Glucose metabolism in South Asian and Nordic women with previous gestational diabetes mellitus

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Glucose metabolism in

South Asian and Nordic women

with previous gestational diabetes mellitus

Archana Sharma

Department of Endocrinology, Division of Medicine Akershus University Hospital

and

Institute of Clinical Medicine University of Oslo

2022

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© Archana Sharma, 2023

Series of dissertations submitted to the Faculty of Medicine, University of Oslo

ISBN 978-82-348-140-2

reproduced or transmitted, in any form or by any means, without permission.

Print production: Graphics Center, University of Oslo.

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FOREWORD

One of my leading lights, Mahatma Gandhi (1869-1948) once said, ‘You may never know what results come of your actions, but if you do nothing, there will be no result’.

This quotation highlights my main goal with the PhD dissertation, to be able to contribute back to the community. However, besides this good-hearted mission, I was also excited to learn more about underlying mechanisms behind diabetes mellitus, statistics, and not to mention my greedy interest in biological reasons for South Asian populations’ higher risk of diabetes (due to my South Asian origin).

And being aware of that this study could mean the end of my weekly consume of Nugatti (a breath-taking Norwegian chocolate spread), I nervously signed the DIAbetes in South Asian (DIASA) 1 project in 2018. But honestly speaking, this research period has made my career path spicier and more fascinating, and I am so happy I did not missed this opportunity!

ACKNOWLEDGEMENTS

Several persons and institutions deserve my sincere gratitude for supporting me throughout this research project and writing the dissertation.

First, I would like to dedicate this dissertation to the memory of my first principal supervisor, PhD Cecilie Wium, who introduced me to this project, but sadly passed away shortly upon study completion. You conceptualised the design, wrote the protocol, obtained the funding, critically revised the first manuscript, and enthusiastically participated in the study inclusion.

And when you got seriously ill in 2019, I have to admit that I thought that this research would be hard to complete. However, you proved me completely wrong! First, you assured me that this project has to go on, then you organised a new principal supervisor for me, and finally, you were still there, although being ill or admitted, you still acted as a supervisor reading systematically through my work, still acted as a colleague, and were one of the most

important drivers for the study. I actually do not think any words can express my feelings and gratitude to you! You will be deeply missed by your colleagues, your patients, your family, and your friends.

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Second, I am eternally grateful to my next principal supervisor, professor Kåre I. Birkeland, who despite his busy days and the circumstances, said yes to become the principal supervisor for the DIASA 1 study. Through your experience, both as a highly recognised diabetes professional and as a researcher, I have grown both as an endocrinologist and as a scientist. I sincerely appreciate your support, our constructive discussions, and your quick responses to my e-mails. I am deeply thankful.

Then, I would like to express a special thanks to my co-supervisors:

Associate professor Ingrid Nermoen, my former boss, who encouraged me to do this research. Without you, I would never had embarked on this PhD programme. Thank you for revising the manuscripts, for your support and your friendship.

PhD. Stina T. Sollid, my colleague, who did an invaluable work in the recruiting process at Vestre Viken Hospital Trust, Drammen, revised the manuscripts and always supported me.

PhD Cecilie Varsi, who guided me through the focus group study. Your positive appearance and constructive criticism of the qualitative design, the conduction and the manuscript made me realise the value of qualitative studies.

I would also like to pay my appreciation to several highly skilled professionals to whom I have been fortunate to work with:

Associate professor Elisabeth Qvigstad, for facilitating the study at Oslo University Hospital Aker, revising the manuscripts, and for the invaluable discussion through your competence in diabetes and pregnancies.

PhD Ahn Thi Tran, for critically revising the manuscripts through concise formulations, and for your competence in ethnicity and diabetes.

PhD. Hanne L. Gulseth, for constructive responses on study designs, revision of the manuscripts, and for your competence in diabetes research.

PhD Christine Sommer, for constructive revision of the manuscripts, and for developing the web-based food frequency questionnaire together with associate professor, Monica H

Carlsen. You both have always been so responsive.

PhD Sindre Lee-Ødegård, for your enthusiastic attitude, and invaluable inputs on hepatic insulin clearance, and not to forget your inspiring figures!

Professor Naveed Sattar and professor, Jason M. R. Gill, at University of Glasgow, have trough their extensive experience in the field of diabetes, and valuable comments on ethnicity and glycated haemoglobin really inspired me. I sincerely appreciate your support.

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I would also like to thank the women who participated in the DIASA 1 study, the Clinical Research Unit at Vestre Viken Hospital Trust, Drammen, Oslo University Hospital (Aker), and Akershus University Hospital, especially the study nurses Karin Pleym, Åse Halsne, Jesini Anurathan and Åshild Stavik, and study coordinator Ellen Hillestad for invaluable help in the recruitment process, and in the examination of the participants. Your enthusiasm and devotion made this research possible. I am special grateful to study nurse Åshild Stavik, who conducted more than half of the study visits. Your patience and optimism really eased my work more than I could ever imagine.

I would also like to thank the statistician, Ragnhild S. Falk, who made me come up with interesting results, and convey a special gratitude to the librarians, who were my roommates and really supported me when things were moving in the wrong directions, especially Åse- Marit Hammersbøen, who additionally taught me a lot about PowerPoint presentations, and EndNote.

I also deeply grateful to the funding from the Research Council of Norway and Akershus University Hospital, who gave me this opportunity for research time and funding at

congresses. Likewise, I would like to emphasise my gratitude to the University of Oslo, who accepted me as a PhD student, and provided me with a bunch of fruitful courses.

Finally, I would like to thank my grandmother, Savitri Devi, my parents, Jugal K. Sharma and Usha R. Sharma, my siblings, Kapil K. Sharma and Varun K. Sharma, my sister-in- law, Geeta Gulati, and my lovely niece, Kartika Sharma, for always standing with me when I needed you most. Without you, I would not have been able to stand here today!

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ABBREVATIONS

ADA American Diabetes Association

AUC Area under the curve

BMI Body mass index

CI Confidence interval

DIASA study DIAbetes in South Asians study

FPG Fasting plasma glucose

FSI Fasting serum insulin

GDM Gestational diabetes mellitus

GWG Gestational weight gain

HbA1c Glycated haemoglobin

HGP Hepatic glucose production

HOMA2-B Homeostatic model assessment2-beta cell

function

HOMA2-S Homeostatic model assessment2-sensitivty

IADPSG International Association of Diabetes and

Pregnancy Study Groups

IFG Impaired fasting glucose

ISI Insulin sensitivity index

NDDG National Diabetes Data Group

OR Odds ratio

OGTT Oral Glucose Tolerance Test

REC Regional Ethical Committee

SD Standard deviation

SLB Street-level bureaucrats

T2D Type 2 diabetes mellitus

WHtR Waist-to-height ratio

WHO World Health Organisation

WHO-IEC World Health Organisation-International

Expert Committee

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SUMMARY

Introduction

The risk of type 2 diabetes (T2D) after gestational diabetes mellitus (GDM) is twice as high in South Asian than in European women. Additionally, South Asian women seem to develop T2D at an earlier age and at a lower body mass index. Notwithstanding this risk, a sizeable proportion of women with GDM do not attend the recommended screening after their delivery, hence miss the opportunity to mitigate their trajectory towards T2D development.

Aims

In this dissertation, we aimed to examine South Asian and Nordic women with previous GDM to (i) advance a deeper knowledge about behavioural mechanisms behind suboptimal follow- up after delivery, (ii) characterise ethnic differences in prevalence rates and early predictors of prediabetes and T2D in Norway, and (iii) understand underlying mechanisms behind South Asian women’s higher risk of T2D short time after delivery.

Material and Methods

In the DIAbetes in South Asians (DIASA) 1 cross-sectional study, we examined 179 South Asian and 108 Nordic women in Norway with an oral glucose tolerance test (OGTT) 1-3 years after a GDM pregnancy. A subgroup of these women were invited to focus group interviews at a separate date, and a theoretical approach was used as an inspiration for thematic data analysis, and to discuss our findings. International accepted diagnostic criteria for diabetes and different diagnostic criteria for prediabetes were used to investigate the prevalence rates of these conditions based on glycated haemoglobin (HbA1c) with and without OGTT measurements. We examined insulin sensitivity, insulin secretion and beta cell

function, included disposition index, by different indexes, and applied deconvolution of C- peptide kinetics to estimate pre-hepatic insulin levels and hepatic insulin clearance. Through regression analyses, we examined the covariate(s) that could mediate the ethnic differences in the estimated indexes.

Results

Through the focus group interviews, we identified five themes that were important for women’s suboptimal follow-ups after GDM. First, all participants accentuated lack of resilience and positive attitude as important factors. Second, emotional distress linked to the

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experience of living with GDM diagnosis was emphasised. Third, a feeling of being ‘caught between a rock and a hard place’ due to diverging expectations from the healthcare providers and family was debated. This feeling maintained motivational barriers such as denial of the diabetes diagnosis and priority of other obligations. Fourth, women expressed a major concern related to the sudden reduction in the frequency of follow-up visits after delivery, perceived as a postpartum abandonment. Finally, lack of tailored information about women’s future diabetes risk was highlighted as an important factor behind women's low adherence to follow-ups after GDM.

We found high prevalence and significant ethnic differences in the proportion of South Asian and Nordic women with prediabetes or diabetes assessed together. By using only HbA1c as a diagnostic marker and applying the World Health Organisation-International Expert

Committee (WHO-IEC) diagnostic criteria, the prevalence rates were 25.8% in South Asian and 6.5% in Nordic women (p ≤0.001). Likewise, when applying the American Diabetes Association (ADA) diagnostic criteria the rates were 58.3% and 22.2%, respectively (p

≤0.001). Further, when combining HbA1c and OGTT measurements the prevalence rates were higher: WHO-IEC criteria 70.6% and 47.2% (p ≤0.001), ADA criteria 87.8% and 65.7% (p

≤0.001). In addition to South Asian ethnicity, GDM before the index pregnancy, the use of glucose-lowering drugs in pregnancy, a higher age, and higher in-pregnancy fasting glucose levels were important risk factors for prediabetes or diabetes after delivery.

In the normal glucose tolerant group, South Asian women had higher area under the curve (AUC) for glucose (p<0.01), pre-hepatic insulin (p<0.01) and peripheral insulin (p<0.01), and lower muscle (p<0.01), hepatic (p<0.001) and whole-body insulin sensitivity indexes (p<0.001) than Nordic women. These South Asian women also displayed lower disposition index (p<0.001), and lower fasting hepatic insulin clearance (p<0.001). Waist-to-height ratio mediated 25-40% of the ethnic differences in insulin sensitivity. In the prediabetes or diabetes group, we found no ethnic differences in AUC levels of glucose or pre-hepatic insulin, but higher AUC for peripheral insulin (p<0.01), lower disposition index (p<0.01), and lower hepatic insulin clearance (p<0.001) levels. Importantly, South Asian normoglycaemic women had higher peripheral insulin levels (p=0.036), and lower hepatic insulin clearance (p<0.001) than Nordic women with prediabetes or diabetes.

Conclusions

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In South Asian and Nordic women with GDM 1-3 years after delivery, we found that one in two South Asian and one in five Nordic women had prediabetes or diabetes. The higher risk for T2D among South Asian women may be linked to lower beta cell function, lower fasting hepatic insulin clearance, and/or lower insulin sensitivity. Notably, normoglycaemic South Asian women displayed a phenotype approaching Nordic women with prediabetes or diabetes. The DIASA I study reinforce the need for more organised public healthcare with tailored information to reduce women’s future diabetes risk after GDM, particularly in South Asian women. Our data also imply that ethnic difference in body composition is important for the progression from normoglycaemia to prediabetes or diabetes after GDM.

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SAMMENDRAG

Introduksjon

Risikoen for type 2 diabetes (T2D) etter gjennomgått svangerskapsdiabetes er dobbelt så høy hos sør-asiatiske sammenliknet med europeiske kvinner. De sør-asiatiske kvinnene utvikler i tillegg T2D i en yngre alder, og ved en lavere kroppsmasseindeks. Likevel møter ikke en stor andel av kvinnene med gjennomgått svangerskapsdiabetes til den anbefalte helsekontrollen etter fødsel, og forringer dermed sine muligheter til å forebygge fremtidig diabetes.

Mål

I denne avhandlingen så vi nærmere på sør-asiatiske- og nordiske kvinner med gjennomgått svangerskapsdiabetes med målsetning om å erverve kunnskap om (i) adferdsmessige mekanismer bak kvinners manglende oppmøte til helsekontroller etter fødsel, (ii) etniske forskjeller i forekomst av prediabetes og T2D, og tidlige risikofaktorer for prediabetes og T2D, samt (iii) bakenforliggende mekanismer for sør-asiatiske kvinners økte risiko for T2D etter fødsel.

Materiale og metode

I DIAbetes in South Asians (DIASA) 1 studien, undersøkte vi 179 sør-asiatiske og 108 nordiske kvinner bosatt i Norge med en oral sukkerbelastningstest (OGTT), ett til tre år etter gjennomgått svangerskapsdiabetes. En subgruppe av disse kvinne deltok også i et

fokusgruppeintervju på et senere tidspunkt. I disse intervjuene brukte vi en teoretisk tilnærming som inspirasjon for den tematiske dataanalysen, og for å diskutere våre funn.

Målinger av glykert hemoglobin (HbA1c), alene eller i kombinasjon med glukosemålinger under OGTT, ble brukt til å kartlegge forekomsten av prediabetes og T2D med ulike diagnostiske kriterier. Insulinsensitivitet, insulinsekresjon og betacellefunksjon, inkludert disposisjonsindeks, ble vurdert ved en rekke ulike indekser. Vi brukte C-peptid

dekonvolusjonsteknikk til å estimere pre-hepatisk insulinnivå og hepatisk insulin clearance.

Regresjonsanalyser ble anvendt for å finne variabler som kunne forklare de etniske forskjellene i de ulike indeksene.

Resultater

Ved bruk av fokusgruppeintervju identifiserte vi fem tema, som illustrerte årsaker til kvinners manglende oppmøte til helsekontroll etter gjennomgått svangerskapsdiabetes. Det første

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temaet var mangelen på ‘motstandsdyktighet’ til å håndtere stress og mangelen på en positiv holdning. Det andre temaet understreket emosjonelt stress relatert til det å leve med diagnosen svangerskapsdiabetes. Det tredje temaet avspeilte kvinnenes følelse av å være ‘klemt mellom barken og veden’, grunnet motstridende forventninger fra helsepersonell og familie. Denne følelsen bidro til å opprettholde eksisterende barrierer slik som det å tvile på

diabetesdiagnosen, og det å prioritere andre forpliktelser. Det fjerde temaet tok opp kvinnenes bekymring relatert til manglende oppfølging fra helsevesenet etter fødsel, oppfattet som postpartum forlatthet. Det siste temaet satte søkelyset på mangelen på persontilpasset informasjon om kvinners fremtidige diabetesrisiko, som en viktig årsak til at kvinnene ikke møtte til de anbefalte helsekontrollene etter gjennomgått svangerskapsdiabetes.

Vi fant høy prevalens og signifikant etnisk forskjell i andelen av sør-asiatiske- og nordiske kvinner med prediabetes og diabetes, - både ved bruk av verdens helseorganisasjon-

internasjonal ekspert komiteens (WHO-IEC), og det amerikanske diabetesforbundets (ADA) diagnostiske kriterier (HbA1c: WHO-IEC 25.8% vs. 6.5% (p ≤0.001), ADA 58.3% vs. 22.2%

(p ≤0.001). Kombinert HbA1c og OGTT målinger: WHO-IEC 70.6% vs. 47.2% (p ≤0.001), ADA 87.8% vs. 65.7% (p ≤0.001)). Viktige risikofaktorer for prediabetes eller diabetes etter gjennomgått svangerskapsdiabetes var sør-asiatisk etnisitet, gjennomgått

svangerskapsdiabetes før siste svangerskap, bruk av glukosesenkende medikamenter i svangerskapet, høy alder, og høye fastende glukoseverdier i svangerskapet.

I gruppen med normal OGTT, hadde sør-asiatiske kvinner høyere areal under kurven (AUC) for glukose (p<0.01), pre-hepatisk insulin (p<0.01), and perifert insulin (p<0.01), og lavere muskel- (p<0.01), hepatisk- (p<0.001), og hel-kroppsinsulinsensitivitetsindeks (p<0.001) sammenliknet med nordiske kvinner. Disse sør-asiatiske kvinnene hadde også lavere

disposisjonsindeks (p<0.001), og lavere fastende hepatisk insulin clearance (p<0.001). Midje- høyde-ratio medierte 25-40% av den etniske forskjellen i insulinsensitivitet. I gruppen med prediabetes eller diabetes, fant vi ingen etnisk forskjell i AUC for glukose- eller pre-hepatisk insulin, men høyere AUC for perifert insulin (p<0.01), lavere disposisjonsindeks (p<0.01), og lavere hepatisk insulin clearance (p<0.001). Sør-asiatiske normoglykemiske kvinner hadde høyere perifert insulinnivå (p=0.036) og lavere hepatisk insulin clearance (p<0.001), enn nordiske kvinner med prediabetes eller diabetes.

Konklusjon

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Vi fant at én av to sør-asiatiske- og én av fem nordiske kvinner hadde prediabetes eller

diabetes, ett til tre år etter gjennomgått svangerskapsdiabetes. Den økte risikoen for T2D blant sør-asiatiske kvinner kan være relatert til lavere betacellefunksjon, lavere fastende hepatisk insulin clearance, og/eller lavere insulinsensitivitet. Et originalt funn var at normoglykemiske sør-asiatiske kvinner fremviste en fenotype påfallende lik nordiske kvinner med prediabetes eller diabetes. DIASA 1 studien understreker behovet for mer organisert offentlig oppfølging i helsevesenet med persontilpasset veiledning for å redusere kvinners fremtidige diabetesrisiko etter gjennomgått svangerskapsdiabetes, særlig hos sør-asiatiske kvinner. Våre data indikerer også at etnisk forskjell i kroppssammensetning er viktig for at normoglykemiske kvinner progredierer til prediabetes eller diabetes etter gjennomgått svangerskapsdiabetes.

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1. LIST OF PAPERS

Paper Ⅰ

Archana Sharma, Kåre I. Birkeland, Ingrid Nermoen, Elisabeth Qvigstad, Anh T. Tran, Christine Somme, Hanne L. Gulseth, Stina T. Sollid, C. Wium, C. Varsi.

Understanding mechanisms behind unwanted health behaviours in Nordic and South Asian women and how they affect their gestational diabetes follow-ups: A qualitative study Diabet Med. 2021;38(10):e14651 (published).

Paper Ⅱ

Archana Sharma, Ingrid Nermoen, Elisabeth Qvigstad, Anh T. Tran, Christine Sommer Naveed Sattar, Jason M.R. Gill, Hanne L. Gulseth, Stina T. Sollid, Kåre I. Birkeland.

High prevalence and significant ethnic differences in actionable HbA1c after gestational diabetes mellitus (submitted).

Paper Ⅲ

Archana Sharma, Sindre Lee-Ødegård, Elisabeth Qvigstad, Christine Sommer, Naveed Sattar, Jason M.R. Gill, Hanne L. Gulseth, Stina T. Sollid, Ingrid Nermoen, Kåre I.

Birkeland.

Beta cell function, hepatic insulin clearance, and insulin sensitivity in South Asian and Nordic women after gestational diabetes mellitus (submitted).

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2. INTRODUCTION

2.1. Diabetes Mellitus

Diabetes mellitus, defined by the World Health Organisation (WHO) as a group of metabolic disorders with increased blood sugar (=glucose) levels (1), has been diagnosed in different ways throughout the history.

Although the diabetes term traces its roots back to the year 1500 BC (2), the WHO did not publish their first diabetes mellitus guidelines before 1965 (3). Thereafter, the WHO and National Diabetes Data Group (NDDG) produced new criteria in the late 1970s (4, 5), before a revised WHO version emerged in 1985 (6). In 1997 the American Diabetes Association (ADA) expert group published their first recommendations (7), while WHO proposed its update in 1999 (8); with almost similar conclusions. Current criteria are based on these definitions. The ADA and WHO added glycated haemoglobin (HbA1c) as a diagnostic test in 2009 (9) and 2011 (10), respectively. The most recent updates from WHO (1) and ADA (11) are from 2019 and 2022. Caused by its simplicity, I will here present the ADA classification of diabetes mellitus:

(i) Type 1 diabetes (caused by an autoimmune beta cell destruction that secondary often leads to an absolute deficiency of insulin)

(ii) Type 2 diabetes (caused by a progressive loss of sufficient beta cell insulin secretion, usually in concert with insulin resistance)

(iii) Specific types of diabetes due to various causes, e.g.:

a) monogenic diabetes syndromes (e.g., neonatal diabetes and maturity-onset diabetes of the young)

b) diseases of the exocrine pancreas (e.g., cystic fibrosis or pancreatitis)

c) drug or chemical-induced diabetes (e.g., glucocorticoid use, drugs used in the treatment for HIV, AIDS, or after organ transplantation)

(iv) Gestational diabetes mellitus (GDM), defined as increased blood glucose levels of variable severity with first presentation in pregnancy, i.e., no overt diabetes before the index pregnancy (8, 12, 13).

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The current ADA (11), WHO (1), and WHO-International Expert Committee (WHO-IEC) (14) guidelines are consistent regarding criteria for the diabetes diagnosis (Table 1). They differ, however, in their definition of individuals at high risk of developing diabetes, defined as prediabetes. These differences are summarised in Table 1.

Based on glucose values from an oral glucose tolerance test (OGTT), prediabetes can be divided into impaired fasting glucose (IFG), and impaired glucose tolerance two hours after ended test (IGT). In 2003 the ADA lowered their cut-off levels for IFG to 5.6-6.9 mmol/L (15), while the WHO’s cut-off levels for IFG remained unchanged at 6.1-6.9 mmol/L (8). The WHO (1, 10, 16) do not recommend the use of HbA1c to diagnose prediabetes, while the ADA (11) and WHO-IEC (14) use different HbA1c cut-offs to diagnose prediabetes, in the range of 39-47 mmol/mol (5.7-6.4%) and 42-47 mmol/mol (6.0-6.4%), respectively.

Despite most of the current literature refers to OGTT as gold-standard test for diagnosing diabetes, its use in practical life is minimal compared to HbA1c. The benefits of using HbA1c

rather than the time-consuming OGTT are less day-to-day variation, and more convenient blood sampling without the need for fasting. In particular, correct storage and transport before analysis, and hence, an increased preanalytical variation, is a far larger problem with glucose than with HbA1c (14). Additionally, seminal meta-analyses have reported stronger association between HbA1c and long-term diabetes complications (17-19) than to OGGT measurements.

Therefore, from a clinical view of point, it is important to examine the prevalence of prediabetes or diabetes based on HbA1c measurements, and compare them to diagnostic categories evaluated by OGTT. As both prediabetes and diabetes should prompt preventive and/or therapeutic efforts from the individual and the healthcare providers, we have jointly named them actionable HbA1c in this dissertation.

Diagnosis Diagnostic criteria

WHO (1, 10, 16)

2019

ADA ⁽¹¹⁾ 2022

WHO-IEC ⁽¹⁴⁾ 2009 Diabetes

Mellitus

HbA1c ≥48 mmol/mol (6.5%)

≥11.1 mmol/L ≥7.0 mmol/L

Symptoms and ≥11.0 mmol/L 2hPG

FPG RPG

Prediabetes HbA1c Not recommended for diagnosis

39-47 mmol/mol (5.7-6.4%)

42-47 mmol/mol (6.0-6.4%) IGT 7.8-11.0 mmol/L 7.8-11.0 mmol/L 7.8-11.0 mmol/L IFG 6.1-6.9 mmol/L 5.6-6.9 mmol/L 6.1-6.9 mmol/L

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Table 1. Diagnostic criteria for diabetes mellitus and prediabetes according to the World Health Organisation (WHO), WHO-International Expert Committee (WHO-IEC), and American Diabetes Association (ADA) recommendations. A diabetes diagnosis requires two tests above the diagnostic cut-off levels unless an individual has random plasma glucose (RPG) ≥ 11.0 and typical symptoms of hyperglycaemia.

2hPG: 2h plasma glucose, FPG: fasting plasma glucose, IFG: impaired fasting glucose, IGT: impaired glucose tolerance, HbA1c: glycated haemoglobin

2.2. Glucose handling during an oral glucose tolerance test

An oral glucose tolerance test (OGTT) is a method used to assess an individual’s capacity to handle a glucose load. After an overnight fast, fasting glucose values are measured, before a glucose load is given. In a clinical setting, blood sampling is then performed after two hours (sometimes also after one hour). The prediabetes and diabetes diagnosis are based on these fasting and 2h OGTT glucose values. In a research setting, multiple blood samples taken during the test provide better opportunities to assess how an individual handles the glucose load.

During the normal fasting state, the total body glucose uptake is precisely matched to the glucose produced by the body, i.e., to the endogenous glucose production that primarily occurs in liver. The majority of the glucose uptake occurs in tissues that are not dependent on insulin (insulin-insensitive), e.g., brain, erythrocytes and abdominal organs such as spleen, pancreas, stomach and intestine (splanchnic tissues). Tissues that requires insulin to enter glucose (primarily muscle, adipose and heart tissue) are called insulin-sensitive tissues. In the fasting state, these tissues only account for a small amount of the glucose uptake

(approximately 25%) (20).

Therefore, hepatic glucose production (HGP) largely controls the fasting glucose levels. HGP production is mainly regulated by insulin produced by pancreatic beta cells (20). Insulin directly impacts HGP by inhibiting glycogenolysis and gluconeogenesis in the liver, and indirectly by inhibiting pancreatic glucagon secretion, lipolysis in muscle and fat tissues, and proteolysis in muscle tissue, which accordingly reduce glycogenolysis and available

gluconeogenic precursors (21). Thus, in the fasting state, the glucose levels multiplied by the insulin levels can principally be interpreted as an index of how well the pancreatic insulin production suppresses the HGP, often taken as a measure of hepatic insulin resistance, or the inversely related hepatic insulin sensitivity. Lower values reflect lower hepatic insulin sensitivity (22). Likewise, the ratio of fasting insulin to fasting glucose levels will largely indicate fasting pancreatic insulin secretion. Higher values indicate greater insulin secretion by the pancreatic beta cells (22).

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The insulin response to a glucose load is biphasic, with a first-phase insulin secretion of 10 minutes, preceding a second-phase insulin secretion. In individuals with type 2 diabetes (T2D), the first-phase insulin response is usually impaired before the second-phase insulin response. The first-phase insulin response can be estimated by an intravenous glucose tolerance test (23). Likewise, the ratio of change in insulin to change in glucose during the first 30 minutes of an OGTT can be used as an estimate of the early insulin response (24).

This response is usually impaired when prediabetes or diabetes is present (23).

During the OGTT, splanchnic tissues still account for a part of the glucose uptake (30-40%), but insulin-sensitive tissues, primarily skeletal muscle cells, are in this situation the major organ for glucose uptake. During the euglycaemic, hyperinsulinemic glucose clamp, often considered as the gold-standard method for testing insulin sensitivity, 80% of the glucose is taken up by skeletal muscle. Adipose tissue uses only a minimal proportion of the glucose, estimated to be 5% during a euglycaemic clamp (20). Importantly, the increasing glucose levels during an OGTT stimulate pancreatic insulin secretion. Both the increased glucose and insulin levels supress HGP. However, the HPG is not always completely suppressed during the OGTT. Therefore, glucose values during an OGTT reflect both hepatic and peripheral (primarily muscle cells) insulin sensitivity, the whole-body insulin sensitivity. Lower values reflect lower whole-body insulin sensitivity (20).

After 60 minutes of the OGTT, the HGP is maximally suppressed and remains unchanged throughout the test. Therefore, glucose uptake after 60 minutes or after the peak glucose value during the OGTT, largely reflects glucose uptake by muscle cells. Accordingly, muscle insulin sensitivity can be estimated as the ratio of glucose fall from its peak value to nadir divided by mean insulin values during the OGTT. Lower values indicate lower muscle insulin sensitivity. Notably, this formula cannot be used in T2D individuals as their glucose levels continue to rise throughout the OGTT test (25).

Another aspect to consider is that peripheral insulin levels are not only dependent on pancreatic insulin secretion, but also on insulin clearance. The liver degrades ~50% of the newly secreted insulin. This means that differences in hepatic degradation, or hepatic insulin clearance, may largely influence the peripheral insulin levels. Lower hepatic insulin

clearance results in higher peripheral insulin levels (Fig. 1). Thus, pre-hepatic insulin levels are important to calculate, not only to estimate hepatic insulin clearance (26), but also to estimate how much pre-hepatic insulin that is secreted per amount of glucose, the beta cell glucose sensitivity. This is increased in insulin resistant individuals with normal beta cell

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function (27). Importantly, pre-hepatic insulin levels are difficult to measure directly in humans. A method used to circumvent this problem is the C-peptide deconvolution technique that utilises that C-peptide and insulin are co-secreted in a similar amount from pancreas (equimolar secretion), but have different hepatic clearance rates. The estimated pre-hepatic insulin levels are, therefore, based on measured C-peptide levels as the hepatic C-peptide clearance is negligible (26) (Fig. 1).

Furthermore, it is important to keep in mind that the pancreatic insulin secretion during an OGTT is not only dependent on the glucose load, but also on other factors such as insulin- stimulating hormones from the intestine, the incretin effect (28). Due to simplicity, in the present dissertation, we will not examine or discuss the incretin effect.

An important previous report showed that the relationship between insulin secretion and insulin sensitivity is constant in individuals within the same glucose tolerance category (29).

This means that the insulin secretion adjusted for insulin resistance, the disposition index, can be estimated from an OGTT, and indicates the beta cell function. Lower values imply lower beta cell function (29).

Fig.1. The relationship between pre-hepatic insulin secretion and hepatic insulin clearance (HIC). Insulin and C- peptide are secreted in an equimolar fashion, but ~ half of the newly secreted insulin is degraded by the liver.

This implies that lower HIC results in higher peripheral insulin levels. The figure was generated by using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 license.

Taken together, an OGTT can provide valuable information about an individual’s insulin secretion, insulin sensitivity, hepatic insulin clearance, and beta cell function; all factors important to consider when assessing an individual’s glucose tolerance. During a normal OGTT, the glucose levels usually peak after 30 minutes and return back to fasting glucose levels within two hours. The peripheral insulin levels peak after 60 minutes. The interval from

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0-60 minutes can, therefore, be taken to reflect the hepatic insulin sensitivity, while the interval from 60-120 minutes basically reflects the peripheral (primarily the muscle) insulin sensitivity. In an individual with prediabetes, the glucose levels usually peak after 60 minutes and do not return to the fasting level. As a consequence of this, the insulin levels continue to rise during the test (30), which may be due to factors such as increased pancreatic insulin secretion, increased hepatic insulin resistance, and/or reduced hepatic insulin clearance. In persons with T2D the glucose levels usually increase steadily during the test and no peak is reached, as the increased insulin demand is not met (25), uncovering a beta cell dysfunction (Fig. 2).

Fig. 2. (a) Plasma glucose and (b) serum insulin (B) levels during an oral glucose tolerance in women with normal glucose tolerance (NGT, solid line), prediabetes (PreDM, dotted line) and a diabetes (DM, dashed line).

Data are means, and collected from the DIAbetes in South Asians (DIASA) 1 study participants.

2.3. Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) affects up to 25% of all pregnancies worldwide (31) and between 13-32 % of all pregnancies in Oslo, Norway (32). The diagnostic criteria for GDM have changed several times throughout the history, and currently no global consensus on the definition of GDM has been reached, see Table 2. In Norway, we applied the WHO 1999

a

b

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definition of GDM (8) until a modified version of the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria was proposed in 2017. These GDM criteria recommend cut-off values of fasting plasma glucose (FPG) ≥ 5.3 mmol/L and 2h OGTT glucose ≥ 9.0 mmol/L (13).

Organisations, year

Test used Plasma glucose cut-off levels

(mmol/L)

GDM diagnosis criteria Fasting 1 h 2 h 3 h

O’Sullivan and Mahan, 1964⁽³³⁾

100g OGGT^† 5.0 9.2 8.1 6.9 ≥2 positive

NDDG, 1979 ⁽⁵⁾ 100g OGGT 5.8 10.6 9.2 8.1 ≥2 positive

Carpenter and Coustan, 1982 ⁽³⁴⁾

100g OGGT 5.3 10.0 8.6 7.8 ≥2 above limit

WHO, 1999 ⁽⁸⁾ 75g OGGT 7.0 - 7.8 - ≥1 above limit

IAPDSG, 2010 ⁽³⁵⁾ ADA, 2011 (¹²) WHO, 2013 ⁽³⁶⁾

75g OGGT 5.1 10.0 8.5 - ≥1 above limit

DIPSI, 2014 ⁽³⁷⁾ 75g OGGT - - 7.8 - ≥1 above limit

ACOG, 2013^††⁽³⁸⁾ ADA, 2014 ^†††⁽³⁹⁾

50 g GCT± 100g OGTT 5.3 10.0 8.6 7.8 ≥2 above limit Norwegian

Directorate of Health, 2017 ⁽¹³⁾

75g OGGT 5.3 - 9.0 - ≥1 above limit

Table 2: Some of the different GDM diagnosis criteria throughout the history.

†Applied whole blood glucose, ^††ACOG: recommends two-step test. ^†††ADA: recommends either the one-step test (an OGTT directly, without a preceding screen test) or the two-step test (a single non-fasting glucose challenge test (GCT) is administered before the test day. If the GCT is positive, it is followed by an OGTT).

ACOG: The American College of Obstetricians and Gynaecologists, ADA: American Diabetes Association, DIPSI: Diabetes in Pregnancy Study Group India, GDM: gestational diabetes mellitus, IADPSG: International Association of Diabetes and Pregnancy Study Groups; OGGT: oral glucose tolerance test; NDDG: National Diabetes Data Group; WHO: World Health Organisation

Interestingly, the pregnancy itself is an insulin resistant state. During the first twelve weeks of a normal pregnancy, insulin sensitivity is improved, but thereafter, insulin sensitivity declines during the pregnancy. Accordingly, insulin secretion increases to maintain normoglycaemia.

In women with pre-pregnancy overweight or genetically compromised beta cell function, the beta cells may fail to adapt to the increased insulin demand, and GDM develops (40). After delivery this demand, and eventual hyperglycaemia disappears. However, a history of GDM is associated with an almost tenfold increased risk of diabetes later in life (41, 42). The highest relative risk is within 3-6 years after delivery (43), but the absolute risk increases steadily after delivery (41, 42). Although data from Norway are scarce, international studies have reported this risk to be twice as high in South Asian compared to European women (42, 44).

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Moreover, prediabetes and diabetes seem to develop earlier and at a lower body mass index (BMI) in South Asian women after GDM (45). Notwithstanding this, a sizeable proportion of women with GDM do not attend the recommended T2D screening after their delivery (46, 47), hence miss the opportunity to reduce their future T2D risk. Several qualitative studies have addressed this issue (48, 49). Themes such as anxiety, shock, frustration, time

constraints, and women’s concern for their unborn child are addressed to explain their negative impact on health-promoting behaviours. Further, healthcare providers that fail to inform women about their increased T2D risk during the pregnancy may also make women resistant to GDM follow-ups after delivery (48, 50, 51). However, limited research has tried to understand mechanisms behind the gap between women's intentions and actions related to health-promoting habits after GDM, defined as unwanted behaviours in this dissertation.

The American, Michael Lipsky (born 13 April 1940), holds a PhD in politics and is

recognised for his theory about street-level bureaucrats (SLB) published in 1980 (52). This theory describes unwanted behaviours among SLB, and clarifies why recommendations are not implemented as intended in circumstances with a heavy workload and few resources.

Although this theory is underused in healthcare research (53), it may be of great importance when trying to understand women's responses to current GDM recommendations and how these influence GDM follow-up.

2.4. South Asians susceptibility for prediabetes and diabetes

2.4.1. Epidemiology

South Asian individuals (considered as subjects living in or with ancestry from Pakistan, India, Bangladesh, and Sri Lanka) account for ~25% of the world’s population (54). They have a twofold increased T2D risk after a GDM diagnosis (42, 44), and up to a fourfold increased T2D risk in general (55) compared to white European individuals. In addition, South Asian individuals develop T2D at a younger age and at a lower BMI, both after GDM (45), and in general (55, 56), than comparable European individuals. Several underlying theories have been raised to explain these findings (Fig. 3) (57). Here, I am presenting the two most common theories related to early changes in development.

(i) Thrifty phenotype hypothesis

This theory proposes that in circumstances with sparse nutrition to the foetus in utero, either due to limited food availability for the mothers or due to for example placenta dysfunction,

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the foetus develops thrifty coping strategies to survive. These thrifty strategies, however, are in the long-term disadvantageous, and contribute to an increased adult T2D risk in times of overnutrition. This theory relates foetal undernutrition to South Asians’ lower birth weight (55, 57, 58) and to their higher risk of glucose deterioration in adulthood.

(ii) The fat or adipose tissue compartment hypothesis

This theory proposes that South Asian individuals have reduced capacity to store fat within healthy adipose tissue compartments. Adipose tissue mass is divided into superficial subcutaneous, deep subcutaneous and visceral adipose tissue. Fat is preferably stored in superficial subcutaneous adipose tissue (that is largely metabolic inactive), and less preferably in deep subcutaneous and visceral adipose tissue (that are metabolic active, and linked to dysglycaemia and dyslipidaemia). Previous findings report that South Asian individuals at similar BMI have lower muscle (i.e., lean) mass, and more visceral fat than white Europeans.

This is linked to South Asians’ higher insulin resistance, and gives rise to the ‘thin-fat’

phenotype (55, 59). Others, however, did not find any differences in the amount of visceral adipose tissue, but more subcutaneous adipose tissue in South Asian than in white American men (60). This opened up for the ‘adipose tissue overflow hypothesis’ that states that South Asian individuals have less developed superficial subcutaneous adipose tissue compartment compared to white Europeans. When total adipose mass increases, South Asian populations expand their deep subcutaneous and visceral adipose tissue at a lower amount of energy accumulation than comparable white Europeans. This earlier overflow to more metabolic active adipose tissue compartments is associated with South Asians higher risk of T2D (61).

2.4.2. Pathophysiology

Several pathogenetic factors explaining South Asians susceptibility for glucose deterioration have been suggested (Fig. 3).

(i) Increased insulin resistance

South Asian neonates, children, and older individuals have higher insulin levels, despite lower birth weight or BMI than white Europeans. These higher insulin levels reflect an insulin resistant state that is present decades before the diabetes diagnosis (55), and is linked to more central fat per given BMI in South Asian populations (55). This is supported by a study showing similar diabetes incidence at a BMI of 23.9 and 30.0 kg/m² in South Asian and European populations (62), and consistent with the fat compartment hypothesis. A recent

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review, however, claims that South Asians’ lower insulin sensitivity is due to their lower lean mass rather than central fat accumulation, as 80% of the glucose uptake after a meal takes place in muscle cells. Low lean mass will result in lower glucose clearance with subsequently higher insulin levels, and peripheral insulin resistance (58). In addition, a defect in fat

degradation, the mitochondrial oxidative function, among South Asian populations is discussed. A defect in this function results in higher levels of intramuscular fat, and harmful fat degradation products that secondary reduce glucose uptake in muscle, fat and liver cells by affecting insulin signalling, and thereby reduce the insulin sensitivity (58). Inconsistent data suggest that South Asian populations also have lower skeletal muscle fat oxidation, and lower oxygen delivery to working muscle cells (i.e., cardiorespiratory fitness) during exercise. The former results in higher muscle insulin resistance, and the latter could explain why South Asian populations in high-income countries are less physical active than their Europeans counterparts (55). Even though aerobic and anaerobic exercise improve mitochondrial function, a review reports that South Asian individuals are more insulin resistant than

comparable white Europeans also after adjusting for physical activity (55). This might explain why lifestyle intervention programmes performed to reduce diabetes incidence have been less successful in South Asian than white American populations, both in general (28-34% (55) vs.

58% reduction (63)), and after GDM (no effect (64) vs. 53% reduction (65)). Considering lifestyle, the migrant South Asians’ diet do contain more fat and carbohydrates than the diet in their native country, but is not poorer in nutrition compared to background population in their adopted countries (55). The main culprit, consistent with the fat compartment hypothesis and overfeeding studies related to fat in a review (55), is that South Asian populations have lower capacity to store fat than comparable European individuals. Subsequently, fat is stored at

‘unhealthy’ (ectopic) compartments in adipose tissues, muscle, and liver (58, 66). All these factors are linked to lower insulin sensitivity. High liver fat is associated with lower hepatic insulin sensitivity (67), which demands higher pre-hepatic insulin secretion to suppress fasting HGP. If the insulin demand is not met, the result will be higher fasting glucose values.

This may explain the higher isolated IFG levels among South Asians compared to other ethnic groups (58).

The increased insulin resistance may, hence, also explain why South Asian individuals transit faster from prediabetes to a diabetes than comparable white Europeans (12-18% vs. 5-11%

per year (55, 66).

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27 (ii) Innate beta cell dysfunction

Traditional theories attribute the higher T2D risk in South Asian populations to increased insulin resistance at an early age, followed by failing beta cells, and a secondary decline in insulin secretion (68, 69). However, recent literature suggests that the mentioned ethnic difference in insulin levels at birth may be due to higher maternal glucose levels in South Asian than in white European women (55). Moreover, emerging literature from the US (66), and from a study conducted in UK and South Asia (56) show that South Asian populations may also have a lower innate beta cell function. The US study reported that South Asians had lower insulin sensitivity and insulin secretion than white Americans, and that both factors were linked to prediabetes and T2D. But only low insulin secretion predicted prediabetes and T2D when comparing individuals with same level of insulin sensitivity or adiposity (66).

They concluded that low insulin secretion, regardless of adiposity, is a strong predictor for prediabetes and T2D. Further, a UK study comparing South Asians in India with white Europeans, found a up to fourfold higher diabetes risk in young, lean Indian individuals than young, lean white European individuals. These young Indian individuals had a lower beta cell function than obese Indian individuals when comparing population with same degree of insulin resistance and family history of diabetes. Notably, they also found a higher genetic burden of T2D risk variants (i.e., alleles) in Indians compared to white Europeans (56).

Altogether, this implies a lower genetically determined beta cell function in South Asian individuals compared to white individuals.

(ii) Lower hepatic insulin clearance

Emerging studies have reported that lower hepatic insulin clearance may be an efficient way to ‘fight’ increased insulin resistance (70, 71). Lower hepatic insulin clearance results in higher peripheral insulin levels, which may contribute to ‘offload’ the beta cells, and hence, improve the insulin secretion. Contrary, higher peripheral insulin levels are also associated with lower insulin sensitivity, and hence, increased risk of developing T2D (72, 73). Although this aspect is important as a majority of the newly secreted insulin is degraded by liver (26), its role in South Asian populations is still unclear.

2.4.3. Risk factors (i) Genetics

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Studies have found genes that are associated with GDM (40) and T2D (55), but not found genes that largely explain the higher T2D prevalence in South Asian populations. Some difference in alleles have, although, been found. Notwithstanding this, studies associating variation in the complete DNA set with T2D, genome-wide association studies (GWAS), have mostly been performed in white individuals.

(i) Age and body composition

Populations of South Asian descent develop T2D at a younger age and at a lower BMI, both after GDM (45) and in general (55, 56, 62) than their European counterparts. Basically, reviews show that South Asians individuals develop T2D 5-10 years earlier than comparable European individuals (55), and that the diabetes incidence in subjects with BMI < 25 kg/m² is threefold to fivefold higher in a South Asian than in a white US population (58).

(ii) Physical inactivity

South Asian populations in high-income countries are less physical active than white European or American populations in general (55, 66). After GDM, however, both South Asian and white populations are less active than the general background population (74, 75).

(iii) Diet

After migration to Western countries, South Asian individuals adapt to westernised diet with higher intake of fat and carbohydrates (55). Higher intakes of saturated and trans fats, and carbohydrates are linked to lower insulin sensitivity, and an increased demand for insulin secretion, respectively (66). Both factors are associated with beta cell exhaustion and an increased risk of glucose deterioration.

(iv) Sociocultural factors

More than 30 million South Asians live abroad (76). Living abroad is linked to a sudden shift in social context and an effort to adapt to a new culture – acculturation. This process is generally associated with emotional distress (48, 77), and an increased GDM and T2D risk.

The obligation towards traditional South Asian diet (51, 77), high in carbohydrates and fat (66), and a negative attitude towards physical activity among some South Asian individuals (77, 78) may also contribute to a higher T2D risk. The role of religion on T2D risk is unclear.

Some studies report that spiritual beliefs may decrease the risk (79), while others report an increased T2D risk (66). Lack of social support and networks are in general related to a

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greater GDM and T2D risk (48, 66, 75, 79, 80), and may affect South Asian populations more due to their high migration rate.

(v) Other factors

Environmental pollutants, limited access to walking areas, sleep disorders, differences in how environmental factors affect gene function (epigenetics), and differences in the amount of small molecules produced as an interaction between environmental factors and genes

(metabolomics) can all increase the T2D risk, and maybe more so in South Asian populations (66). These factors are, however, beyond the scope of this dissertation.

Genetics (Unknown factors)

Beta cell dysfunction

Early changes in development

↑ Visceral and deep subcutaneous fat

Physical inactivity ↓ Muscle mass ↓IS ↑T2D risk

Diet ↑ Hepatic fat ↑ Peripheral

insulin Sociocultural factors ↑ Intramuscular fat

Others*

(Unknown factors)

Mitochondrial oxidative dysfunction

↓HIC?

Fig. 3 Possible mechanisms behind South Asians higher T2D risk.

*Environmental pollutants, limited access to walking areas, sleep disorders, epigenetics, and metabolomics.

HIC: hepatic insulin clearance, IS: insulin sensitivity, T2D: type 2 diabetes. The Figure was partly generated by using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 license.

The underlying causes of South Asian individuals’ higher risk of T2D are still not completely understood, and the role of hepatic insulin clearance has not been explored yet. Thus,

advancing knowledge about mechanisms behind this excess risk is of great importance.

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3. AIMS AND RESEARCH QUESTIONS

3.1. General aims

The aim of the DIAbetes in South Asians (DIASA) 1 study was to characterise South Asian women short time after a GDM pregnancy to acquire a deeper knowledge of their prediabetes and T2D risk compared to a Nordic background population. We aimed to assess both

behavioural and pathogenetic factors.

3.2. Specific research questions

In women with a GDM diagnosis 1-3 years previously:

(i) What are the mechanisms behind South Asian and Nordic women’s suboptimal diabetes follow-up by comparing their GDM experiences, their health and disease perceptions, and factors important to facilitate or interrupt health-promoting behaviours?

(Paper I)

(ii) Do South Asian women have a higher prevalence of prediabetes or diabetes measured by HbA1c alone or combined with OGTT measurements compared to Nordic women?

(Paper II)

(iii) Is it possible to find early predictors for prediabetes or diabetes? (Paper II)

(iv) Can the higher prediabetes and diabetes risk in South Asian compared to Nordic women be explained by differences in insulin sensitivity, insulin secretion, beta cell function and hepatic insulin clearance? (Paper III)

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4. MATERIAL AND METHODS

4.1. Study design and settings

This dissertation includes a paper using qualitative research methodology (paper I), and two papers reporting on quantitative research (paper II and III). It is based on the DIASA 1 study, a trial investigating glucose tolerance in South Asian and Nordic women with a history of GDM 12-36 (±3) months previously.

The quantitative cross-sectional study took place at Vestre Viken Hospital Trust, Drammen, Oslo University Hospital, Oslo (Aker), and Akershus University Hospital, Lørenskog,

between September 2018, and December 2021. We further invited a subgroup of these women to participate in the qualitative study scheduled on another day. This qualitative part took place at Akershus University Hospital between September 2018, and October 2019.

4.2. Sample size

(i) The quantitative part

We calculated the sample size based on the primary outcome measurement, i.e., the prevalence of prediabetes or diabetes in South Asian and Nordic women after GDM.

According to previous literature, this was expected to be 35% in South Asian and 20% in Nordic women. With a 2:1 group size ratio, and an α of 0.05, we needed 324 women to obtain a power of 80% to find ethnic differences in the prevalence. We also calculated for a dropout rate of 1-2%.

However, due the COVID-19 pandemic, and a subsequently delayed recruitment (!), we performed an interim analysis in 2021. We found higher prevalence rates, and greater difference between the ethnic groups than expected. Therefore, this interim analysis showed that the included 271 women in 2021 had more than 80% power to detect significant

differences between the groups. Accordingly, our primary outcome measurement was based on these 271 women.

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In the focus group interviews, the sample size was not predefined. The focus group interviews continued until no new information about the research question was obtained, i.e., until we reached the data saturation (81). We increased the information power in our study sample by applying factors such as a precise research question, only including women with previous GDM who could answer the study aims (i.e., purposive sample), recruiting women from different ethnicities, and using an endocrinologist as an interviewer, assisted by an experienced interviewer in qualitative research. All these factors contributed to higher information power in the sample, and hence, reduced the needed sample size. To compare differences and elicit the study aims, women were grouped according to their ethnicity, as it is easier to share experiences among similar others (82). We did not apply a theoretical model to frame our research question, and used a cross-case (rather than an in-depth longitudinal) analysis that required more participants to obtain appropriate information power (81).

Therefore, the final number of participants and groups were determined by both data

saturation and information power, and comprised six focus group interviews with 28 women.

4.3. Study population

(i) The quantitative part

DIASA 1 study personnel at Vestre Viken Hospital and Akershus University Hospital

searched in medical records for South Asian and Nordic women with a diagnosis of GDM 1-3 years previously. At Oslo University Hospital two midwifes assisted in this process. We selected participants based on the GDM criteria used in the actual period, which were the WHO 1999 (8), or the Norwegian modified IADPSG criteria (13). When we found cases recorded as GDM in pregnancy, but with missing OGTT values (n = 6), we registered FPG 5.3-6.9 mmol/L as diagnostic of GDM.

Thereafter, DIASA study personnel sent invitation letters to all eligible women, followed by a telephone invitation to the South Asian population within 2 weeks to ensure that study

information was delivered in an understandable language.

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Inclusion criteria: Exclusion criteria:

-Age ≥ 18 years -Pregnant

-South Asian origin (Pakistan, India, Sri Lanka, Bangladesh) or

-Exclusive breastfeeding at time of inclusion

-Nordic origin (Norway, Sweden, Denmark, Finland, Iceland)

-Known T2D treated with medication at time of inclusion

- GDM 1-3 year prior to inclusion -Known T1D

-FPG ≥ 14 mmol/L at inclusion

-Systolic blood pressure ≥ 180 mmHg, or diastolic blood pressure ≥ 120 mmHg -Known serious disease (cancer, heart failure NYHA class III-IV, liver disease) -Active infectious disease at inclusion -A major surgical procedure within the last 3 months before inclusion

-Intake of systemic corticosteroids (duration > 2 weeks) within the last 3 months before inclusion

- Hypothyroidism with unstable Levaxin treatment, or TSH levels outside reference range the last 3 months before inclusion - Any condition that the investigator think will threaten the participant’s safety or compliance with the protocol

Table 3. Inclusion and exclusion criteria in the DIAbetes in South Asian (DIASA) 1 study.

FPG: fasting plasma glucose, GDM: gestational diabetes mellitus, NYHA: New York Heart Association. T1D:

type 1 diabetes T2D: type 2 diabetes

Of a total of 1220 (449 South Asian and 771 Nordic) women assessed for eligibility, 179 South Asian and 108 Nordic women participated in the study. Among the South Asians, 270 women did not participate due to newly diagnosed diabetes or a new pregnancy (n = 45), or they declined to participate or were not contactable (n = 225). As the Nordic women were invited through a letter only, we did not gather information related to their non-attendance (Fig. 4). None of the women withdrew due to the oral glucose load, and we did not registered any serious adverse event.

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Fig. 4. Participant flow-chart. GDM: gestational diabetes mellitus, T2D: type 2 diabetes

(ii) The qualitative part

By the end of the focus group interview inclusion period, 96 women were included in the DIASA 1 study and had undergone an OGTT. Of these, 19 women refused to participate in focus group interviews, and 49 women were unreachable or felt ill the day of the interview.

Finally, ten Nordic, nine Pakistani, six Sri Lankan, and three Indian women attended one of the six (two Nordic, two Pakistani, one Sri Lankan, and one mixed South Asian) focus group interviews.

4.4. Data collection

(i) The quantitative part (a) Oral glucose tolerance test

We informed all women to avoid unusual hard physical activity, and unusual diet such as high or low carbohydrate diet two days before test. At the test day, all women attended a five-point OGGT at their respective study hospitals, starting between 08.00-10.00 am, after at least an 8 hours overnight fast that also included no smoke or snuff (Fig. 5). On arrival, they signed an

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informed consent in their native language, before delivering a urine test to exclude infection or pregnancy. Then, the study personnel inserted a catheter in an elbow vein (the antecubital vein) for blood sampling. A slow sodium chloride 0.9% infusion kept the catheter open during the OGTT. Blood samples for glucose, insulin, C-peptide, HbA1c, routine clinical chemistry, lipids, incretins and DNA were taken. This dissertation only includes biomarkers related to glucose, insulin, C-peptide and HbA1c. HbA1c was collected at time point 0 minute, while glucose, insulin and C-peptide were collected at 0, 15, 30, 60 and 120 minutes after intake of 75 g anhydrated glucose. All women were asked to consume the glucose load within 5 minutes. During the OGTT, the women remained fasted and answered questionnaires about demographics, their previous pregnancy, physical activity and quality of life. In addition, the women were asked to perform a web-based food frequency questionnaire during the last hour of the OGTT. This dissertation only includes data related to demographics, previous

pregnancy, and physical activity.

Fig. 5. Overview of the procedures during the five-point oral glucose tolerance test (OGTT). Figure was produced in Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 license.

(b) Clinical and anthropometric measurements

Before the OGTT, we measured blood pressure, weight, height, waist and hip circumference.

Blood pressure was calculated as the mean of the last two of three measurements in the sitting position after five min rest (electronic sphygmomanometer Riester Ri Champion N, Rudolf Riester GmbH, Germany). Anthropometric tests were measured by maximum two different

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persons at the respective hospitals. Women were examined in an upright position with light clothing and without shoes. We used calibrated digital weights to measure weight. Height was measured by a standard altimeter. We measured waist and hip circumference with a

standardised stretch‐resistant tape (Gulick II Measuring Tape), and ensured that the tape only snugged around the body. Waist circumference was measured at the midpoint between the lower rib, and the highest level of the hip (iliac crest) when women were breathing out. Hip circumference was measured at the widest level of the buttocks (trochanter major).

(c) Definitions

Prediabetes was defined according to WHO, WHO-IEC, and ADA criteria in paper II, and according to WHO-IEC criteria in paper III (Table 1).

Diabetes was defined as in Table 1, and consistent with international standards. International standards require two separate tests for a clinical diabetes diagnosis in individuals without diabetes symptoms. For research purposes, however, it is considered appropriate to perform only one measurement, and therefore, we recorded one test as diagnostic for diabetes in the DIASA 1 trial.

(d) Calculations

In paper III we calculated different indexes for insulin sensitivity, insulin secretion, beta cell function, and hepatic insulin clearance. We used the following formula to estimate these indexes:

- Insulin sensitivity. We divided insulin sensitivity into hepatic, muscle, and whole-body insulin sensitivity. Hepatic insulin sensitivity was based on homeostatic model

assessment2-sensitivty (HOMA2-S), and calculated by plotting fasting serum insulin (FSI) [pmol/L], and fasting plasma glucose (FPG) [mmol/L] into the HOMA

calculator (22, 83). Muscle insulin sensitivity was based on the muscle-insulin sensitivity index (muscle-ISI), and calculated by plotting plasma glucose [mmol/L] and insulin values [pmol/L] into the muscle-ISI calculator (84). This calculator estimate muscle insulin sensitivity as the ratio of reduction in glucose values from peak to nadir, and mean insulin values during the OGTT (25). Whole-body insulin sensitivity was based on the Matsuda insulin sensitivity index (Matsuda ISI), and calculated manually by using this formula:

10,000/√ (FSI [uIU/mL] × FPG [mg/dL]) × (mean OGTT insulin [uIU/mL]) × (mean OGTT glucose [mg/dL]) (85).

Glucose metabolism in South Asian and Nordic women with previous gestational diabetes mellitus