, the primary site of interest is the duodenum, as this is where the device dwells; however, the impact of diet on the duodenal MAM is not well characterised. Further, dietary data was collected in the form of a monthly 24 hour recall, and this did not always occur at the time of specimen collection. Furthermore, there was no explicit capture of probiotic consumption, particularly in supplement form. Unless patients reported consumption of probiotic foods in their 24 hour recall, this was not captured, despite the relevant effect on the microbiota. From the dietary data that was collected (Chapter 3 177

Section 3.3.5), participants significantly reduced the amount of food they were eating, including carbohydrates, and more specifically dietary fibre, and this would impact the microbiota profile. However, regardless of the dietary data collection methodology, it is difficult to discern if the impacts on the microbiota are associated with changes in dietary intake or the diverse changes in body weight and metabolism that occurred in parallel as a results of DJBS treatment.

In summary, there were significant taxonomic changes in the MAM and stool microbiota induced by treatment with the DJBS. These were mirrored by changes in functional genetic profile that would indicate an enhancement in bacterial carbohydrate metabolism, in the context of reduced carbohydrate intake. A distinct microbial community colonised the mucosa-adjacent device surface, largely representing organisms that are more typical to the distal small intestine and large bowel than the duodenum. Whilst significant changes in the stool microbiota were observed, the post-explant stool data suggests these changes are transient. Further work in a larger sample size could aim to delineate the role of the microbiota in weight loss responsiveness and device tolerability, and further characterise the microbiota in the case of SAEs, in particular hepatic abscess.

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Chapter 6: Effect of the Duodenal-Jejunal Bypass Sleeve on Histological and Immune Parameters

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6.1 INTRODUCTION The proximal GIT is an important site for nutrient uptake, and thus an attractive target for intervention in obesity management. Contact of nutrients with the small intestinal mucosa appears important for maintenance of normal architecture, which in turn is important for nutrient absorption585. Surgical models of duodenal-jejunal bypass have reported villous atrophy in the excluded limb. In a rodent model, duodenal villous blunting was observed 14 days after duodenal-jejunal bypass surgery586. In the jejunum; however, there was an increase in epithelial cell proliferation and intestinal wall circumference. Therefore, these intestinal adaptations appear to occur in response to nutrient contact.

Conceptually, the DJBS separates bile and pancreatic secretions from chyme for 60 cm, thereby excluding nutrients from the duodenal mucosa and reducing the absorptive surface during small intestinal transit. The impact of DJBS treatment on the excluded mucosa has not been studied in humans. In a porcine model, Tarnoff et al. investigated the morphology of the duodenum and jejunum following 90 days of DJBS implantation587. In these animals, focal chronic inflammation was reported immediately adjacent to the anchor barbs, with normal mucosa observed in close proximity to the barbs. Additionally, normal mucosa was observed in duodenal and jejunal tissue that had been in contact with the sleeve. Conversely, a rat model has utilised a DJBS anchored in the gastric antrum and extending to the ligament of Treitz588. In this model, after 27 days of DJBS implantation flattening of the duodenal submucosa was observed, with a significant increase in villi length. This phenomenon was not seen in the pair-fed sham operated control animals, suggesting this occurs independent of changes in body weight. Cumulatively, these results suggest the DJBS may exert a more modest effect on duodenal morphology than surgical exclusion.

The DJBS is also a foreign body in the duodenum, with the anchor barbs implanting into the duodenal mucosa. This may induce a local inflammatory response, although this has not been assessed in humans. In the porcine study by Tarnoff et al587, local inflammation was reported immediately adjacent to the anchor barbs, but immune cell populations were not quantified. In other populations, immune cell infiltration has been associated with an increase in GI symptoms589, 590. Thus, the effect of DJBS implantation on immune cell infiltration in the duodenum warrants further investigation, as this may be a relevant modulator of device tolerability.

Further, obesity has been associated with chronic, low grade systemic inflammation. This inflammation is largely driven by the secretion of pro-inflammatory cytokines from adipose tissue deposits591. Weight loss could also benefit patients with obesity by reducing inflammation, which has been recognised as a key factor in the pathogenesis of a number of obesity-related

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comorbidities, including T2DM592. In obesity, T cells, a key component of the adaptive immune system, have been implicated in adipose tissue inflammation593 and insulin resistance594. Knowledge of the impact of weight loss on T cell phenotype in the circulation is limited. In two studies, weight loss following adjustable gastric banding was not associated with any significant alteration in CD4+ T cell population595, 596, whilst RYGB has been associated with a reduction in CD4+ T cells597. Similarly, no significant alteration in CD8+ T cells was observed after significant weight loss following adjustable gastric banding596. More broadly, a reduction in circulating cytokines has been observed following weight loss, achieved by dietary intervention597-599 or bariatric surgery597, 600, 601.

There have been conflicting reports of the impact of obesity on T cell phenotypes in the circulation in cross-sectional studies. The majority of studies have either reported no significant correlation602-604 or a positive correlation between proportion of CD4+ T cells and BMI605-609. Conversely, a number of studies have reported an inverse relationship between the proportion of CD8+ T cells and BMI603, 604, 607, 608.

Even fewer studies have reported on the impact of weight loss following DJBS treatment on immune parameters. Involvement of specific immune cell populations have not been reported following DJBS treatment. Only one study of 17 patients with obesity and T2DM has reported a minimal impact of DJBS treatment on plasma markers of inflammation (TNF-α and IL-6) after three months of DJBS treatment471. However, after six months of DJBS treatment, these levels were no longer significantly different to baseline471. Defining changes in systemic immune activation is important, as this may be relevant for device efficacy outcomes, in particular glycaemic control outcomes.

6.1.1 Aims and Hypotheses The aim of this study was to determine the impact of DJBS treatment on local and systemic inflammation. A post-hoc secondary analysis was conducted to determine any links between the immune system and the GI microbiota.

It was hypothesised that implantation of a DJBS for 48 weeks would:

• Impact the morphology of villi in the duodenum; measured by assessment of histological specimens; • Trigger a local immune response due to the presence of a foreign body in the duodenum; measured by immune cell infiltration visualised on histological slides; and • Reduce systemic markers of inflammation due to weight loss; cytokines produced by LPS-stimulated PBMCs and PBMC phenotype measured by flow cytometry.

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6.2 METHODS

6.2.1 Histology Duodenal mucosal biopsy samples were taken at baseline and explant for routine histology. Villous architecture was assessed as part of routine clinical histological assessment by anatomical pathologists from Queensland Pathology. Additionally, a consultant anatomical pathologist quantified immune cell populations from histological slides. Specifically, one tissue section was selected and IELs were quantified (per 500 enterocytes). In the same tissue section, intact eosinophils were counted. The area of tissue was quantified using Visopharm software (Visopharm). The number of eosinophils in the duodenal tissue section were expressed as a count per mm2. For further details, see Chapter 2 Section 2.6.1.

6.2.2 PBMC Phenotyping and Cytokine Production Following PBMC Culture Whole blood was collected for the assessment of systemic immune cell phenotype and immune activation at baseline and explant. PBMCs were isolated from whole blood and subjected to flow cytometry analysis. A 13 fluorophore panel was used to assess T cell phenotype using the following markers: cytotoxic T cells: CD3+CD8+, T helper cells: CD3+CD4+, central memory T cells: CD45RA- CD45RO+CD62L+CCR7+, effector memory T cells: CD45RA-CD45RO+CD62L-CCR7-, effector T cells: CD45RA+CD45RO-CD62L-CCR7-, naïve T cells: CD45RA+CD45RO-CD62L+CCR7+ and gut homing T cells: α4+β7+CCR9+. For further details, see Chapter 2 Section 2.6.2.1 and Section 2.6.2.2.

The concentrations of TNF, IL-6, IL-8 and IL-10 from LPS-stimulated cultured PBMC supernatant were measured by ELISA. For further details, see Chapter 2 Section 2.6.2.3 and Section 2.6.2.4.

Results were compared to a control group of 30 patients referred for investigation of iron-deficiency anaemia or positive faecal occult blood test who underwent immunological assessment as part of a separate study (HREC/13/QPAH/690).

6.2.3 Statistical Analysis All quantitative measures were described as mean and standard deviation and were assessed for normality of distribution using the Shapiro-Wilk normality test. Normally distributed data was analysed using paired t-tests, which compared pre- to post-procedure measurements. Non-normal data was assessed by the non-parametric Wilcoxon signed rank test. To assess the relationship between immunological parameters and the GI microbiota, bivariate correlations were performed to screen for candidate variables for univariate linear regression analysis. Taxa were only screened where the minimum mean RA of that taxon was at least 1%. Non-normally distributed data was square root transformed. Any variables that were significant on univariate analysis were added to a

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multiple linear regression model with backward elimination. Significance of results was determined by a two-tailed p value of less than 0.05.

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6.2.4 Schedule of Investigations

• Duodenal tissue biopsy for histological assessment of: • Morphology • Immune cell populations (IELS and eosinophils) • Blood collection for: • Systemic immune cell phenotyping (flow cytometry of PBMCs) Baseline • Systemic immune activation (IL-6, IL-8, IL-10 and TNF; ELISA of LPS-stimulated PBMCs)

• Duodenal tissue biopsy for histological assessment of: • Morphology • Immune cell populations (IELs and eosinophils) • Blood collection for: • Systemic immune cell phenotyping (flow cytometry of PBMCs) Explant • Systemic immune activation (IL-6, IL-8, IL-10 and TNF; ELISA of LPS-stimulated PBMCs)

Figure 6.1: Schedule of histological and immune parameter investigations.

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6.3 RESULTS

6.3.1 Histology Duodenal villous architecture was assessed during routine clinical histological assessment by Queensland Pathology. At baseline, 18 of 19 patients had normal villous architecture. One patient was reported to have mildly disturbed villous architecture. At explant, 11 of 19 patients were reported to have normal villous architecture. In seven of 19 patients, architectural distortions such as blunting and focal erosion were observed. One patient specimen had insufficient tissue material to assess architecture. To determine if there was a relationship between atrophic changes and weight loss, a subgroup analysis was conducted. There was no significant difference in weight loss between those with normal morphology and those with villous blunting (p=0.32).

Immune cell populations in the duodenal mucosa were quantified by a consultant anatomical pathologist. There was a significant increase in eosinophils in the duodenal mucosa from baseline to explant (baseline: 58±34 eosinophils per mm2 vs. explant: 112±69 eosinophils per mm2; n=19; p<0.01; Figure 6.2A). IELs in the duodenum also significantly increased from baseline to explant (baseline: 14±10 IELs/100 enterocytes vs. explant: 17±10 IELs/500 enterocytes; n=19; p=0.05; Figure 6.2B).

Figure 6.2: Histological analysis of immune cell populations.

Eosinophils in the duodenal mucosa (per mm2) (A) and IELs (B), counted per 500 enterocytes, from baseline to explant. There was a significant increase in the number of eosinophils per mm2 of duodenal mucosa from baseline to explant (n=19; p=0.0039). There was a significant increase in the number of IELs per 500 enterocytes from baseline to explant (n=19; p=0.0456). Each patient is displayed as an individual line on the graph. (B) Reference range of 25 IELs/100 enterocytes610 represented by dotted line. Results assessed by paired t-test (A) and Wilcoxon signed rank test (B).

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A control group of patients that did not receive the DJBS, with a positive FOBT or iron deficiency anaemia were included for comparison. These patients were chosen to represent a diverse range of body habitus, from lean to severe obesity (BMI range: 20-41 kg/m2). The baseline number of eosinophils in the duodenal mucosa was significantly higher, compared the control group (26±13 eosinophils per mm2; n=16; p<0.001; Figure 6.3A), or control patients with obesity (n=7, p=0.01). There was no significant difference in duodenal eosinophil count between the overall control group and those patients with obesity (26±13 vs 24±10 eosinophils per mm2; Figure 3A). At explant, the number of eosinophils in the duodenal mucosa was also higher than the control group (p<0.0001). Again, in comparison to control patients with obesity, the number of eosinophils was higher at DJBS explant (p=0.0002). There was no significant difference in the number of IELs per 100 enterocytes at baseline or explant, compared to the control group (14±6 IELs/100 enterocytes; n=13), or only the control patients with obesity (15±9 IELs/100 enterocytes; n=6; Figure 6.3B).

Figure 6.3: Histological analysis of immune cell populations compared to a control group.

The number of eosinophils in the duodenal mucosal was significantly higher at both baseline and explant, compared to the entire control group (vs baseline p=0.0013, vs explant p<0.0001) and control patients with obesity (vs baseline p=0.0069, vs explant p=0.0002). The number of IELs per 100 enterocytes was not significantly different at baseline or explant, compared to the control group, or control patients with obesity. Results analysed by unpaired t-test (eosinophils baseline vs control and explant vs control) or Mann-Whitney test (eosinophils baseline vs controls with obesity and explant vs controls with obesity and IELs for all comparisons).

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6.3.2 PBMC Phenotyping A panel of 13 antibodies was used to assess circulating T cell phenotype from PBMCs collected at baseline and within three weeks after device explant. The T cell subgroups were defined as: cytotoxic T cells: CD3+CD8+, T helper cells: CD3+CD4+, central memory T cells: CD45RA- CD45RO+CD62L+CCR7+, effector memory T cells: CD45RA-CD45RO+CD62L-CCR7-, effector T cells: CD45RA+CD45RO-CD62L-CCR7-, naïve T cells: CD45RA+CD45RO-CD62L+CCR7+ and gut homing T cells: α4+β7+CCR9+.

There was a significant decrease in the proportion of T helper (CD4+) cells from baseline to explant (baseline: 45.4±13.9% vs. explant: 38.6±13.2%; n=12; p<0.01; Figure 6.4A). Comparing the control group to the DJBS patients, there was no difference in the proportion of CD4+ T cells at baseline; however, at explant, there was a significantly lower proportion of CD4+ T cells in the DJBS cohort (p=0.02; Figure 6.4B). There was no significant difference in CD4+ T cells proportion between the DJBS cohort and control patients with obesity at any time-point.

Figure 6.4: T helper cells (CD4+) populations from baseline to explant and compared to a control group.

There was a significant reduction in the proportion of T helper cells from baseline to explant (A; n=12; p=0.0052). T helper cells were not significantly different at baseline compared to the control group. However, there was a significantly lower proportion or T helper cells at explant, compared to controls (B; p=0.0324). When compared only to controls with obesity, there was no significant difference in the proportion of CD4+ T cells. A) Each patient is displayed as an individual line on the graph, B) grouped data presented. A) assessed by paired t-test, B) assessed by unpaired t-test, when comparing to controls.

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The proportion of cytotoxic (CD8+) cells did not significantly change from baseline to explant (baseline: 23.0±14.8% vs. 27.6±15.2%; n=12; p=0.13; Figure 6.5A). Compared to controls, the proportion of CD8+ T cells was significantly lower at baseline (p=0.0009) and there was a trend toward a lower proportion at explant (p=0.05; Figure 6.5B). Compared to control patients with obesity, the proportion of CD8+ T cells was significantly lower at baseline (n=13 controls; p=0.01), but not explant (n=13 controls; p=0.23).

Figure 6.5: Cytotoxic T cell (CD8+) populations from baseline to explant and compared to a control group.

There was no significant change in the proportion of cytotoxic T cells from baseline to explant (A; n=13; p=0.13). Cytotoxic T cells were significantly lower at both baseline (B; p=0.0009) and there was a trend at explant (B; p=0.0535), compared to the control group. When only control patients with obesity (n=13) were included, there was a significantly lower proportion of cytotoxic T cells at baseline (B; p=0.0107), but not explant (B; p=0.2254). A) Each patient is displayed as an individual line on the graph, B) grouped data presented. A) assessed by Wilcoxon signed rank test, B) baseline vs control/controls with obesity and explant vs controls with obesity assessed by Mann Whitney test, explant vs control assessed by unpaired t-test.

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More specifically, both CD4+ and CD8+ T cell subgroups in the DJBS cohort were assessed. There was no significant change in CD4+ effector T cells (baseline: 46.1±18.3% vs. explant: 52.5±23.8%; n=12; p=0.17; Figure 6.6A), CD4+ central memory T cells (baseline: 3.0±2.0% vs. explant: 2.3±2.5%; n=12; p=0.20; Figure 6.6B) or CD4+ effector memory T cells (baseline: 73.2±8.7% vs. explant: 74.9±17.2%; n=12; p=0.76; Figure 6.6C). However, there was a significant reduction in CD4+ naïve T cells from baseline to explant (baseline: 17.9±10.8% vs. explant: 11.8±11.2%; n=12; p=0.003; Figure 6.6D).

Figure 6.6: CD4+ T cell subtypes from baseline to explant.

There was no significant change in CD4+ effector T cells (A; p=0.1766), CD4+ central memory T cells (B; p=0.2036) or CD4+ effector memory T cells (C; p=0.7554). There was a significant decrease in CD4+ naïve T cells (D; p=0.0030). Each patient is displayed as an individual line on the graph. A, C and D assessed by paired t-test. B assessed by Wilcoxon signed rank test.

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There was a trend toward a significant increase in CD8+ effector T cells (baseline: 94.2±3.8% vs. explant: 96.6±3.2%; n=12; p=0.06; Figure 6.7A). There was no significant change in CD8+ effector memory T cells (baseline: 88.7±4.4% vs. explant: 90.6±10.7%; n=12; p=0.30; Figure 6.7B), CD8+ central memory T cells (baseline: 0.6±0.7% vs. explant: 0.4±0.5%; n=12; p=0.37; Figure 6.7C) or CD8+ naïve T cells from baseline to explant (baseline: 0.4±0.5% vs. explant: 0.2±0.3%; n=12; p=0.13; Figure 6.7D).

Figure 6.7: CD8+ T cell subtypes from baseline to explant.

There was a trend toward a significant increase in CD8+ effector T cells (A; p=0.0640). There was no significant change in CD8+ effector memory T cells (B; p=0.3013), CD8+ central memory T cells (C; p=0.3652) or CD8+ naïve T cells (D; p=0.1299). Each patient is displayed as an individual line on the graph. A, B and C assessed by Wilcoxon signed rank test, D assessed by paired t-test.

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We also assessed gut homing T cell markers on PBMCs. There was a significant reduction in gut homing T cells from baseline to explant (baseline: 1.9±1.6% vs. explant: 0.5±0.2%; n=11; p=0.01; Figure 6.8A). At baseline, DJBS patients had a significantly higher proportion of gut homing T cells, compared to both the overall control population (p<0.001) and controls with obesity (p=0.001; Figure 6.8B). However, after device explant, the proportion of gut homing T cells was not different compared to controls or controls with obesity.

Figure 6.8: Gut homing T cells from baseline to explant.

There was a significant reduction in gut homing T cells from baseline to explant (A; p=0.0124). At baseline, DJBS patients had a significantly higher proportion of gut homing T cells, compared to both the overall control population (p=0.0006) and controls with obesity (p=0.0012). However, after device explant, the proportion of gut homing T cells was not different compared to controls (p=0.7117) or control patients with obesity (p=0.1813). A) Each patient is displayed as an individual line on the graph. B) Group data. A) assessed by Wilcoxon signed rank test. B) Between group comparisons by Mann Whitney U test.

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6.3.3 Cytokine Production Following PBMC Culture The production of four cytokines (IL-6, TNF, IL-8 and IL-10) following LPS stimulation of PBMCs was assessed at baseline and explant. There was no significant change in IL-6 production (baseline: 27,730±15,202 pg/mL vs. explant: 26,451±17,177 pg/mL; n=10; p=0.77; Figure 6.9A), TNF production (baseline: 3,490±1,540 pg/mL vs. explant: 2,981±1,959 pg/mL; n=10; p=0.32; Figure 6.9B) or IL-8 production (baseline: 100,359±58,306 pg/mL vs. explant: 96,231±51,018 pg/mL; n=11; p=0.78; Figure 6.9C). However, there was a significant increase in IL-10 production from baseline to explant (baseline: 98±111 pg/mL vs. explant: 186±160 pg/mL; n=11; p=0.03; Figure 6.9D).

Figure 6.9: Cytokine production following LPS stimulation from baseline to explant.

There was no significant change in IL-6 production (A; p=0.7674), TNF production (B; p=0.3223) or IL-8 production (C; p=0.7772). There was a significant increase in IL-10 production (D; p=0.0273). Each patient is displayed as an individual line on the graph. A and C assessed by paired t-test. B and D assessed by Wilcoxon signed rank test. 192

Compared to the control group, there was no significant difference in IL-6 production at baseline or explant (Figure 6.10A). However, IL-10 was significantly higher at explant, compared to both the entire control group (explant: 203±156 pg/mL vs. control: 61±117 pg/mL; p=0.002; Figure 6.10B) and control patients with obesity (50±43 pg/mL; p=0.02). Due to differences in PBMC culture duration, TNF could not be compared to the control group. IL-8 was not measured in the control group.

Figure 6.10: Cytokine production following LPS stimulation compared to a control group.

IL-6 production was not significantly different at baseline or explant, compared to the control group, or control patients with obesity. IL-10 production was significantly higher at explant, compared to the entire control group (p=0.0024) and control patients with obesity (p=0.0194). Results analysed by unpaired t-text (IL-6 explant vs control and explant vs controls with obesity) or Mann-Whitney test (IL-6 baseline vs control and baseline vs controls with obesity and explant vs controls with obesity, IL-10 baseline vs control and baseline vs controls with obesity, IL-10 explant vs control and explant vs controls with obesity).

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6.3.4 Correlation with the Gastrointestinal Microbiota An exploratory analysis was conducted to determine the relationship between immunological parameters and the RA of GI microbes. To constrain the analyses, only taxa with a minimum mean RA of 1% were included. Univariate linear regression analyses of immunological parameters in comparison with the RA of taxon with a mean RA of >1% are displayed in Table 6.1 below.

A number of relationships between T cell proportions and the RA of microbial taxa were observed. At baseline, positive univariate relationships were observed between CD4+ and CD8+ T cell subtypes and the RA of Prevotella and Neisseria in the baseline duodenal biopsies. At explant, the RA of bacterial taxa in the gastric biopsies was associated with a number of immune parameters. On multivariate analysis, the proportion of CD8+ T cells was positively associated with the RA of Dialister and Prevotella in the gastric biopsies (r=0.85, r2=0.72, p=0.003). The proportion of CD4+ central memory T cells was positively associated with the RA of Klebsiella in the gastric biopsies on multivariate analysis (r=0.62, r2=0.38, p=0.02). The proportion of CD8+ central memory T cells was negatively associated with the RA of Dialister in the gastric biopsies on multivariate analysis (r=-0.66, r2=0.43, p=0.01). At explant, the RA of Klebsiella and members of the Gammaproteobacteria class in the duodenal biopsies was negatively associated with the proportion of CD4+ effector memory T cells in the circulation (r=-0.92; r2=0.84; p<0.001). There were no relationships between the microbiota and the proportion of gut homing T cells, CD4+ naïve T cells, and CD4+ or CD8+ effector T cells at baseline or explant.

Isolated PBMCs were cultured with LPS and cytokine production was measured. At baseline, there were no significant relationships between bacterial taxa and cytokine production. At explant, IL-6 production was negatively associated with the RA of member of the Gammaproteobacteria class and bacterial load in the explant gastric biopsies on multivariate analysis (r=0.89; r2=0.78; p=0.002). At explant, IL-8 production was negatively associated with the RA of members of the Enterobacteriaceae family in the explant gastric biopsies on multivariate analysis (r=0.88; r2=0.68; p=0.002).

Eosinophils in the duodenal tissue at baseline were associated with the RA of Veillonella in the baseline duodenal biopsies. IELs were positively associated with the RA of Rothia and bacterial load in the duodenum on multivariate analysis (r=0.96; r2=0.92; p=0.02). At explant, immune cell numbers in the duodenal mucosa were associated with the RA of taxa in the explant duodenal biopsies. Specifically, the number of eosinophils in the duodenal mucosa was negatively associated with the RA of Veillonella and bacterial load in the duodenal biopsies on multivariate analysis (r=0.84, r2=0.71, p=0.001). The number of IELs in the duodenal mucosa was positively associated with the RA of Lactobacillus and Dialister in the explant duodenal biopsies (r=0.64, r2=0.41, p=0.02). 194

Table 6.1: The relationship between immunological parameters and the RA of microbial taxa or bacterial load isolated from gastric and duodenal biopsies.

Variable Baseline Explant Positive Negative Positive Negative CD4+ T cells Nil Nil Nil Nil CD8+ T cells Nil Nil Gastric: Dialister (r=0.77; Gastric: Gammaproteobacteria (r=- p=0.004) and Prevotella 0.69; p=0.01) and Klebsiella (r=-0.66; (r=0.66; p=0.02) p=0.02)

Duodenum: Dialister (r=0.66; p=0.02) and Prevotella (r=0.62; p=0.03) CD4+ central Nil Nil Gastric: Klebsiella (r=0.62; Nil memory T p=0.02) and cells Gammaproteobacteria (r=0.59; p=0.03) CD4+ effector Gastric: nil Nil Nil Duodenum: Gammaproteobacteria memory T (r=-0.80; p=0.002) and Klebsiella (r=- cells Duodenum: 0.64; p=0.03) Prevotella (r=0.77; p=0.03) CD8+ naïve T Nil Nil Gastric: Klebsiella (r=0.54; Nil cells p=0.05) CD8+ central Duodenum: Nil Gastric: Klebsiella (r=0.62; Gastric: Dialister (r=-0.66; p=0.01) memory T Prevotella p=0.02) cells (r=0.73; p=0.04) Gammaproteobacteria (r=0.62; p=0.02) CD8+ effector Duodenum: Nil Nil Nil memory T Neisseria (r=0.78; cells p=0.02) IL-6 Nil Nil Gastric: Prevotella (r=0.61; Gastric: Enterobacteriaceae (r=-0.85; p=0.05) p=0.001), Klebsiella (r=-0.67; p=0.02) and Gammaproteobacteria (r=-0.62; p=0.05); bacterial load (r=-0.61; p=0.05)

Duodenum: Enterobacteriaceae (r=- 0.76; p=0.01) TNF Nil Nil Duodenum: Lactobacillus Nil (r=0.57; p=0.03) IL-8 Nil Nil Gastric: Enterobacteriaceae Nil (r=0.83; p=0.002)

Duodenum: Enterobacteriaceae (r=0.76; p=0.01) IL-10 Nil Nil Nil Gastric: Enterobacteriaceae (r=-0.61; p=0.02) Eosinophils in Nil Duodenum: Duodenum: Fusobacterium Duodenum: Veillonella (r=-0.62; the duodenal Veillonella (r=- (r=0.55; p=0.02); bacterial p=0.01) mucosa 0.84; p=0.01) load (r=0.56; p=0.03) IELs Duodenum: Nil Duodenum: Lactobacillus Nil Rothia (r=0.77; (r=0.51; p=0.03) and p=0.03), bacterial Dialister (r=0.49; p=0.04) load (r=0.64; p=0.03)

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6.4 DISCUSSION Obesity is a chronic, low-grade inflammatory state and this inflammation is critical for the pathogenesis of a number of obesity related comorbidities, including T2DM. Treatments for obesity related comorbidities often centre on weight loss, which can improve inflammation401, 406. One of the most effective weight loss methods, the RYGB, separates the nutrient stream from bile and pancreatic secretions to facilitate weight loss. The DJBS mimics this endoscopically, using a fluoropolymer sleeve to separate bile and pancreatic secretions from the chyme. However, the effects of this nutrient diversion by the DJBS on the morphology of the small intestine are unknown. This is the first study to assess the effect of DJBS treatment on duodenal morphology and duodenal immune cell infiltration in humans and assess systemic T cell phenotype. Overall, a modest impact on duodenal morphology was observed; however, a marked local immune response occurred in the duodenum following DJBS treatment. The changes in systemic T cell phenotype observed broadly suggest an increase in immune activation following DJBS treatment.

A number of factors are involved in maintaining normal villous morphology, which is important for nutrient absorption585. Villous morphology appears to be related to contact with the nutrient stream586. Nutrient exclusion (total parenteral nutrition) has been associated with atrophic changes in animal models, with enteral feeding restoring villous morphology611. In approximately 50% of our patients, DJBS treatment had no impact on villous morphology. This suggests that at least a portion of luminal contents may still have contact with the duodenal mucosa. Bile acid recirculation has also been implicated in maintenance of villous morphology612. This is unlikely to have been negatively impacted by DJBS treatment, as two studies have reported an increase in circulating bile acids after DJBS treatment469, 478. Atrophic changes have also been associated with an increase in intestinal permeability613. This may be linked to a rare adverse event of DJBS treatment, as bacterial translocation across a permeable intestinal epithelium is a possible mechanism for the development of hepatic abscess. Further investigations into the impact of DJBS treatment on duodenal morphology and mucosal integrity may provide insight into the mechanisms of hepatic abscess formation.

The DJBS is endoscopically implanted into the wall of the duodenum via a self-expanding metal stent. This anchor site may provide a location for a local immune response to develop due to the presence of the foreign body. Immune cells, such as eosinophils and IELs, are resident in the healthy small intestine233; however, the impact of obesity or T2DM on eosinophils in the duodenal mucosa is not known. In this study, at baseline, duodenal eosinophils were increased compared with duodenal histology performed in a control cohort (n=16), including those with obesity (n=7), or a previously reported Australian control cohort590. In the Walker et al. study, controls were not stratified by weight, nor was BMI reported. In our study, the severity of obesity differed significantly between 196

the groups. Despite there being six patients with severe obesity in the control cohort, the mean BMI was still significantly higher in the DJBS group at baseline (DJBS: 43.7±5.3 vs control:35.4±3.6 kg/m2). This may help explain the increased eosinophils counts in our cohort at baseline; however, further investigation in larger cohorts is required.

The increase in eosinophils observed may also be linked with the increase in GI symptoms seen in the in situ phase, as duodenal eosinophilia has previously been associated with GI symptoms in FGID patients590. There was no significant difference in the proportion of eosinophils in the blood from baseline to explant (from full blood count; data not shown), suggesting a localised eosinophil response. It is possible that increased intestinal permeability facilitates bacterial translocation, leading to increased eosinophil recruitment614.

There was also a significant increase in IELs from baseline to explant, further suggesting a chronic inflammatory response615, 616. Whilst IELs were statistically significantly increased, the clinical impact was modest. At baseline, all but two patients had a normal number of duodenal IELs (defined as <25/ 100 enterocytes610). At device explant, one of the patients with high baseline values remained high, and three other patients who were previously within normal clinical limits had elevated IELs. Despite this, there was no significant difference in the number of IELs at baseline or explant, compared to controls (n=13), or controls with obesity (n=6). However, it may be the function of these cells rather than the overall number that may be of importance617. For example, tissue resident γδ IELs have been implicated in mucosal repair after inflammatory damage616 and could represent an important population mediating tolerability of the device. Further phenotypic characterisation of IELs by flow cytometry would be an interesting avenue for further investigation.

In the literature, a number of patients have undergone early explant due to ‘device intolerance’. In our cohort, three patients underwent early explant due to abdominal pain. Statistical analyses could not reliably be performed on this subgroup. However, these patients were unremarkable from the rest of the cohort on the basis of histological assessment. Two of these three patients had normal duodenal morphology at explant. Only one patient had elevated IELs at explant and all were below the post-explant mean for the number of eosinophils in the duodenal mucosa. Within the constraints of a small sample size, this suggests local immunological changes are not associated with device intolerance. However, it may be the functional aspects of the infiltrated immune cells that mediate tolerance (e.g. perhaps infiltrated cells in those with persistent pain are not those involved in mucosal repair). Further characterisation of the local immune cell populations may provide further insight.

Weight loss has been demonstrated to impact circulating immune markers222, 223 and this is the first study to assess changes in systemic T cell phenotype following DJBS treatment. The overall

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circulating CD8+ T cell proportion was not significantly altered by DJBS treatment, but the proportion of CD8+ T cells was significantly lower than controls. This has previously been reported in a cross-sectional study of 10 patients with obesity and 10 lean subjects608. This study also reported a significantly higher proportion of CD4+ T cells in patients with obesity, compared to lean controls. In our cohort, CD4+ T cells were significantly lower at explant, compared to controls, contrasting the literature605-609. However, we also observed a high proportion of CD4 and CD8 double positive T cells, and this may distort our findings. Further investigations to define the relevance of double positive T cell populations in obesity are required.

Characterisation of circulating T cell subtypes was performed to further discriminate response to treatment. There was a significant decrease in the proportion of CD4+ naïve T cells and a trend towards a significant increase in CD8+ effector T cells from baseline to explant. Broadly, these results suggest an increase in antigen presentation and activation of cells, leading to an increase in CD8+ effector T cells618. This may be associated with an increase in microbial antigen as a result of colonisation of the DJBS device.

There was a significant reduction in circulating gut homing T cell markers from baseline to explant. At baseline only, patients in the DJBS cohort had a significantly higher proportion of gut homing T cells in the circulation than the control group, or control patients with obesity. There was a trend toward a significant correlation between reduction in BMI and change in gut homing T cells (p=0.0656). This suggests obesity may play a role in the elevated proportion observed at baseline, although to date no literature has reported the effect of obesity on gut homing T cell markers. In our cohort, we did not have a matched tissue sample for quantification of lamina propria gut homing T cells. However, in the control cohort, 17 patients had lamina propria gut homing T cells quantified. In this group, there was no correlation between the proportion of gut homing T cell markers across the two sites (r=-0.34; p=0.18). Overall, this suggests that changes in the circulation may not reflect what is occurring in the tissue.

The GI microbiota, and in particular the MAM, provide a significant source of potential antigens and are in constant contact with the host. However, despite recent advances, the relationship between the MAM and the immune system is poorly understood. Treatment with the DJBS increases the amount of bacterial antigen present due to colonisation of the device surface by microbes. As such, an exploratory analysis was performed to assess the relationship between the RA of taxa with a minimum mean RA of 1% and immunological parameters. In the duodenal mucosa, there was a negative relationship between the number of eosinophils and the RA of Veillonella in the duodenal biopsy samples at both baseline and explant. Conversely, there was a positive relationship between the number of eosinophils in the mucosa and the RA of Fusobacterium at explant. This is the first time 198

a relationship between intestinal microbes and eosinophils in the duodenal mucosa has been reported. However, Veillonella is a normal upper GIT microbe. In healthy adults, it is a highly abundant coloniser of the duodenum619, 620. Some Fusobacterium species have been demonstrated to invade the epithelium, and this may induce a local immune response621. The number of IELs in the duodenum was positively associated with the RA of Rothia in the duodenal biopsies at baseline and Dialister and Lactobacillus in the duodenal biopsies at explant. The impact of these taxa on IELs has not previously been reported. However, a high abundance of Rothia in the stool has been reported in patients with primary sclerosing cholangitis, suggesting it may be a relevant taxon in the context of GI inflammation228. Finally, bacterial load was positively associated with both the number of IELs at baseline and the number of eosinophils in the duodenal mucosa at explant. Therefore, it may be that the quantity of bacteria is also important, particularly to the local immune response, in addition to the composition of the microbial community present.

A number of relationships were observed between circulating immune markers and the microbiota. At baseline, the RA of “typical” upper GIT microbes were positively associated with CD4+ and CD8+ effector memory T lymphocytes and CD8+ central memory T lymphocytes. This aligns with the literature, which has demonstrated an interaction between memory T lymphocytes and commensal microbes in healthy individuals622. At explant, members of the Gammaproteobacteria class, Klebsiella, Dialister and Prevotella were associated with a number of immunological parameters. Klebsiella is a member of the Enterobacteriaceae family and the Gammaproteobacteria class. At class and family level, the proliferation of these taxa has been reported to be greater in inflammatory bowel disease623-625 , demonstrating a link between inflammatory state and these taxa. Similarly, Dialister and Prevotella have also been linked to inflammatory conditions such as spondyloarthritis, rheumatoid arthritis and the metabolic syndrome626, 627. Furthermore, there was a positive relationship between Gammaproteobacteria and Klebsiella and central memory T lymphocyte populations; whereas there was a negative relationship between these taxa and effector memory T lymphocyte populations. This suggests a differential response in lymphoid and non-lymphoid tissues as central memory T lymphocytes act within lymphoid tissue when restimulated while effector memory T lymphocytes act within peripheral tissues233.

Members of the Gammaproteobacteria class and Klebsiella were also positively associated with IL-6 production, whilst members of the Enterobacteriaceae family were negatively associated with production of IL-10 following LPS stimulation. While these pro-inflammatory bacteria could be linked to cytokine production, overall, there was actually a significant increase in IL-10 production following LPS stimulation of PBMCs following DJBS treatment. These results suggest an interplay between inflammation and microbial colonisation that may be relevant for device efficacy, as the 199

RA of Gammaproteobacteria was negatively associated with weight loss (see Chapter 5 Section 5.3.6). Further investigation is required to confirm these findings.

In summary, DJBS treatment resulted in a significant chronic local immune response, in particular leading to an increase in eosinophils in the mucosa. This occurred without alterations to duodenal morphology in the majority of patients. In the circulation, DJBS treatment partially normalised CD4+ T cell populations. Overall, assessment of T cell subtype suggested an increase in T cell activation following DJBS treatment. It is possible this is associated with the presence of microbes in the GIT, but further work in a larger cohort should attempt to validate these relationships. Overall, there was no pronounced improvement in systemic immune activation. This may be due to the strong local immune response, which may overcome the improvements that would be expected with a reduction in obesity-related inflammation secondary to weight loss. Future studies should aim to include more patients and time-points of sampling to better identify the progression of the immune response following DJBS treatment. Finally, future studies should aim to assess changes in immune cell phenotype in the lamina propria of the duodenum. This may provide insight into complications such as ‘device intolerance’ that lead to elective early explant.

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Chapter 7: General Discussion

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7.1 SUMMARY Obesity and T2DM represent a significant health burden to society. Most treatment modalities are ineffective at the population level for these highly prevalent conditions. Lifestyle and pharmacotherapeutic measures are front line treatments. Lifestyle modification interventions are attractive, as they can be applied to a broad target audience, and practitioners and resources to facilitate lifestyle changes are relatively readily available. Lifestyle modification interventions target a reduction in energy intake and an increase in energy expenditure by behavioural means. However, long-term efficacy with diet-based approaches is poor271-273. The majority of pharmacotherapeutic obesity treatments act centrally, and long-term efficacy is contingent on continual medication usage.

Bariatric surgery is considered the most effective obesity treatment, but use in the public health care setting is limited due to costs. In Australia, bariatric surgery is primarily performed in a private health care setting. To address a gap in the treatment continuum, minimally invasive endoscopic techniques have been developed that aim to mimic physiological changes induced by bariatric surgery. A number of these techniques are approved for use in the clinical setting; however, limited data are available regarding the precise mechanisms of action. Indeed, a better understanding of the mechanisms of action of the current surgical and endoscopic treatment options would be of value to refine or individualise interventions in order to optimise patient outcomes and ultimately design of next generation techniques that offer safe, effective and durable solutions.

The GIT is an obvious target for obesity management. The proximal GIT is of critical importance, as it is the site at which the majority of absorption of ingested nutrients occurs. Bariatric surgery specifically targets the proximal GIT and brings about weight loss by making permanent anatomical changes. These approaches have been demonstrated to be efficacious in inducing significant and sustainable weight loss. Therefore, endoscopic techniques seek to mimic some of the beneficial anatomical changes to the proximal GIT that occur with bariatric surgery.

In this study, we explored the DJBS. This is a 60 cm fluoropolymer sleeve endoscopically implanted into the proximal small intestine. Whilst clinical efficacy has been demonstrated, little is known about the mechanisms of action of the DJBS. It has been suggested that as a RYGB mimetic, the DJBS may cause weight loss by inducing fat malabsorption446. The impact of the DJBS on gastric emptying has also been investigated, with conflicting findings456, 485. The relevance of impacts on other parameters of GI function, such as nutrient absorption or sensory function have not previously been explored.

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It is now recognised that the complex interplay between the GI microbiota and immune homeostasis can contribute to obesity, weight gain/loss and metabolic outcomes628. However, the impacts of the DJBS on the GI microbiota and local/systemic immune parameters have not been reported. This is of relevance, as the device is implanted directly into the duodenum, and the device surface is in contact with the mucosa. The surface of the DJBS appears to offer an ideal growth environment for microbes. Microbial and immunological responses may be relevant for device efficacy, particularly as there are differences in the GI microbiota in obesity and T2DM, compared to lean controls171, 542 and these differences are reduced following weight loss194, 374. Finally, the relevant effect of the GI microbiota on key device-related complications, such as pyogenic liver abscess, have not been reported.

Although our primary interest was in defining potential mechanisms of action, we first wanted to define the clinical response to DJBS treatment in our centre. We demonstrated that treatment with the DJBS was efficacious in our cohort. Clinically significant reductions in body weight were observed in all patients. There were associated improvements in markers of metabolic health, including glycaemic control, cardiovascular disease risk and markers of liver function.

In parallel, we reported dietary intake and HRQoL outcomes for the first time. There was a significant reduction in dietary energy intake over the intervention period. This was driven by a reduction in food intake rather than an improvement in diet quality. Significant improvements in HRQoL were also reported across the intervention period. Despite significant weight gain over the one year post-explant follow-up period, a trend toward a significant improvement in MetSSS persisted.

In addition to assessing efficacy, we also examined the effect of DJBS treatment on GI function, the GI microbiota and the immune system. This revealed an increase in meal-related symptoms, and changes in the intestinal microbiota concurrent with mucosal inflammation. These key findings provide insight into the potential mechanisms by which the DJBS induces weight loss. These findings also identity potential factors associated with device tolerability and safety, as discussed below.

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DJBS treatment increases meal-related GI symptoms, facilitating weight loss by reducing energy intake We aimed to assess the impact of the DJBS on GI function. In the literature, there were suggestions that device placement may alter GI motility456, 485 or fat metabolism446. To investigate these possibilities, we assessed the impact of DJBS treatment on gastric emptying and measured intraluminal lipolytic activity. Contrary to our expectations, no impact on gastric emptying was observed. A previous study of the DJBS485 found that gastric emptying was significantly delayed after device placement and there was a trend towards a relationship between weight loss and gastric retention at four hours, suggesting a relevance in the weight loss response. In our cohort, no relationship between weight loss and gastric emptying was observed, but gastric emptying was not delayed by device placement. Further investigation is required to determine the links between gastric emptying and weight loss following DJBS treatment.

We detected no significant change in intraluminal lipolytic activity following DJBS treatment. The concept of fat malabsorption as a relevant mechanism to induce weight loss had been extrapolated from the bariatric surgery literature. The DJBS is seen as an endoscopic RYGB mimetic due to its ability to separate luminal contents from bile and pancreatic secretions in a similar way to the biliopancreatic limb of the RYGB. However, it has been reported that fat malabsorption after RYGB is only significant when at least 70 cm of jejunum is included in the biliopancreatic limb284. If this is required, the DJBS is unlikely to cause significant fat malabsorption due to its limited projection into the jejunum.

My research group has a long standing interest in FGIDs and as part of our studies of the DJBS we also assessed its effect on GI sensory function. We found there was a significant increase in GI symptoms following a meal study, and also on a self-reported questionnaire measuring symptom intensity over the preceding seven days. This is the first study to postulate a possible role for GI symptoms in the weight loss response to DJBS placement. Previous studies have reported GI symptoms as adverse events of treatment445, 447-452, 454-459, 461, 462, 465, 469, 475, 482. We believe that meal- related GI symptoms are important to weight loss with the DJBS, contributing to a reduction in dietary energy intake.

More specifically, epigastric pain was a prominent symptom following DJBS placement. One possible mechanism for this is that following a meal, gastric accommodation increases the pressure placed on the barbs that anchor the DJBS, triggering a pain response. This would be more pronounced with a larger meal, possibly prompting a reduction in food intake. Other explanations for the reduction in energy intake that were not assessed in our cohort include changes in appetite control biomarkers, or changes in self-reported appetite or food preferences. 204

DJBS treatment induces changes in the GI microbiota, and these changes may be linked to a key adverse event of treatment This study also aimed to explore the impact of DJBS treatment on the GI microbiota, across a number of sites in the GIT. To the candidate’s knowledge, this is the first study that has assessed the MAM and the biofilm formed on the device surface after DJBS placement. DJBS treatment resulted in a strong taxonomic shift in the gastric and duodenal MAM away from taxa that could be considered the core microbiota of the upper GIT544, 629, 630. In the duodenum, a possible explanation for the changes observed is the lack of available nutrients at the mucosa due to the presence of the sleeve. This may explain the high abundance of more large bowel-type microbiota, such as members of the Enterobacteriaceae family, as the distal intestine is an area with lower nutrient availability. As part of the study protocol, all patients were placed on a PPI. Yet, in the stomach, the changes seen in the gastric MAM were somewhat distinct from what would be anticipated with acid suppression, such as reductions in acidogenic taxa that would typically be enriched with chronic PPI use such as Rothia and Actinomyces554. Transient taxonomic changes were also observed in the stool microbiota while the device was in situ, without impacting the overall community profile or alpha diversity. At eight weeks post-implant, the predominant taxon was Lactobacillus, which was also a primary coloniser of the device surface. As with the MAM, the changes observed in the stool during the treatment period largely reflected a shift away from the organisms that typically make up the core of a healthy stool microbiota631-633. This may be a flow through effect of microbes shed from the device surface into the lumen. Overall, these data demonstrate that DJBS treatment alters the microbiota along the length of the gut.

Whilst we didn’t assess the MAM post-explant, long-term data from other cohorts suggest a relative stability of the GI microbiota634. DJBS removal (and cessation of PPI) would allow a return to the baseline physiological conditions in the stomach and duodenum, thus removing the key stimulus for a shift in the MAM. Further, the resolution of GI symptoms after device explant may lead to a return to prior eating habits, which would also influence the microbiota. Given the links between alterations of the microbiota in obesity171, 548, 635, 636, murine models demonstrating a causative role of the microbiota in weight gain637, and improved energy harvesting in obesity170, 638, it is possible that a shift back to a baseline microbiota profile may precipitate weight regain. Indeed, during the intervention period, the relative abundance (RA) of microbial taxa in the MAM and the stool could be linked with weight loss outcomes. A greater magnitude of weight loss was associated with a lesser deviation from a “typical” site-specific core microbiota. Assessment of the MAM following device removal would allow for the relationship between these communities and long-term weight outcomes to be established.

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In addition to changes in the MAM and stool microbiota, a distinct microbial community was also formed on the device surface. The mucosa-adjacent device surface was colonised by taxa that were either absent from, or only low in abundance on the duodenal mucosa prior to device implantation. At explant, the duodenal mucosa was more dominantly colonised by microbes that are “typical” in the upper GIT such as Streptococcus, Prevotella and Veillonella, whereas the collocated device surface was more dominantly colonised by organisms that are typically lower in abundance in the upper GIT, such as Klebsiella, Bifidobacterium and members of the Enterobacteriaceae family. The differences between what grows on the mucosa and what grows on the device may be due to specific growth requirements, including the ability of organisms to adhere, form a biofilm, or colonise and utilise mucin. It is difficult to determine the relationship between the MAM and the device communities. It is likely that the MAM is altered by the DJBS being in place, due to changes in pH (associated with acid suppression), nutrient availability and oxygen environment. These changes will also influence what is able to grow on the device surface.

Diet is a relevant mediator of the microbiota profile and DJBS treatment resulted in significant changes in dietary intake. Whilst dietary constituents have been associated with microbial taxa in the stool, there is no literature on the impact of diet on the gastric or duodenal MAM, despite this being an area with high nutrient availability. Therefore, we conducted an exploratory analysis to determine the relationship between dietary intake (collected by 24 hour recall) and the stool microbiota and MAM. In our cohort, a number of the associations between dietary constituents and the MAM match what has been reported in studies that have assessed this in the stool. There were also novel observations, such as the relationship between Gammaproteobacteria and Klebsiella and protein intake, which has not previously been reported. This may be related to a regional variation between the proximal and distal GIT and/or mucosa-adjacent vs luminal environments. However, our data may also be impacted by measurement imprecision. A 24 hour recall was used to assess dietary intake. This tool captures recent not habitual dietary intake, which may have a more pronounced effect on shaping microbiota composition639.

Finally, the development of pyogenic liver abscess is a rare complication of DJBS treatment. To date, there are no published data on the microbes found in these abscesses. In our cohort, there were no cases of hepatic abscess. However, some of the changes in the MAM and taxa that colonised the mucosa-adjacent device surface are consistent with taxa previously isolated from sequenced pyogenic material from typical liver abscesses565, 566. In particular, Klebsiella, which was at very low abundance in the duodenal MAM at baseline, but was highly abundant on the mucosa-adjacent device surface at explant, has commonly been reported in cases of hepatic abscess in patients with

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T2DM565, 566. This suggests that modifications of the device that inhibit bacterial growth may help to maintain clinical outcomes and possibly reduce the likelihood of developing hepatic abscess.

DJBS treatment induces a local immune response in the duodenum and does not improve systemic markers of inflammation Following DJBS implantation, a modest atrophy of duodenal mucosal villi was found in seven of 19 patients. This may be relevant for weight reduction, by influencing nutrient absorption, and requires further investigation. Despite the modest impact on villous architecture, a pronounced impact on local immune cell infiltration was observed after device implantation. There was significant increase in duodenal eosinophils and IELs from baseline to explant. There are a number of possible explanations for this. Firstly, peristalsis is likely to cause small movements in the anchor barbs, which would act as a local irritant, triggering a local immune response. In addition, the anchor barbs are likely to be colonised by microbes, and the movements induced by peristalsis have the potential to allow these bacteria entry into the mucosa. Finally, the overall quantity of bacteria in close contact with the mucosa is increased by the biofilm formed on the device surface. This increases the amount of microbial antigen present, which may trigger a local immune response.

The MAM is of great relevance to the local immune response, as these microbes have direct contact with the host. Significant relationships between bacterial taxa and the number of eosinophils in the duodenal mucosa at both baseline and explant were observed. Specifically, the eosinophil count in the duodenum had an inverse correlation with the RA of “typical” upper GIT microbes, and a positive correlation with Fusobacterium (more typical of the distal GIT). Similar relationships were observed between the number of IELs and bacterial taxa, with a positive association between IEL count and the RA of Lactobacillus and Dialister, which are typically present in low abundance in the duodenum. These results suggest that perturbation from a “typical” upper GI MAM results in increased local immune infiltration.

The pro-inflammatory cytokine response of PBMCs to LPS stimulation was not significantly changed by DJBS treatment. A major source of cytokine production in obesity is the adipose tissue640. At explant, all but two patients were still obese. Therefore, despite the significant weight loss observed, the PBMCs will still be exposed to adipose tissue-derived cytokines and thus be primed to respond accordingly.

However, specific changes in T lymphocyte phenotype were noted. The proportion of CD4+ naïve T lymphocytes was significantly decreased, whilst there was a trend towards an increase in the proportion of CD8+ effector T lymphocytes. This implies that DJBS placement is associated with an increase in T lymphocyte activation. This may be secondary to increased translocation of bacteria or

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bacterial products, either across the epithelial membrane or via perforations induced by the anchor barbs.

Overall, these results suggest the DJBS is triggering a strong local immune response. Despite the significant weight loss that occurred across the intervention period, DJBS treatment appears ineffective in improving global inflammation. This may be related to the local immune response, which may overcome the beneficial effects of weight loss, or may be related to changes in the gut microbiota that perpetuate a systemic immune response.

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7.2 STRENGTHS AND WEAKNESSES

7.2.1 Strengths There are a number of strengths of this study. This study combined clinical techniques with microbiological and immunological techniques to characterise the mechanisms of action of the DJBS. A number of novel clinical investigations were conducted, including the first reports of dietary intake, HRQoL and functional exercise capacity following DJBS implantation. We also explored the impact of DJBS treatment on hepatic elastography measures, for which there is only one prior report455. Overall, the assessment methodologies were selected on the basis of their clinical utility and validation, in addition to ensuring patient safety. This is a strength as we aimed to reduce participant burden to ensure maximum retention of study participants.

A key strength of the microbiota work is the diversity of sites that were repeatedly sampled. The majority of studies that assess the effect of weight loss on the microbiota use stool samples, which reflect the luminal contents of the large intestine. Assessment of the MAM provides insight into the microbial community that is in direct contact with the host, and therefore more likely to be mediating a relevant physiological response.

There is a large amount of inter-individual variability in gut microbiota profiles. Therefore the longitudinal sampling at each site, combined with repeated measures statistical techniques, increase confidence that changes in the microbiota are occurring as a result of DJBS treatment. The inclusion of a control group for the microbiota and immune parameters also allows for identification of how obesity and comorbidities are influencing the results, compared to a cohort processed in an identical manner in the same centre, using the same protocols. The inclusion of controls with a diverse range of BMIs allows us to compare our cohort at baseline to determine if there is already an altered profile (as may be expected in obesity), and if the changes observed are related to DJBS treatment.

This study has also included novel exploratory investigations of the relationship between diet and the microbiota, and the microbiota and the immune system. These investigations are inherently valuable in a region of the GIT where little is known about the interplay between diet, the microbiota and the immune system.

7.2.2 Weaknesses Sample Size, Cohort Study Design and Data Collection A limitation of the study is the small number of study participants. We initially aimed to recruit 50 patients. However, this was not possible as the device was removed from the ARTG and we decided to discontinue the use of this device in our centre. Secondary to this, a pilot group of patients who underwent a limited number of investigations were retrospectively included. This introduced a 209

methodological bias as review schedules and investigations were different. However, outcome data was collected within the same post-explant duration of up to three weeks.

We did not have a control group who received only dietary counselling, instead conducting a cohort study. This limits definitive conclusions about the impact of the DJBS on body weight compared to dietary modification and exercise. However, the weight loss observed in this cohort exceeds that expected following 12 months of lifestyle intervention aimed at reducing energy intake269.

Missing data is a common feature of clinical studies, and in particular weight loss interventions, where up to an 80% drop-out rate has been reported641. In addition to the four early explants, interim data collection points were missed for a number of other patients. As a consequence, only pre- to post-procedure statistical analyses were conducted for a number of measures. This reduced the power of the study, as we initially designed the study to use repeated measures statistical analysis methodologies.

We also experienced an approximately 50% loss to follow-up at the one year post-explant interval. Therefore our conclusions regarding long-term outcomes are limited. However, given the majority of patients assessed had regained a large amount of weight, it is possible that weight gain resulted in reduced motivation to attend follow-up appointments in those that were lost to follow-up.

Dietary Assessment Methodology and Recall Bias Unless using biomarker calibrated methods (such as urinary nitrogen as a marker of protein intake), which are more invasive for the patient, all dietary data collection methods are subject to recall bias. However, all interviews were conducted by an Accredited Practising Dietitian with training in conducting detailed interviews to maximise the precision of the data set. Despite this, underreporting of energy intake, a key parameter on which we have postulated, is significant in patients with obesity642. The precision of the dataset could be improved by triangulating multiple 24 hour recalls collected within a short time frame, albeit this increases the burden for both participant and researcher.

One important aim was to integrate the dietary data set with data from sequencing of the GI microbiota. We used a 24 hour recall, which provides a snapshot of recent intake. However, this data was not collected close to microbiota sample collection. This results in the assumption that neither the diet nor the microbiota has changed in the time that has elapsed between dietary data collection and microbiota sampling. Major dietary change has been demonstrated to alter the stool microbiota very rapidly151. In future studies, it would be valuable to have a 24 hour recall that was matched to specimen collection, in addition to a measure of long-term dietary intake, such as a validated food frequency questionnaire.

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Use of “Gold Standard” Techniques At the time of study design, considerations around the patient burden of a large number of investigations (27 appointments required over the 48 week intervention period) were made. As a result, this study was not able to employ a number of “gold standard” techniques. A 13C mixed triglyceride breath test was used for the assessment of intraluminal lipolytic activity. This was selected in preference to the gold standard of 72 hour faecal fat. This investigation has a high patient burden and provides a significant calorie load that is counterintuitive in a weight loss intervention. This test would also be inappropriate if the device did induce fat malabsorption, which was our hypothesis.

The gold standard of assessment of gastric emptying is gastric scintigraphy. This method was not selected due to the risk of ionising radiation exposure for patients. The method selected, the 13C octanoic acid breath test, has been validated against scintigraphy71, 72.

Whilst a “gold standard” of microbiota assessment does not exist, the use of metagenomic sequencing, where the entire genome of a sample is sequenced, would allow for a greater granularity in the data obtained643. It would also provide definitive information about the functional capabilities of the microbes present in the sample, instead of the inferences made from the 16S data set in the present study. However, this method was not selected due to the small sample size (underpowered for such detailed analysis).

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7.3 FUTURE DIRECTIONS The investigations in this study have identified a number of mechanisms associated with DJBS-induced weight loss. We identified relevant changes in gut sensory function, the GI microbiota and immune parameters following DJBS placement. Our results strongly suggest the induction of meal-related GI symptoms are a key component of the weight loss response. These findings need to be validated in a larger cohort. Assessments of other mechanisms for dietary intake reduction should be conducted in concert with assessment of GI symptoms. Whilst previous studies have shown mixed changes in appetite control biomarkers446, 451, 456, 469, 476, 477, 482, subjective measures of appetite, changes in food preferences and eating behaviours should also be assessed.

The current prototype of the DJBS has limitations and some of these may explain the high SAE rate observed in patients treated with the DJBS. In particular, the development of pyogenic hepatic abscess is a major concern. One of the key findings of this study is the taxonomic changes in the MAM and the growth of bacterial taxa on the mucosa-adjacent device surface that are similar to taxa previously reported in pyogenic liver abscess material565, 566. To date, two mechanisms for this have been proposed: i) ascending cholangitis, and ii) bacterial translocation to the liver secondary to perforation of mucosal surfaces via the anchor barbs563. It is also possible that bacterial translocation across the epithelial membrane occurs. Future studies should aim to assess this. It is recommended this is done using biopsy specimens, with assessment of tight junctions using genetic or protein markers (such as zonulin) or scanning electron microscope assessment of tight junctions. This is recommended as the impact of the DJBS on clinical measures of permeability, specifically the absorption of lactulose and rhamnose in the small intestine, is not known.

Future investigations of endoscopic obesity treatments, or indeed any investigations with access to high quality data on dietary intake, the duodenal MAM and immune function would bolster knowledge about the interplay of these factors. This is relevant for diet-microbiota interactions in the duodenum, as it is the site of absorption for simple sugars and amino acids, which are key energy sources for commensal microbes. Methodological recommendations on better capturing these relationships include measuring both long-term dietary intake (e.g. via a validated food frequency questionnaire) and recent intake (via 24 hour recall on the day of biological sampling); aseptic sampling of the duodenal microbiota; the use of metagenomic sequencing methods; and taking matched biopsy samples for histology and/or immunohistochemistry.

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7.4 CONCLUSIONS In conclusion, significant improvements in body weight and markers of metabolic health occurred during the 48 week DJBS treatment period. However, the majority of changes were not maintained at one year post-explant. Despite this, there was a persistent trend toward an overall improvement in metabolic syndrome severity.

Contrasting our expectations, we found no evidence that DJBS-induced weight loss was associated with changes in gastric emptying or fat absorption. Instead, meal-related GI symptoms increased following DJBS placement and this was associated with a significant reduction in energy intake. In particular, the epigastric pain score was increased nearly five-fold following DJBS placement.

After initiation of the study, concerns were raised in relation to safety. This was associated with the development of pyogenic hepatic abscess, a rare SAE of treatment reported in up to 3% of patients in other trials. In response to this, we investigated the GI microbiota. After placement of the DJBS, a substantial shift in the duodenal MAM occurred. We observed a significant increase in the RA of taxa such as Fusobacterium and Klebsiella that have previously been associated with hepatic abscess in other cohorts. These bacteria were also dominant colonisers of the DJBS device surface. Therefore, our data suggest that assessment of the GI microbiota, and any changes induced by the DJBS or similar devices should be included as part of longitudinal safety assessment.

DJBS treatment was associated with chronic local inflammation and modest changes in villous architecture in the duodenum. Systemically, immune activation appears to be increased. The relationship between local immune changes and GI symptoms and systemic immunity need to be further defined.

Overall, this study shows that the DJBS induces meal-related symptoms by interfering with “normal” upper gut digestive function. This results in a reduced energy intake and subsequent weight loss, as well as metabolic improvements. Unfortunately, long-term effects after removal of the device appear to be limited. Therefore interventions, including bariatric surgery or minimally invasive endoscopic procedures, that alter upper gut function and induce meal-related symptoms are a rational approach to achieve more sustainable weight loss if other measures have failed.

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APPENDIX

1.1 Human Research Ethics Committee Approval and Site Specific Approval

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1.4 Microbiology Primers and Reactions Illumina 16S rRNA sequencing primers

Amplicon PCR

16S rRNA forward primer: 917-F with Illumina adapter (adapter underlined):

TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGAATTGRCGGGGRCC

16S rRNA reverse primer: 1392-R with Illumina adapter (adapter underlined):

GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACGGGCGGTGWGTRC

Universal forward primer: 926-F with Illumina adapter (adapter underlined):

TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGAAACTYAAAKGAATTGRCGG

Product size: 556 bp (917-F and 1392-R) or 499 bp (926-F and 1392-R)

Barcoding PCR

Forward Index Primer (P5 seq primer region – Index I5 – Adapter (underlined)):

AATGATACGGCGACCACCGAGATCTACACiiiiiiiiTCGTCGGCAGCGTCAGATGTGTATAA GAGACAG

Reverse Index Primer (P7 seq primer region – Index I7 – Adapter (underlined)):

CAAGCAGAAGACGGCATACGAGATiiiiiiiiGTCTCGTGGGCTCGGAGATGTGTATAAGAG ACAG

Product size: 625 bp

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Index Kit A

I5 Index Sequence S502 CTCTCTAT S503 TATCCTCT S505 GTAAGGAG S506 ACTGCATA S507 AAGGAGTA S508 CTAAGCCT S510 CGTCTAAT S511 TCTCTCCG

I7 Index Sequence N701 TAAGGCGA N702 CGTACTAG N703 AGGCAGAA N704 TCCTGAGC N705 GGACTCCT N706 TAGGCATG N707 CTCTCTAC N710 CGAGGCTG N711 AAGAGGCA N712 GTAGAGGA N714 GCTCATGA N715 ATCTCAGG

Index Kit B

I5 Index Sequence S502 CTCTCTAT S503 TATCCTCT S505 GTAAGGAG S506 ACTGCATA S507 AAGGAGTA S508 CTAAGCCT S510 CGTCTAAT S511 TCTCTCCG

I7 Index Sequence N716 ACTCGCTA N718 GGAGCTAC N719 GCGTAGTA N720 CGGAGCCT N721 TACGCTGC N722 ATGCGCAG N723 TAGCGCTC N724 ACTGAGCG N726 CCTAAGAC N727 CGATCAGT N728 TGCAGCTA N729 TCGACGTC

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PCR reaction set up – 16S rRNA sequencing and qPCR qPCR

Volume per sample (µL) Reagent 0.2 Forward Primer (16S rRNA or human β-actin) 0.2 Reverse Primer (16S rRNA or human β-actin) 10 Power SYBR Green Master Mix 9.6 gDNA template (15 ng diluted in Ultrapure water) 20 Total

qPCR cycle program

95 °C 10 minutes 40 cycles of 95 °C 15 seconds 60 °C 1 minute

Melt Curve: 95 °C – 60 °C – 95 °C

16S rRNA sequencing reaction set up

Amplicon PCR

Volume per Reagent sample (µL) 2 Forward Primer (917-F or 926-F), with Illumina adapter (10 µM) 2 Reverse Primer (1392-R), with Illumina adapter (10 µM) 25 Q5® High Fidelity DNA Polymerase(New England Biolabs, MA, USA) 21 gDNA template (diluted in Ultrapure water to 100 ng for biopsies and 20 ng for device and stool samples) 50 Total

PCR cycle program

Temperature Time 98 °C 3 minutes 25 cycles of 95 °C 20 seconds 51 °C 20 seconds 72 °C 20 seconds 72 °C 2 minutes

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Barcoding PCR

Volume per Reagent sample (µL) 5 Nextera XT index 1 primer (N7xx) 5 Nextera XT index 2 primer (N5xx) 25 Q5® High Fidelity DNA Polymerase(New England Biolabs, MA, USA) 5 PCR water 10 gDNA template (from cleaned amplicon PCR) 50 Total

PCR cycle program

Temperature Time 95 °C 3 minutes 25 cycles of 95 °C 30 seconds 55 °C 30 seconds 72 °C 30 seconds 72 °C 5 minutes

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1.5 Contaminants Removed from Sequencing Results Table 1S: Full list of OTUs present in negative controls removed as contaminants prior to analysis (917 primer set). The number following the taxonomic description is the Greengenes identification number.

OTU p__Actinobacteria; f__Microbacteriaceae 179312 p__Actinobacteria; f__Microbacteriaceae New Reference OTU 11347 p__Actinobacteria; f__Microbacteriaceae New Reference OTU 28099 p__Actinobacteria; f__Microbacteriaceae; s__NA New Reference OTU 8606 p__Actinobacteria; g__Cryocola; s__NA 223593 p__Actinobacteria; g__Microbacterium; s__NA 1760354 p__Actinobacteria; f__Micrococcaceae 526833 p__Actinobacteria; g__Propionibacterium; s__Acnes New Reference OTU 13881 p__Firmicutes; g__Staphylococcus; s__NA New Reference OTU 27998 p__Firmicutes; g__Staphylococcus; s__Aureus New Reference OTU 14856 p__Firmicutes; g__Staphylococcus; s__Epidermidis New Reference OTU 11839 p__Firmicutes; g__Planifilum; s__NA 1725586 p__Firmicutes; g__Enterococcus; s__NA 303838 p__Firmicutes; g__Enterococcus; s__NA 593781 p__Firmicutes; o__Clostridiales 351309 p__Firmicutes; o__Clostridiales 352222 p__Firmicutes; o__Clostridiales 354097 p__Firmicutes; f__Clostridiaceae 346666 p__Firmicutes; g__Allobaculum; s__NA 1105860 p__Firmicutes; g__Allobaculum; s__NA 274912 p__Firmicutes; g__Allobaculum; s__NA 277143 p__Firmicutes; g__Allobaculum; s__New Reference OTU 18502 p__Proteobacteria; o__Rhizobiales New Reference OTU 94795 p__Proteobacteria; f__Bradyrhizobiaceae New Reference OTU 14380 p__Proteobacteria; g__Paracoccus 812921 p__Proteobacteria; g__Sphingomonas New Reference OTU 85436 p__Proteobacteria; g__Achromobacter; s__NA 846710 p__Proteobacteria; f__Comamonadaceae 275296 p__Proteobacteria; f__Comamonadaceae 838837 p__Proteobacteria; g__Comamonas; s__NA 576010 p__Proteobacteria; g__Delftia; s__NA 816420 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU 106355 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU 95717 p__Proteobacteria; f__Oxalobacteraceae 335466 p__Proteobacteria; f__Oxalobacteraceae 332293 p__Proteobacteria; f__Oxalobacteraceae 341936 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU 103772 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU 64692 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU 74027 p__Proteobacteria; g__Ralstonia; s__NA 783719 p__Proteobacteria; g__Ralstonia; s__NA New Reference OTU 36969

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OTU p__Proteobacteria; g__Ralstonia; s__NA New Reference OTU 54789 p__Proteobacteria; g__Ralstonia; s__NA New Reference OTU 31241 p__Proteobacteria; g__Ralstonia; s__NA New Reference OTU 66558 p__Proteobacteria; g__Ralstonia; s__NA New Reference OTU 41823 p__Proteobacteria; f__Moraxellaceae New Reference OTU 25185 p__Proteobacteria; g__Enhydrobacter 574102 p__Proteobacteria; g__Enhydrobacter; s__NA New Reference OTU 12057 p__Proteobacteria; f__Pseudomonadaceae New Reference OTU 41836 p__Proteobacteria; g__Pseudomonas; s__NA 573184 p__Proteobacteria; g__Pseudomonas; s__NA 813945 p__Proteobacteria; g__Pseudomonas; s__NA 827497 p__Proteobacteria; g__Pseudomonas; s__NA 817734 p__Proteobacteria; g__Pseudomonas; s__NA 780261 p__Proteobacteria; f__Xanthomonadaceae 884604 Abbreviations: f; family, g; genus, NA; not assigned, o; order, OTU; operational taxonomic unit, p; phylum s; species

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Table 2S: Full list of OTUs present in negative controls removed as contaminants prior to analysis (926 primer set). The number following the taxonomic description is the Greengenes identification number.

OTU p__Actinobacteria; f__Microbacteriaceae New Reference OTU 102495 p__Actinobacteria; f__Microbacteriaceae New.CleanUp.ReferenceOTU 1094657 p__Actinobacteria; g__Cryocola; s__NA 223593 p__Actinobacteria; g__Microbacterium; s__NA 814909 p__Actinobacteria; g__Microbacterium; s__chocolatum New Reference OTU201158 p__Actinobacteria; g__Micrococcus; s__luteus 338075 p__Actinobacteria; g__Micrococcus; s__luteus 526833 p__Actinobacteria; g__Propionibacterium; s__acnes New Reference OTU 203234 p__Actinobacteria; g__Propionibacterium; s__acnes New Reference OTU155864 p__Firmicutes; g__Staphylococcus; s__NA New Reference OTU235149 p__Firmicutes; g__Staphylococcus; s__epidermidis New Reference OTU139851 p__Firmicutes; o__Lactobacillales 667621 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU103939 p__Proteobacteria; f__Oxalobacteraceae 335466 p__Proteobacteria; f__Oxalobacteraceae 332293 p__Proteobacteria; f__Oxalobacteraceae 341936 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU 97535 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU210997 p__Proteobacteria; f__Oxalobacteraceae New Reference OTU 62539 p__Proteobacteria; g__Ralstonia; s__NA 783719 p__Proteobacteria; g__Enhydrobacter 574102 p__Proteobacteria; g__Enhydrobacter; s__NA New Reference OTU151729 p__Proteobacteria; g__Pseudomonas; s__NA 813945 p__Proteobacteria; g__Pseudomonas; s__NA 827497 p__Proteobacteria; g__Pseudomonas; s__NA 817734 p__Proteobacteria; g__Pseudomonas; s__NA 780261 Abbreviations: f; family, g; genus, NA; not assigned, o; order, OTU; operational taxonomic unit, p; phylum s; species

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1.6 Full List of OTUs Recovered in Samples Table 3S: Full list of OTUs present in samples, sorted alphabetically.

The number following the taxonomic description is the Greengenes identification number.

OTU Biopsy Device Stool k__Bacteria New.ReferenceOTU106958 Y p__Actinobacteria; f__Coriobacteriaceae 1106384 Y Y p__Actinobacteria; f__Coriobacteriaceae 87668 Y Y p__Actinobacteria; f__Coriobacteriaceae 196140 Y p__Actinobacteria; f__Coriobacteriaceae New.CleanUp.ReferenceOTU272839 Y Y p__Actinobacteria; f__Coriobacteriaceae New.ReferenceOTU100235 Y Y p__Actinobacteria; f__Coriobacteriaceae New.ReferenceOTU36643 Y Y p__Actinobacteria; g__Actinomyces 1066410 Y Y p__Actinobacteria; g__Actinomyces 4318624 Y Y p__Actinobacteria; g__Actinomyces 4350499 Y Y p__Actinobacteria; g__Actinomyces 565136 Y Y p__Actinobacteria; g__Adlercreutzia 198774 Y p__Actinobacteria; g__Atopobium 2163609 Y Y p__Actinobacteria; g__Atopobium 251702 Y Y p__Actinobacteria; g__Bifidobacterium 362214 Y Y p__Actinobacteria; g__Bifidobacterium 4426298 Y Y Y p__Actinobacteria; g__Bifidobacterium New.CleanUp.ReferenceOTU81261 Y p__Actinobacteria; g__Bifidobacterium New.ReferenceOTU103436 Y Y p__Actinobacteria; g__Bifidobacterium New.ReferenceOTU10855 Y Y p__Actinobacteria; g__Bifidobacterium New.ReferenceOTU13067 Y Y p__Actinobacteria; g__Bifidobacterium New.ReferenceOTU37474 Y Y p__Actinobacteria; g__Bifidobacterium New.ReferenceOTU60116 Y Y p__Actinobacteria; g__Bifidobacterium__s__animalis New.ReferenceOTU11994 Y Y p__Actinobacteria; g__Bifidobacterium__s__animalis Y New.ReferenceOTU102970 p__Actinobacteria; g__Bifidobacterium__s__animalis New.ReferenceOTU85119 Y Y p__Actinobacteria; g__Collinsella 193575 Y p__Actinobacteria; g__Collinsella 302647 Y p__Actinobacteria; g__Collinsella 365181 Y Y Y p__Actinobacteria; g__Collinsella 414949 Y Y p__Actinobacteria; g__Collinsella 415315 Y Y p__Actinobacteria; g__Collinsella New.CleanUp.ReferenceOTU235731 Y Y p__Actinobacteria; g__Collinsella New.ReferenceOTU2563 Y Y p__Actinobacteria; g__Collinsella New.ReferenceOTU47451 Y Y p__Actinobacteria; g__Collinsella New.ReferenceOTU57797 Y Y p__Actinobacteria; g__Collinsella New.ReferenceOTU6884 Y Y p__Actinobacteria; g__Collinsella__s__aerofaciens New.ReferenceOTU111175 Y p__Actinobacteria; g__Collinsella__s__aerofaciens 1811927 Y Y p__Actinobacteria; g__Collinsella__s__aerofaciens 196140 Y Y p__Actinobacteria; g__Collinsella__s__aerofaciens New.ReferenceOTU19159 Y Y 281

OTU Biopsy Device Stool p__Actinobacteria; g__Rothia 532388 Y Y p__Actinobacteria; g__Slackia 471157 Y Y p__Bacteroidetes; f__S24-7 3293886 Y p__Bacteroidetes; f__S24-7 333695 Y p__Bacteroidetes; f__S24-7 357305 Y p__Bacteroidetes; g__Bacteroides 1531298 Y Y p__Bacteroidetes; g__Bacteroides 198449 Y Y Y p__Bacteroidetes; g__Bacteroides 198530 Y Y p__Bacteroidetes; g__Bacteroides 270094 Y p__Bacteroidetes; g__Bacteroides 323231 Y Y Y p__Bacteroidetes; g__Bacteroides 326626 Y p__Bacteroidetes; g__Bacteroides 359538 Y p__Bacteroidetes; g__Bacteroides 363887 Y Y p__Bacteroidetes; g__Bacteroides 364029 Y p__Bacteroidetes; g__Bacteroides 369449 Y p__Bacteroidetes; g__Bacteroides 568118 Y Y Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU10143 Y Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU102603 Y Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU106777 Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU12980 Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU18528 Y Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU24310 Y Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU36879 Y Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU49181 Y Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU54418 Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU72131 Y p__Bacteroidetes; g__Bacteroides New.ReferenceOTU89660 Y Y p__Bacteroidetes; g__Bacteroides__s__fragilis 130007 Y p__Bacteroidetes; g__Bacteroides__s__fragilis 1795137 Y Y Y p__Bacteroidetes; g__Bacteroides__s__fragilis 1918384 Y Y p__Bacteroidetes; g__Bacteroides__s__fragilis 2944933 Y Y p__Bacteroidetes; g__Bacteroides__s__uniformis 323619 Y p__Bacteroidetes; g__Bacteroides__s__uniformis 584541 Y p__Bacteroidetes; g__Dysgonomonas New.ReferenceOTU40751 Y p__Bacteroidetes; g__Parabacteroides__s__distasonis 577294 Y p__Bacteroidetes; g__Porphyromonas 970138 Y Y p__Bacteroidetes; g__Porphyromonas__s__endodontalis 573034 Y Y p__Bacteroidetes; g__Prevotella 1011398 Y Y Y p__Bacteroidetes; g__Prevotella 2228 Y Y p__Bacteroidetes; g__Prevotella 269907 Y Y p__Bacteroidetes; g__Prevotella 369077 Y p__Bacteroidetes; g__Prevotella 4295238 Y Y p__Bacteroidetes; g__Prevotella 4307006 Y Y p__Bacteroidetes; g__Prevotella 535359 Y p__Bacteroidetes; g__Prevotella 610111 Y Y p__Bacteroidetes; g__Prevotella 851822 Y Y p__Bacteroidetes; g__Prevotella 851923 Y Y 282

OTU Biopsy Device Stool p__Bacteroidetes; g__Prevotella 851961 Y Y p__Bacteroidetes; g__Prevotella 926362 Y Y p__Bacteroidetes; g__Prevotella New.CleanUp.ReferenceOTU507256 Y p__Bacteroidetes; g__Prevotella New.ReferenceOTU113938 Y p__Bacteroidetes; g__Prevotella__s__copri 545061 Y p__Bacteroidetes; g__Prevotella__s__copri 840914 Y p__Bacteroidetes; g__Prevotella__s__intermedia 246785 Y Y p__Bacteroidetes; g__Prevotella__s__melaninogenica 535359 Y Y p__Bacteroidetes; g__Prevotella__s__nigrescens 2195 Y Y p__Bacteroidetes; g__Prevotella__s__nigrescens 3746312 Y p__Bacteroidetes; g__Prevotella__s__pallens 2222 Y Y p__Bacteroidetes; g__Prevotella__s__tannerae 2887205 Y p__Bacteroidetes; g__Prevotella__s__tannerae 38227 Y Y p__Euryarchaeota; g__Methanobrevibacter 849440 Y p__Euryarchaeota; g__Methanobrevibacter New.ReferenceOTU105789 Y p__Firmicutes; f__Clostridiaceae 328059 Y p__Firmicutes; f__Clostridiaceae 346666 Y p__Firmicutes; f__Erysipelotrichaceae 580008 Y p__Firmicutes; f__Lachnospiraceae 184729 Y p__Firmicutes; f__Lachnospiraceae 195186 Y p__Firmicutes; f__Lachnospiraceae 198909 Y p__Firmicutes; f__Lachnospiraceae 2148365 Y Y p__Firmicutes; f__Lachnospiraceae 230741 Y p__Firmicutes; f__Lachnospiraceae 3175741 Y Y p__Firmicutes; f__Lachnospiraceae 369709 Y p__Firmicutes; f__Lachnospiraceae 4087649 Y Y p__Firmicutes; f__Lachnospiraceae 524219 Y p__Firmicutes; f__Lachnospiraceae 574290 Y p__Firmicutes; f__Lachnospiraceae New.CleanUp.ReferenceOTU103377 Y p__Firmicutes; f__Lachnospiraceae New.CleanUp.ReferenceOTU1075895 Y p__Firmicutes; f__Lachnospiraceae New.CleanUp.ReferenceOTU1164950 Y p__Firmicutes; f__Lachnospiraceae New.CleanUp.ReferenceOTU209487 Y p__Firmicutes; f__Lachnospiraceae New.CleanUp.ReferenceOTU499544 Y p__Firmicutes; f__Lachnospiraceae New.CleanUp.ReferenceOTU93368 Y p__Firmicutes; f__Lachnospiraceae New.ReferenceOTU100062 Y p__Firmicutes; f__Lachnospiraceae New.ReferenceOTU100098 Y p__Firmicutes; f__Lachnospiraceae New.ReferenceOTU100192 Y p__Firmicutes; f__Lachnospiraceae New.ReferenceOTU100341 Y p__Firmicutes; f__Ruminococcaceae 1062061 Y p__Firmicutes; f__Ruminococcaceae 1813887 Y p__Firmicutes; f__Ruminococcaceae 196902 Y p__Firmicutes; f__Ruminococcaceae 198053 Y p__Firmicutes; f__Ruminococcaceae 215855 Y p__Firmicutes; f__Ruminococcaceae 515869 Y p__Firmicutes; f__Ruminococcaceae 538322 Y p__Firmicutes; f__Ruminococcaceae 566899 Y p__Firmicutes; f__Ruminococcaceae 761968 Y 283

OTU Biopsy Device Stool p__Firmicutes; f__Ruminococcaceae New.CleanUp.ReferenceOTU926888 Y p__Firmicutes; f__Veillonellaceae 3581088 Y Y p__Firmicutes; g__Acidaminococcus 179067 Y Y Y p__Firmicutes; g__Allobaculum 277143 Y p__Firmicutes; g__Blautia 367790 Y p__Firmicutes; g__Blautia 532203 Y p__Firmicutes; g__Blautia New.CleanUp.ReferenceOTU1168251 Y p__Firmicutes; g__Blautia New.CleanUp.ReferenceOTU240620 Y p__Firmicutes; g__Blautia New.CleanUp.ReferenceOTU30692 Y p__Firmicutes; g__Blautia New.CleanUp.ReferenceOTU418707 Y p__Firmicutes; g__Blautia New.CleanUp.ReferenceOTU758589 Y p__Firmicutes; g__Blautia New.CleanUp.ReferenceOTU830420 Y p__Firmicutes; g__Blautia New.ReferenceOTU102422 Y p__Firmicutes; g__Bulleidia 851938 Y Y p__Firmicutes; g__Catenibacterium 330294 Y p__Firmicutes; g__Clostridium__s__perfringens 355471 Y p__Firmicutes; g__Coprococcus 355175 Y Y p__Firmicutes; g__Coprococcus New.CleanUp.ReferenceOTU1009188 Y p__Firmicutes; g__Dialister 2160415 Y Y Y p__Firmicutes; g__Dialister 2866420 Y Y p__Firmicutes; g__Dialister 535901 Y Y Y p__Firmicutes; g__Dorea New.CleanUp.ReferenceOTU444829 Y p__Firmicutes; g__Dorea New.ReferenceOTU100101 Y p__Firmicutes; g__Dorea New.ReferenceOTU109445 Y p__Firmicutes; g__Enterococcus 303838 Y p__Firmicutes; g__Enterococcus 539447 Y p__Firmicutes; g__Enterococcus 543824 Y p__Firmicutes; g__Enterococcus 593781 Y p__Firmicutes; g__Enterococcus 667621 Y Y p__Firmicutes; g__Enterococcus New.ReferenceOTU4904 Y p__Firmicutes; g__Eubacterium__s__biforme 523132 Y p__Firmicutes; g__Eubacterium__s__biforme 527208 Y p__Firmicutes; g__Faecalibacterium__s__prausnitzii 523892 Y p__Firmicutes; g__Gemella 4384936 Y Y p__Firmicutes; g__Gemella 529233 Y Y p__Firmicutes; g__Granulicatella New.ReferenceOTU14113 Y Y p__Firmicutes; g__Granulicatella New.ReferenceOTU61217 Y Y p__Firmicutes; g__Lactobacillus 133892 Y Y p__Firmicutes; g__Lactobacillus 137580 Y Y Y p__Firmicutes; g__Lactobacillus 221299 Y Y Y p__Firmicutes; g__Lactobacillus 255367 Y Y p__Firmicutes; g__Lactobacillus 266445 Y Y p__Firmicutes; g__Lactobacillus 276632 Y p__Firmicutes; g__Lactobacillus 288521 Y Y p__Firmicutes; g__Lactobacillus 316438 Y Y p__Firmicutes; g__Lactobacillus 318764 Y Y p__Firmicutes; g__Lactobacillus 329402 Y 284

OTU Biopsy Device Stool p__Firmicutes; g__Lactobacillus 331248 Y Y Y p__Firmicutes; g__Lactobacillus 340960 Y Y Y p__Firmicutes; g__Lactobacillus 354225 Y Y Y p__Firmicutes; g__Lactobacillus 354911 Y Y p__Firmicutes; g__Lactobacillus 354971 Y Y Y p__Firmicutes; g__Lactobacillus 383885 Y Y p__Firmicutes; g__Lactobacillus 4321285 Y p__Firmicutes; g__Lactobacillus 4337090 Y Y p__Firmicutes; g__Lactobacillus 484444 Y Y p__Firmicutes; g__Lactobacillus 509452 Y Y Y p__Firmicutes; g__Lactobacillus 519673 Y Y Y p__Firmicutes; g__Lactobacillus 533133 Y Y p__Firmicutes; g__Lactobacillus 536754 Y p__Firmicutes; g__Lactobacillus 539647 Y Y Y p__Firmicutes; g__Lactobacillus 541135 Y Y p__Firmicutes; g__Lactobacillus 549756 Y Y Y p__Firmicutes; g__Lactobacillus 563654 Y Y Y p__Firmicutes; g__Lactobacillus 564037 Y p__Firmicutes; g__Lactobacillus 573010 Y Y p__Firmicutes; g__Lactobacillus 573214 Y p__Firmicutes; g__Lactobacillus 574021 Y Y Y p__Firmicutes; g__Lactobacillus 581474 Y Y Y p__Firmicutes; g__Lactobacillus 582884 Y Y Y p__Firmicutes; g__Lactobacillus 584001 Y Y Y p__Firmicutes; g__Lactobacillus 584571 Y Y p__Firmicutes; g__Lactobacillus 586093 Y Y Y p__Firmicutes; g__Lactobacillus 586968 Y Y Y p__Firmicutes; g__Lactobacillus 588197 Y Y Y p__Firmicutes; g__Lactobacillus 590168 Y Y Y p__Firmicutes; g__Lactobacillus 592160 Y Y p__Firmicutes; g__Lactobacillus 593376 Y Y p__Firmicutes; g__Lactobacillus 716286 Y Y p__Firmicutes; g__Lactobacillus 725198 Y Y Y p__Firmicutes; g__Lactobacillus 805007 Y Y Y p__Firmicutes; g__Lactobacillus 808562 Y Y p__Firmicutes; g__Lactobacillus 818672 Y p__Firmicutes; g__Lactobacillus 823803 Y Y Y p__Firmicutes; g__Lactobacillus 851733 Y Y Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU1204606 Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU1285289 Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU229135 Y Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU292621 Y Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU388917 Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU600388 Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU675106 Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU744612 Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU744633 Y 285

OTU Biopsy Device Stool p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU859886 Y p__Firmicutes; g__Lactobacillus New.CleanUp.ReferenceOTU980504 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100078 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100080 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100146 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100298 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100383 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100507 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100549 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100589 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100668 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU100849 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU10089 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU101020 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU101346 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU101393 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU102320 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU10236 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU102911 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU103058 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU103611 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU10377 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU104376 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU106105 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU107798 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU108675 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU11157 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU113378 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU11593 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU116651 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU118335 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU119630 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU130718 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU136613 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU140599 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU14451 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU15184 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU167255 Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU20734 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU24218 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU24936 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU3201 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU49108 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU63911 Y Y p__Firmicutes; g__Lactobacillus New.ReferenceOTU87765 Y Y p__Firmicutes; g__Lactobacillus__s__reuteri 564037 Y Y 286

OTU Biopsy Device Stool p__Firmicutes; g__Lactobacillus__s__reuteri New.ReferenceOTU100628 Y p__Firmicutes; g__Lactobacillus__s__reuteri New.ReferenceOTU104864 Y p__Firmicutes; g__Lactobacillus__s__vaginalis 536754 Y Y p__Firmicutes; g__Megasphaera 173744 Y Y Y p__Firmicutes; g__Megasphaera 41128 Y Y Y p__Firmicutes; g__Megasphaera 518686 Y Y p__Firmicutes; g__Megasphaera 539819 Y Y Y p__Firmicutes; g__Moryella 714766 Y Y p__Firmicutes; g__Parvimonas 851704 Y Y Y p__Firmicutes; g__Peptostreptococcus__s__anaerobius 532521 Y Y p__Firmicutes; g__Ruminococcus 333096 Y p__Firmicutes; g__Ruminococcus New.CleanUp.ReferenceOTU672208 Y p__Firmicutes; g__Ruminococcus__s__gnavus Y New.CleanUp.ReferenceOTU1129070 p__Firmicutes; g__Selenomonas 258034 Y p__Firmicutes; g__Selenomonas 28118 Y p__Firmicutes; g__Selenomonas 295019 Y Y Y p__Firmicutes; g__Selenomonas 4297955 Y Y Y p__Firmicutes; g__Selenomonas 4310995 Y Y p__Firmicutes; g__Selenomonas 4387963 Y p__Firmicutes; g__Selenomonas New.ReferenceOTU14349 Y p__Firmicutes; g__Streptococcus 1098340 Y Y p__Firmicutes; g__Streptococcus 1141646 Y p__Firmicutes; g__Streptococcus 349024 Y Y Y p__Firmicutes; g__Streptococcus 516966 Y Y Y p__Firmicutes; g__Streptococcus New.CleanUp.ReferenceOTU379777 Y Y p__Firmicutes; g__Streptococcus New.CleanUp.ReferenceOTU885632 Y p__Firmicutes; g__Streptococcus New.ReferenceOTU101 Y p__Firmicutes; g__Streptococcus New.ReferenceOTU100710 Y Y p__Firmicutes; g__Streptococcus New.ReferenceOTU101677 Y Y p__Firmicutes; g__Streptococcus New.ReferenceOTU102486 Y p__Firmicutes; g__Streptococcus New.ReferenceOTU104459 Y Y p__Firmicutes; g__Streptococcus New.ReferenceOTU13477 Y Y p__Firmicutes; g__Streptococcus New.ReferenceOTU137100 Y p__Firmicutes; g__Streptococcus New.ReferenceOTU30299 Y Y p__Firmicutes; g__Streptococcus New.ReferenceOTU32130 Y Y p__Firmicutes; g__Streptococcus__s__anginosus 217734 Y Y p__Firmicutes; g__Streptococcus__s__anginosus 2959075 Y p__Firmicutes; g__Streptococcus__s__anginosus 561636 Y Y p__Firmicutes; g__Streptococcus__s__anginosus 797560 Y Y p__Firmicutes; g__Streptococcus__s__anginosus New.ReferenceOTU42852 Y Y p__Firmicutes; g__Streptococcus__s__infantis 517754 Y Y p__Firmicutes; g__Veillonella 222433 Y Y p__Firmicutes; g__Veillonella 2283862 Y p__Firmicutes; g__Veillonella 518743 Y Y p__Firmicutes; g__Veillonella 561537 Y Y p__Firmicutes; g__Veillonella New.ReferenceOTU23742 Y 287

OTU Biopsy Device Stool p__Firmicutes; g__Veillonella New.ReferenceOTU60029 Y Y p__Firmicutes; g__Veillonella__s__parvula 4352537 Y p__Firmicutes; g__Veillonella__s__parvula 518743 Y p__Firmicutes; o__Clostridiales 193731 Y p__Firmicutes; o__Clostridiales 321005 Y p__Firmicutes; o__Clostridiales 337403 Y p__Firmicutes; o__Clostridiales 365484 Y p__Firmicutes; o__Clostridiales 470382 Y p__Firmicutes; o__Clostridiales 798164 Y p__Firmicutes; o__Clostridiales New.CleanUp.ReferenceOTU666868 Y p__Firmicutes; o__Clostridiales New.ReferenceOTU100675 Y p__Fusobacteria; g__Cetobacterium__s__somerae 825815 Y Y p__Fusobacteria; g__Cetobacterium__s__somerae 828162 Y Y Y p__Fusobacteria; g__Cetobacterium__s__somerae New.ReferenceOTU100037 Y Y p__Fusobacteria; g__Fusobacterium 1749615 Y p__Fusobacteria; g__Fusobacterium 1750702 Y p__Fusobacteria; g__Fusobacterium 1875986 Y Y p__Fusobacteria; g__Fusobacterium 34791 Y Y p__Fusobacteria; g__Fusobacterium 4479600 Y Y p__Fusobacteria; g__Fusobacterium 545299 Y Y Y p__Fusobacteria; g__Leptotrichia 2480553 Y Y p__Fusobacteria; g__Leptotrichia 251967 Y Y p__Fusobacteria; g__Leptotrichia 4483174 Y Y Y p__Lentisphaerae; f__Victivallaceae 158404 Y Y Y p__Proteobacteria; c__Gammaproteobacteria 808486 Y Y p__Proteobacteria; c__Gammaproteobacteria New.ReferenceOTU101477 Y Y p__Proteobacteria; c__Gammaproteobacteria New.ReferenceOTU102522 Y p__Proteobacteria; c__Gammaproteobacteria New.ReferenceOTU102591 Y p__Proteobacteria; c__Gammaproteobacteria New.ReferenceOTU127027 Y p__Proteobacteria; f__Aeromonadaceae 423025 Y p__Proteobacteria; f__Aeromonadaceae 834097 Y Y p__Proteobacteria; f__Aeromonadaceae 837030 Y Y p__Proteobacteria; f__Enterobacteriaceae 1109844 Y Y Y p__Proteobacteria; f__Enterobacteriaceae 1111874 Y Y p__Proteobacteria; f__Enterobacteriaceae 168254 Y Y p__Proteobacteria; f__Enterobacteriaceae 1935544 Y Y p__Proteobacteria; f__Enterobacteriaceae 2016095 Y Y p__Proteobacteria; f__Enterobacteriaceae 228556 Y Y p__Proteobacteria; f__Enterobacteriaceae 2651538 Y Y p__Proteobacteria; f__Enterobacteriaceae 268101 Y Y p__Proteobacteria; f__Enterobacteriaceae 332958 Y Y p__Proteobacteria; f__Enterobacteriaceae 3908647 Y p__Proteobacteria; f__Enterobacteriaceae 4439606 Y p__Proteobacteria; f__Enterobacteriaceae 510870 Y Y p__Proteobacteria; f__Enterobacteriaceae 544824 Y Y p__Proteobacteria; f__Enterobacteriaceae 588216 Y Y Y p__Proteobacteria; f__Enterobacteriaceae 687940 Y Y 288

OTU Biopsy Device Stool p__Proteobacteria; f__Enterobacteriaceae 788593 Y p__Proteobacteria; f__Enterobacteriaceae 797229 Y Y Y p__Proteobacteria; f__Enterobacteriaceae 808486 Y p__Proteobacteria; f__Enterobacteriaceae 812379 Y p__Proteobacteria; f__Enterobacteriaceae 819636 Y Y Y p__Proteobacteria; f__Enterobacteriaceae 820346 Y Y p__Proteobacteria; f__Enterobacteriaceae 822899 Y Y p__Proteobacteria; f__Enterobacteriaceae 823118 Y Y Y p__Proteobacteria; f__Enterobacteriaceae 825033 Y Y p__Proteobacteria; f__Enterobacteriaceae 833731 Y Y Y p__Proteobacteria; f__Enterobacteriaceae New.CleanUp.ReferenceOTU1065258 Y p__Proteobacteria; f__Enterobacteriaceae New.CleanUp.ReferenceOTU226028 Y p__Proteobacteria; f__Enterobacteriaceae New.CleanUp.ReferenceOTU52125 Y Y p__Proteobacteria; f__Enterobacteriaceae New.CleanUp.ReferenceOTU648007 Y p__Proteobacteria; f__Enterobacteriaceae New.CleanUp.ReferenceOTU652965 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU100065 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU100205 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU100333 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU10037 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU100485 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU100937 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU100973 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU100999 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU101 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU101122 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU101818 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU101916 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU102806 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU103662 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU103828 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU105580 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU106446 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU111272 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU111780 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU112161 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU15281 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU156121 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU193868 Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU19530 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU22731 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU23274 Y Y p__Proteobacteria; f__Enterobacteriaceae New.ReferenceOTU87048 Y Y p__Proteobacteria; g__Acinetobacter 562618 Y p__Proteobacteria; g__Acinetobacter New.ReferenceOTU104469 Y p__Proteobacteria; g__Actinobacillus__s__parahaemolyticus 9610 Y Y p__Proteobacteria; g__Aggregatibacter 92231 Y Y p__Proteobacteria; g__Aggregatibacter__s__segnis 4295455 Y 289

OTU Biopsy Device Stool p__Proteobacteria; g__Aggregatibacter__s__segnis New.ReferenceOTU54822 Y Y p__Proteobacteria; g__Citrobacter 786708 Y p__Proteobacteria; g__Erwinia New.ReferenceOTU127469 Y p__Proteobacteria; g__Erwinia New.ReferenceOTU32508 Y Y p__Proteobacteria; g__Haemophilus__s__parainfluenzae Y Y New.CleanUp.ReferenceOTU280946 p__Proteobacteria; g__Haemophilus__s__parainfluenzae Y Y New.ReferenceOTU100511 p__Proteobacteria; g__Haemophilus__s__parainfluenzae Y Y New.ReferenceOTU100889 p__Proteobacteria; g__Helicobacter__s__pylori 132837 Y Y p__Proteobacteria; g__Klebsiella 103166 Y Y p__Proteobacteria; g__Klebsiella 511908 Y p__Proteobacteria; g__Klebsiella 546384 Y Y p__Proteobacteria; g__Klebsiella 786708 Y Y p__Proteobacteria; g__Klebsiella 807118 Y Y p__Proteobacteria; g__Klebsiella 822899 Y p__Proteobacteria; g__Klebsiella New.CleanUp.ReferenceOTU288972 Y Y p__Proteobacteria; g__Klebsiella New.CleanUp.ReferenceOTU571714 Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU101071 Y Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU101209 Y Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU102659 Y Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU103022 Y Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU103878 Y Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU105022 Y Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU14298 Y Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU4524 Y Y p__Proteobacteria; g__Klebsiella New.ReferenceOTU57954 Y Y p__Proteobacteria; g__Morganella__s__morganii 631768 Y Y p__Proteobacteria; g__Neisseria New.CleanUp.ReferenceOTU184570 Y Y p__Proteobacteria; g__Neisseria New.ReferenceOTU15692 Y Y p__Proteobacteria; g__Neisseria New.ReferenceOTU29961 Y Y p__Proteobacteria; g__Proteus 4484484 Y p__Proteobacteria; g__Serratia 820346 Y p__Proteobacteria; g__Sutterella 1820513 Y p__Proteobacteria; g__Sutterella 189076 Y p__Synergistetes; g__TG5 68345 Y Y Y p__Verrucomicrobia; g__Akkermansia__s__muciniphila 358757 Y Y Y p__Verrucomicrobia; g__Akkermansia__s__muciniphila 359105 Y p__Verrucomicrobia; g__Akkermansia__s__muciniphila 587822 Y Abbreviations: c; class, f; family, g; genus, o; order, OTU; operational taxonomic unit, p; phylum s; species, Y; recovered in samples

290

1.7 Full Statistical Analyses for Microbiota Data Table 4S: Full statistical analyses for gastric biopsies from baseline to explant (FDR q assessed by linear regression modelling).

Phylum FDR q Change from Baseline Fusobacteria 0.0048 Increase Synergistetes 0.04 Increase Firmicutes 0.7 Decrease Lentisphaerae 0.7 Increase Verrucomicrobia 0.7 Increase Actinobacteria 0.82 Decrease Bacteroidetes 0.82 Increase Proteobacteria 0.92 Decrease

291

FDR q Change from Genus Baseline Rothia 0.00024 Decrease Streptococcus 0.00035 Decrease Dialister 0.0039 Increase Fusobacterium 0.0039 Increase Unclassified Enterobacteriaceae 0.0039 Increase Unclassified Gammaproteobacteria 0.0039 Increase Lactobacillus 0.0041 Increase Klebsiella 0.0041 Increase Actinomyces 0.0048 Decrease Granulicatella 0.0048 Decrease Bifidobacterium 0.0072 Increase Helicobacter 0.012 Decrease Selenomonas 0.013 Increase Neisseria 0.021 Decrease Bulleidia 0.021 Decrease Erwinia 0.024 Increase Megasphaera 0.024 Increase TG5 0.024 Increase Unclassified Coriobacteriaceae 0.053 Increase Morganella 0.062 Increase Unclassified Aeromonadaceae 0.071 Increase Slackia 0.071 Increase Haemophilus 0.071 Decrease Gemella 0.13 Decrease Moryella 0.15 Decrease Unclassified Veillonellaceae 0.19 Increase Parvimonas 0.2 Increase Collinsella 0.22 Increase Bacteroides 0.22 Increase Acidaminococcus 0.22 Decrease Actinobacillus 0.23 Decrease Prevotella 0.33 Decrease Atopobium 0.35 Decrease Leptotrichia 0.4 Increase Enterococcus 0.47 Increase Cetobacterium 0.5 Increase Unclassified Victivallaceae 0.5 Increase Akkermansia 0.51 Increase Unclassified Lachnospiraceae 0.54 Increase Porphyromonas 0.54 Decrease Aggregatibacter 0.89 Decrease Veillonella 0.89 Increase Peptostreptococcus 1 Decrease Coprococcus 1 Unchanged Unclassified S247 NaN Unchanged

292

Change from OTU FDR q Baseline p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 0.00006 Decrease p__Fusobacteria__g__Fusobacterium_34791 0.000078 Increase p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU379777 0.00098 Decrease p__Firmicutes__g__Streptococcus__s__infantis_517754 0.00098 Decrease p__Proteobacteria__f__Enterobacteriaceae_823118 0.00098 Increase p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0.002 Decrease p__Actinobacteria__g__Actinomyces_4350499 0.002 Decrease p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 0.0022 Decrease p__Proteobacteria__c__Gammaproteobacteria_808486 0.0028 Increase p__Proteobacteria__f__Enterobacteriaceae_822899 0.0028 Increase p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.003 Increase p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.0037 Increase p__Proteobacteria__f__Enterobacteriaceae_819636 0.0037 Increase p__Firmicutes__g__Lactobacillus_563654 0.0037 Increase p__Actinobacteria__g__Actinomyces_565136 0.0037 Decrease p__Proteobacteria__f__Enterobacteriaceae_332958 0.0066 Increase p__Firmicutes__g__Lactobacillus_574021 0.0074 Increase p__Proteobacteria__f__Enterobacteriaceae_228556 0.0076 Increase p__Proteobacteria__f__Enterobacteriaceae_2016095 0.0076 Increase p__Actinobacteria__g__Bifidobacterium_4426298 0.0076 Increase p__Firmicutes__g__Lactobacillus_586093 0.0076 Increase p__Firmicutes__g__Lactobacillus_592160 0.011 Increase p__Proteobacteria__g__Klebsiella_786708 0.012 Increase p__Firmicutes__g__Lactobacillus_539647 0.015 Increase p__Proteobacteria__f__Enterobacteriaceae_510870 0.02 Increase p__Firmicutes__g__Lactobacillus_533133 0.02 Increase p__Firmicutes__g__Lactobacillus_331248 0.02 Increase p__Firmicutes__g__Lactobacillus_582884 0.021 Increase p__Proteobacteria__f__Enterobacteriaceae_588216 0.021 Increase p__Firmicutes__g__Streptococcus_349024 0.025 Decrease p__Proteobacteria__f__Enterobacteriaceae_797229 0.027 Increase p__Firmicutes__g__Selenomonas_4297955 0.027 Increase p__Proteobacteria__f__Enterobacteriaceae_1109844 0.029 Increase p__Firmicutes__g__Selenomonas_295019 0.029 Increase p__Firmicutes__g__Lactobacillus_590168 0.029 Increase p__Firmicutes__g__Lactobacillus_573010 0.036 Increase p__Proteobacteria__g__Haemophilus__s__parainfluenzae_New.Reference OTU100511 0.075 Decrease p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.075 Increase p__Firmicutes__g__Lactobacillus_340960 0.088 Increase p__Firmicutes__g__Lactobacillus_354225 0.11 Increase p__Firmicutes__g__Megasphaera_41128 0.11 Increase p__Actinobacteria__g__Collinsella__s__aerofaciens_1811927 0.12 Increase p__Actinobacteria__g__Collinsella_365181 0.12 Increase p__Firmicutes__g__Lactobacillus_823803 0.14 Increase p__Bacteroidetes__g__Prevotella__s__nigrescens_2195 0.18 Increase p__Fusobacteria__g__Fusobacterium_545299 0.2 Increase 293

Change from OTU FDR q Baseline p__Firmicutes__g__Veillonella_561537 0.26 Increase p__Bacteroidetes__g__Bacteroides_568118 0.31 Increase p__Firmicutes__g__Veillonella_518743 0.49 Decrease p__Firmicutes__g__Streptococcus_New.ReferenceOTU13477 0.74 Increase

294

Table 5S: Full statistical analyses for duodenal biopsies from baseline to explant (FDR q assessed by linear regression modelling).

Phylum FDR q Change from Baseline Fusobacteria 0.031 Increase Firmicutes 0.16 Decrease Synergistetes 0.16 Increase Lentisphaerae 0.34 Unchanged Bacteroidetes 0.45 Decrease Actinobacteria 0.45 Increase Proteobacteria 0.47 Increase Verrucomicrobia 1 Unchanged

295

FDR q Change FDR q Change from from Genus Baseline Genus Baseline Streptococcus 2.9E-07 Decrease Peptostreptococcus 1 Decrease Dialister 0.000031 Increase Akkermansia 1 Unchanged Haemophilus 0.0013 Decrease Unclassified S247 NaN Unchanged Helicobacter 0.0043 Decrease Lactobacillus 0.0056 Increase Klebsiella 0.0062 Increase Unclassified Increase Gammaproteobacteria 0.0075 Gemella 0.0099 Decrease Bifidobacterium 0.011 Increase Selenomonas 0.011 Increase Neisseria 0.013 Decrease Megasphaera 0.019 Increase Unclassified Increase Enterobacteriaceae 0.026 Fusobacterium 0.026 Increase Granulicatella 0.026 Decrease Bulleidia 0.033 Decrease Slackia 0.044 Increase Rothia 0.056 Decrease Prevotella 0.056 Decrease Leptotrichia 0.062 Increase Unclassified Increase Aeromonadaceae 0.073 Unclassified Increase Coriobacteriaceae 0.11 Morganella 0.11 Increase TG5 0.11 Increase Moryella 0.13 Decrease Actinomyces 0.13 Decrease Parvimonas 0.15 Increase Erwinia 0.15 Increase Collinsella 0.15 Increase Veillonella 0.16 Decrease Actinobacillus 0.17 Decrease Aggregatibacter 0.21 Decrease Unclassified Unchanged Victivallaceae 0.22 Acidaminococcus 0.22 Decrease Coprococcus 0.28 Unchanged Porphyromonas 0.28 Decrease Enterococcus 0.29 Increase Bacteroides 0.32 Increase Unclassified Increase Veillonellaceae 0.38 Cetobacterium 0.54 Increase Unclassified Increase Lachnospiraceae 0.55 Atopobium 0.8 Decrease 296

Change from OTU FDR q Baseline p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 2.8E-08 Decrease p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU379777 2.2E-06 Decrease p__Firmicutes__g__Streptococcus__s__infantis_517754 9.5E-06 Decrease p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 0.00014 Decrease p__Fusobacteria__g__Fusobacterium_34791 0.00061 Increase p__Firmicutes__g__Streptococcus_349024 0.00076 Decrease p__Proteobacteria__g__Haemophilus__s__parainfluenzae_New.Reference OTU100511 0.0023 Decrease p__Firmicutes__g__Lactobacillus_592160 0.0023 Increase p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0.0033 Decrease p__Proteobacteria__g__Klebsiella_786708 0.0035 Increase p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.0035 Increase p__Proteobacteria__f__Enterobacteriaceae_819636 0.0035 Increase p__Proteobacteria__f__Enterobacteriaceae_822899 0.0042 Increase p__Proteobacteria__c__Gammaproteobacteria_808486 0.0043 Increase p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.0047 Increase p__Firmicutes__g__Lactobacillus_331248 0.0075 Increase p__Firmicutes__g__Lactobacillus_574021 0.0076 Increase p__Proteobacteria__f__Enterobacteriaceae_823118 0.0079 Increase p__Proteobacteria__f__Enterobacteriaceae_332958 0.0079 Increase p__Firmicutes__g__Lactobacillus_582884 0.012 Increase p__Proteobacteria__f__Enterobacteriaceae_228556 0.012 Increase p__Firmicutes__g__Lactobacillus_590168 0.014 Increase p__Firmicutes__g__Lactobacillus_573010 0.016 Increase p__Firmicutes__g__Selenomonas_295019 0.017 Increase p__Actinobacteria__g__Bifidobacterium_4426298 0.019 Increase p__Proteobacteria__f__Enterobacteriaceae_510870 0.019 Increase p__Firmicutes__g__Selenomonas_4297955 0.022 Increase p__Firmicutes__g__Lactobacillus_563654 0.023 Increase p__Proteobacteria__f__Enterobacteriaceae_2016095 0.024 Increase p__Firmicutes__g__Lactobacillus_533133 0.037 Increase p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.042 Increase p__Firmicutes__g__Lactobacillus_340960 0.052 Increase p__Actinobacteria__g__Actinomyces_4350499 0.056 Decrease p__Firmicutes__g__Lactobacillus_539647 0.068 Increase p__Firmicutes__g__Megasphaera_41128 0.09 Increase p__Actinobacteria__g__Collinsella_365181 0.092 Increase p__Firmicutes__g__Lactobacillus_354225 0.092 Increase p__Actinobacteria__g__Collinsella__s__aerofaciens_1811927 0.11 Increase p__Firmicutes__g__Lactobacillus_586093 0.12 Increase p__Firmicutes__g__Veillonella_518743 0.12 Decrease p__Actinobacteria__g__Actinomyces_565136 0.17 Decrease p__Proteobacteria__f__Enterobacteriaceae_797229 0.19 Increase p__Proteobacteria__f__Enterobacteriaceae_588216 0.2 Increase p__Proteobacteria__f__Enterobacteriaceae_1109844 0.28 Increase p__Firmicutes__g__Lactobacillus_823803 0.29 Increase p__Bacteroidetes__g__Bacteroides_568118 0.48 Increase 297

Change from OTU FDR q Baseline p__Fusobacteria__g__Fusobacterium_545299 0.54 Increase p__Bacteroidetes__g__Prevotella__s__nigrescens_2195 0.6 Increase p__Firmicutes__g__Veillonella_561537 0.69 Increase p__Firmicutes__g__Streptococcus_New.ReferenceOTU13477 0.99 Increase

298

Table 6S: Full statistical analyses for mucosal adjacent device surface compared to luminal adjacent device surface (FDR q assessed by linear regression modelling).

Phylum FDR q Proteobacteria 0.00031 Synergistetes 0.028 Firmicutes 0.14 Verrucomicrobia 0.48 Bacteroidetes 0.75 Actinobacteria 0.79 Lentisphaerae 0.79 Fusobacteria 0.9

299

Genus FDR q Klebsiella 0.00009 Streptococcus 0.00009 Atopobium 0.000097 Unclassified.cGammaproteobacteria 0.00028 Parvimonas 0.00031 Unclassified.Coriobacteriaceae 0.00052 Prevotella 0.0011 Porphyromonas 0.0011 Bacteroides 0.0032 Slackia 0.0032 Megasphaera 0.0047 Veillonella 0.0047 Bulleidia 0.0053 Rothia 0.0071 Gemella 0.0075 Unclassified.Enterobacteriaceae 0.012 Actinomyces 0.014 TG5 0.016 Peptostreptococcus 0.032 Morganella 0.037 Cetobacterium 0.092 Unclassified.Lachnospiraceae 0.12 Leptotrichia 0.12 Lactobacillus 0.12 Granulicatella 0.19 Unclassified.Veillonellaceae 0.22 Helicobacter 0.24 Aggregatibacter 0.25 Neisseria 0.28 Akkermansia 0.34 Bifidobacterium 0.4 Dialister 0.64 Moryella 0.64 Unclassified.Victivallaceae 0.84 Collinsella 0.84 Unclassified.Aeromonadaceae 0.84 Actinobacillus 0.84 Selenomonas 0.84 Erwinia 0.84 Acidaminococcus 0.88 Haemophilus 0.89 Enterococcus 0.99 Fusobacterium 0.99 Unclassified.S247 NaN Coprococcus NaN

300

OTU FDR q p__Proteobacteria__g__Klebsiella_786708 4.1E-06 p__Proteobacteria__f__Enterobacteriaceae_819636 0.000048 p__Firmicutes__g__Lactobacillus_331248 0.00017 p__Proteobacteria__c__Gammaproteobacteria_808486 0.00032 p__Proteobacteria__f__Enterobacteriaceae_2016095 0.00037 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.00037 p__Proteobacteria__f__Enterobacteriaceae_228556 0.00037 p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.00037 p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 0.00089 p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.0018 p__Proteobacteria__f__Enterobacteriaceae_332958 0.0023 p__Firmicutes__g__Streptococcus_349024 0.0032 p__Fusobacteria__g__Fusobacterium_545299 0.0032 p__Firmicutes__g__Streptococcus_New.ReferenceOTU13477 0.0032 p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0.0043 p__Firmicutes__g__Lactobacillus_582884 0.0066 p__Firmicutes__g__Lactobacillus_354225 0.0066 p__Bacteroidetes__g__Prevotella__s__nigrescens_2195 0.0066 p__Proteobacteria__f__Enterobacteriaceae_822899 0.0066 p__Firmicutes__g__Veillonella_561537 0.008 p__Firmicutes__g__Lactobacillus_533133 0.012 p__Firmicutes__g__Lactobacillus_539647 0.013 p__Firmicutes__g__Lactobacillus_573010 0.013 p__Firmicutes__g__Lactobacillus_586093 0.013 p__Firmicutes__g__Veillonella_518743 0.013 p__Bacteroidetes__g__Bacteroides_568118 0.013 p__Firmicutes__g__Lactobacillus_574021 0.014 p__Firmicutes__g__Lactobacillus_340960 0.021 p__Firmicutes__g__Lactobacillus_590168 0.022 p__Actinobacteria__g__Actinomyces_4350499 0.03 p__Firmicutes__g__Streptococcus__s__infantis_517754 0.034 p__Firmicutes__g__Lactobacillus_823803 0.039 p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 0.039 p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU379777 0.084 p__Proteobacteria__f__Enterobacteriaceae_823118 0.11 p__Firmicutes__g__Megasphaera_41128 0.12 p__Proteobacteria__f__Enterobacteriaceae_588216 0.13 p__Actinobacteria__g__Actinomyces_565136 0.13 p__Actinobacteria__g__Bifidobacterium_4426298 0.14 p__Firmicutes__g__Selenomonas_295019 0.19 p__Firmicutes__g__Lactobacillus_592160 0.22 p__Actinobacteria__g__Collinsella_365181 0.33 p__Proteobacteria__f__Enterobacteriaceae_797229 0.35 p__Firmicutes__g__Selenomonas_4297955 0.38 p__Proteobacteria__f__Enterobacteriaceae_1109844 0.4 p__Proteobacteria__g__Haemophilus__s__parainfluenzae_New.ReferenceOTU100511 0.62 p__Proteobacteria__f__Enterobacteriaceae_510870 0.69 301

OTU FDR q p__Actinobacteria__g__Collinsella__s__aerofaciens_1811927 0.79 p__Firmicutes__g__Lactobacillus_563654 0.87 p__Fusobacteria__g__Fusobacterium_34791 0.91

302

Table 7S: Full statistical analyses for top mucosal adjacent device surface compared to explant duodenal biopsy (FDR q assessed by linear regression modelling).

Phylum FDR q Compared to Device Bacteroidetes 0.00025 Increase Fusobacteria 0.00025 Increase Synergistetes 0.0026 Increase Proteobacteria 0.028 Decrease Actinobacteria 0.35 Decrease Verrucomicrobia 0.35 Increase Lentisphaerae 0.35 Decrease Firmicutes 0.41 Increase

303

FDR q Compared FDR q Compared Genus to Device Genus to Device Prevotella 4.4E-09 Increase Acidaminococcus 0.32 Decrease Parvimonas 3.1E-08 Increase Collinsella 0.35 Decrease Streptococcus 1.4E-06 Increase Aggregatibacter 0.4 Increase Bulleidia 0.00002 Increase Unclassified S247 NaN Unchanged Porphyromonas 0.000038 Increase Veillonella 0.000043 Increase Dialister 0.000057 Increase Atopobium 0.00006 Increase Selenomonas 0.00011 Increase Actinomyces 0.00016 Increase Granulicatella 0.00024 Increase Fusobacterium 0.00032 Increase Peptostreptococcus 0.00081 Increase Haemophilus 0.0022 Increase TG5 0.0029 Increase Rothia 0.0061 Increase Gemella 0.0067 Increase Erwinia 0.0083 Decrease Slackia 0.016 Increase Leptotrichia 0.017 Increase Neisseria 0.021 Increase Unclassified Increase Aeromonadaceae 0.024 Unclassified Decrease Enterobacteriaceae 0.024 Moryella 0.024 Increase Klebsiella 0.026 Decrease Bifidobacterium 0.037 Decrease Actinobacillus 0.051 Increase Megasphaera 0.071 Increase Unclassified Increase Coriobacteriaceae 0.2 Helicobacter 0.2 Decrease Coprococcus 0.2 Increase Enterococcus 0.2 Decrease Cetobacterium 0.2 Increase Unclassified Increase Veillonellaceae 0.21 Bacteroides 0.25 Decrease Unclassified Decrease Gammaproteobacteria 0.29 Unclassified Decrease Lachnospiraceae 0.29 Morganella 0.29 Increase Akkermansia 0.29 Increase Lactobacillus 0.31 Decrease Unclassified Decrease Victivallaceae 0.32 304

OTU FDR Compared to Device p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 0.00006 Increased p__Actinobacteria__g__Actinomyces_4350499 0.00006 Increased p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU379777 0.00008 Increased p__Bacteroidetes__g__Prevotella__s__nigrescens_2195 0.00017 Increased p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 0.00017 Increased p__Firmicutes__g__Veillonella_561537 0.00017 Increased p__Firmicutes__g__Streptococcus_349024 0.00017 Increased p__Firmicutes__g__Streptococcus__s__infantis_517754 0.00017 Increased p__Actinobacteria__g__Actinomyces_565136 0.0002 Increased p__Firmicutes__g__Veillonella_518743 0.00024 Increased p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.00034 Increased p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0.00071 Increased p__Proteobacteria__g__Haemophilus__s__parainfluenzae_New.Reference OTU100511 0.00081 Increased p__Fusobacteria__g__Fusobacterium_34791 0.001 Increased p__Fusobacteria__g__Fusobacterium_545299 0.001 Increased p__Firmicutes__g__Streptococcus_New.ReferenceOTU13477 0.001 Increased p__Firmicutes__g__Selenomonas_295019 0.0013 Increased p__Firmicutes__g__Selenomonas_4297955 0.0047 Increased p__Proteobacteria__g__Klebsiella_786708 0.0068 Decreased p__Proteobacteria__f__Enterobacteriaceae_588216 0.011 Decreased p__Proteobacteria__f__Enterobacteriaceae_819636 0.011 Decreased p__Proteobacteria__f__Enterobacteriaceae_228556 0.014 Decreased p__Actinobacteria__g__Bifidobacterium_4426298 0.022 Decreased p__Proteobacteria__f__Enterobacteriaceae_2016095 0.023 Decreased p__Proteobacteria__f__Enterobacteriaceae_332958 0.026 Decreased p__Proteobacteria__f__Enterobacteriaceae_822899 0.029 Decreased p__Proteobacteria__f__Enterobacteriaceae_510870 0.063 Decreased p__Firmicutes__g__Lactobacillus_340960 0.07 Decreased p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.1 Decreased p__Firmicutes__g__Lactobacillus_539647 0.11 Decreased p__Firmicutes__g__Lactobacillus_533133 0.16 Decreased p__Firmicutes__g__Lactobacillus_586093 0.23 Decreased p__Firmicutes__g__Lactobacillus_823803 0.26 Decreased p__Firmicutes__g__Lactobacillus_563654 0.29 Decreased p__Firmicutes__g__Lactobacillus_574021 0.3 Decreased p__Proteobacteria__c__Gammaproteobacteria_808486 0.35 Decreased p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.35 Decreased p__Firmicutes__g__Lactobacillus_590168 0.38 Decreased p__Firmicutes__g__Lactobacillus_582884 0.49 Increased p__Firmicutes__g__Lactobacillus_331248 0.5 Decreased p__Firmicutes__g__Megasphaera_41128 0.5 Increased p__Firmicutes__g__Lactobacillus_592160 0.51 Increased p__Proteobacteria__f__Enterobacteriaceae_797229 0.51 Decreased p__Bacteroidetes__g__Bacteroides_568118 0.53 Decreased p__Actinobacteria__g__Collinsella__s__aerofaciens_1811927 0.61 Decreased p__Proteobacteria__f__Enterobacteriaceae_823118 0.75 Decreased 305

OTU FDR Compared to Device p__Firmicutes__g__Lactobacillus_354225 0.82 Decreased p__Proteobacteria__f__Enterobacteriaceae_1109844 0.85 Increased p__Firmicutes__g__Lactobacillus_573010 0.98 Decreased p__Actinobacteria__g__Collinsella_365181 0.98 Decreased

306

Table 8S: Full statistical analyses for top mucosal adjacent device surface compared to baseline duodenal biopsy (FDR q assessed by linear regression modelling).

Compared to Phylum FDR q Device Bacteroidetes 0.035 Increase Proteobacteria 0.24 Decrease Firmicutes 0.24 Increase Synergistetes 0.24 Increase Actinobacteria 0.36 Decrease Lentisphaerae 0.36 Decrease Fusobacteria 0.43 Decrease Verrucomicrobia 0.95 Unchanged

307

Compared to Genus FDR q Device Streptococcus 0 Increase Prevotella 1.7E-13 Increase Gemella 4.2E-11 Increase Haemophilus 3.5E-06 Increase Rothia 3.5E-06 Increase Granulicatella 3.5E-06 Increase Atopobium 9.8E-06 Increase Actinomyces 0.000011 Increase Neisseria 0.00012 Increase Porphyromonas 0.00023 Increase Bulleidia 0.00023 Increase Peptostreptococcus 0.0003 Increase Veillonella 0.00043 Increase Unclassified.Enterobacteriaceae 0.00043 Decrease Moryella 0.00086 Increase Klebsiella 0.00089 Decrease Helicobacter 0.0014 Increase Parvimonas 0.005 Increase Unclassified.cGammaproteobacteria 0.005 Decrease Lactobacillus 0.01 Decrease Erwinia 0.033 Decrease Actinobacillus 0.035 Increase Bifidobacterium 0.045 Decrease Dialister 0.091 Decrease Morganella 0.11 Decrease Unclassified.Aeromonadaceae 0.13 Decrease TG5 0.18 Increase Acidaminococcus 0.18 Increase Collinsella 0.21 Decrease Megasphaera 0.22 Decrease Aggregatibacter 0.25 Increase Enterococcus 0.26 Decrease Slackia 0.27 Decrease Bacteroides 0.29 Decrease Selenomonas 0.37 Decrease Unclassified.Coriobacteriaceae 0.41 Decrease Unclassified.Lachnospiraceae 0.56 Decrease Cetobacterium 0.6 Decrease Leptotrichia 0.64 Decrease Fusobacterium 0.64 Decrease Unclassified.Veillonellaceae 0.71 Increase Akkermansia 0.97 Unchanged Unclassified.Victivallaceae 1 Unchanged Unclassified.S247 NaN Unchanged Coprococcus NaN Unchanged

308

Compared to OTU FDR q Device p__Firmicutes__g__Streptococcus_New.ReferenceOTU100710 0 Increase p__Firmicutes__g__Streptococcus_New.CleanUp.ReferenceOTU379777 0 Increase p__Firmicutes__g__Streptococcus_New.ReferenceOTU32130 0 Increase p__Firmicutes__g__Streptococcus__s__infantis_517754 6.2E-14 Increase p__Bacteroidetes__g__Prevotella__s__melaninogenica_535359 2.3E-12 Increase p__Firmicutes__g__Streptococcus_349024 8.1E-09 Increase p__Actinobacteria__g__Actinomyces_565136 1E-07 Increase p__Actinobacteria__g__Actinomyces_4350499 1.1E-06 Increase p__Proteobacteria__g__Haemophilus__s__parainfluenzae_ New.ReferenceOTU100511 1.6E-06 Increase p__Proteobacteria__f__Enterobacteriaceae_823118 0.000035 Decrease p__Bacteroidetes__g__Prevotella__s__nigrescens_2195 0.000095 Increase p__Proteobacteria__f__Enterobacteriaceae_819636 0.00022 Decrease p__Firmicutes__g__Veillonella_518743 0.00046 Increase p__Firmicutes__g__Streptococcus_New.ReferenceOTU13477 0.00075 Increase p__Proteobacteria__f__Enterobacteriaceae_228556 0.00097 Decrease p__Proteobacteria__f__Enterobacteriaceae_822899 0.00097 Decrease p__Proteobacteria__f__Enterobacteriaceae_2016095 0.001 Decrease p__Proteobacteria__f__Enterobacteriaceae_332958 0.0011 Decrease p__Proteobacteria__g__Klebsiella_New.ReferenceOTU102659 0.0013 Decrease p__Fusobacteria__g__Fusobacterium_545299 0.0018 Increase p__Proteobacteria__g__Klebsiella_786708 0.0029 Decrease p__Proteobacteria__g__Klebsiella_New.ReferenceOTU14298 0.0036 Decrease p__Proteobacteria__c__Gammaproteobacteria_808486 0.0048 Decrease p__Firmicutes__g__Lactobacillus_592160 0.013 Decrease p__Firmicutes__g__Lactobacillus_331248 0.024 Decrease p__Proteobacteria__f__Enterobacteriaceae_510870 0.032 Decrease p__Firmicutes__g__Lactobacillus_590168 0.032 Decrease p__Firmicutes__g__Lactobacillus_533133 0.032 Decrease p__Firmicutes__g__Lactobacillus_539647 0.033 Decrease p__Firmicutes__g__Veillonella_561537 0.033 Increase p__Firmicutes__g__Lactobacillus_574021 0.034 Decrease p__Bacteroidetes__g__Prevotella__s__tannerae_38227 0.038 Increase p__Firmicutes__g__Lactobacillus_563654 0.038 Decrease p__Proteobacteria__f__Enterobacteriaceae_588216 0.038 Decrease p__Firmicutes__g__Lactobacillus_340960 0.043 Decrease p__Actinobacteria__g__Bifidobacterium_4426298 0.044 Decrease p__Firmicutes__g__Lactobacillus_582884 0.083 Decrease p__Firmicutes__g__Lactobacillus_573010 0.083 Decrease p__Fusobacteria__g__Fusobacterium_34791 0.083 Decrease p__Firmicutes__g__Selenomonas_4297955 0.12 Decrease p__Firmicutes__g__Megasphaera_41128 0.12 Decrease p__Actinobacteria__g__Collinsella_365181 0.14 Decrease p__Actinobacteria__g__Collinsella__s__aerofaciens_1811927 0.17 Decrease p__Proteobacteria__f__Enterobacteriaceae_797229 0.18 Decrease p__Firmicutes__g__Lactobacillus_354225 0.19 Decrease p__Firmicutes__g__Lactobacillus_586093 0.24 Decrease 309

Compared to OTU FDR q Device p__Firmicutes__g__Lactobacillus_823803 0.32 Decrease p__Proteobacteria__f__Enterobacteriaceae_1109844 0.39 Decrease p__Firmicutes__g__Selenomonas_295019 0.45 Decrease p__Bacteroidetes__g__Bacteroides_568118 0.45 Decrease

310

Table 9S: Full statistical analyses for eight weeks post-implant stool samples compared to baseline (FDR q assessed by linear regression modelling).

Phylum FDR q Change from Baseline Fusobacteria 0.0041 Increased Proteobacteria 0.0041 Increased Unclassified Bacteria 0.053 Increased Bacteroidetes 0.12 Decreased Verrucomicrobia 0.34 Increased Synergistetes 0.34 Increased Lentisphaerae 0.34 Increased Actinobacteria 0.68 Decreased Firmicutes 0.68 Increased Euryarchaeota 0.83 Decreased

311

FDR q Compared to FDR q Compared to Genus Baseline Genus Baseline 0.0000 Increased Adlercreutzia 0.54 Increased Citrobacter 24 Dorea 0.54 Decreased 0.0000 Increased Decreased Enterococcus 24 Ruminococcus 0.57 Increased Bifidobacterium 0.0025 Increased Dialister 0.61 Unclassified Increased Methanobrevibacter 0.89 Decreased Clostridiaceae 0.0025 Catenibacterium 0.9 Increased Fusobacterium 0.0025 Increased Megasphaera 0.91 Decreased Lactobacillus 0.0025 Increased Acidaminococcus 0.93 Increased Clostridium 0.0025 Increased Cetobacterium NaN Unchanged Serratia 0.0037 Increased Aggregatibacter NaN Unchanged Acinetobacter 0.0059 Increased Unclassified Decreased Lachnospiraceae 0.0086 Unclassified Increased Enterobacteriaceae 0.013 Klebsiella 0.013 Increased Veillonella 0.019 Increased Unclassified Increased Gammaproteobacteria 0.025 Unclassified Bacteria 0.048 Increased Erwinia 0.048 Increased Streptococcus 0.059 Increased Unclassified Decreased Clostridiales 0.088 Blautia 0.088 Decreased Sutterella 0.15 Decreased Coprococcus 0.16 Increased Selenomonas 0.16 Increased Unclassified Decreased Erysipelotrichaceae 0.16 Unclassified Decreased Coriobacteriaceae 0.16 Unclassified Increased Aeromonadaceae 0.16 Parvimonas 0.16 Increased Bacteroides 0.2 Decreased Faecalibacterium 0.22 Decreased Proteus 0.22 Increased Collinsella 0.26 Decreased Parabacteroides 0.29 Decreased Akkermansia 0.29 Increased Unclassified Decreased Ruminococcaceae 0.32 TG5 0.32 Increased Unclassified Increased Victivallaceae 0.32 Dysgonomonas 0.32 Increased Eubacterium 0.39 Decreased Unclassified.S247 0.39 Decreased Prevotella 0.39 Decreased Leptotrichia 0.53 Unchanged 312

OTU FDR q Compared to baseline p__Proteobacteria__g__Citrobacter_786708 0.00005 Increased p__Proteobacteria__f__Enterobacteriaceae_819636 0.0006 Increased p__Actinobacteria__g__Bifidobacterium_4426298 0.003 Increased p__Firmicutes__g__Lactobacillus_563654 0.0032 Increased p__Fusobacteria__g__Fusobacterium_545299 0.0033 Increased p__Firmicutes__g__Lactobacillus_582884 0.0036 Increased p__Firmicutes__g__Lactobacillus_329402 0.0036 Increased p__Firmicutes__g__Lactobacillus_549756 0.0036 Increased p__Firmicutes__f__Lachnospiraceae_New.CleanUp.ReferenceOTU93368 0.0041 Decreased p__Firmicutes__f__Lachnospiraceae_New.ReferenceOTU100192 0.0041 Decreased p__Proteobacteria__f__Enterobacteriaceae_New.ReferenceOTU100973 0.0041 Increased p__Firmicutes__g__Lactobacillus_New.CleanUp.ReferenceOTU388917 0.0079 Increased p__Firmicutes__g__Lactobacillus_586093 0.0088 Increased p__Proteobacteria__f__Enterobacteriaceae_823118 0.0093 Increased p__Firmicutes__g__Veillonella__s__parvula_518743 0.0093 Increased p__Proteobacteria__g__Klebsiella_822899 0.011 Increased p__Firmicutes__g__Lactobacillus_539647 0.011 Increased p__Firmicutes__g__Lactobacillus_590168 0.014 Increased p__Firmicutes__f__Lachnospiraceae_369709 0.014 Decreased p__Firmicutes__o__Clostridiales_470382 0.016 Decreased p__Firmicutes__g__Lactobacillus_725198 0.017 Increased p__Proteobacteria__f__Enterobacteriaceae_812379 0.02 Increased p__Firmicutes__g__Streptococcus_516966 0.02 Increased p__Firmicutes__g__Lactobacillus_340960 0.02 Increased p__Firmicutes__g__Lactobacillus_New.ReferenceOTU100589 0.02 Increased p__Firmicutes__g__Veillonella__s__parvula_4352537 0.021 Increased p__Firmicutes__f__Lachnospiraceae_New.CleanUp.ReferenceOTU1164950 0.028 Decreased p__Firmicutes__g__Lactobacillus_823803 0.043 Increased p__Firmicutes__g__Lactobacillus_331248 0.043 Increased p__Firmicutes__g__Veillonella_2283862 0.052 Increased p__Bacteroidetes__g__Bacteroides_323231 0.069 Decreased p__Firmicutes__g__Megasphaera_41128 0.072 Increased p__Firmicutes__g__Lactobacillus_354225 0.073 Increased p__Firmicutes__f__Lachnospiraceae_New.ReferenceOTU100062 0.085 Decreased p__Firmicutes__f__Ruminococcaceae_New.CleanUp.ReferenceOTU926888 0.097 Decreased p__Bacteroidetes__g__Bacteroides_568118 0.099 Decreased p__Firmicutes__g__Lactobacillus_581474 0.099 Increased p__Actinobacteria__f__Coriobacteriaceae_196140 0.12 Decreased p__Actinobacteria__g__Collinsella_193575 0.13 Decreased p__Firmicutes__g__Lactobacillus_354971 0.21 Increased p__Firmicutes__g__Blautia_532203 0.22 Decreased p__Bacteroidetes__g__Parabacteroides__s__distasonis_577294 0.23 Decreased p__Firmicutes__g__Blautia_New.CleanUp.ReferenceOTU30692 0.23 Decreased p__Firmicutes__g__Blautia_367790 0.26 Decreased p__Proteobacteria__f__Enterobacteriaceae_588216 0.27 Increased p__Actinobacteria__g__Collinsella_365181 0.27 Decreased p__Proteobacteria__f__Enterobacteriaceae_797229 0.47 Increased 313

OTU FDR q Compared to baseline p__Bacteroidetes__g__Bacteroides_326626 0.62 Decreased p__Firmicutes__g__Ruminococcus_333096 0.68 Decreased p__Bacteroidetes__g__Bacteroides_364029 0.73 Decreased

314

Table 10S: Full statistical analyses for eight weeks post-implant stool samples compared to explant (FDR q assessed by linear regression modelling).

Phylum FDR q Change from 8 weeks Fusobacteria 0.11 Decreased Unclassified Bacteria 0.11 Decreased Bacteroidetes 0.11 Decreased Proteobacteria 0.11 Decreased Verrucomicrobia 0.15 Decreased Euryarchaeota 0.34 Increased Firmicutes 0.34 Increased Lentisphaerae 0.42 Decreased Actinobacteria 0.42 Increased Synergistetes 0.42 Increased

315

FDR q Compared FDR q Compared Genus to 8 weeks Genus to 8 weeks Enterococcus 0.0098 Decreased Parabacteroides 0.43 Decreased Lactobacillus 0.017 Decreased Methanobrevibacter 0.43 Increased Bifidobacterium 0.018 Decreased Acidaminococcus 0.44 Decreased Fusobacterium 0.031 Decreased Collinsella 0.44 Increased Unclassified Decreased Klebsiella 0.51 Decreased Enterobacteriaceae 0.044 Catenibacterium 0.52 Decreased Citrobacter 0.072 Decreased Adlercreutzia 0.52 Increased Veillonella 0.084 Decreased Dialister 0.52 Decreased Unclassified Decreased Unclassified Decreased Gammaproteobacteria 0.085 Decreased Victivallaceae 0.52 Erwinia 0.085 Decreased Unclassified Increased Clostridium 0.54 Lachnospiraceae 0.085 Leptotrichia 0.54 Increased Unclassified Increased Unclassified.S247 0.54 Decreased Ruminococcaceae 0.085 TG5 0.56 Increased Unclassified Bacteria 0.13 Decreased Eubacterium 0.64 Decreased Acinetobacter 0.14 Decreased Bacteroides 0.64 Decreased Serratia 0.16 Decreased Dorea 0.64 Decreased Unclassified Increased Unclassified Decreased Erysipelotrichaceae 0.21 Clostridiaceae 0.65 Megasphaera 0.21 Decreased Faecalibacterium 0.75 Increased Akkermansia 0.21 Decreased Streptococcus 0.77 Increased Decreased Selenomonas 0.22 Unclassified Increased Unclassified Clostridiales 0.26 Increased Coriobacteriaceae 0.77 Unclassified Decreased Coprococcus 0.78 Decreased Aeromonadaceae 0.4 Sutterella 0.84 Increased Parvimonas 0.4 Decreased Prevotella 0.87 Decreased Ruminococcus 0.43 Increased Dysgonomonas 0.97 Increased Proteus 0.43 Decreased Cetobacterium 1 Increased Blautia 0.43 Increased Aggregatibacter NaN Unchanged

316

Table 11S: KEGG ortholog pathways that were significantly different between the gastric biopsies of DJBS patients at explant, compared to control patients.

Pathways that were significantly Pathways that were significantly higher in DJBS at explant FDR q higher in DJBS at explant FDR q Other transporters 0.00057 Chagas disease American trypanosomiasis. 0.0069 Pertussis 0.00057 Biosynthesis of unsaturated fatty Signal transduction mechanisms 0.0077 acids 0.00057 Butanoate metabolism 0.0079 Bile secretion 0.00057 Glutathione metabolism 0.009 Phenylalanine metabolism 0.00057 Alpha-linolenic acid metabolism 0.0099 Transcription related proteins 0.00057 Flagellar assembly 0.011 Prion diseases 0.00057 Membrane and intracellular structural molecules 0.014 Geraniol degradation 0.00057 Biosynthesis and biodegradation of Carotenoid biosynthesis 0.00067 secondary metabolites 0.017 Shigellosis 0.00067 Two component system 0.018 Fluorobenzoate degradation 0.00067 Chloroalkane and chloroalkene degradation 0.021 Other ion-coupled transporters 0.00067 Others 0.021 Lipid metabolism 0.00074 Energy metabolism 0.022 Beta-alanine metabolism 0.00088 Ascorbate and aldarate metabolism 0.023 Vibrio cholerae infection 0.00095 Bacterial motility proteins 0.024 Metabolism of cofactors and vitamins 0.001 Carbohydrate metabolism 0.024 Caprolactam degradation 0.001 Starch and sucrose metabolism 0.026 Pores ion channels 0.0014 Styrene degradation 0.028 Amino acid metabolism 0.0014 Vibrio cholerae pathogenic cycle 0.029 Fatty acid metabolism 0.0014 Tropane piperidine and pyridine Pentose and glucoronate alkaloid biosynthesis 0.029 interconversions 0.0015 Lysine degradation 0.031 Function unknown 0.0015 Bacterial chemotaxis 0.031 Secondary bile acid biosynthesis 0.0015 Electron transfer carriers 0.033 Primary bile acid biosynthesis 0.0015 Protein kinases 0.034 Phenylpropanoid biosynthesis 0.0015 Chlorocyclohexane and Isoquinoline alkaloid biosynthesis 0.034 chlorobenzene degradation 0.0015 Inositol phosphate metabolism 0.037 Ethylbenzene degradation 0.0024 Atrazine degradation 0.039 Bacterial invasion of epithelial cells 0.0024 Glyoxylate and dicarboxylate metabolism 0.043 Tyrosine metabolism 0.0036 Valine leucine and isoleucine Glycan biosynthesis and metabolism 0.0037 degradation 0.044 Drug metabolism – cytochrome P450 0.0037 Lipopolysaccharide biosynthesis Limonene and pinene degradation 0.0037 proteins 0.044 Retinol metabolism 0.0037 Tryptophan metabolism 0.047 Metabolism of xenobiotics by Cyanoamino acid metabolism 0.049 cytochrome P450 0.0037 Transcription factors 0.0037 Nucleotide metabolism 0.0037 Biosynthesis of siderophore group nonribosomal peptides 0.004 Toluene degradation 0.0057 Inorganic ion transport and metabolism 0.0057 African trypanosomiasis 0.0063 Aminobenzoate degradation 0.0064 317

Pathways that were significantly lower Pathways that were significantly lower in DJBS at explant FDR q in DJBS at explant FDR q Protein export 0.00057 Purine metabolism 0.014 Bacterial toxins 0.00057 Flavone and flavonol biosynthesis 0.023 Phenylalanine tyrosine and tryptophan Penicillin and cephalosporin biosynthesis 0.024 biosynthesis 0.00057 Polycyclic aromatic hydrocarbon Selenocompound metabolism 0.029 degradation 0.00057 Prenyltransferases 0.029 Phosphatidylinositol signalling system 0.00067 Basal transcription factors 0.031 Aminoacyl tRNA biosynthesis 0.00067 Sporulation 0.034 Ribosome biogenesis in eukaryotes 0.00067 Translation proteins 0.037 Pantothenate and CoA biosynthesis 0.00069 Protein folding and associated processing 0.041 Lysine biosynthesis 0.00093 Zeatin biosynthesis 0.045 Photosynthesis 0.001 Photosynthesis proteins 0.001 Nucleotide excision repair 0.0011 Type 1 diabetes mellitus 0.0012 RNA polymerase 0.0012 Folate biosynthesis 0.0012 Restriction enzyme 0.0014 Base excision repair 0.0016 Ribosome 0.0019 Thiamine metabolism 0.002 Meiosis – yeast 0.0021 DNA replication 0.0023 Taurine and hypotaurine metabolism 0.0024 Primary immunodeficiency 0.003 Ribosome Biogenesis 0.003 Mismatch repair 0.0032 Amino acid related enzymes 0.0032 Ether lipid metabolism 0.0037 Terpenoid backbone biosynthesis 0.0037 D-alanine metabolism 0.0037 Homologous recombination 0.0037 DNA replication proteins 0.0037 Cysteine and methionine metabolism 0.0043 D-glutamine and D-glutamate metabolism 0.0049 Amoebiasis 0.0052 Cell cycle – Caulobacter 0.0064 Butirosin and neomycin biosynthesis 0.0064 Pyrimidine metabolism 0.0074 Translation factors 0.0092 Apoptosis 0.011 DNA repair and recombination proteins 0.011 RNA degradation 0.011 Tuberculosis 0.012 Alzheimer’s disease 0.013 Peptidoglycan biosynthesis 0.013

318

Table 12S: KEGG ortholog pathways that were significantly different between the duodenal biopsies of DJBS patients at explant, compared to control patients.

Pathways that were significantly Pathways that were significantly higher in DJBS at explant FDR q higher in DJBS at explant FDR q Shigellosis 0.000074 Synthesis and degradation of ketone bodies 0.0014 Bacterial motility proteins 0.0001 Pertussis 0.0014 Vibrio cholerae infection 0.0001 Prion diseases 0.0019 Flagellar assembly 0.0001 Tryptophan metabolism 0.002 Bacterial chemotaxis 0.0002 Benzoate degradation 0.0027 Alpha linolenic acid metabolism 0.00032 Ethylbenzene degradation 0.0032 Fluorobenzoate degradation 0.00033 Butanoate metabolism 0.0037 Tyrosine metabolism 0.00033 Cyanoamino acid metabolism 0.0039 Metabolism of cofactors and vitamins 0.00033 Signal transduction mechanisms 0.0041 Lipid metabolism 0.00033 Biosynthesis of unsaturated fatty acids 0.0041 Styrene degradation 0.00035 Retinol metabolism 0.0042 Other transporters 0.00043 Bacterial secretion system 0.0042 Transcription related proteins 0.00047 Bacterial invasion of epithelial cells 0.0048 Bile secretion 0.00047 Glycan biosynthesis and metabolism 0.0049 Secondary bile acid biosynthesis 0.00047 Geraniol degradation 0.0049 Primary bile acid biosynthesis 0.00047 Amino acid metabolism 0.0052 Phenylalanine metabolism 0.00047 Sulfur relay system 0.0071 Flavonoid biosynthesis 0.00067 Nucleotide metabolism 0.0088 Caprolactam degradation 0.00067 Glyoxylate and dicarboxylate Chlorocyclohexane and chlorobenzene metabolism 0.009 degradation 0.00067 Inorganic ion transport and metabolism 0.0092 Fatty acid metabolism 0.00067 Glutathione metabolism 0.0098 Other ion-coupled transporters 0.00067 Naphthalene degradation 0.01 Carotenoid biosynthesis 0.00067 Vibrio cholerae pathogenic cycle 0.011 Atrazine degradation 0.00067 X 1.1.1-trichloro-2-2-bis (4- Metabolism of xenobiotics by chlorophenyl) ethane (DDT) cytochrome P450 0.00067 degradation 0.015 Biosynthesis and biodegradation of secondary metabolites 0.00067 Phosphotransferase system - PTS. 0.015 Phenylpropanoid biosynthesis 0.00069 Lysine degradation 0.016 Drug metabolism – cytochrome P450 0.00073 ABC transporters 0.019 Replication, recombination and repair Others 0.022 proteins 0.00077 Nitrotoluene degradation 0.022 Pentose and glucoronate Biosynthesis of siderophore group interconversions 0.0008 nonribosomal peptides 0.023 Two component system 0.00082 Beta-alanine metabolism 0.028 Propanoate metabolism 0.00082 Ascorbate and aldarate metabolism 0.028 Transcription factors 0.0009 Arginine and proline metabolism 0.03 Limonene and pinene degradation 0.001 Parkinson’s disease 0.038 Electron transfer carriers 0.0012 Cardiac muscle contraction 0.038 Secretion system 0.0013 Aminobenzoate degradation 0.038 Chloroalkane and chloroalkene degradation 0.0013 Bisphenol degradation 0.043 Function unknown 0.0013

319

Pathways that were significantly Pathways that were significantly lower in DJBS at explant FDR q lower in DJBS at explant FDR q Folate biosynthesis 0.000048 Other glycan degradation 0.0032 Pantothenate and CoA biosynthesis 0.000048 Zeatin biosynthesis 0.0037 Ribosome Biogenesis 0.000048 Lipid biosynthesis proteins 0.0037 Phenylalanine tyrosine and tryptophan Cysteine and methionine metabolism 0.0041 biosynthesis 0.000048 Prenyltransferases 0.0042 Protein export 0.0001 Photosynthesis 0.0042 Homologous recombination 0.00013 Type 1 diabetes mellitus 0.0046 Ribosome biogenesis in eukaryotes 0.00019 Glycosphingolipid biosynthesis – DNA replication 0.00043 ganglio series 0.0048 Drug metabolism – other enzymes 0.00047 Proteasome 0.0049 Peptidoglycan biosynthesis 0.00052 Cell cycle - Caulobacter 0.0049 Primary immunodeficiency 0.00052 Photosynthesis proteins 0.005 One carbon pool by folate 0.00057 Phosphatidylinositol signalling system 0.0054 Translation proteins 0.00067 Nicotinate and nicotinamide metabolism 0.0054 DNA replication proteins 0.00067 Valine leucine and isoleucine Peroxisome 0.00067 biosynthesis 0.0064 Meiosis - yeast 0.00067 Translation factors 0.0064 Ribosome 0.00067 Terpenoid backbone biosynthesis 0.0067 Glycine serine and threonine Selenocompound metabolism 0.0067 metabolism 0.00067 RNA polymerase 0.0091 Lysosome 0.00069 Apoptosis 0.0099 DNA repair and recombination proteins 0.00075 Streptomycin biosynthesis 0.01 Adipocytokine signalling pathway 0.00075 Polyketide sugar unit biosynthesis 0.013 Amino acid related enzymes 0.00077 D-glutamine and D-glutamate RNA degradation 0.00085 metabolism 0.013 Bacterial toxins 0.00088 MAPK signalling pathway - yeast 0.013 Glycosphingolipid biosynthesis – globo Aminoacyl tRNA biosynthesis 0.013 series 0.00097 Vitamin B6 metabolism 0.013 Purine metabolism 0.001 Amoebiasis 0.015 Base excision repair 0.0012 Sphingolipid metabolism 0.016 Nucleotide excision repair 0.0012 Protein folding and associated Lysine biosynthesis 0.0014 processing 0.016 N-glycan biosynthesis 0.0014 Citrate cycle TCA cycle. 0.016 Mismatch repair 0.0014 Ubiquitin system 0.02 PPAR signalling pathway 0.0016 Penicillin and cephalosporin biosynthesis 0.02 Pyrimidine metabolism 0.0017 Alzheimer’s disease 0.028 Glycosaminoglycan degradation 0.0018 Polycyclic aromatic hydrocarbon D-alanine metabolism 0.033 degradation 0.0019 Protein digestion and absorption 0.037 Tuberculosis 0.0019 Ether lipid metabolism 0.037 Thiamine metabolism 0.0021 Insulin signalling pathway 0.047 Restriction enzyme 0.0032 Chromosome 0.047

320