research on diabetes in pregnancy

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The top 10 research priorities in diabetes and pregnancy according to women, support networks and healthcare professionals

Göher ayman.

1 National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford UK

James A. Strachan

2 Medical Sciences Division, University of Oxford, John Radcliffe Hospital, Oxford UK

Niamh McLennan

3 MRC Centre for Reproductive Health, University of Edinburgh, Queen's Medical Research Institute, Edinburgh UK

Reem Malouf

Jack lowe‐zinola.

4 The Royal Wolverhampton Hospital NHS Trust, New Cross Hospital, Wolverhampton UK

Nia Roberts

5 Bodleian Health Care Libraries, University of Oxford, Oxford UK

Fiona Alderdice

6 School of Nursing and Midwifery, Queen’s University Belfast, Belfast UK

Iuliana Berneantu

Niki breslin, caroline byrne.

7 Cambridge University Hospitals NHS Foundation Trust, Cambridge UK

Sonya Carnell

David churchill.

8 University of Wolverhampton, Wolverhampton UK

Jeannie Grisoni

Jane e. hirst.

9 Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford UK

10 John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford UK

Anna Morris

11 Diabetes UK, Wells Lawrence House, London UK

Helen R. Murphy

12 Norwich Medical School, University of East Anglia, Norwich UK

13 Division of Women's Health, St Thomas’ Campus, King's College London, London UK

14 Elsie Bertram Diabetes Centre, Norfolk and Norwich University Hospital, Norwich UK

Jane O’Brien

15 Stockport NHS Foundation Trust, Stepping Hill Hospital, Stockport UK

Caroline Schmutz

16 JDRF the Type 1 Diabetes Charity, London UK

Kamini Shah

Ankita s. singal, mark w. j. strachan.

17 Metabolic Unit, Western General Hospital, Edinburgh UK

Katherine Cowan

18 James Lind Alliance, National Institute for Health Research Evaluation, Trials and Studies Coordinating Centre, University of Southampton, Southampton UK

Marian Knight

Associated data.

The top 10 and the full long‐list of indicative questions will be made available on the NPEU project website www.npeu.ox.ac.uk/jla‐psp and JLA website www.jla.nihr.ac.uk/priority‐setting‐partnerships/diabetes‐and‐pregnancy . For further information, please contact the team at ku.ca.xo.uepn@PSPALJ .

To undertake a Priority Setting Partnership (PSP) to establish priorities for future research in diabetes and pregnancy, according to women with experience of pregnancy, and planning pregnancy, with any type of diabetes, their support networks and healthcare professionals.

The PSP used established James Lind Alliance (JLA) methodology working with women and their support networks and healthcare professionals UK‐wide. Unanswered questions about the time before, during or after pregnancy with any type of diabetes were identified using an online survey and broad‐level literature search. A second survey identified a shortlist of questions for final prioritisation at an online consensus development workshop.

There were 466 responses (32% healthcare professionals) to the initial survey, with 1161 questions, which were aggregated into 60 unanswered questions. There were 614 responses (20% healthcare professionals) to the second survey and 18 questions shortlisted for ranking at the workshop. The top 10 questions were: diabetes technology, the best test for diabetes during pregnancy, diet and lifestyle interventions for diabetes management during pregnancy, emotional and well‐being needs of women with diabetes pre‐ to post‐pregnancy, safe full‐term birth, post‐natal care and support needs of women, diagnosis and management late in pregnancy, prevention of other types of diabetes in women with gestational diabetes, women's labour and birth experiences and choices and improving planning pregnancy.

Conclusions

These research priorities provide guidance for research funders and researchers to target research in diabetes and pregnancy that will achieve greatest value and impact.

Novelty statement

• Women report a lack of consistent evidence‐based information to help them manage their diabetes in the period before to after pregnancy.
• The top 10 questions for research in diabetes and pregnancy according to women, their support networks and healthcare professionals were identified.
• Joint top priorities for research were diabetes technology and identifying the best test for diabetes in pregnancy.
• These questions will inform funders of research and researchers towards addressing areas of great need and impact.

1. INTRODUCTION

Approximately one in every 10 women will experience a pregnancy complicated by either pre‐existing or gestational diabetes. 1 Rates are increasing as a result of increased rates of obesity and pregnancy at a later age. 2 Although most women have healthy pregnancies and healthy babies, diabetes increases the risk of complications during pregnancy and birth, and can have long‐term effects. Compared to the maternity population without diabetes, the risks are two to six times greater for adverse outcomes such as congenital anomalies, stillbirth, preterm birth, infant death within the first month of life, together with long‐term risks of adverse cardiovascular outcomes in both mothers and children. 3 , 4

Many pregnant women with diabetes report a lack or inconsistency of information, leaving many of their questions unanswered. National guidelines and high‐quality systematic reviews highlight variable quality, heterogeneity and reliability of research. Consequently, treatment guidelines are insufficiently evidenced in line with current context and available healthcare options. 5 , 6 However, with limited funding and resources available for research, it is important to ensure that the research that is undertaken is of highest value and impact.

Healthcare research led by industry and researchers often does not address the issues that are most important for people living with the condition, or those who support them. 7 The James Lind Alliance (JLA), a UK‐based initiative established in 2004, aims to address this mismatch. Through Priority Setting Partnerships (PSP), the JLA supports the identification of the research questions that matter most to patients and the healthcare professionals that care for them. Sharing the outputs of PSPs with health research funders helps to align the work they fund towards addressing the areas of need prioritised by those directly affected and involved.

Previous successful PSPs have been conducted in diabetes in the UK. The type 1 diabetes PSP identified two questions in pregnancy, but both fell outside the top 10 priorities: ‘What impact do changing hormones, for example, during menstruation, pregnancy and menopause, have on blood glucose levels in women with type 1 diabetes?’; ‘Is it safe to continue insulin analogues in preconception and pregnancy in type 1 diabetes? ’ 8 No priorities specific to pregnancy were identified in the type 2 diabetes PSP top 10. 9 There have also been prioritisation exercises using different but overlapping methodology to PSPs in Canada and ​the USA but focussing on gestational diabetes. 10 , 11 However, women's health and pregnancy, particularly in relation to diabetes, are not prioritised, despite being consistently identified as an area of much needed research. 2 , 12 , 13

A PSP was therefore established between the University of Oxford, Diabetes UK, Diabetes Research and Wellness Foundation, JDRF the type 1 diabetes charity, and JLA, on World Diabetes Day 2018. The PSP aimed to find out the priorities for future research in diabetes and pregnancy, according to women and their support networks (families, partners, friends and carers) with experience of pregnancy, or planning pregnancy, with any type of diabetes and healthcare professionals.

2. PARTICIPANTS AND METHODS

The PSP employed the established JLA methodology. 14

2.1. Establishing the PSP

The PSP was overseen by a steering group representing key stakeholders (Supplementary Table  S1 ) and was chaired by a senior JLA advisor to ensure transparency of the process, and fair and equal involvement of all members. The group agreed the scope (Table  1 ) and was responsible for the completeness and appropriateness of the process, ensuring involvement of key stakeholder groups, approval of categorisation, grouping and phrasing of questions and interpretation of data. The protocol was prospectively published online at www.jla.nihr.ac.uk/priority‐setting‐partnerships/diabetes‐and‐pregnancy .

Scope of the James Lind Alliance priority setting partnership in diabetes and pregnancy

2.2. Initial survey—identifying questions

Women and their support networks (partners, families, friends and carers) with experience of pregnancy or planning pregnancy with any type of diabetes and healthcare professionals were invited via an open survey (26 June–15 November 2019) to suggest up to three questions they felt were important to answer. These could be any questions about the time before, during or after pregnancy with any type of diabetes. The scope was intentionally broad so that the submissions reflected public need.

The survey was available, in English, online and on paper. Targeted efforts to maximise responses, particularly from underrepresented groups, included direct approaches in diabetes and pregnancy clinics, outreach through relevant support groups, professional networks and conferences, diabetes, pregnancy and birth charities’ websites and communication channels and social media platforms. Concerted efforts were made to hear the voices of ethnic minorities working with organisations, support groups and community champions, which aim to address health inequalities. Representation across different ethnic minorities was monitored through broad groupings.

2.3. Categorisation and grouping

The submitted questions were organised using NVivo qualitative data analysis software (QSR International Pty Ltd. Version 12, 2018). Initial data cleaning was manually completed with any issues about the clinical aspects or interpretation of the submitted questions resolved with the steering group. The questions were analysed using content analysis with an initial stage of open coding of the question content, followed by the grouping of codes into categories. 15 To retain the integrity of the initial submissions, some questions were mapped to two or more categories. Independent second checks were conducted with members of the steering group to ensure potential impact of individual bias, and missed or misinterpreted categorisation was minimised. The steering group further consolidated the categories into groups and summarised the initial survey submissions under an indicative question. Indicative questions were formulated to capture the issues raised by the submitted questions within each group, whether originating from single or multiple respondents.

2.4. Evidence checking

A broad level and pragmatic evidence checking strategy was taken (January – May 2020) with the aim of ascertaining whether there was evidence of substantial uncertainty for each indicative question. The search was restricted to the Cochrane Database of Systematic Reviews ( www.cochranelibrary.com ) , systematic reviews published since 2017 using Medline or PubMed and National Institute of Clinical Excellence (NICE) and Scottish Intercollegiate Guidelines Network (SIGN) national diabetes and pregnancy guidelines. 5 , 6 Expanded evidence searches, including evidence highlighted by the steering group, were applied on a question‐by‐question basis after finalisation of the list of indicative questions. Research underway or recently completed but not available as published was not included as evidence. Where part of the question had sufficient evidence, the question phrasing was amended to reflect the remaining uncertainty.

2.5. Interim survey and prioritisation

The interim survey presented the long list of indicative questions in groups by phase. The order of the groups and individual questions within the groups were randomised each time the survey was entered. Participants were invited to pick up to 10 that they felt were most important to answer. Due to the Covid‐19 pandemic and social distancing restrictions, the survey was offered online only. Following a pilot mid‐May, the survey ran for nine weeks (29 May–31 July 2020).

2.6. Interim ranking and shortlisting

Every selection made by an individual respondent had equal weighting and no weighting changes were made if fewer than 10 questions were selected. However, to account for the differences in observed voting patterns and the number of respondents from different groups, ranking was tallied separately for: women and support networks, healthcare professionals, ethnic minorities and diabetes type, namely type 1, type 2 and other, and gestational diabetes. Within each of the groups, the total points for each question were put into rank order. The questions ranked in the top 10 for the two main groups (women/support networks and healthcare professionals), and the top three, and at least eight of the top 10, for each of the other subgroups, were shortlisted. In total, 18 questions were shortlisted for the final workshop, the maximum number considered feasible by the steering group for effective discussion online.

2.7. Final workshop—agreeing the top 10

The final stage involved a 1‐day workshop (2 October 2020) using the established JLA approach, which was adapted to be delivered online. 14 Twenty‐five participants were identified initially through phased targeted approaches to prioritise representation from ethnic minority groups, the devolved nations and Crown dependencies, support networks and specific health professions, for example, psychologists and GPs as underrepresented groups, followed by open invitation. Contacts collated through the surveys, and special interest groups and partner communication channels were used. Participants were screened for possible conflicts of interest and whether they were highly research active in the area. The participants were split into four breakout groups balanced by representation between women, support networks and healthcare professionals, and by experience of diabetes and healthcare specialist.

In breakout groups, the attendees participated in a series of discussion and ranking exercises to jointly rank the shortlist of indicative questions and agree the top 10 most important for future research to answer. The workshop and discussions were facilitated by trained JLA advisors to ensure equal and open participation. Four steering group members joined as observers only. Technical support was made available, and a contact point for emotional support was provided should any participant be upset by the process or discussions. The participants were invited to provide anonymous feedback on the prioritised questions and the workshop generally.

2.8. Ethics

The Medical Sciences Interdepartmental Research Ethics Committee, University of Oxford confirmed that the project did not require ethics committee approval.

The top 10 areas of most needed research in diabetes and pregnancy identified were: diabetes technology at any stage pre‐ to post‐pregnancy, the best test for diabetes during pregnancy, diet and lifestyle interventions for diabetes management during pregnancy, emotional and well‐being needs of women with diabetes pre‐ to post‐pregnancy, safe birth at full term, post‐natal care and support needs of women, diagnosis and management late in pregnancy, prevention of other types of diabetes in women with gestational diabetes, women's labour and birth experiences and choices and improving planning for pregnancy (Table  2 ).

Top 10 priorities for research in diabetes and pregnancy according to women, their support networks and healthcare professionals

The indicative questions are presented in final rank order, with phase of pregnancy, the question concerns, the number of initial survey questions grouped within the indicative question and the interim survey ranking results by group; HCPs ‐ Healthcare professionals; T1D – Type 1 diabetes; T2D – Type 2 diabetes; GDM – Gestational diabetes mellitus.

The responses at each stage of the process are summarised in Figure  1 . Participant demographics at each stage of the process are in Table  3 . The survey submission counts and rankings for the 60 indicative questions are available in Supplementary Table  S2 .

Summary of the James Lind Alliance prioritisation process showing how the top 10 questions in diabetes and pregnancy were identified. ^Ongoing studies were not included as evidence as it would not be possible to know if they answer the question

Participant demographics at each stage of the priority setting process

3.1. Initial survey

Four hundred and sixty‐six responses were submitted (64% women and support networks, 32% healthcare professionals and 4% other/not answered) suggesting 1161 questions covering the whole perinatal period (Supplementary Table  S3 ).

Initial questions submitted by women and support networks were mainly in relation to post‐birth effects on themselves and their child, diabetes management during pregnancy and understanding the risks for diabetes in pregnancy. The long‐term effects of diabetes in pregnancy on the child (risks of the child developing diabetes and any wider health effects) being the most frequently asked question (20.1% of women and support networks’ submissions). This group more specifically raised questions about breastfeeding (8.9%) and labour and birth (8.6%) in terms of informed choice, continuity/availability of care and emotional support more generally. Healthcare professionals’ questions were mainly about pre‐pregnancy care, and diagnosis and clinical management of diabetes in pregnancy. How to improve preconception care was most frequently asked (9.7% of healthcare professionals’ submissions), closely followed by the value and methods of diagnosis and management of diabetes late in pregnancy, that is, after 34 weeks (8.5%). Modes of delivering care, improving uptake and access to services and motivational interventions were more specifically raised by this group. Common to both groups were questions about individualised and risk‐based care, optimal management of diabetes, prevention of diabetes and safety of medications.

One hundred and forty‐two categories were extracted and broadly organised by the phase of pregnancy: pre‐pregnancy (62 questions, 6.3%), pregnancy (376, 38.2%), labour and birth (87, 8.8%) and post‐birth (373, 37.9%). Technology (20, 2.0%), mental health and well‐being (20, 2.0%) and health services (46, 4.7%) were identified as cross‐cutting categories. A total of 934 questions were within scope, of which 50 mapped to more than one category, and consolidated into 60 indicative questions. Rarely was there a need to specify a type of diabetes within an indicative question, which reflects the significant overlap in priorities regardless of diabetes type. The main distinctions were questions relating to gestational diabetes, due to its transient nature and diagnosis in pregnancy. All 60 indicative questions were considered to have substantial uncertainty following evidence checks. The evidence check summary is provided in Supplementary Table  S4 .

3.2. Interim survey

Six hundred and fourteen submissions (80% women and support networks and 20% healthcare professionals) were received in the interim survey. In the interim survey rankings, there were notable differences between women and support networks, and healthcare professionals (Figure  2a ). Four of the top 10 ranked questions for healthcare professionals were below the 45th ranking for women and support networks. Women and support networks ranked the long‐term effects of diabetes in pregnancy on the child's general health (non‐diabetes‐related) highest. Varying standards and advice across hospitals and giving birth at full term were also in the top three.

Interim survey question ranking comparisons between respondent groups. (a) Main groups: Indicative questions are ordered by rank position for women and support networks (60th to 1s t place; left to right). (b) Diabetes type: Indicative questions are ordered by rank position for the group that indicated interest/experience in gestational diabetes (60th to 1st place; left to right). ‘Other’ types were grouped with type 2 diabetes due to low number and greatest similarity in rankings. T1D – Type 1 diabetes; T2D – Type 2 diabetes; GDM – Gestational diabetes mellitus

Voting patterns varied for the main groups between surveys (Figure  3 ) with overall movement towards labour and birth and cross‐cutting categories. For example, despite being rarely asked in the initial survey, the use of technology became the highest ranked for healthcare professionals in the interim survey.

Survey submissions by main group. The initial and interim survey submissions for women and support networks, and healthcare professionals proportioned by phase of pregnancy

For ethnic minorities, representation was below national population figures for Black and Black British groups in both surveys (1.6% and 0.5% respectively; national 3.0%), and for Asian and Asian British groups (4.1%; national 7.0%) in the interim survey. With low numbers, the votes across 60 questions became too dispersed to discern a strong pattern. However, peaks in voting overlapped the top 10 for women and support networks, and healthcare professionals, with the eight highest voted questions already shortlisted for the final workshop.

Comparing the interim survey rankings by types of diabetes, there were some clear differences (Figure  2b ). Technology was ranked the top priority for type 1 and type 2/other diabetes groups, but ranked low for the gestational diabetes group. Post‐birth support came in second for type 1, but ranked low for both type 2/other and gestational diabetes groups, whereas testing for diabetes during pregnancy and prevention of developing diabetes ranked high for type 2/other and gestational diabetes groups but low for the type 1 diabetes group.

3.3. Final workshop and top 10

The diversity and balance in experiences and expertise of workshop attendees were strong (Table  3 ). Participants uncovered unexpected overlaps between questions, for example, technology for diabetes care was considered to include telemedicine which was subsequently ranked lower; labour and birth choices was considered to overlap with safety of giving birth at full term and the need for induction. Groups also highlighted recurring themes that linked with multiple questions. For example, technology was considered to improve understanding of diabetes management and reduce burden, linking with mental health and well‐being. Therefore, with further such links highlighted, both these questions were more highly ranked as the discussions progressed.

Consistently, top ranking questions through the surveys were regarding the long‐term health impact of maternal diabetes on the child. The questions submitted in the initial survey were clearly split into risk of the child developing diabetes or the risk of wider health conditions. Ranking in the top 10 of all groups, both questions were shortlisted for the final workshop. However, neither reached the final top 10 (positions 11 and 12 respectively). Workshop discussions and post‐workshop feedback from participants indicated several reasons for this apparent discrepancy. Firstly, the questions were considered to be addressed within the broader research agenda of child health, and not necessarily within pregnancy or women's health. The other questions all affected or contributed to the child outcomes, so it was felt they would help address these questions too. Some participants considered that the answers to the questions about long‐term child outcomes would add burden to women already concerned by many factors associated with dealing with the responsibility of a pregnancy and diabetes. For some, ‘how can it be prevented’ was important wording, as it balanced the concerns of adding burden of information on risks, with learning about what can be done to prevent these being realised. One of the questions did not include the specific wording about prevention and so was ranked less highly. A further possible reason for these questions receiving less priority was because the votes were split; if presented as a single question, this may have meant a higher final ranking. A final possible reason may have been that the workshop consisted of people with recent experience, that is, within the last five years.

Feedback was received from 10 women and support network representatives, and 10 healthcare professionals from the workshop:

‘It was really good having different perspectives. Moving to a second smaller group was also useful, as it showed how varied priorities can be between 2 groups, despite having a similar mix of people from the different backgrounds’ ‘Although the final top 10 was not the same as my personal top 10 that I had prepared, discussing all of the 18 questions, and reflecting on them as a group, meant I was satisfied with it. It was good to hear the views of women with different forms of diabetes and look at the questions from their perspective’ .

Further excerpts of the feedback and the JLA’s review of the online format of the workshop are available at www.jla.nihr.ac.uk/development‐of‐online‐priority‐setting‐workshop.htm .

4. DISCUSSION

4.1. main findings.

We supported women, their support networks and healthcare professionals to jointly identify the top 10 questions for research in diabetes and pregnancy. The final list includes priorities of relevance to all the stakeholders, all types of diabetes and preconception to long‐term post‐pregnancy. These questions were generated through a robust and inclusive process, which can be trusted and used by funders of research and researchers to inform their research activities towards addressing evidence gaps of great need and impact.

There is consistency across many areas also identified by the modified prioritisation exercises in Canada and the USA focussing on gestational diabetes, such as screening, risk factors, prevention and clinical management of diabetes in the woman. 10 , 11 For example, whether there is a better test for diabetes during pregnancy than the oral glucose tolerance test was ranked joint first for similar reasons of application, interpretation and practicality, with no apparent consensus on the thresholds, methods and timing to test for diabetes in pregnancy, due to small‐sized studies. Our participants raised further questions such as the possible use of testing to predict gestational diabetes, the stratification of testing by risk and testing within special populations, such as those who have undergone gastric bypass surgery, and open up further opportunities for research.

There are also some clear differences in priorities including preconception care, late pregnancy diagnosis, labour and birth experiences and post‐natal care needs, particularly breastfeeding. The role of diabetes technology was ranked joint first position, and while explicably not featuring in the gestational diabetes exercises, technology‐specific questions formed the top three of the JDRF UK type 1 diabetes PSP final top 10. There have been significant developments in diabetes technology in the last few years with ongoing innovations. 16 In the time since the final workshop, the National Institute of Clinical Excellence has completed their review and approved the funding of continuous glucose monitoring for pregnant women with type 1 diabetes, in line with The NHS Long Term Plan . 17 , 18 However, our results endorse their recommendations for further large‐scale research in different monitoring methods and systems (i.e. insulin pumps) in women with diabetes of different types, through all stages preconception to post‐pregnancy, and importantly with wider population diversity. There are also wider facets to consider such as apps, automation, data integration platforms and data sharing in supporting the management of diabetes.

Risks to the child's health was the number one priority identified by women in the Canadian exercise, and ranked highest in the USA exercise, but strikingly fell outside our top 10. However, the possible reasons for this, based on differential prioritisation of two related summary questions, made clear that the impact on the child is still considered highly important for separate study.

Important overarching issues were noted around continuity of care and support (particularly post‐birth), consistency in care standards and advice, joint decision making (particularly in labour and birth) and the burden for women of being diagnosed with and managing diabetes. The consequences on women's well‐being and mental health were at the core of much of the discussion at the final workshop. The initial survey submissions also raised further unanswered questions about the impact of a ‘medicalised’ pregnancy, withdrawal of intensive clinical involvement postpartum and support needs of women if adverse outcomes in their child linked to diabetes are realised. There is ongoing lack of evidence on the support pregnant women with diabetes may need as previously echoed by the gestational diabetes prioritisation exercises. However, the Diabetes UK Too Often Missing report highlights pregnancy as a time of particular high risk for emotional and psychosocial impact of diabetes requiring increased awareness and support. 19

5. STRENGTHS AND LIMITATIONS

This is the first priority setting exercise to focus on all different types of diabetes and at any stage before, during and after pregnancy, including long‐term post‐pregnancy.

The initial survey was completed in 2019 before the Covid‐19 pandemic and lockdowns, but the interim prioritisation survey and final workshop took place during the pandemic over 2020. Due to the restrictions, the interim survey and final workshop could only be completed online. Despite an increased response rate in the interim survey, the online‐only nature will have imposed limitations on who could take part. Reliance on online‐only means of communication and participation may also have affected outreach, particularly to ethnic minority groups, which was achieved in the initial survey mainly through the support of community champions and face‐to‐face approaches in hospital clinics. However, despite our best efforts, representation was below what may be expected based on national population statistics, particularly Black and Black British groups. 20 Therefore, the results may not be representative of the priorities of ethnic minority groups.

Although the indicative questions were established before the 2020 Covid‐19 pandemic, it is possible that the voting in the interim survey and, consequently, the shortlist of questions for the final workshop have been influenced. The final workshop participants were advised to consider the shortlisted questions thinking longer term beyond the pandemic so that this would not unduly influence the results for the future. More generally, it is not assured that if the exercise was redone that the same priorities would be identified and assigned the same rank.

6. CONCLUSIONS

Further research is needed to provide evidence‐based healthcare for women, with or at risk of diabetes complications, who are planning pregnancy or are pregnant, to ensure the best outcomes for them and their children in the short and long term.

The Covid‐19 pandemic has highlighted the importance of inclusive research. Pregnant women, those planning pregnancy or breastfeeding are often actively excluded from clinical trials, perpetuating the population as a vulnerable group. 21 The addition of co‐morbidities, such as diabetes, complicates matters further. As well as improved health and well‐being for generations of families, interventions which improve outcomes for pregnancy with diabetes provide significant opportunity in terms of cost savings. 13

The questions identified are areas that still have significant uncertainty and are considered to be of most importance by the beneficiaries of that research. This work presents further opportunity for funders and researchers to focus future research to address the priorities of women, support networks and health professionals.

CONFLICT OF INTEREST

GA, FA, IB, NB, CB, SC, DC, KC, JG, MK, JLZ, FM, RM, NM, AM, JO, NR, CS, KS, AS and JAS none declared. JEH is a member of the NICE Diabetes Committee and holds a UKRI Future Leaders fellowship. HRM sits on a scientific advisory board for Medtronic (insulin pump and CGM manufacturer) and has received research support from Medtronic, Dexcom and Abbott Diabetes Care Inc. (CGM devices). MWJS has received honoraria from Astellas, AstraZeneca, Eli Lilly, Merck, Sanofi, Eisai and Bristol Myers Squibb, and has contributed to advisory boards for Novo Nordisk, Eisai and Servier.

AUTHOR CONTRIBUTIONS

GA and MK conceived the idea for the project and led the PSP. GA had full access to the data, coordinated the PSP process, drafted the surveys, analysed and led the interpretation of the data and wrote the first draft of the manuscript. GA, FA, IB, NB, CB, SC, DC, KC, MD, JG, JH, MK, AM, HM, JO, CS, KS, AS and MS are members of the Diabetes and Pregnancy Priority Setting Partnership (PSP) steering group and were responsible for the development of the protocol, and conduct, integrity and oversight of this work. All steering group members contributed to the outreach and communication activities to maximise participation and awareness. KC facilitated and chaired the PSP process, steering group and final workshop. GA, JS, NM, NR, RM, JL‐Z, FM and MK completed the evidence checks. MK and KC are joint senior authors. All authors reviewed and approved the manuscript and revised it for intellectual content.

Supporting information

Acknowledgements.

The project team thank everyone who contributed through the surveys and took part in the final workshop, our experts by experience steering group members, Rachel Connor, JDRF UK, and Lucy McKillop, University of Oxford, who were advisors to the steering group, our partners and the many people and organisations who supported at various stages throughout the project, sharing their time, expertise, communications and meeting facilities, without whom this project would not have been successful.

Marian Knight and Katherine Cowan should be considered joint senior authors.

The Diabetes and Pregnancy Priority Setting Partnership was funded by Diabetes Research and Wellness Foundation (SCA/PP/12/19), and John Fell Fund and Nuffield Department of Population Health, University of Oxford. The funders had no involvement in the design, conduct, analyses and interpretation of data, or the reporting and submission of results of this project. MK is an NIHR Senior Investigator. The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care.

DATA AVAILABILITY STATEMENT

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Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis

• Related content
• Peer review
• Wenrui Ye , doctoral student 1 2 ,
• Cong Luo , doctoral student 3 ,
• Jing Huang , assistant professor 4 5 ,
• Chenglong Li , doctoral student 1 ,
• Zhixiong Liu , professor 1 2 ,
• Fangkun Liu , assistant professor 1 2
• 1 Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
• 2 Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China
• 3 Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China
• 4 National Clinical Research Centre for Mental Disorders, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
• 5 Department of Psychiatry, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
• Correspondence to: F Liu liufangkun{at}csu.edu.cn
• Accepted 18 April 2022

Objective To investigate the association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for at least minimal confounding factors.

Design Systematic review and meta-analysis.

Data sources Web of Science, PubMed, Medline, and Cochrane Database of Systematic Reviews, from 1 January 1990 to 1 November 2021.

Review methods Cohort studies and control arms of trials reporting complications of pregnancy in women with gestational diabetes mellitus were eligible for inclusion. Based on the use of insulin, studies were divided into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. Subgroup analyses were performed based on the status of the country (developed or developing), quality of the study, diagnostic criteria, and screening method. Meta-regression models were applied based on the proportion of patients who had received insulin.

Results 156 studies with 7 506 061 pregnancies were included, and 50 (32.1%) showed a low or medium risk of bias. In studies with no insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and infant born large for gestational age (1.57, 1.25 to 1.97). In studies with insulin use, when adjusted for confounders, the odds of having an infant large for gestational age (odds ratio 1.61, 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31), were higher in women with gestational diabetes mellitus than in those without diabetes. No clear evidence was found for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and small for gestational age between women with and without gestational diabetes mellitus after adjusting for confounders. Country status, adjustment for body mass index, and screening methods significantly contributed to heterogeneity between studies for several adverse outcomes of pregnancy.

Conclusions When adjusted for confounders, gestational diabetes mellitus was significantly associated with pregnancy complications. The findings contribute to a more comprehensive understanding of the adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

Review registration PROSPERO CRD42021265837.

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Introduction

Gestational diabetes mellitus is a common chronic disease in pregnancy that impairs the health of several million women worldwide. 1 2 Formally recognised by O’Sullivan and Mahan in 1964, 3 gestational diabetes mellitus is defined as hyperglycaemia first detected during pregnancy. 4 With the incidence of obesity worldwide reaching epidemic levels, the number of pregnant women diagnosed as having gestational diabetes mellitus is growing, and these women have an increased risk of a range of complications of pregnancy. 5 Quantification of the risk or odds of possible adverse outcomes of pregnancy is needed for prevention, risk assessment, and patient education.

In 2008, the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) study recruited a large multinational cohort and clarified the risks of adverse outcomes associated with hyperglycaemia. The findings of the study showed that maternal hyperglycaemia independently increased the risk of preterm delivery, caesarean delivery, infants born large for gestational age, admission to a neonatal intensive care unit, neonatal hypoglycaemia, and hyperbilirubinaemia. 6 The obstetric risks associated with diabetes, such as pregnancy induced hypertension, macrosomia, congenital malformations, and neonatal hypoglycaemia, have been reported in several large scale studies. 7 8 9 10 11 12 The HAPO study did not adjust for some confounders, however, such as maternal body mass index, and did not report on stillbirths and neonatal respiratory distress syndrome, raising uncertainty about these outcomes. Other important pregnancy outcomes, such as preterm delivery, neonatal death, and low Apgar score in gestational diabetes mellitus, were poorly reported. No comprehensive study has assessed the relation between gestational diabetes mellitus and various maternal and fetal adverse outcomes after adjustment for confounders. Also, some cohort studies were restricted to specific clinical centres and regions, limiting their generalisation to more diverse populations.

By collating the available evidence, we conducted a systematic review and meta-analysis to quantify the short term outcomes in pregnancies complicated by gestational diabetes mellitus. We evaluated adjusted associations between gestational diabetes mellitus and various adverse outcomes of pregnancy.

This meta-analysis was conducted according to the recommendations of Cochrane Systematic Reviews, and our findings are reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (table S16). The study was prospectively registered in the international database of prospectively registered systematic reviews (PROSPERO CRD42021265837).

Search strategy and selection criteria

We searched the electronic databases PubMed, Web of Science, Medline, and the Cochrane Database of Systematic Reviews with the keywords: “pregnan*,” “gestatio*” or “matern*” together with “diabete*,” “hyperglycaemia,” “insulin,” “glucose,” or “glucose tolerance test*” to represent the exposed populations, and combined them with terms related to outcomes, such as “pregnan* outcome*,” “obstetric* complicat*,” “pregnan* disorder*,” “obstetric* outcome*,” “haemorrhage,” “induc*,” “instrumental,” “caesarean section,” “dystocia,” “hypertensi*,” “eclampsia,” “premature rupture of membrane,” “PROM,” “preter*,” “macrosomia,” and “malformation,” as well as some abbreviated diagnostic criteria, such as “IADPSG,” “DIPSI,” and “ADIPS” (table S1). The search strategy was appropriately translated for the other databases. We included observational cohort studies and control arms of trials, conducted after 1990, that strictly defined non-gestational diabetes mellitus (control) and gestational diabetes mellitus (exposed) populations and had definite diagnostic criteria for gestational diabetes mellitus (table S2) and various adverse outcomes of pregnancy.

Exclusion criteria were: studies published in languages other than English; studies with no diagnostic criteria for gestational diabetes mellitus (eg, self-reported gestational diabetes mellitus, gestational diabetes mellitus identified by codes from the International Classification of Diseases or questionnaires); studies published after 1990 that recorded pregnancy outcomes before 1990; studies of specific populations (eg, only pregnant women aged 30-34 years, 13 only twin pregnancies 14 15 16 ); studies with a sample size <300, because we postulated that these studies might not be adequate to detect outcomes within each group; and studies published in the form of an abstract, letter, or case report.

We also manually retrieved reference lists of relevant reviews or meta-analyses. Three reviewers (WY, CL, and JH) independently searched and assessed the literature for inclusion in our meta-analysis. The reviewers screened the titles and abstracts to exclude ineligible studies. The full texts of relevant records were then retrieved and assessed. Any discrepancies were resolved after discussion with another author (FL).

Data extraction

Three independent researchers (WY, CL, and JH) extracted data from the included studies with a predesigned form. If the data were not presented, we contacted the corresponding authors to request access to the data. We extracted data from the most recent study or the one with the largest sample size when a cohort was reported twice or more. Sociodemographic and clinical data were extracted based on: year of publication, location of the study (country and continent), design of the study (prospective or retrospective cohort), screening method and diagnostic criteria for gestational diabetes mellitus, adjustment for conventional prognostic factors (defined as maternal age, pregestational body mass index, gestational weight gain, gravidity, parity, smoking history, and chronic hypertension), and the proportion of patients with gestational diabetes mellitus who were receiving insulin. For studies that adopted various diagnostic criteria for gestational diabetes mellitus, we extracted the most recent or most widely accepted one for subsequent analysis. For studies adopting multivariate logistic regression for adjustment of confounders, we extracted adjusted odds ratios and synthesised them in subsequent analyses. For unadjusted studies, we calculated risk ratios and 95% confidence intervals based on the extracted data.

Studies of women with gestational diabetes mellitus that evaluated the risk or odds of maternal or neonatal complications were included. We assessed the maternal outcomes pre-eclampsia, induction of labour, instrumental delivery, caesarean section, shoulder dystocia, premature rupture of membrane, and postpartum haemorrhage. Fetal or neonatal outcomes assessed were stillbirth, neonatal death, congenital malformation, preterm birth, macrosomia, low birth weight, large for gestational age, small for gestational age, neonatal hypoglycaemia, neonatal jaundice, respiratory distress syndrome, low Apgar score, and admission to the neonatal intensive care unit. Table S3 provides detailed definitions of these adverse outcomes of pregnancy.

Risk-of-bias assessment

A modified Newcastle-Ottawa scale was used to assess the methodological quality of the selection, comparability, and outcome of the included studies (table S4). Three independent reviewers (WY, CL, and JH) performed the quality assessment and scored the studies for adherence to the prespecified criteria. A study that scored one for selection or outcome, or zero for any of the three domains, was considered to have a high risk of bias. Studies that scored two or three for selection, one for comparability, and two for outcome were regarded as having a medium risk of bias. Studies that scored four for selection, two for comparability, and three for outcome were considered to have a low risk of bias. A lower risk of bias denotes higher quality.

Data synthesis and analysis

Pregnant women were divided into two groups (gestational diabetes mellitus and non-gestational diabetes mellitus) based on the diagnostic criteria in each study. Studies were considered adjusted if they adjusted for at least one of seven confounding factors (maternal age, pregestational body mass index, gestational weight gain, gravidity, parity, smoking history, and chronic hypertension). For each adjusted study, we transformed the odds ratio estimate and its corresponding standard error to natural logarithms to stabilise the variance and normalise their distributions. Summary odds ratio estimates and their 95% confidence intervals were estimated by a random effects model with the inverse variance method. We reported the results as odds ratio with 95% confidence intervals to reflect the uncertainty of point estimates. Unadjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy were quantified and summarised (table S6 and table S14). Thereafter, heterogeneity across the studies was evaluated with the τ 2 statistics and Cochran’s Q test. 17 18 Cochran’s Q test assessed interactions between subgroups. 18

We performed preplanned subgroup analyses for factors that could potentially affect gestational diabetes mellitus or adverse outcomes of pregnancy: country status (developing or developed country according to the International Monetary Fund ( www.imf.org/external/pubs/ft/weo/2020/01/weodata/groups.htm ), risk of bias (low, medium, or high), screening method (universal one step, universal glucose challenge test, or selective screening based on risk factors), diagnostic criteria for gestational diabetes mellitus (World Health Organization 1999, Carpenter-Coustan criteria, International Association of Diabetes and Pregnancy Study Groups (IADPSG), or other), and control for body mass index. We assessed small study effects with funnel plots by plotting the natural logarithm of the odds ratios against the inverse of the standard errors, and asymmetry was assessed with Egger’s test. 19 A meta-regression model was used to investigate the associations between study effect size and proportion of patients who received insulin in the gestational diabetes mellitus population. Next, we performed sensitivity analyses by omitting each study individually and recalculating the pooled effect size estimates for the remaining studies to assess the effect of individual studies on the pooled results. All analyses were performed with R language (version 4.1.2, www.r-project.org ) and meta package (version 5.1-0). We adopted the treatment arm continuity correction to deal with a zero cell count 20 and the Hartung-Knapp adjustment for random effects meta models. 21 22

Patient and public involvement

The experience in residency training in the department of obstetrics and the concerns about the association between gestational diabetes mellitus and health outcomes inspired the author team to perform this study. We also asked advice from the obstetrician and patients with gestational diabetes mellitus about which outcomes could be included. The covid-19 restrictions meant that we sought opinions from only a limited number of patients in outpatient settings.

Characteristics of included studies

Of the 44 993 studies identified, 156 studies, 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 involving 7 506 061 pregnancies, were eligible for the analysis of adverse outcomes in pregnancy ( fig 1 ). Of the 156 primary studies, 133 (85.3%) reported maternal outcomes and 151 (96.8%) reported neonatal outcomes. Most studies were conducted in Asia (39.5%), Europe (25.5%), and North America (15.4%). Eighty four (53.8%) studies were performed in developed countries. Based on the Newcastle-Ottawa scale, 50 (32.1%) of the 156 included studies showed a low or medium risk of bias and 106 (67.9%) had a high risk of bias. Patients in 35 (22.4%) of the 156 studies never used insulin during the course of the disease and 63 studies (40.4%) reported treatment with insulin in different proportions of patients. The remaining 58 studies did not report information about the use of insulin. Table 1 summarises the characteristics of the study population, including continent or region, country, screening methods, and diagnostic criteria for the included studies. Table S5 lists the key excluded studies.

Search and selection of studies for inclusion

Characteristics of study population

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Associations between gestational diabetes mellitus and adverse outcomes of pregnancy

Based on the use of insulin in each study, we classified the studies into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. We reported odds ratios with 95% confidence intervals after controlling for at least minimal confounding factors. In studies with no insulin use, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and an infant born large for gestational age (1.57, 1.25 to 1.97) ( fig 2 and fig S1). In studies with insulin use, adjusted for confounders, the odds of an infant born large for gestational age (odds ratio 1.61, 95% confidence interval 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31) were higher in women with than in those without gestational diabetes mellitus ( fig 3) . In studies that did not report the use of insulin, women with gestational diabetes mellitus had increased odds ratio for pre-eclampsia (1.46, 1.21 to 1.78), induction of labour (1.88, 1.16 to 3.04), caesarean section (1.38, 1.20 to 1.58), premature rupture of membrane (1.13, 1.06 to 1.20), congenital malformation (1.18, 1.10 to 1.26), preterm delivery (1.51, 1.19 to 1.93), macrosomia (1.48, 1.13 to 1.95), neonatal hypoglycaemia (11.71, 7.49 to 18.30), and admission to the neonatal intensive care unit (2.28, 1.26 to 4.13) (figs S3 and S4). We found no clear evidence for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and infant born small for gestational age between women with and without gestational diabetes mellitus in all three subgroups ( fig 2, fig 3, and figs S1-S4). Table S6 shows the unadjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy.

Findings of meta-analysis of association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjusting for at least minimal confounding factors, in studies in patients who never used insulin during the course of the disease (no insulin use). NA=not applicable

Findings of meta-analysis of association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjusting for at least minimal confounding factors, in studies where different proportions of patients were treated with insulin (insulin use). NA=not applicable

Subgroup, meta-regression, and sensitivity analyses

Subgroup analyses, based on risk of bias, did not show significant heterogeneity between the subgroups of women with and without gestational diabetes mellitus for most adverse outcomes of pregnancy ( table 2 and table 3 ), except for admission to the neonatal intensive care unit in studies where insulin use was not reported (table S7). Significant differences between subgroups were reported for country status and macrosomia in studies with (P<0.001) and without (P=0.001) insulin use ( table 2 and table 3 ), and for macrosomia (P=0.02) and infants born large for gestational age (P<0.001) based on adjustment for body mass index in studies with insulin use (table S8). Screening methods contributed significantly to the heterogeneity between studies for caesarean section (P<0.001) and admission to the neonatal intensive care unit (P<0.001) in studies where insulin use was not reported (table S7). In most outcomes, the estimated odds were lower in studies that used universal one step screening than those that adopted the universal glucose challenge test or selective screening methods ( table 2 and table 3 ). Diagnostic criteria were not related to heterogeneity between the studies for all of the study subgroups (no insulin use, insulin use, insulin use not reported). The subgroup analysis was performed only for outcomes including ≥6 studies.

Subgroup analysis according to country status, diagnostic criteria, screening method, and risk of bias for adverse outcomes of pregnancy in women with gestational diabetes mellitus compared with women without gestational diabetes mellitus in studies with no insulin use

Subgroup analysis according to country status, diagnostic criteria, screening method, and risk of bias for adverse outcomes of pregnancy in women with gestational diabetes mellitus compared with women without gestational diabetes mellitus in studies with insulin use

We applied meta-regression models to evaluate the modification power of the proportion of patients with insulin use when sufficient data were available. Significant associations were found between effect size estimate and proportion of patients who had received insulin for the adverse outcomes caesarean section (estimate=0.0068, P=0.04) and preterm delivery (estimate=−0.0069, P=0.04) (table S9).

In sensitivity analyses, most pooled estimates were not significantly different when a study was omitted, suggesting that no one study had a large effect on the pooled estimate. The pooled estimate effect became significant (P=0.005) for low birth weight when the study of Lu et al 99 was omitted, however (fig S5). We found evidence of a small study effect only for caesarean section (Egger’s P=0.01, table S10). Figure S6 shows the funnel plots of the included studies for various adverse outcomes (≥10 studies).

Principal findings

We have provided quantitative estimates for the associations between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for confounding factors, through a systematic search and comprehensive meta-analysis. Compared with patients with normoglycaemia during pregnancy, patients with gestational diabetes mellitus had increased odds of caesarean section, preterm delivery, low one minute Apgar score, macrosomia, and an infant born large for gestational age in studies where insulin was not used. In studies with insulin use, patients with gestational diabetes mellitus had an increased odds of an infant born large for gestational age, or with respiratory distress syndrome or neonatal jaundice, or requiring admission to the neonatal intensive care unit. Our study was a comprehensive analysis, quantifying the adjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy. The study provides updated critical information on gestational diabetes mellitus and adverse outcomes of pregnancy and would facilitate counselling of women with gestational diabetes mellitus before delivery.

To examine the heterogeneity conferred by different severities of gestational diabetes mellitus, we categorised the studies by use of insulin. Insulin is considered the standard treatment for the management of gestational diabetes mellitus when adequate glucose levels are not achieved with nutrition and exercise. 179 Our meta-regression showed that the proportion of patients who had received insulin was significantly associated with the effect size estimate of adverse outcomes, including caesarean section (P=0.04) and preterm delivery (P=0.04). This finding might be the result of a positive linear association between glucose concentrations and adverse outcomes of pregnancy, as previously reported. 180 However, the proportion of patients who were receiving insulin indicates the percentage of patients with poor glycaemic control in the population and cannot reflect glycaemic control at the individual level.

Screening methods for gestational diabetes mellitus have changed over time, from the earliest selective screening (based on risk factors) to universal screening by the glucose challenge test or the oral glucose tolerance test, recommended by the US Preventive Services Task Force (2014) 181 and the American Diabetes Association (2020). 182 The diagnostic accuracy of these screening methods varied, contributing to heterogeneity in the analysis.

Several studies have tried to pool the effects of gestational diabetes mellitus on pregnancy outcomes, but most focused on one outcome, such as congenital malformations, 183 184 macrosomia, 185 186 or respiratory distress syndrome. 187 Our findings of increased odds of macrosomia in gestational diabetes mellitus in studies where insulin was not used, and respiratory distress syndrome in studies with insulin use, were similar to the results of previous meta-analyses. 188 189 The increased odds of neonatal respiratory distress syndrome, along with low Apgar scores, might be attributed to disruption of the integrity and composition of fetal pulmonary surfactant because gestational diabetes mellitus can delay the secretion of phosphatidylglycerol, an essential lipid component of surfactants. 190

Although we detected no significant association between gestational diabetes mellitus and mortality events, the observed increase in the odds of neonatal death (odds ratio 1.59 in studies that did not report the use of insulin) should be emphasised to obstetricians and pregnant women because its incidence was low (eg, 3.75% 87 ). The increased odds of neonatal death could result from several lethal complications, such as respiratory distress syndrome, neonatal hypoglycaemia (3.94-11.71-fold greater odds), and jaundice. These respiratory and metabolic disorders might increase the likelihood of admission to the neonatal intensive care unit.

For the maternal adverse outcomes, women with gestational diabetes mellitus had increased odds of pre-eclampsia, induction of labour, and caesarean section, consistent with findings in previous studies. 126 Our study identified a 1.24-1.46-fold greater odds of pre-eclampsia between patients with and without gestational diabetes mellitus, which was similar to previous results. 191

Strengths and limitations of the study

Our study included more studies than previous meta-analyses and covered a range of maternal and fetal outcomes, allowing more comprehensive comparisons among these outcomes based on the use of insulin and different subgroup analyses. The odds of adverse fetal outcomes, including respiratory distress syndrome (P=0.002), neonatal jaundice (P=0.05), and admission to the neonatal intensive care unit (P=0.005), were significantly increased in studies with insulin use, implicating their close relation with glycaemic control. The findings of this meta-analysis support the need for an improved understanding of the pathophysiology of gestational diabetes mellitus to inform the prediction of risk and for precautions to be taken to reduce adverse outcomes of pregnancy.

The study had some limitations. Firstly, adjustment for at least one confounder had limited power to deal with potential confounding effects. The set of adjustment factors was different across studies, however, and defining a broader set of multiple adjustment variables was difficult. This major concern should be looked at in future well designed prospective cohort studies, where important prognostic factors are controlled. Secondly, overt diabetes was not clearly defined until the IADPSG diagnostic criteria were proposed in 2010. Therefore, overt diabetes or pre-existing diabetes might have been included in the gestational diabetes mellitus groups if studies were conducted before 2010 or adopted earlier diagnostic criteria. Hence we cannot rule out that some adverse effects in newborns were related to prolonged maternal hyperglycaemia. Thirdly, we divided and analysed the subgroups based on insulin use because insulin is considered the standard treatment for the management of gestational diabetes mellitus and can reflect the level of glycaemic control. Accurately determining the degree of diabetic control in patients with gestational diabetes mellitus was difficult, however. Finally, a few pregnancy outcomes were not accurately defined in studies included in our analysis. Stillbirth, for example, was defined as death after the 20th or 28th week of pregnancy, based on different criteria, but some studies did not clearly state the definition of stillbirth used in their methods. Therefore, we considered stillbirth as an outcome based on the clinical diagnosis in the studies, which might have caused potential bias in the analysis.

Conclusions

We performed a meta-analysis of the association between gestational diabetes mellitus and adverse outcomes of pregnancy in more than seven million women. Gestational diabetes mellitus was significantly associated with a range of pregnancy complications when adjusted for confounders. Our findings contribute to a more comprehensive understanding of adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

What is already known on this topic

The incidence of gestational diabetes mellitus is gradually increasing and is associated with a range of complications for the mother and fetus or neonate

Pregnancy outcomes in gestational diabetes mellitus, such as neonatal death and low Apgar score, have not been considered in large cohort studies

Comprehensive systematic reviews and meta-analyses assessing the association between gestational diabetes mellitus and adverse pregnancy outcomes are lacking

What this study adds

This systematic review and meta-analysis showed that in studies where insulin was not used, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean delivery, preterm delivery, low one minute Apgar score, macrosomia, and an infant large for gestational age in the pregnancy outcomes

In studies with insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of an infant large for gestational age, or with respiratory distress syndrome or neonatal jaundice, or requiring admission to the neonatal intensive care unit

Future primary studies should routinely consider adjusting for a more complete set of prognostic factors

Ethics statements

Ethical approval.

Not required.

Data availability statement

Table S11 provides details of adjustment for core confounders. Supplementary data files contain all of the raw tabulated data for the systematic review (table S12). Tables S13-15 provide the raw data and R language codes used for the meta-analysis.

Contributors: WY and FL developed the initial idea for the study, designed the scope, planned the methodological approach, wrote the computer code and performed the meta-analysis. WY and CL coordinated the systematic review process, wrote the systematic review protocol, completed the PROSPERO registration, and extracted the data for further analysis. ZL coordinated the systematic review update. WY, JH, and FL defined the search strings, executed the search, exported the results, and removed duplicate records. WY, CL, ZL, and FL screened the abstracts and texts for the systematic review, extracted relevant data from the systematic review articles, and performed quality assessment. WY, ZL, and FL wrote the first draft of the manuscript and all authors contributed to critically revising the manuscript. ZL and FL are the study guarantors. ZL and FL are senior and corresponding authors who contributed equally to this study. All authors had full access to all the data in the study, and the corresponding authors had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: The research was funded by the National Natural Science Foundation of China (grants 82001223 and 81901401), and the Natural Science Foundation for Young Scientist of Hunan Province, China (grant 2019JJ50952). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the National Natural Science Foundation of China and the Natural Science Foundation for Young Scientist of Hunan Province, China for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

The lead author (the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: The dissemination plan targets a wide audience, including members of the public, patients, patient and public communities, health professionals, and experts in the specialty through various channels: written communication, events and conferences, networks, and social media.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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• Published: 27 February 2024

Metabolic diseases

New insights into the genetics of diabetes in pregnancy

• Aminata Hallimat Cissé 1 &
• Rachel M. Freathy   ORCID: orcid.org/0000-0003-4152-2238 1  

Nature Genetics volume  56 ,  pages 358–359 ( 2024 ) Cite this article

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• Genome-wide association studies
• Gestational diabetes

Gestational diabetes is a complex metabolic condition thought to have a strong genetic predisposition. A large genome-wide association study of participants from Finland sheds light on the genetic contributors, opening avenues for research into mechanisms that underlie glucose regulation in pregnancy to improve the health of mothers and babies.

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Cissé, A.H., Freathy, R.M. New insights into the genetics of diabetes in pregnancy. Nat Genet 56 , 358–359 (2024). https://doi.org/10.1038/s41588-024-01675-0

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• Published: 08 August 2022

A scoping review of gestational diabetes mellitus healthcare: experiences of care reported by pregnant women internationally

• Sheila Pham 1 ,
• Kate Churruca 1 ,
• Louise A. Ellis 1 &
• Jeffrey Braithwaite 1  

BMC Pregnancy and Childbirth volume  22 , Article number:  627 ( 2022 ) Cite this article

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Gestational diabetes mellitus (GDM) is a condition associated with pregnancy that engenders additional healthcare demand. A growing body of research includes empirical studies focused on pregnant women’s GDM healthcare experiences. The aim of this scoping review is to map findings, highlight gaps and investigate the way research has been conducted into the healthcare experiences of women with GDM.

A systematic search of primary research using a number of databases was conducted in September 2021. Studies were included if they had an explicit aim of focusing on GDM and included direct reporting of participants’ experiences of healthcare. Key data from each study was extracted into a purposely-designed form and synthesised using descriptive statistics and thematic analysis.

Fifty-seven articles were included in the analysis. The majority of studies used qualitative methodology, and did not have an explicit theoretical orientation. Most studies were conducted in urban areas of high-income countries and recruitment and research was almost fully conducted in clinical and other healthcare settings. Women found inadequate information a key challenge, and support from healthcare providers a critical factor. Experiences of prescribed diet, medication and monitoring greatly varied across settings. Additional costs associated with managing GDM was cited as a problem in some studies. Overall, women reported significant mental distress in relation to their experience of GDM.

Conclusions

This scoping review draws together reported healthcare experiences of pregnant women with GDM from around the world. Commonalities and differences in the global patient experience of GDM healthcare are identified.

Peer Review reports

Gestational diabetes mellitus (GDM) is defined as any degree of hyperglycaemia recognised for the first time during pregnancy, including type 2 diabetes mellitus diagnosed during pregnancy as well as true GDM which develops in pregnancy [ 1 ]. GDM is associated with a number of adverse maternal and neonatal outcomes, including increased birth weight and increased cord-blood serum C-peptide levels [ 2 ], as well as greater risk of future diabetes [ 3 ].

The global incidence and health burden of GDM is increasing [ 4 ] and the cost of healthcare relating to GDM significant. In 2019, the International Diabetes Federation estimated the annual global diabetes-related health expenditure, which includes GDM, reached USD$760 billion [ 4 ]. In China, for example, the annual societal economic burden of GDM is estimated to be ¥19.36 billion ($5.59 billion USD) [ 5 ].

GDM is estimated to affect 7–10% of all pregnancies worldwide, though the absence of a universal gold standard for screening means it is difficult to achieve an accurate estimation of prevalence [ 6 ], and the prevalence of GDM varies considerably depending on the data source used [ 7 ]. In Australia, for example, between 2000 and 01 and 2017-18, the rate of diagnosis for GDM tripled from 5.2 to 16.1% (3); furthermore, in 2017-18, there were around 53,700 hospitalisations for a birth event where gestational diabetes was recorded as the principal and/or additional diagnosis [ 8 ]. Important risk factors for GDM include being overweight/obese, advanced maternal age and having a family history of diabetes mellitus (DM), with all these risk factors dependent on foreign-born racial/ethnic minority status [ 9 ]. However, primarily directing research to understanding risk factors does not necessarily lead to better pregnancy care, particularly where diabetes is concerned, and developing better interventions requires consideration of women’s beliefs, behaviours and social environments [ 10 ].

To date there have been numerous systematic and scoping reviews focused on women’s experiences of GDM, which provide a comprehensive overview of numerous issues. However, gaps remain. In 2014, Nielsen et al. [ 11 ] reviewed qualitative and quantitative studies to investigate determinants and barriers to women’s use of GDM healthcare services, finding that although most women expressed commitment to following health professional advice to manage GDM, compliance with treatment was challenging. Their review also noted that only four out of the 58 included studies were conducted in low-income countries. In their follow-up review, Nielsen et al. specifically focused on research from low and middle income countries (LMIC) to examine barriers and facilitators for implementing programs and services for hyperglycaemia in pregnancy in those settings [ 12 ] and identified a range of factors such as women reporting treatment is “expensive, troublesome and difficult to follow”.

In 2014, Costi et al. [ 13 ] reviewed 22 qualitative studies on women’s experiences of diabetes and diabetes management in pregnancy, including both pre-existing diabetes and GDM. From their synthesis of study findings, they concluded that health professionals need to take a more whole-person approach when treating women with GDM, and that prescribed regimes need to be more accommodating [ 13 ]. Another 2014 review by Parsons et al. [ 14 ] conducted a narrative meta-synthesis of qualitative studies. Their 16 included studies focused on the experiences of women with GDM, including healthcare support and information, but the focus of their meta-synthesis was focused on perceptions of diabetes risk and views on future diabetes prevention.

In a systematic review of qualitative and survey studies from 2015, Van Ryswyck et al. [ 15 ] included 42 studies and had similar findings to Parsons et al. [ 14 ], also emphasising their findings regarding the emotional responses of women who have experienced GDM. Specifically, Van Ryswyck et al. [ 15 ] identified that women’s experiences ran the gamut of emotions from “very positive to difficult and confusing”, with a clear preference for non-judgmental and positively focused care. Most recently, the 2020 systematic review of qualitative studies by He et al. [ 16 ] synthesised findings from 10 studies to argue that understanding the experiences of women with GDM can aid health care professionals to better understand those under their care and to develop more feasible interventions to reduce the risk of DM. A further systematic review of qualitative studies by Craig et al. [ 17 ] focused on women’s psychosocial experiences of GDM diagnosis, one important aspect of healthcare experience, highlighting future directions for research into the psychosocial benefits and harms of a GDM diagnosis.

There has been insufficient consideration of epistemological assumptions and other aspects of research design which may affect how such studies are framed, which participants are included, how data is collected and subsequently what findings are spotlighted. While women’s experiences of GDM healthcare are often broadly included in reviews, they are not often the exclusive focus with healthcare experiences folded into accounts of living with GDM [ 11 ], healthcare service implementation [ 12 ], diabetes and pregnancy [ 13 ], understanding of future risk [ 14 ] and seeking postpartum care after GDM [ 15 ].

To address this gap, the aim of this review was to map the literature, identify gaps in knowledge and investigate the ways research has been conducted into GDM healthcare experiences. The research questions were:

When, where and how has knowledge been produced about women’s experiences of GDM healthcare?

What findings have been reported about women’s experience of GDM healthcare?

A scoping review was selected as the most appropriate method given our multiple aims relate to mapping the field of GDM healthcare experiences [ 18 ]. The reporting of this scoping review was guided by an adaptation of the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) reporting guidelines [ 19 ].

Search strategy

The search strategy was designed in consultation with a research librarian. The following databases were used: Scopus, PubMed, CINAHL, Web of Science, MEDLINE, Embase and Joanna Briggs Institute EBP. These databases were searched on 27 September 2021 by the first author using the keywords and MESH terms outlined in Table  1 . No limits were set on publication date, study design or country of origin. The reference lists of included articles were also examined to identify other potential articles (i.e. snowballing).

Study selection

References were downloaded into Endnote before being exported into the online systematic review platform Rayyan [ 20 ]. Titles and abstracts were first screened against inclusion criteria by the first author and uncertainties about article inclusion were referred to the second and third authors for a decision. A second reviewer independently screened a subset (5%) of titles and abstracts of studies for eligibility to ensure inclusion criteria were consistently applied. Studies were included if they reported primary (empirical) research in the English-language in a published peer-reviewed journal. Studies had to have an explicit aim of focusing on GDM and include direct reporting of participants’ experiences of healthcare. The experience of healthcare is here understood as being the patient experience of care occurring in formal clinical settings, including interactions with providers and other aspects of care prescribed by healthcare professionals. Exclusion criteria were reviews of any kind, research that was not empirical (e.g. personal accounts) and conference abstracts.

Data extraction and synthesis

Data from studies including authors, year published, study design, setting, sample size, recruitment site, stated theoretical approach, data collection method, languages and findings, were extracted into a custom template developed in Microsoft Excel. Findings were further summarised through an iterative coding process and used to develop a series of categories that broadly captured women’s experiences of GDM healthcare.

Search results

A total of 2856 articles were identified as potentially relevant to the research question from database searches. After removing duplicates ( n  = 811) and excluding non-relevant studies by screening titles and abstracts ( n  = 2045) and identifying an additional study through snowballing ( n  = 1), 112 articles were examined for inclusion through a full text assessment. Of these, 57 articles were included in this review, with 55 studies being excluded with reasons for exclusion documented. Figure  1 outlines the process of data gathering and Additional file: Appendix 1 for summarised study characteristics.

The process of data gathering

Publication dates

All of the included studies were published from 2005 onwards, except for one early study published in 1994 [ 21 ]. There has been an overall increase in the number of studies published each year to 2020 (see Fig.  2 ).

Included studies published over time

Research settings

For the vast majority of studies ( n  = 55, 91%), recruitment of women with GDM was conducted via hospitals, clinics and healthcare providers, with one of these studies also conducting additional recruitment via workplaces [ 22 ]. Electronic databases were used in two studies for recruitment, with one study using a national diabetes database in Australia [ 23 ] and another using electronic health data in the United States [ 24 ]. Two studies which targeted Indigenous populations relied on pre-existing relationships; a Canadian study gained entry to an Indigenous population by building on pre-existing relationships with the Mi’kmaq communities [ 25 ] and an Australian study which focused on Aboriginal populations relied on existing research networks [ 26 ]. Only one study recruited completely outside clinical, healthcare and research settings using advertisements and community notices in targeted areas of Atlanta, Georgia in the United States [ 27 ].

A handful of studies ( n  = 5, 9%) were based in countries classified as low- and lower middle-income; there were no countries considered ‘least developed’ [ 28 ]. For the most part, included studies were concentrated in a relatively small number of high-income countries, with the top six countries for research on women’s experiences of GDM healthcare being Australia ( n  = 11), Canada ( n  = 8), Sweden ( n  = 7), the United States ( n  = 6), the United Kingdom ( n  = 4) and China ( n  = 4). The remaining studies were spread across a number of countries, largely one study per setting: Austria [ 29 ], Brazil [ 30 ], Denmark [ 31 ], Ghana [ 32 ], India [ 33 ], Indonesia [ 34 ], Iran [ 35 , 36 ], Malaysia [ 37 ], New Zealand [ 38 , 39 ], Norway [ 40 ], Singapore [ 41 ], South Africa [ 42 , 43 ], Vietnam [ 44 ], Zimbabwe [ 45 ] (see Fig.  3 ).

Settings of included studies

Forty-eight of the studies (84%) were conducted with participants in urban areas and the remaining studies ( n  = 9) were conducted in regional and rural areas of Australia [ 26 , 46 ], Canada [ 25 , 47 , 48 , 49 ], China [ 50 ], Tamil Nadu in India [ 33 ], and the state of New York in the United States [ 51 ]. A number of studies were conducted by the same research team and published in multiple installments; these studies were conducted in Lund, Sweden (6 studies), southeastern China (4 studies) and Melbourne, Australia (4 studies).

Participants

The majority of studies specifically focused on women diagnosed with GDM as the sole target group, though two studies also interviewed comparative groups of women with different conditions such as DM [ 27 , 52 ]. Several studies targeted women as well as healthcare professionals, including nurses, clinicians, general practitioners, with data being compared between groups [ 26 , 27 , 32 , 36 , 41 , 46 , 47 , 53 , 54 ]. In one study it was noted how some participants had pre-existing medical conditions, such as hypertension and HIV, and that their co-morbidities directly contributed to their perspective on GDM [ 36 ].

Depending on the nature of the study design—whether qualitative, mixed methods or quantitative—the range of participants varied greatly, from a small number of interview and focus group participants ( n  = 8) [ 55 ] through to large datasets such as the open-ended responses on a cross-sectional survey ( n  = 393) [ 23 ]. While there was some stratification of participants based on individual factors, such as body mass index [ 56 ] as well as glycaemic targets set [ 38 ], the main categorisation made was often in relation to ethnicity in studies from countries such as Australia, Sweden and the United States, where the focus on ethnic differences was built into the design of studies. For example, this included directly comparing ethnic groups, such as Swedish-born versus African-born [ 57 ], or comparing groups of women by their ethnicity, namely Caucasian, Arabic and Chinese [ 58 ].

Study designs

The studies varied in how they understood, described and measured women’s experiences of GDM healthcare. Of the 57 included studies, 50 (88%) used qualitative study designs. Only four studies (7%) had quantitative designs and three (5%) employed mixed-methods [ 29 ]. The vast majority of studies ( n  = 49, 86%) were cross-sectional, with seven studies [ 21 , 51 , 56 , 59 , 60 , 61 , 62 ] interviewing the same women at multiple time points. In terms of methodologies used, all the qualitative studies featured various types of interviews and/or focus groups. These were largely conducted face-to-face or via telephone. Seven studies employed more than one qualitative method to collect data [ 36 , 43 , 47 , 55 , 63 , 64 , 65 ] and, in addition, three studies used mixed methods to collect data [ 29 , 41 , 46 ]. One study focused on First Nations women in Canada used a focused ethnographic approach [ 49 ], and another 2021 study focused on South Asian women in Australia using ethnography [ 54 ]. The quantitative studies comprised four survey studies using questionnaires [ 37 , 38 , 52 , 66 ].

Theoretical approaches

The majority of studies did not specify a theoretical approach ( n  = 31, 54%), and relied on general data analysis approaches such as thematic analysis. Where a theory was referred to, it was largely used as a guiding framework for study design and data collection, and data analysis where applicable (see Additional file: Appendix 1 ). The three most popular theoretical approaches were the Health Belief Model ( n  = 6), Grounded Theory ( n  = 3) and phenomenology ( n  = 8), with the last of these specifically including hermeneutic [ 67 ] and interpretative approaches [ 63 , 68 ]. Two of the studies that focused on Indigenous populations used culturally-sensitive qualitative methodologies designed to respect and recognise Indigenous worldviews, namely the Two-Eyed Seeing Approach [ 25 ] and the Kaupapa Māori methodology [ 39 ]. Another study [ 47 ] focused on an Indigenous population discussed qualitative research in general being the most “flexible and interpretive methodology” and how using open-ended interviewing creates a dialogue which recognises Indigenous oral traditions and knowledge.

Data collection

Studies varied in when they captured data during the pregnancy and postpartum periods. Where the focus of a study was specifically on healthcare, women’s experiences were often elicited by researchers directly; otherwise, healthcare experience was generally revealed in relation to broader questions within the research framing, such as looking at factors that influence migrant women’s management of GDM [ 69 , 70 ] or examining barriers and possible solutions to nonadherence to antidiabetic therapy [ 71 ].

Almost all studies were conducted in a primary language of the research team, with fluency in the primary language largely requisite for participation. However, there were 14 studies involving multicultural populations that allowed women to use their preferred language as research teams consisted of multilingual researchers, research assistants or interpreters (see Table 2 ).

Study findings on women with GDM experiences of healthcare

The findings from the 57 included studies were categorised into a number of salient aspects of formal healthcare experience, then further categorised as being positive and/or negative experiences depending on how participants’ self-reports were described and quoted by study authors. Where there was not an explicit reference to sentiment in the study, it has not been recorded in this review.

Mental distress

Mental distress included acute emotional reactions such as shock and stress, as well as ongoing psychological challenges in coping with GDM. The vast majority of included studies noted mental distress of some kind ( n  = 48, 84%), inferring that mental distress was inextricably part of women’s experiences of GDM and intertwined with healthcare experience.

Patient-provider interactions

From the moment diagnosis of GDM occurs, a cornerstone of women’s healthcare experience is interactions with providers, which differs depending on the model of care offered. ‘Interactions’ can be broadly defined as interpersonal encounters where communication occurs directly through conversations at consultations as well as group sessions, or interactions via other means such as text messages, emails and phone calls. Forty-four studies ( n  = 44, 77%) discussed patient-provider interactions in their findings; these were positive experiences ( n  = 9, 20%), negative experiences ( n  = 16, 36%), or ambivalent, being both positive and negative ( n  = 19, 43%). As an example of positive experience, one study reported “women were happy with the care provided in managing their GDM, acknowledging that the care was better than in their home country.” [ 62 ] In terms of negative experiences, women felt, for example, healthcare providers could be “preachy” [ 55 ] and discount their own expertise in their bodies [ 21 ]. One study [ 40 ] specifically examined the difference in women’s experiences with primary and secondary healthcare providers, and found that overall they received better care from the latter. More generally, the participants from one study emphasised the importance of a humanistic approach to care [ 76 ].

Treatment satisfaction

Treatment satisfaction was a measure reported in two quantitative studies [ 37 , 52 ], and the mixed-methods study [ 29 ]. The Diabetes Treatment Satisfaction Questionnaire (DTSQ) was used in two studies to measure satisfaction [ 29 , 37 ]. The study by Anderberg et al. [ 52 ] used its own purposely developed instrument and found 89% of women with GDM marked “satisfied”, 2% marked “neutral” and no one indicated dissatisfaction. In the study by Hussain et al. [ 37 ], which used the DTSQ, 122 (73.5%) patients reported they were satisfied with treatment and 44 (26.5%) were unsatisfied; overall, the majority of patients were satisfied with treatment but retained a ‘negative’ attitude towards GDM. The study by Trutnovsky et al. [ 29 ] went further in its analysis as women responded to the DTSQ at three different phases – before treatment, during early treatment and during late treatment – and found that overall treatment satisfaction was high, and significantly increased between early and late treatment.

Diet prescribed

Diet is a fundamental component of treatment for GDM. Once diagnosed, many women are prescribed modified diets to maintain blood sugar levels, which they record on paper or by using an electronic monitor at specified times. Thirty-nine studies ( n  = 39, 68%) included findings and discussion about women’s experiences of prescribed diet, and of those studies ( n  = 33, 84%) this is captured as generally a negative experience. In some studies, women’s experience of the prescribed diet was reported as being both positive and negative ( n  = 4, 10%); only one study ( n  = 1, 3%) recorded it as a positive experience [ 38 ]. The difficulty of following a new diet during pregnancy was a key reason as to why the experience was negative, as well as practical considerations such as being able to easily access fresh food in remote areas [ 26 ]. In studies with multicultural populations, negative experience related to managing the advice in conjunction with culturally-based diets. As noted in the two studies led by Bandyopadhyay, women had difficulty maintaining their traditional diet due to the new restrictions placed upon them [ 54 , 62 ].

Medication prescribed

Medication for GDM primarily involves some form of insulin, which is prescribed to manage blood sugar levels. Twenty-one studies ( n  = 21, 37%) included findings and discussion about women’s experiences of GDM medication and of those, it was mostly reported as being a negative experience ( n  = 13, 62%), with various reasons captured including insufficient time to “figure things out” [ 77 ] and causing feelings of anxiety and failure [ 78 ]. However, in a few studies prescribed medication was noted as being a positive experience ( n  = 3, 14%), or both a positive and negative experience ( n  = 5, 24%). In one study, a participant stated, “the fact that I’m on insulin makes it easy” [ 68 ].

Monitoring captures both the direct monitoring conducted by healthcare providers, primarily blood and blood sugar level tests as well as ultrasounds, as well as self-monitoring women were required to carry out and which was often then verified by healthcare professionals. Twenty studies ( n  = 20, 35%) included findings and discussion about women’s experiences of monitoring and of those it was seen as being negative ( n  = 14, n  = 70%), both positive and negative ( n  = 5, 25%) and positive ( n  = 1, n  = 5%). In the one study that reported positive experiences only, a participant reported that she thought it was good “they are monitoring us all the time” [ 30 ]. Studies reporting negative experiences with monitoring had participants citing reasons such as feeling over-scrutinised [ 65 ].

Access to timely healthcare

Access to healthcare can be a challenge in certain settings, and, even when access is possible, timeliness can be an issue. Of the 31 studies ( n  = 31, 54%) that referred to access in their findings, the vast majority of these studies ( n  = 28) reported access to timely healthcare being a negative experience, with reasons cited including geographic distance [ 39 , 46 ], difficulties in being able to make a booking to be seen at a hospital [ 79 ] and then, when being seen, not having enough time with a healthcare provider [ 27 , 44 ]. In one of the two studies reporting positive experiences [ 52 ], all questions relating to accessibility indicated satisfaction (97%); in the other of the two studies [ 38 ], the majority of women (68%) appreciated that health professionals took time to listen and explain.

Provision of information

Information to support women is critical in managing their GDM diagnosis. Ongoing management came from meetings with healthcare providers—described in one study as being “frontline support” [ 79 ]— alongside sources focused on diet, medication, exercise and other pertinent information. Across all the studies which discussed how provision of information by healthcare providers was received ( n  = 38, 67%), it was noted as largely negative ( n  = 24, 63%) and both positive and negative ( n  = 10, 18%), though there were discussions of positive experiences ( n  = 4, 7%). Considered together, all the studies suggested how crucial clear information is to a positive experience of healthcare. For women, having inadequate knowledge about how to cope was a source of disempowerment and, across the majority of studies ( n  = 44, 77%), participants reported they found information from providers was insufficient. Interestingly, one of these studies found the insufficiency was actually due to the information being “too much” [ 26 ], while another study [ 59 ] found there was a desire for “more frequent controls and dietary advice”. The inappropriate timing of information was also reported in a number of studies [ 31 , 58 , 79 , 80 , 81 ]. One study noted how participants found one group of healthcare providers, midwives and nurses provided better information than general practitioners [ 40 ], while another noted the contradictory nature of advice from different providers [ 82 ]. Language barriers were also identified as a problem with information provision with a lack of information available in a woman’s preferred language [ 69 ].

Financial issues

Direct healthcare costs including out-of-pocket medical consultation fees, medication and medical equipment were primarily raised by participants in the United States [ 27 ], Ghana [ 32 ] and Zimbabwe [ 45 ], with the last of these reporting that some participants discussed “the related costs of treatment … resulted in participants foregoing some of the tests and treatments ordered” [ 45 ]. A study from Canada noted a number of participants with refugee status discussed the “economic challenge” of managing GDM and that the cost of diabetes care “was quite high and difficult to manage” [ 83 ]. Several indirect costs were also discussed across the studies. In a number of studies ( n  = 7), the additional cost of purchasing healthy food to manage GDM was brought up as being a burden [ 25 , 27 , 38 , 42 , 48 , 51 , 84 ]. However, in one study, women said the costs related to food went down as being able to buy take-away (fast foods) became restricted [ 38 ]. Loss of income [ 46 ] as well as daycare costs were cited [ 25 ], as was additional transportation and hospital parking costs [ 39 , 46 , 56 ]. Finally, women in one study reported having to change occupations and even quit work to manage GDM [ 21 ].

The growing number of research studies relaying women’s GDM healthcare experience is encouraging, given increasing incidence and health burden. As this review demonstrates, there are important commonalities across all studies, suggesting that some aspects of GDM healthcare experience seem to be universal; mental distress, for example, was reported in most studies. In contrast, other aspects of GDM healthcare experience seem to relate to factors specific to local settings; financial issues were mainly raised in settings where healthcare is not universal or is not readily affordable. Related financial issues were raised by participants in a number of rural-based studies, revealing something of a difference between urban and rural healthcare settings regardless of country context.

All of the included studies relied on women’s self-reporting without necessarily involving other measures, which broadly fell into two categories: women currently undergoing care for GDM at the time of study data collection and those looking back on past experience. Included studies were overwhelmingly qualitative in design, with relatively small numbers of participants for each category; put together, though, they paint a broad picture of women’s GDM healthcare experience across a range of settings. As the phenomenon being examined here is women’s experiences, qualitative methodologies are vital given the experience of health, illness and medical intervention cannot be quantified [ 85 ]. On the other hand, quantitative studies are able to include far more participants, though it is important to note not necessarily greater applicability and generalisability; when both types of studies are considered together as in mixed-methods study designs, there is a possibility of corroboration, elaboration, complementarity and even contradiction [ 85 ].

Recruiting women through clinical and other healthcare settings, as almost all of the included studies did, necessarily leads to biased samples of participants likely to be ‘compliant’ with healthcare requirements and treatment regimens. As one study noted, compliance was high despite limited understanding of GDM and dietary requirements, as well as why change was required [ 71 ]. This scenario occurs against the backdrop of the inherent power imbalance which exists in patient-provider relationships in reproductive healthcare [ 86 ]. A few of the included studies demonstrated reflexivity for this issue, with the studies most sensitive to these concerns focused on Indigenous populations. This power imbalance also exists in patient-researcher relationships [ 87 ]; a critical way to mitigate this effect is to actively include participants in research design, which only one included study reported doing 75]. This suggests an important direction for future studies, building on recent work involving patients to establish research priorities for GDM [ 88 ]. Indeed, many of the included studies did incorporate ideas about improving healthcare as proposed by the women themselves. For example, in one study, participants reported that small group sessions with medical practitioners and more detailed leaflets would be useful [ 44 ], suggesting how current sessions could be run better.

Culturally sensitive qualitative methodologies were employed with Indigenous populations and those learnings could be further extended to other groups of research participants. GDM is known to be more common in foreign-born racial minorities [ 9 ], so it is encouraging that some studies focused on these particular groups and had study designs that included interpreters. However, this line of research is arguably under-developed given most studies excluded minoritised women who did not have a high degree of fluency in the dominant language. Language barriers were identified as a problem with information provision with GDM healthcare [ 69 , 70 ], and it is possible to extend this idea to research contexts themselves. Not being able to use the language one feels most fluent in clearly affects the way GDM healthcare experiences are reported.

Treatment satisfaction was used in both quantitative and mixed-method studies, but as a solo measure the insights it can provide is limited; we do not exactly know why or how, for example, women’s satisfaction improves later in GDM care [ 29 ]. However, a number of the studies provide possible answers. Persson et al. [ 61 ] describe the process women underwent “from stun to gradual balance” due to a process of adaptation that became easier “with increasing knowledge” about how to self-manage GDM. Ge et al. [ 89 ] found that women developed a philosophical attitude over time to reach a state of acceptance, and such a shift in attitude would clearly have an impact on how healthcare is received and understood. These findings suggest the benefit of both time and experience, and the role of these factors could be better examined with more longitudinal studies.

In this scoping review, under half of the included studies explicitly drew on theory. But as argued by Mitchell and Cody [ 90 ], regardless of whether it is acknowledged, theoretical interpretation occurs in qualitative research. Explicitly incorporating theoretical approaches are valuable in strengthening research design when such conceptual thinking clearly informs the research process; here, examining women’s lived experiences without articulating the theoretical bases which underpins research design and analysis leads to a lack of acknowledgement of relevant context as to how both treatment and research occurs. For example, gender exerts a significant influence upon help-seeking and healthcare delivery [ 91 ], and particularly for GDM. In future, it might be useful to further consider the value of theory in elucidating women’s experiences to address biases in research design to further the fields of study which relate to women’s GDM experiences [ 90 ].

Finally, much of this research has been generated in a small number of wealthy countries. GDM is a growing problem in low income settings and yet, as Nielsen et al. [ 92 ] describe, detection and treatment of GDM is hindered due to “barriers within the health system and society”. Going further, Goldenberg et al. suggest that due to competing concerns, “diagnosing and providing care to women with diabetes in pregnancy is not high on the priority lists in many LMIC”. [ 93 ] Similar barriers exist with GDM research endeavours; ensuring that evaluation of healthcare includes women’s experiences of GDM healthcare would be valuable to researchers in these settings and beyond. Thus there are clear gaps in practice as well as the research literature in considering women’s experiences of GDM healthcare internationally.

Implications

Research into women’s experience of GDM healthcare continues to accumulate and continued research efforts will contribute to far greater understanding of how we might best support women and improve healthcare outcomes. However, there is room for improvement, such as by following participants longitudinally, using mixed methods and taking more reflexive and theoretically informed approaches to researching women’s experiences of GDM healthcare. There is a need highlighted for more culturally sensitive research techniques as well as including women in the study design process, and not just as research subjects to be instrumentalised for developing recommendations for clinical delivery.

Strengths and limitations

Secondary analyses of primary research are challenging to conduct when the pool of included studies is highly heterogeneous. In this scoping review, in order to synthesise a large group of diverse studies, summarising results in terms of positive and negative experiences of GDM healthcare was reductive but necessary. This key strength of our review, inspired by sentiment analysis [ 94 ], shows the utility in capturing overall polarity of feelings as it highlights the ambivalence of healthcare experience. An additional strength was involving a research librarian to help design the searches and advise on relevant databases.

There are several limitations. For our search strategy, we used a broad set of terms relating to patient experience, but there is no standard set of terminology about this type of research, so it is possible some studies were missed. Only studies in English were included, so any studies published in other languages were missed. We did not conduct a critical appraisal on the included studies, which was a limitation; however, this was a purposeful choice in order to include a wide range of studies, including from research settings that are not as well-resourced.

This scoping review identifies commonalities in how GDM healthcare is delivered and received in settings around the world, with women’s experiences varying depending on what model of care is applied alongside other factors. Documenting experiences of GDM healthcare is a vital way to inform future policy and research directions, such as more theoretically informed longitudinal and mixed method approaches, and co-designed studies.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

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Acknowledgements

Jeremy Cullis, Clinical Librarian at Macquarie University, provided invaluable assistance with the database search strategy.

SP is being supported by a Macquarie Research Excellence Scholarship, funded by both Macquarie University and the Australian Government’s Research Training Program.

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SP led and executed the study with design support, input and advice from KC, LAE and JB. SP supported by KC assessed the literature. LAE provided statistical and methodological expertise alongside the other authors, and JB provided strategic advice. All authors reviewed and provided editorial suggestions on SP’s draft and agreed with the final submitted version. All authors read and approved the final manuscript.

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Characteristics of the studies included in the scoping review

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Pham, S., Churruca, K., Ellis, L.A. et al. A scoping review of gestational diabetes mellitus healthcare: experiences of care reported by pregnant women internationally. BMC Pregnancy Childbirth 22 , 627 (2022). https://doi.org/10.1186/s12884-022-04931-5

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Hyperglycemia during pregnancy is a double-edged sword, affecting both mothers and their offspring and creating a vicious cycle that can affect multiple generations. Research in this field over the past 30 years has greatly improved our understanding of this disease and formed the basis of improved strategies to improve the health of mothers and their babies. Despite this progress, gestational and preexisting diabetes continue to have significant effects on both short- and long-term health of mothers and their offspring. In this article, I provide an overview of the work that my colleagues and I have done to advance the knowledge base around diabetes and pregnancy in four areas: 1 ) diabetes risk after gestational diabetes mellitus (GDM), including racial and ethnic disparities; 2 ) the pathophysiology of GDM and subsequent diabetes in Hispanic women; 3 ) diabetes prevention and β-cell preservation following GDM; and 4 ) evidence for multiple potential developmental effects in offspring that vary according to the timing of exposure and severity of maternal diabetes during pregnancy. This research continues the legacy of Norbert Freinkel and the concepts that he contributed to the field of diabetes and pregnancy. With the epidemic of obesity, increasing rates of type 1 and type 2 diabetes in youth, and rising prevalence of GDM across all racial and ethnic groups, we have a lot more work to do to combat this disease to break the vicious cycle.

In his 1980 Banting Award lecture, “Of Pregnancy and Progeny” ( 1 ), Norbert Freinkel hypothesized that abnormal nutrient mixtures in the mother could disrupt fetal development and lead to long-range changes in offspring, including behavioral, anthropometric, and metabolic teratology—the concept of “fuel-mediated teratogenesis.” Research over the ensuing 40 years has largely supported his original ideas, providing a wealth of specific information on the breadth and depth of long-term health effects related to maternal hyperglycemia and metabolic aberrations in pregnancy. Those effects mimic a double-edged sword, affecting both mothers and their offspring in a vicious cycle that can impact multiple generations. The growing body of knowledge in this field has helped to increase public health awareness of the associated risks and served as a base for development of strategies to improve maternal and child health.

However, gestational and preexisting diabetes continue to impart significant morbidity to mothers and offspring, and rising rates of gestational diabetes mellitus (GDM), type 2 diabetes (T2D), and type 1 diabetes (T1D) in women of reproductive age amplify the impact of these conditions within and across generations. In this article, I provide an overview of work that my colleagues and I have done to better understand the health impacts of diabetes during pregnancy in four areas: 1 ) future diabetes risk after pregnancies complicated by GDM, including racial and ethnic disparities; 2 ) pathophysiology of GDM and subsequent diabetes in one racial and ethnic group, namely, Hispanic women; 3 ) diabetes prevention and β-cell preservation following GDM; and 4 ) multiple potential developmental effects of maternal diabetes on offspring that vary by the timing of exposure and severity of maternal diabetes during pregnancy.

My work in the field of GDM research started in the 1990s while working closely with two pioneers of GDM research, Dr. Thomas Buchanan and Dr. Siri Kjos, at the University of Southern California (USC). Together we made several novel findings by analyzing data collected by Dr. Kjos from a clinical cohort of high-risk pregnancies at the Los Angeles County/USC Medical Center. A large majority of the patients were Hispanic, and GDM was diagnosed by National Diabetes Data Group criteria ( 2 ). We found that these women had almost 50% risk of developing diabetes within 5 years after the index pregnancy. Elevated glucose levels during oral glucose tolerance tests in pregnancy or postpartum, along with a relatively early gestational age at diagnosis of GDM, predicted the highest risk of future diabetes ( 3 , 4 ). In addition, one subsequent pregnancy tripled the long-term diabetes risk and each 10 lb of weight gain during follow-up doubled the diabetes risk. These two effects were independent of each other and all other identified risk factors ( 5 ). Furthermore, progestin-only contraception while breastfeeding added additional risk, while combination hormonal contraceptives had no detrimental effects ( 6 – 8 ). These findings provided important information relevant to patient management. They also provided hints into the pathogenesis of diabetes after GDM. Greater hyperglycemia during and after pregnancy identified women who were already close to diabetes; relatively little deterioration was required to develop diabetes during follow-up. However, data for individuals with weight gain, individuals who used progestin-only contraception, and, in particular, individuals who had one additional pregnancy suggested that insulin resistance may be accelerating the decline in β-cell compensation that leads to diabetes. That suggestion was borne out in subsequent studies discussed below.

Meta-analysis conducted by others showed that women with GDM had an approximately sevenfold higher long-term diabetes risk than women without GDM ( 9 ). Moreover, GDM was known to be rising in frequency across all racial and ethnic groups ( 10 ), in parallel with the epidemic of obesity and increasing trend in T1D and T2D in children and adolescents ( 11 ). However, less was known about whether the long-term diabetes risks after GDM are the same across racial and ethnic groups. Using electronic medical records from Kaiser Permanente Southern California (KPSC), we found that 10.3% of the 139,334 singleton pregnancies delivered at KPSC between 1995 and 2008 had GDM by the Carpenter and Coustan criteria ( 12 , 13 ). The rate was highest for women who self-identified as Asian and Pacific Islander (17.1%), followed by self-identified Hispanic (11.4%), non-Hispanic White (NHW) (7.4%), and Black (6.9%) women ( 13 ) ( Fig. 1A ). We assessed incidence rates of diabetes by race and ethnicity for up to a decade after delivery of GDM pregnancies and age-matched and race- and ethnicity-matched control participants who delivered in the same year. As expected, all the GDM groups had a much higher rate of diabetes during follow-up than their counterparts who did not have GDM ( Fig. 1B ). However, the pattern of variation in long-term diabetes rates among racial and ethnic groups with GDM did not parallel the variation in their rates of GDM during pregnancy. Specifically, Black women had the lowest rate of GDM during pregnancy but the highest rate of diabetes over the next decade. Asian and Pacific Islander women had the opposite pattern, with the highest rate of GDM but the second lowest rate of subsequent diabetes. Hispanic women demonstrated the best concordance, with the second highest rate of GDM and the second highest rate of subsequent diabetes. NHW women had a GDM rate that was nearly as low as that of Black women combined with the lowest rate of diabetes during follow-up ( Fig. 1C ). Compared with counterparts who did not have GDM, the relative risks (95% CI) of developing diabetes associated with GDM were 9.9 (7.5–13.1) for Black, 7.7 (6.8–8.7) for Hispanic, 6.5 (5.2–8.0) for NHW, and 6.3 (5.0–7.9) for Asian and Pacific Islander women after adjusting for potential confounders. Adjusting for maternal obesity measured by prepregnancy BMI reduced the risk estimates associated with GDM for all racial and ethnic groups but did not explain the differences of future diabetes risk across racial and ethnic groups ( 13 ). Thus, this large observational study revealed important racial and ethnic differences between GDM risk and future diabetes risk. The discrepancies suggest different pathophysiologies among groups. Mechanistic studies are needed to understand the differences in more detail, along with influences of genetics, environment, lifestyle, and other factors that may contribute to these differences.

GDM and subsequent diabetes risk by race and ethnicity. A : GDM prevalence, including 139,334 singleton pregnancies between 1995 and 2008, with GDM by the Carpenter and Coustan criteria. B : Diabetes cumulative incidence after GDM. Both panels A and B were adapted with permission from Xiang et al. ( 13 ). C : Rank order of GDM prevalence and diabetes cumulative incidence by race and ethnicity. Red and green circles highlight discordance between GDM prevalence and diabetes incidence in Asian Pacific Islander (API) (red circles) and Black (green circles) groups, respectively.

Pregnancy is normally attended by progressive insulin resistance that begins near midpregnancy and progresses through the third trimester due to increased maternal adiposity and the insulin-desensitizing effects of hormonal products of the placenta ( 14 ). This is a normal pathophysiologic response of pregnancy that is thought to ensure adequate nutrient supply from the mother to meet the needs of a growing fetus ( 15 ).

To understand the pathophysiology of GDM and future diabetes risk, our group designed a detailed study of glucose regulation in Hispanic women with GDM diagnosed by using the criteria of the Third International Workshop Conference on GDM ( 16 ). We recruited 175 Hispanic women during the third trimester of pregnancy, 150 with GDM and 25 without GDM, and they were frequency matched on age, prepregnancy BMI, and gestational age to the GDM group. All women had a 100-g oral glucose tolerance test, euglycemic clamp at physiologic hyperinsulinemia, and frequently sampled intravenous glucose tolerance tests (IVGTT) between 28 and 34 weeks gestation. Compared with their non-GDM counterparts, women with GDM had significantly lower glucose clearance, higher glucose production, higher levels of free fatty acids at baseline of the clamp, and less stimulation of glucose clearance and suppression of glucose production and fatty acid levels compared with controls ( 17 ). The GDM group also had a marked reduction in the disposition index from the IVGTTs, a measure of acute β-cell compensation for the degree of insulin resistance ( 17 ). These results demonstrate that GDM in Hispanic women is characterized by all the major defects in glucose regulation, i.e., insulin resistance in muscles, liver, and fat and reduced β-cell compensation, observed in patients with T2D, albeit to a lesser degree. Testing of a large subset of these women ∼6 months after pregnancy revealed that their defect in β-cell compensation was similar to that observed during the third trimester (−69% vs. −62%, respectively) ( 14 ). Similar patterns were observed for Black women ( 18 ) and White women ( 19 ). Thus, GDM in large part represents the detection of pre-existing defects in β-cell compensation for chronic insulin resistance in relatively young women.

We continued following these women with oral glucose tolerance tests and IVGTT at 12−15-month intervals for up to 12 years after the index pregnancy unless their fasting glucose reached 140 mg/dL or they were lost to follow-up. Forty-three percent developed diabetes, with an average annual incidence rate of 7.2% ( 20 ). Body weight and fasting and postchallenge glucose increased significantly; β-cell compensation measured by disposition index declined significantly ( 20 , 21 ). The strongest factor associated with falling β-cell compensation was weight gain, an association explained statistically by a combination of increasing insulin resistance (40%), falling adiponectin, and rising C-reactive protein (31% combined effect) ( 21 ). This study also revealed that low insulin sensitivity and β-cell function at baseline and the rate of their deterioration during follow-up contributed to the risk of T2D development ( 20 ). In addition, high habitual daily caloric intake was associated with worsening insulin resistance, falling β-cell compensation, and rising glucose levels independent of weight gain ( 22 ). This study provides the longest follow-up of which we are aware, and it uses detailed physiological measurements to characterize the natural history of glucose regulation following pregnancies complicated by GDM. It provides evidence that obesity and related metabolic changes may be important drivers of the β-cell decline that leads to diabetes. Interventions to increase exercise and reduce body weight should reduce diabetes risk, as was demonstrated in the Diabetes Prevention Program ( 23 ).

An important observation from this longitudinal study is that the relationship between changes in circulating glucose levels and changes in β-cell function is nonlinear ( 24 ). Both fasting and 2h glucose rise slowly with the decline in β-cell compensation over many years ( Fig. 2 in Xiang et al. [ 24 ]), followed by more rapid increase in glucose per change in β-cell compensation when compensation gets very low (∼10% of normal). These findings define a pathogenesis for T2D in Hispanic women with prior GDM that is characterized by a relatively long-term decline in acute β-cell compensation for chronic insulin resistance, attended initially by slowly rising glucose levels. Only relatively late in this process do glucose levels rise rapidly and into the diabetic range. The implication of these findings is that simply measuring glucose itself may not reveal the underlying stage of diabetes risk.

GDM presents an opportunity for diabetes prevention. Based on the results of our longitudinal studies of glucose regulation described above, we hypothesized that amelioration of chronic insulin resistance would preserve β-cell function and delay or prevent the onset of T2D in high-risk Hispanic women with prior GDM. We randomized 266 Hispanic women with history of GDM to placebo or the insulin-sensitizing thiazolidinedione medication troglitazone in a 1:1 ratio (TRIPOD [Troglitazone in the Prevention of Diabetes] study) ( 25 ). During a median follow-up of 30 months on blinded medication, treatment reduced the diabetes incidence by 55% compared with placebo and prevented the worsening of β-cell compensation over time ( 25 , 26 ). Open-label continuation of treatment with another thiazolidinedione drug, pioglitazone (the PIPOD [Pioglitazone in Prevention of Diabetes] study), revealed that pioglitazone stopped the decline in β-cell compensation that occurred during placebo treatment in the TRIPOD study and maintained the stability of β-cell compensation that had occurred during troglitazone treatment in the TRIPOD study ( 27 ). The risk of diabetes was lowest in women with the largest reduction in total IVGTT insulin area after 3 months of treatment in the TRIPOD study and after 1 year of treatment in the PIPOD study. These findings suggest β-cell unloading is important in diabetes prevention, a concept that has been used in β-cell preservation outside of pregnancy by treating obesity ( 28 ).

Thiazolidinedione treatment in the TRIPOD study was also associated with a 31% reduction in the progression of subclinical atherosclerosis, measured by ultrasound as carotid intima-media thickness (CIMT), in these premenopausal women ( 29 , 30 ). Importantly, reduction in CIMT progression occurred only in treated women who had an increase in insulin sensitivity ( 29 ). Continued treatment with open-label pioglitazone slowed CIMT progression in women who had been on placebo in the TRIPOD study and maintained a relatively low rate of progression in women who had been on troglitazone ( 30 ). The similarity of findings between the PIPOD and TRIPOD studies supports a class effect of thiazolidinedione drugs to enhance insulin sensitivity, reduce insulin secretory demands, preserve β-cell function, and slow the progression of subclinical atherosclerosis.

Racial and Ethnic Differences in Rates of Large-for-Gestational-Age Newborns

Freinkel proposed that pregnancy is a form of tissue culture ( 1 ) in which the mix of nutrients and other substances provided from mother to fetus affects fetal development, with both short- and long-term implications for health of the offspring. Potential mechanisms include fetal overnutrition, inflammation and altered cytokines, oxidative stress, immune activation and hyperinsulinemia, and possibly epigenetic changes ( 31 – 36 ).

Newborns being large for gestational age (LGA) is the most common short-term impact associated with maternal hyperglycemia during pregnancy ( 37 , 38 ). To examine this impact across different racial and ethnic groups, we conducted a large birth cohort study including ∼350,000 women with singleton births at KPSC from 1995–2010 and assessed rates of LGA for pregnancies complicated by GDM from ten racial and ethnic groups (Hispanic, NHW, Black, Filipino, Chinese, Asian Indian, Vietnamese, Korean, Japanese, and Pacific Islander) ( 39 ). Quantile regression was applied to birthweights with adjustment for gestational age at delivery and parity in each racial and ethnic and sex subgroup, and stratified by birth year to define birth weight percentiles for that subgroup. LGA was defined as birth weight >90th percentile specific to each racial and ethnic group. We found that infants of Black women with GDM had the highest LGA rate (17.2%), followed by infants of Pacific Islander (16.2%), Hispanic (14.5%), NHW (13.1%), Asian Indian (12.8%), and Filipino (11.6%) women with GDM. All other Asian women with GDM had LGA rates comparable to the expected 10% (9.6–11.1%). Compared with NHW women with GDM, the LGA rate was significantly greater for Black women with GDM after adjustment for maternal characteristics except for obesity measures. Further adjustment for maternal prepregnancy BMI and gestational weight gain in the subcohort with available data greatly attenuated the elevated LGA risk for Black women. Our results suggest that variation across racial and ethnic groups in excessive fetal growth associated with GDM can be explained to a significant degree by maternal obesity and gestational weight gain. Studies are needed to test whether reducing obesity and appropriate weight gain will eliminate the disparities in LGA risk associated with GDM.

Maternal Diabetes, Maternal Obesity, and Childhood Obesity Risk

The investigation of the effect of GDM on obesity in offspring is confounded by maternal obesity, which is itself an important risk factor for both conditions. Excessive gestational weight gain (EGWG) above the Institution of Medicine recommendation and breastfeeding are also important factors affecting obesity risk, especially for infancy and early childhood. We assessed the interplay among four risk factors (GDM, prepregnancy obesity, gestational weight gain, and breastfeeding) and their independent contributions to childhood overweight at 2 years of age. Using electronic medical record data from 15,710 mother–offspring pairs with delivery at KPSC in 2011, we found that women with GDM were less likely to have EGWG than women without GDM ( 40 ). Maternal GDM was not associated with childhood overweight at 2 years of age. Maternal preexisting obesity and overweight, EGWG, and breastfeeding ≤6 months were all significantly, independently, and positively associated with overweight in children ( 40 ).

We extended this study to assess growth trajectory from ages 2 to 6 years, added maternal preexisting T1D and T2D as potential risk factors, and separated GDM to those who did or did not receive antidiabetes medications during pregnancy ( 41 ). Data were from 71,892 children born at KPSC from 2007–2011. Three distinct BMI trajectory groups were identified in the children: group 1 (59% of the cohort) had stable relatively lower BMI; group 2 (35% of the cohort) had stable intermediate BMI; and group 3 (6% of the cohort) had high and increasing BMI over time ( 41 ). Being in the high and increasing BMI group (i.e., group 3) was strongly associated with maternal prepregnancy obesity and overweight, modestly associated with maternal T1D, T2D, GDM with medication treatment and EGWG, and slightly associated with relatively short duration of breastfeeding (≤6 months). Individuals with GDM not receiving medication treatment during pregnancy had little association with high and increasing BMI over ages 2–6 years ( 41 ).

We further extended our studies to BMI trajectory in offspring from birth to age 10 years ( 42 ). Data were from 218,227 singleton children born in 2008–2015 at KPSC. We saw a catchup growth pattern associated with diabetes during pregnancy ( Fig. 2A ). On average, BMI was 0.1–0.2 kg/m 2 (∼1% relative to the BMI in the group not exposed to diabetes) lower at 6 months for all diabetes-exposed groups ( 42 ). At age 3, the growth patterns started separating with the highest BMI in maternal T1D and T2D groups, followed by the GDM group receiving medication, then the GDM group not receiving medication, and finally the groups with no maternal diabetes. The absolute differences in BMI across these exposure groups increased with age, being ∼0.5 kg/m 2 (∼3%) higher at age 5 years and ∼1.5–2 kg/m 2 (10%) higher at age 9–10 years ( Fig. 2A and Table 2 in Sidell et al. [ 42 ]). By age 7 years, BMI was significantly higher for the GDM group receiving medication compared with the group with no diabetes. Our data showed a hierarchical BMI growth pattern in offspring exposed to different types of diabetes during pregnancy after adjusting for important covariates, including maternal obesity, starting as early as 3 years of age.

A : Covariate-adjusted BMI from birth to age 10 years in offspring of mothers with no diabetes, GDM not treated with medications (GDM no meds), GDM treated with medications (GDM with meds), and preexisting T2D or T1D diabetes. Adapted with permission from Sidell et al. ( 42 ). See the text and Table 2 of Sidell et al. ( 42 ) for significance of differences among groups. B : Crude cumulative incidence of ASD and adjusted hazard ratios (HR) in offspring of mothers with no diabetes, GDM diagnosed after 26 weeks gestation (GDM≤26wks), GDM diagnosed by 26 weeks gestation (GDM>26wks), or preexisting T2D or T1D. Adapted with permission from Xiang et al. ( 44 ).

Maternal Diabetes and Risk of Autism Spectrum Disorders in Children

Our group is one of the first to carefully delineate potential links between diabetes in pregnancy and neurobehavior developmental disorders in offspring. Using a birth cohort of 322,323 children born at KPSC from 1995 to 2009 with follow-up until 2012, we found that GDM, as a whole, was not associated with a diagnosis of autism spectrum disorders (ASD) in children. However, gestational age at GDM diagnosis was negatively associated with risk of ASD, suggesting that early exposure to elevated glucose during pregnancy was associated with ASD ( 43 ). We divided the women with GDM into three tertiles based on gestational age at diagnosis of GDM, rounded to the nearest week for clinical relevance: GDM diagnosed at 26 weeks or earlier (mean of 16 weeks; 30% of women with GDM), after 26 weeks but prior to 30 weeks (mean of 28 weeks; 30% of women with GDM), and 30 weeks or later (mean of 32 weeks; 40% of women with GDM). We found that exposure to GDM diagnosed by 26 weeks gestation was associated with risk of ASD in offspring, but GDM diagnosed after 26 weeks gestation was not ( 43 ). Of note, individuals with GDM diagnosed early had higher rates of being treated with antidiabetes medications during pregnancy, but that treatment was not associated with risk of ASD after adjustment for gestational age at GDM diagnosis ( 43 ). Preexisting T1D and T2D were both associated with increased risk of ASD in children. The hierarchy of risk was highest for T1D, followed by T2D and then GDM diagnosed at ≤26 weeks gestation ( Fig. 2B ) ( 44 ).

To test whether the association between maternal diabetes and ASD might be related to severity of hyperglycemia in early pregnancy, we analyzed first-trimester HbA 1c levels for an association with ASD risk in children. We found that HbA 1c >6.5% in the first trimester (99% had a diagnosis of T1D, T2D, or GDM) was associated with a 1.8-fold increased risk of ASD in offspring ( 45 ). More recent work from my group found that maternal diabetes during pregnancy was associated with increased risk of ASD and associated gastrointestinal disturbances ( 46 ), and maternal preexisting diabetes and GDM diagnosed at ≤26 weeks, as well as maternal obesity. were all associated with increased scores on autism screening questionnaires among typically developing children ( 47 ).

Taken together, our studies suggest that timing of exposure is important, and abnormal glycemia in early pregnancy plays an important role in a child’s ASD risk. Our observations are consistent with studies showing that the early trimester intrauterine environment (e.g., folic acid supplementation and environmental exposure) is important for the risk of ASD in children ( 48 , 49 ).

Maternal Diabetes and Risk of Attention Deficit Hyperactivity Disorder in Children

Using the KPSC birth cohort, which includes 333,182 children born in 1995–2012, we comprehensively examined risks of another neurodevelopmental condition, attention deficit hyperactivity disorder (ADHD), in association with different types, severity, and timing of exposure to maternal diabetes during pregnancy. We found significant associations between maternal T1D and T2D in pregnancy and ADHD risk in children. For GDM, we found no association overall with ADHD and, unlike ASD, no association between gestational age at diagnosis of GDM and ADHD risk. Instead, we found a significant association between individuals with GDM receiving antidiabetes medication treatment and ADHD in children ( 50 ). The ADHD risk hierarchy was individuals with T1D followed by those with T2D and then those with GDM receiving antidiabetes medication. Having GDM but not receiving antidiabetes medication is not associated with ADHD risk ( Fig. 3A ). Our results suggest that severity of maternal diabetes (T1D vs. T2D vs. GDM requiring antidiabetes medications) influences the risk of ADHD in offspring. The similarity and difference between timing and severity of diabetes on ASD and ADHD risk could represent both overlaps and differences in the etiology of the two conditions.

A : Crude cumulative incidence of ADHD and adjusted hazard ratios (HR) of ADHD following exposure to different types of diabetes, as defined in the legend to Fig. 2A . Adapted with permission from Xiang et al. ( 50 ). B : Crude cumulative incidence and adjusted HR for asthma by types of diabetes as defined in the legend to Fig. 2A . Adapted with permission from Martinez et al. ( 51 ).

Maternal Diabetes and Other Childhood Health Outcomes

We also examined risks of asthma, depression, and anxiety in children following exposure to different types of diabetes and timing of maternal diabetes during pregnancy. For asthma, we found that the risk of asthma was predominately observed following exposure to maternal preexisting T2D, the risk was small for individuals with GDM receiving medication, and the risk was not increased for individuals with GDM not receiving medication among 97,554 singletons born at KPSC between 2007 and 2011 ( Fig. 3B ) ( 51 ). We did not assess the presence of preexisting T1D due to the small sample size.

For the association of depression and anxiety with maternal diabetes, we found that individuals with maternal preexisting T1D, T2D, and GDM receiving medications were all significantly associated with depression and anxiety from childhood to young adulthood among 439,590 singletons born at KPSC between 1995 and 2015. There was no association with depression and anxiety for individuals with GDM who were not treated with antidiabetes medication during pregnancy. The significant associations were predominantly observed for offspring ages 5–12 and 13–18 years ( 52 ).

In summary, our large and multiethnic birth cohort studies show that diabetes during pregnancy can affect a broad spectrum of short- and long-term health outcomes in offspring. The hierarchy of risk, from high to low, is preexisting T1D and T2D, followed by GDM diagnosed early or GDM treated with antidiabetes medications during pregnancy. Individuals with GDM that were diagnosed late during gestation, or who did not receive antidiabetes medications, did not increase these long-term risks in their offspring. Our results suggest that both the degree of glycemic level and timing of exposure matter for long-term offspring health ( Fig. 4 ). Taken together, our results support Freinkel’s prescient proposal more than 40 years ago that fuel-mediated teratogenesis may manifest in many ways, including behavioral, anthropometric, and metabolic teratology ( 1 ).

Potential long-range effects on offspring caused by different types of diabetes during pregnancy.

Despite much progress over the past 40 years, many unknowns remain at the intersection of diabetes and pregnancy. Three areas where gains in knowledge could have important implications for the personal care of diabetes during pregnancy should be highlighted. First, we need to understand racial and ethnic differences in the pathophysiology of GDM and subsequent diabetes development, such that targeted approaches specific to each racial and ethnic group can be developed to mitigate the risk. Second, more studies are needed to understand the relative contributions of maternal factors such as obesity, weight gain, hyperglycemia, and hyperlipidemia ( 15 ) to perinatal and long-term risks in offspring of mothers with diabetes. Such understanding could have important implications for antenatal management to mitigate risk. Third, much work is needed to understand mechanisms, timing of exposure, and severity of maternal diabetes that predisposes to neurocognitive disorders in children, so that clinical approaches to early detection and treatment ( 53 ) can be developed and tested to help women at high risk manage their glucose levels ( 54 ) to minimize both short- and long-term impacts to the offspring.

The 2022 Norbert Freinkel Award Lecture was presented at the American Diabetes Association’s 82nd Scientific Sessions, New Orleans, LA, 4 June 2022.

Acknowledgments. I thank my colleague and mentor Dr. Thomas Buchanan for his inspirational education and guidance in diabetes pathophysiology and gestational diabetes as well as for his friendship and support; Dr. Richard Bergman for providing the scholarship to support my initial education and training in diabetes pathophysiology; Drs. Siri Kjos, Richard Watanabe, and Katie Page for their friendship and collaborations; my extraordinary team members at KPSC and the USC GDM Study Group who have made all of this research possible; and my parents, my husband (Jeff), and my children (Marshall and Brandon) for their love and support. Special thanks go to patients who participated in our GDM studies and to patients of Kaiser Permanente for helping us improve care by using the information collected by KPSC electronic health record systems.

Funding. The work reported here reflected work with funding from multiple funders, especially the National Institutes of Health (R01DK46374, R21DK066243, R01DK61628, R01DK100302, R56ES028121, R01DK116858, and R01ES029963) and KPSC Direct Community Benefit funds.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

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The Top 10 research priorities for diabetes in pregnancy

Published: 17 November 2020

A new list of ten questions chosen by patients and clinicians will help guide future research in diabetes and pregnancy to deliver maximum value and impact.

The Diabetes and Pregnancy Priority Setting Partnership (PSP) , led by the National Perinatal Epidemiology Unit (NPEU) at the Nuffield Department of Population Health, University of Oxford, has announced the results of their collaborative project involving hundreds of women, their families and healthcare professionals. This has identified ten key priorities for diabetes and pregnancy research.

Diabetes affects around 38,000 women giving birth in the UK and rates are rising. This can cause complications during pregnancy and birth, and may have long-term effects for mother and child, such as cardiovascular disease. The COVID-19 pandemic has put even greater pressure on resources and funding across the health sector, hence it is important that research in diabetes and pregnancy is centred on the greatest needs of those affected.

‘Healthcare research is often led by industry and researchers. But there can be a mismatch between the research they do and the issues that are most important for people living with the condition, or those that support them’ says Dr Goher Ayman, project co-lead at NPEU.

To find out the perspective of patients and clinicians, NPEU launched the Diabetes and Pregnancy PSP in partnership with the James Lind Alliance, Diabetes UK, JDRF the type 1 diabetes research charity and Diabetes Research and Wellness Foundation. PSPs use a method developed by the James Lind Alliance, which works to raise awareness of research questions which are directly relevant and important to patients and clinicians.

In a survey conducted between June and November 2019, women, their families and support networks, and healthcare professionals were invited to submit their unanswered questions about the time before, during or after pregnancy with diabetes of any type. More than 450 people across the UK took part, submitting over 1,100 questions. The research team grouped these questions into themes and checked which had not been answered by previous research.

A long-list of 60 questions was then sent out in a second survey between May and July 2020. This asked participants to select the questions they felt were most important. The results were used to draw up a shortlist of 18 questions. At a workshop held online in October 2020, the participants jointly agreed the Top 10 priority research questions from the shortlist.

To date, most research has focused on gestational diabetes, which develops during pregnancy. However the ten questions reflect the entire birth journey, from planning pregnancy to postnatal support, and apply to any type of diabetes. The research team presented the results on 17th November 2020 at the Diabetes UK Professional Conference.

The priorities will be shared with funding bodies, research institutes and scientific societies to use them as a starting point for deciding future research projects and programmes.

‘When these priorities are acted on, we are making sure that research will deliver the most impact and value for women and their families, closing the loop of the process’ says Goher.

Ruth Unstead-Joss, participant in the Diabetes and Pregnancy PSP, said:

'There was a good mix of people at the Diabetes and Pregnancy PSP workshop and everyone was very respectful of each other’s thoughts and feelings. As well as women with first-hand experience of different types of diabetes during pregnancy, we also had clinicians who work on a day-to-day basis with people affected by diabetes in pregnancy. Having a diversity of perspectives was essential to generate a strong list of overall priorities. I'm hopeful that this will influence the future of research and practice, leading to better care and outcomes for women, and their babies and families.’

The research questions are:

• How can diabetes technology be used to improve pregnancy, birth, and mother and child health outcomes?
• What is the best test to diagnose diabetes in pregnant women?
• For women with diabetes, what is the best way to manage blood sugar levels using diet and lifestyle during pregnancy?
• What are the emotional and mental well-being needs of women with diabetes before, during, and after pregnancy, and how can they best be supported?
• When is it safe for pregnant women with diabetes to give birth at full term compared with early delivery via induction or elective caesarean?
• What are the specific postnatal care and support needs of women with diabetes and their infants?
• What is the best way to test for and treat diabetes in late pregnancye. after 34 weeks?
• What is the best way to reduce the risk or prevent women with gestational diabetes developing other types of diabetes any time after pregnancy?
• What are the labour and birth experiences of women with diabetes, and how can their choices and shared decision making be enhanced?
• How can care and services be improved for women with diabetes who are planning pregnancy?

The project team would like to thank everyone who contributed through the surveys and took part in the final workshop, the PSP funders the Diabetes Research and Wellness Foundation and the University of Oxford (John Fell Fund and Nuffield Department of Population Health), the PSP steering group members and partners Diabetes UK, JDRF the type 1 diabetes charity, James Lind Alliance, and the many people and organisations who supported at various stages throughout the project, without whom this project would not have been successful.

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• Introduction
• Conclusions
• Article Information

a Values are rounded either up or down to a multiple of 5 for patient confidentiality purposes.

b Fatal events occurring at any point between 20 weeks’ gestation in the second pregnancy and 12 weeks post partum. Five deaths were related to a fatal cardiovascular disease (CVD) event while the remaining 15 fatalities were related to obstetrical complications related to childbirth, major trauma, and suicide.

The log-rank test suggested significant differences in event-free survival across exposure groups. GD indicates gestational diabetes.

The corresponding unadjusted hazard ratios (HRs) among women with no GD, GD in first pregnancy, GD in second pregnancy and GD in both pregnancies, in Panel A were 4.80 (95% CI, 4.48-5.14), 9.09 (95% CI, 8.67-9.53), and 19.05 (95% CI, 18.10-19.91) for GD in first pregnancy, GD in second pregnancy, and GD in both pregnancies, respectively. The corresponding unadjusted HRs among those in panel B were 1.89 (95% CI, 1.75-2.05) and 3.96 (95% CI, 3.66-4.28) for GD in second pregnancy and GD in both pregnancies, respectively. The corresponding unadjusted HR among those in panel C was 2.09 (95% CI, 1.97-2.22) for GD in both pregnancies.

eTable 1. ICD Codes

eTable 2. Comparing Effect Estimates of the Diabetes Outcome, With and Without Inclusion of Years of Pregnancy, with Account for Temporal Trends in the Screening and Diagnosis of Gestational Diabetes

eTable 3. Associations of Incident Diabetes With Gestational Diabetes Occurrences When Including Women With Stillbirth Pregnancies in Cohort

eFigure 1. Timeline of Applied Exclusions and Exposure Ascertainment Windows

eFigure 2. Distribution of Gestational Diabetes Exposure Categories

Data Sharing Statement

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Mussa J , Rahme E , Dahhou M , Nakhla M , Dasgupta K. Incident Diabetes in Women With Patterns of Gestational Diabetes Occurrences Across 2 Pregnancies. JAMA Netw Open. 2024;7(5):e2410279. doi:10.1001/jamanetworkopen.2024.10279

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Incident Diabetes in Women With Patterns of Gestational Diabetes Occurrences Across 2 Pregnancies

• 1 Department of Medicine, McGill University, Montreal, Quebec, Canada
• 2 Centre for Outcomes Research and Evaluation (CORE), Research Institute of the McGill University Health Centre (RI-MUHC), Montreal, Quebec, Canada
• 3 Department of Pediatrics, McGill University, Montreal, Quebec, Canada

Question   Across 2 pregnancies, are the order and number of gestational diabetes occurrences linked to maternal diabetes risk in the years after second pregnancy?

Findings   In this cohort study of 431 980 women, those with a first occurrence of gestational diabetes in a second pregnancy had a 76% higher risk for diabetes development than women who had gestational diabetes in the first pregnancy but not in the second, a statistically significant difference. The highest risk was in women with gestational diabetes in both pregnancies.

Meaning   These findings suggest that considering gestational diabetes history in each pregnancy results in more accurate diabetes risk estimation than a simple yes/no dichotomy of past gestational diabetes occurrence.

Importance   Gestational diabetes is a type 2 diabetes risk indicator, and recurrence further augments risk. In women with a single occurrence across 2 pregnancies, it is unclear whether first- vs second-pregnancy gestational diabetes differ in terms of risk.

Objective   To compare the hazards of incident diabetes among those with gestational diabetes in the first, in the second, and in both pregnancies with women without gestational diabetes in either.

Design, Setting, and Participants   This was a retrospective cohort study with cohort inception from April 1, 1990, to December 31, 2012. Follow-up was April 1, 1990, to April 1, 2019. Participants were mothers with 2 singleton deliveries between April 1, 1990, and December 31, 2012, without diabetes before or between pregnancies, who were listed in public health care insurance administrative databases and birth, stillbirth, and death registries in Quebec, Canada. Data were analyzed from July to December 2023.

Exposure   Gestational diabetes occurrence(s) across 2 pregnancies.

Main outcomes and measures   Incident diabetes from the second delivery until a third pregnancy, death, or the end of the follow-up period, whichever occurred first.

Results   The 431 980 women with 2 singleton deliveries studied had a mean (SD) age of 30.1 (4.5) years at second delivery, with a mean (SD) of 2.8 (1.5) years elapsed between deliveries; 373 415 (86.4%) were of European background, and 78 770 (18.2%) were at the highest quintile of material deprivation. Overall, 10 920 women (2.5%) had gestational diabetes in their first pregnancy, 16 145 (3.7%) in their second, and 8255 (1.9%) in both (12 205 incident diabetes events; median [IQR] follow-up 11.5 [5.3-19.4] years). First pregnancy–only gestational diabetes increased hazards 4.35-fold (95% CI, 4.06-4.67), second pregnancy–only increased hazards 7.68-fold (95% CI, 7.31-8.07), and gestational diabetes in both pregnancies increased hazards 15.8-fold (95% CI, 15.0-16.6). Compared with first pregnancy–only gestational diabetes, second pregnancy–only gestational diabetes increased hazards by 76% (95% CI, 1.63-1.91), while gestational diabetes in both pregnancies increased it 3.63-fold (95% CI, 3.36-3.93).

Conclusions and relevance   In this retrospective cohort study of nearly half a million women with 2 singleton pregnancies, both the number and ordinal pregnancy of any gestational diabetes occurrence increased diabetes risk. These considerations offer greater nuance than an ever or never gestational diabetes dichotomy.

Gestational diabetes (GD) affects 14% of pregnancies globally. 1 A recent meta-analysis 2 estimates its occurrence is associated with a 10-fold risk increase for type 2 diabetes. Whether risks vary with the order of GD occurrences is not well-studied. We hypothesized that new GD occurrence in a second pregnancy implies transition to a higher risk profile, while a single occurrence in a first pregnancy implies the converse.

One challenge is that GD is conditional on pregnancy (ie, cannot occur without pregnancy) and the number of pregnancies itself is associated with type 2 diabetes risk. The lowest risk occurs in those with 1 pregnancy. 3 Two previous studies 4 , 5 tried to improve comparability among participants by requiring that all have at least 2 pregnancies. Both reported a 2.4-fold increase in hazards with GD recurrence compared with its absence in the second pregnancy. They did not include women without any GD or women with a new occurrence of GD in a second pregnancy.

In the longer term, first-pregnancy GD may motivate some to adopt behaviors demonstrated to reduce diabetes risk, 6 , 7 lowering GD recurrence rates and type 2 diabetes development. In contrast, some women without GD in the first pregnancy may enter a higher risk trajectory, related to excess gestational weight gain, 8 postpartum weight retention, 9 weight gain between pregnancies, 10 , 11 and motherhood demands impeding healthy eating and physical activity. 12 The delineation of differences in future incident type 2 diabetes risk between GD occurrence in a first pregnancy compared with new occurrence in a second could allow further personalization of approaches to type 2 diabetes prevention. 13 We therefore examined patterns of GD absence, occurrence, and recurrence across 2 pregnancies and their associations with diabetes.

The McGill University Health Centre’s research ethics board and Quebec Access to Information Commission approved the protocol. These bodies waived informed consent because the study involved deidentified data, analyses at the Quebec Statistical Institute’s secure data centers, and rounded frequencies to multiples of 5. This cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines.

We conducted a retrospective cohort study in Quebec, Canada. We examined health administrative databases of the public health insurance plan linked to birth, stillbirth, and death registries by the Quebec Statistical Institute (probabilistic linkage). We obtained mothers’ residential territory and month and year of birth from the public health insurance registry. The Physician Services Claims and Hospitalization Discharge Databases include diagnostic codes (eTable 1 in Supplement 1 ) and hospitalization dates; we used these to define outcomes, exposures, and other variables alongside data from birth and stillbirth registries (offspring birthdates, gestational age at birth, birthweight, parental country of birth and first language, and years of maternal education). We also had access to the mothers’ Institut national de santé publique du Québec (INSPQ) material and social deprivation index, derived from the 6-digit postal code in the public health registry. 14 The Institut national de santé publique du Québec (INSPQ) material and social deprivation index is computed from small-area census data. Specifically, the material indices are derived from average income, proportions without high school diploma, and employment to population ratio among those 15 years and older. The social indices are derived from the proportion of the population who are single-parent families, aged 15 years and older living alone, and aged 15 years and older who are separated, divorced, or widowed. To assign the INSPQ index for each woman, we first checked availability of this variable in the index year (year of second delivery).

We considered women with 2 or more consecutive singleton deliveries between April 1, 1990, and December 31, 2012, who were alive at 12 weeks following the second delivery (index date) (eFigure 1 in Supplement 1 ). We excluded mothers with missing offspring gestational age (required to distinguish diabetes from GD), 15 and those with diabetes or hypertension before or between pregnancies. We applied the validated Canadian Chronic Disease Surveillance System (CCDSS) diabetes 16 , 17 and hypertension 18 definitions of 2 outpatient or 1 hospitalization diagnostic code(s) to (1) the 2-year period before 20 weeks’ gestation in first pregnancy and (2) the period from 12 weeks after the first delivery until 20 weeks’ gestation in the second pregnancy. All required an opportunity to develop GD for both of the pregnancies considered, thus gestation 20 or more weeks was required. In the primary analysis, we required the same partner for each offspring to minimize heterogeneity of within-household factors. 19 - 21 This resulted in the removal of stillbirths, for whom paternal data were unavailable; in a sensitivity analysis, we removed the paternal data requirement. Lastly, we excluded those with 2 outpatient visits or 1 hospitalization for cardiovascular disease, the most common diabetes consequence, before the index date.

We adapted a validated health administrative database GD definition 22 that applies diabetes and GD diagnostic codes to a pregnancy-specific period. We started this period at 20 weeks’ gestation instead of the 120-day predelivery date used in the validation study, as the information we had on gestational age allowed us to conform with clinical definitions, which considers type 2 diabetes before 20 weeks’ gestation as preexisting. 15 We extended the period beyond delivery to 12 weeks post partum, as screening for type 2 diabetes after pregnancy is generally advised by this time. 23 , 24 We required 2 outpatient and/or 1 hospitalization code to maximize specificity (99.5%) and maintain sensitivity (94.1%), as recommended in the validation study. 22 Our 4 mutually exclusive exposure categories were absence of GD, its presence in only first pregnancy, in only the second, and in both.

Our primary outcome was incident diabetes, using the previously described CCDSS definition. 16 , 17 These diagnostic codes cannot differentiate between type 1 and type 2 diabetes, but because 95% of incident diabetes among adults is type 2 diabetes, they primarily capture incident type 2 diabetes. Follow-up was until the first of incident diabetes, the 120-day time point before a third delivery (we did not have gestational age data for any third pregnancy), death, or the end of the study period (April 1, 2019).

For both the first and second pregnancies, we considered other pregnancy and offspring-related factors associated with type 2 diabetes development, specifically, gestational hypertension (with or without preeclampsia), preterm delivery (<37 weeks), and small-for-gestational-age (SGA) and large-for-gestational-age (LGA) status. 25 , 26 For gestational hypertension, we applied the CCDSS hypertension definition to the same pregnancy periods for which we defined GD, and we also considered diagnostic codes for gestational hypertension and preeclampsia.

We considered time between deliveries, comorbid conditions, maternal age at index date, deprivation level (see Table 1 footnotes), 14 preexisting paternal diabetes and hypertension (validated CCDSS definitions 16 , 17 applied from 2 years before 20 weeks’ gestation in the first pregnancy to 12 weeks following the second delivery), and ethnocultural background (African/Caribbean [if born in West/South/East/Central Africa or first language was of Caribbean or African descent], Arabic [if born in the Arab league or first language was of Arabic or other North African or South-West Asian descent], Asian [if born in West/East/Central/South/Southeast/Pacific Asia or first language descends from these regions], European [if born in North America, South America, Central America, Mexico, East/South/Southern/West Europe, or Australia and first language was English, French, or other European language], and other [if first language was of Indigenous descent) based on participant-reported place of birth and primary language recorded on the mandatory birth declaration and incorporated into the birth registry. Ethnocultural background was assessed in this study because those with background other than European have a higher baseline risk for diabetes.

We computed baseline characteristics (counts and proportions for categorical variables and mean [SD] for continuous variables) and compared them across exposure groups (Pearson χ 2 tests for proportions; 1-way analysis of variance for means, as applicable). We calculated type 2 diabetes incidence rates (IR). We assessed for interactions ( P  < .05 for interaction terms) and multicollinearity (Cramer V > 0.10) among exposures and covariates. We constructed Kaplan-Meier curves and compared these through log-rank testing. We evaluated proportional hazards assumptions (log-minus-log survival plots, Schoenfeld residuals) and performed some transformations to fulfill these (age as a spline variable and binary gestational hypertension category defined as presence in either or both pregnancies vs neither).

We constructed multivariable Cox proportional hazards models to compute hazard ratios (HR) for type 2 diabetes, first with GD absence in either pregnancy as the reference group. We then examined models with GD in only the first pregnancy as the reference group and finally with GD in only the second as the reference group. We retained covariates based on univariate association with type 2 diabetes where P  ≤ .25, multivariable association (stepwise selection) where P  ≤ .05, and reduced bayesian information criteria values with inclusion (see eMethods in Supplement 1 for omitted variables).

In a sensitivity analysis, we evaluated the change in associations when including calendar years of each pregnancy in our models to account for temporal trends in the screening and diagnosis of GD over the years. 27 In another sensitivity analysis, we retained women with stillbirth deliveries and accounted for stillbirths in the model, along with miscarriages between pregnancies. In a third sensitivity analysis, we applied indirect adjustments for obesity and smoking status to our main results, using established methods. 28 , 29 This bias analysis required external estimates for the HRs of obesity and of smoking with incident type 2 diabetes in women, which we respectively estimated as 3.90 (obesity vs no obesity) 30 and 1.13 (smoking vs not smoking). 31 This method also required external cohort data for obesity and smoking prevalence in groups of women corresponding to our main exposure categories. We used the Canadian Community Health Survey (Cycle 2.2) for this purpose 32 ; 13% were in the obesity category and 24% smoked cigarettes. We applied the following formula for the indirect obesity adjustment:

HR (corrected for obesity)  = HR (from our analysis) / HR (related to obesity, from literature) P oe  − P e  × P o

(see eMethods in Supplement 1 , similar formula applied for smoking; P oe  = proportion within specific GD category who have obesity; P e  = proportion of those with specific GD category among all women with 2 consecutive singleton pregnancies; P o  = proportion with obesity among all women with 2 consecutive singleton pregnancies). We performed analyses with SAS version 9.4 (SAS Institute). Data were analyzed from July 2023 to December 2023.

The 431 980 women analyzed ( Figure 1 ) had a mean (SD) age of 30.1 (4.5) years, and a mean (SD) of 2.8 (1.5) years elapsed between deliveries. Overall, 8550 women were African or Caribbean, 17 315 were from Arab-speaking regions, 14 615 were Asian, 373 415 were of European background, 78 770 (18.2%) at the highest material deprivation level (INSPQ deprivation index: quintile 5), 62 605 (14.5%) had 1 or more SGA offspring, 64 195 (14.9%) had 1 or more LGA offspring, 35 290 (8.2%) had 1 or more preterm delivery, and 34 145 (7.9%) had 1 or more gestational hypertension occurrence. In terms of the main exposure, 10 920 (2.5%) had GD in only their first pregnancy, 16 145 (3.7%) had GD in only their second, and 8255 (1.9%) in both (eFigure 2 in Supplement 1 ). Those without GD in either pregnancy (396 660 participants) ( Table 1 ) were younger with higher proportions of European background and lower proportions with deprivation, comorbid conditions, LGA offspring, preterm births, and partners with diabetes and hypertension.

Over a median (IQR) of 11.5 (5.3-19.4) years (5 298 940 total person years), 12 205 mothers developed type 2 diabetes. The IRs per 1000 person-years rose across the no GD (IR, 1.4), GD in first pregnancy only (IR, 6.7), GD in second pregnancy only (IR, 12.4), and GD in both pregnancies (IR, 25.5) categories. Kaplan-Meier curves suggested significant differences in event-free survival across groups ( Figure 2 ). The proportional hazards assumption applied. We did not detect interactions or multicollinearity.

In adjusted models, compared with those without GD, those with GD in first pregnancy had a 4.35-fold higher hazard for type 2 diabetes (95% CI, 4.06-4.67) ( Figure 3 A), those with GD in the second pregnancy had a 7.68-fold increase (95% CI, 7.31-8.07), and those with GD in both pregnancies demonstrated a 15.80-fold increase (95% CI, 15.00-16.61). Compared with those with GD in the first pregnancy, women with GD in the second had 76% higher hazards (95% CI, 1.63-1.91) ( Figure 3 B) and those with GD in both pregnancies had a 3.63-fold increase (95% CI, 3.36-3.93). Hazards were 2.06-fold higher among women with GD in both pregnancies (95% CI, 1.94-2.19) ( Figure 3 C) compared with those with GD in the second pregnancy.

Inclusion of calendar years for each pregnancy did not importantly alter HRs (eTable 2 in Supplement 1 ). In another sensitivity analysis including women with stillbirth pregnancies in our study cohort (435 685 participants; 12 415 events), stillbirths were associated with 19% increased hazards (HR, 1.19; 95% CI, 1.04-1.38), compared with women without stillbirth deliveries (eTable 3 in Supplement 1 ). Miscarriages between pregnancies were not conclusively associated with incident diabetes (HR, 1.03; 95% CI, 0.97-1.09). Furthermore, retaining stillbirths among the 2 pregnancies examined and considering miscarriages between pregnancies did not importantly alter the association between GD occurrences and incident diabetes.

Indirect adjustments for obesity (no GD: reference; GD in first pregnancy HR, 2.72; 95% CI, 2.46-2.83; GD in second pregnancy HR, 5.48; 95% CI, 5.22-5.76; GD in both pregnancies HR, 9.62; 95% CI, 9.15-10.10) (see eMethods in Supplement 1 for other comparisons) somewhat attenuated the HRs for the incident type 2 diabetes outcome. Indirect adjustments for smoking (no GD: reference; GD in first pregnancy HR, 4.23; 95% CI, 3.94-4.53; GD in second pregnancy HR, 7.44; 95% CI, 7.09-7.83; GD in both pregnancies HR, 15.50; 95% CI, 14.70-16.21) did not significantly alter the HRs.

Gestational hypertension in either or both pregnancies was associated with a 65% increase in hazards for diabetes development ( Table 2 ). Compared with appropriate size for gestational age of both offspring, LGA was consistently associated with diabetes development, with a 60% increase in hazards whether it occurred in the first or second pregnancies, and a doubling when it occurred in both pregnancies or when LGA occurred in 1 pregnancy and SGA in the other. Preterm delivery in 1 or both pregnancies was associated with a 10% to 20% increase in hazards of diabetes compared with full-term delivery in both pregnancies. Deprivation levels were associated with a stepwise increase in hazards. All ethnocultural groups other than European had higher diabetes hazards compared with European women, and all comorbid conditions considered were associated with increased hazards. Paternal diabetes was associated with a 43% increase in hazards for maternal diabetes development.

Among nearly half a million mothers with 2 consecutive singleton pregnancies, our analyses demonstrate that GD in only the second pregnancy was associated with higher hazards for type 2 diabetes development than GD in only the first pregnancy. The highest hazards are with GD occurrence in both pregnancies. Compared with women without GD in either pregnancy, there were 4.35-fold, 7.68-fold, and 15.80-fold greater hazards for type 2 diabetes with GD in the first, in the second, and in both pregnancies, respectively. Indirect adjustments for obesity somewhat attenuated these values to 2.72-fold, 5.48-fold, and 9.62-fold, respectively, but the magnitude remained high, and the differences persisted. Direct comparisons between GD groups were also conclusive. For example, compared with first pregnancy–only GD, second pregnancy–only GD increased hazards by 76%, while GD in both pregnancies increased hazards 3.63-fold.

We did not identify any prior study that compared GD in a first pregnancy with GD in a second among women with 2 pregnancies. Our specific estimate for the increase in hazards associated with GD recurrence (HR, 3.63; HR, 2.21 with indirect adjustment for obesity) compared with women with a GD occurrence in only a first pregnancy was similar to the greater than 2-fold increase in hazards reported in 2 previous studies 4 , 5 that restricted analyses to women with at least 2 pregnancies. Other studies that examined GD recurrence had a higher degree of variability in numbers of pregnancies. One reported a 16% increase in hazards with GD recurrence 33 while the other estimated a 2-fold increase. 34 We identified a single study that examined hazards of type 2 diabetes after pregnancy in relationship to numbers of prior GD pregnancies (Sister Study). 35 It differed in several other respects from ours. As such, its interpretation applies to an older group of women, several years beyond pregnancy. In the Sister Study, women with a history of 2 GD pregnancies experienced 6.2-fold higher hazards for type 2 diabetes in middle age compared with women with no GD pregnancies. The reference group included women without pregnancies. The investigators accounted for time since last GD pregnancy and self-reported weight. In our study, women with 2 pregnancies and GD in both had a 15.8-fold increase in hazards for type 2 diabetes development between their 30s and 40s, starting soon after their second pregnancy, compared with women without GD in either pregnancy. We indirectly adjusted for obesity and demonstrated that the association was attenuated to a 10-fold increase in hazards.

Our key discovery is that a single GD occurrence in a first pregnancy is associated with lower hazards for type 2 diabetes than a single GD occurrence in a second pregnancy. A subgroup analysis of the American Diabetes Prevention Program among women with a GD history showed that healthy diet-induced weight loss and higher physical activity levels could reduce type 2 diabetes risk. 6 , 7 Women with a first GD pregnancy may be motivated to adopt behavioral changes that both prevent GD in a second pregnancy and lower hazards of incident type 2 diabetes development thereafter. Supporting this, a recent cohort study 11 determined that among women without GD in their first pregnancy, weight loss between pregnancies was associated with reduced risk for new occurrence of GD in a subsequent pregnancy. In another study 10 among women with excess weight and GD in a first pregnancy, weight loss between pregnancies lowered the risk for GD recurrence in a second pregnancy.

In our analyses, the women without GD in the first pregnancy who developed GD in the second may have gained excess weight in the first pregnancy and had difficulty losing it 9 or gained weight between pregnancies. 10 , 11 In an Australian investigation 11 among women without GD in their first pregnancy, higher levels of weight gain between pregnancies were associated with stepwise increases in the risk of new occurrence of GD in second pregnancy. For many women, the additional responsibilities of motherhood 12 may challenge efforts to engage in behaviors to enhance personal health. Furthermore, the metabolic stresses inherent to pregnancy may impair their β-cell function, 36 making them more susceptible to developing GD in the second pregnancy, and ultimately to type 2 diabetes development.

Alongside health behaviors and physiological changes, our analyses reinforce the importance of social factors, including material and social deprivation and non-European background. The underpinnings of such associations likely stem from other related upstream characteristics, such as food insecurity, 37 local environments not conducive to physical activity, 38 , 39 and structural inequity. 40 Partner diabetes was another risk indicator for maternal type 2 diabetes development in our analyses, with a 43% increase in hazards. This is consistent with our prior studies 41 , 42 demonstrating increases in hazards for the development of diabetes in fathers whose partners had GD compared with those whose partners did not. Shared partner type 2 diabetes risk may be related to shared health behaviors, resources, social factors, and household environments. 20 , 43 - 46 Assortative mating (similar demographics, attitudes, behaviors, and traits at the outset) may also play a role. 46 - 49

Our large sample size of nearly half a million women was possible through linkage of Quebec’s health administrative and vital statistics databases. Limitations to these data include lack of information on GD management, prepartum weight status, gestational weight gain, smoking status, and laboratory values. GD and weight excess are intimately associated. However, all of our models accounted for LGA in both the first and second pregnancies, a strong correlate of prepregnancy and gestational weight gain. 50 , 51 Furthermore, we performed indirect adjustments for obesity using established methods. 28 , 29 We could not corroborate International Statistical Classification of Diseases and Related Health Problems, Ninth and Tenth Revision –coded diagnoses of diabetes with laboratory data, but to mitigate for misclassification, we applied validated definitions of GD and diabetes. 16 , 17 The diagnostic codes do not reliably distinguish between type 1 and type 2 diabetes, but given that 95% of diabetes onset among adults is type 2 diabetes, the majority of events captured with our codes are type 2 diabetes. We acknowledge that women with more than 1 GD occurrence may already be undergoing more frequent screening for diabetes than women with only 1 occurrence, perhaps partly accounting for their higher diabetes hazards. We also acknowledge potential misclassification of ethnocultural background, as second- and third-generation women and/or Indigenous women would have been classified as European if their first language was English, French, or another European language. Lastly, we did not examine women without pregnancies or women with a single pregnancy, but our focus was women with 2 consecutive deliveries; restriction to women with 2 or more deliveries overcame some methodological challenges, as discussed.

Our retrospective cohort study suggests that among women with 2 consecutive singleton pregnancies, without diabetes before or between pregnancies, the absence of GD in a second pregnancy following GD in the first suggests that the mother is taking effective diabetes prevention measures. If confirmed, she should be encouraged to continue. New onset GD or recurrent GD in a second pregnancy, however, should inspire urgent action for prevention or adjustments to ongoing efforts. We also confirmed the importance of material deprivation and ethnocultural background in type 2 diabetes risk estimation, and we identified paternal diabetes as a factor associated with risk for type 2 diabetes development in mothers. Our results provide a personalized medicine–oriented pathway to diabetes risk estimation in women. This should be coupled with tailored prevention programs and equitable referral pathways to reduce the burden of type 2 diabetes and its complications.

Accepted for Publication: March 7, 2024.

Published: May 9, 2024. doi:10.1001/jamanetworkopen.2024.10279

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Mussa J et al. JAMA Network Open .

Corresponding Author: Kaberi Dasgupta, MD, Centre for Outcomes Research and Evaluation of the RI-MUHC, 5252 boul de Maisonneuve Ouest, Office 3E.09, Montréal, QC H4A 3S5, Canada ( [email protected] ).

Author Contributions: Dr Dasgupta had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Mussa, Rahme, Dasgupta.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Mussa, Dasgupta.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Mussa, Rahme, Dahhou.

Obtained funding: Dasgupta.

Administrative, technical, or material support: Mussa.

Supervision: Rahme, Dasgupta.

Conflict of Interest Disclosures: None reported.

Funding/Support: The Heart and Stroke Foundation of Canada funded this study (grant No. HSF G-12-000251 to Dr Dasgupta). The Société Québécoise d’Hypertension Artérielle and McGill’s Faculty of Medicine provided additional support through doctoral studentships for Mr Mussa.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: The authors thank the Quebec Statistical Institute (Institut de la statistique du Québec) for data linkage and hosting us at their secured Centers for Access to Research Data.

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Gestational diabetes

On this page, when to see a doctor, risk factors, complications.

Gestational diabetes is diabetes diagnosed for the first time during pregnancy (gestation). Like other types of diabetes, gestational diabetes affects how your cells use sugar (glucose). Gestational diabetes causes high blood sugar that can affect your pregnancy and your baby's health.

While any pregnancy complication is concerning, there's good news. During pregnancy you can help control gestational diabetes by eating healthy foods, exercising and, if necessary, taking medication. Controlling blood sugar can keep you and your baby healthy and prevent a difficult delivery.

If you have gestational diabetes during pregnancy, generally your blood sugar returns to its usual level soon after delivery. But if you've had gestational diabetes, you have a higher risk of getting type 2 diabetes. You'll need to be tested for changes in blood sugar more often.

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Most of the time, gestational diabetes doesn't cause noticeable signs or symptoms. Increased thirst and more-frequent urination are possible symptoms.

If possible, seek health care early — when you first think about trying to get pregnant — so your health care provider can check your risk of gestational diabetes along with your overall wellness. Once you're pregnant, your health care provider will check you for gestational diabetes as part of your prenatal care.

If you develop gestational diabetes, you may need checkups more often. These are most likely to occur during the last three months of pregnancy, when your health care provider will monitor your blood sugar level and your baby's health.

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Researchers don't yet know why some women get gestational diabetes and others don't. Excess weight before pregnancy often plays a role.

Usually, various hormones work to keep blood sugar levels in check. But during pregnancy, hormone levels change, making it harder for the body to process blood sugar efficiently. This makes blood sugar rise.

Risk factors for gestational diabetes include:

• Being overweight or obese
• Not being physically active
• Having prediabetes
• Having had gestational diabetes during a previous pregnancy
• Having polycystic ovary syndrome
• Having an immediate family member with diabetes
• Having previously delivered a baby weighing more than 9 pounds (4.1 kilograms)
• Being of a certain race or ethnicity, such as Black, Hispanic, American Indian and Asian American

Gestational diabetes that's not carefully managed can lead to high blood sugar levels. High blood sugar can cause problems for you and your baby, including an increased likelihood of needing a surgery to deliver (C-section).

Complications that may affect your baby

If you have gestational diabetes, your baby may be at increased risk of:

• Excessive birth weight. If your blood sugar level is higher than the standard range, it can cause your baby to grow too large. Very large babies — those who weigh 9 pounds or more — are more likely to become wedged in the birth canal, have birth injuries or need a C-section birth.
• Early (preterm) birth. High blood sugar may increase the risk of early labor and delivery before the due date. Or early delivery may be recommended because the baby is large.
• Serious breathing difficulties. Babies born early may experience respiratory distress syndrome — a condition that makes breathing difficult.
• Low blood sugar (hypoglycemia). Sometimes babies have low blood sugar (hypoglycemia) shortly after birth. Severe episodes of hypoglycemia may cause seizures in the baby. Prompt feedings and sometimes an intravenous glucose solution can return the baby's blood sugar level to normal.
• Obesity and type 2 diabetes later in life. Babies have a higher risk of developing obesity and type 2 diabetes later in life.
• Stillbirth. Untreated gestational diabetes can result in a baby's death either before or shortly after birth.

Complications that may affect you

Gestational diabetes may also increase your risk of:

• High blood pressure and preeclampsia. Gestational diabetes raises your risk of high blood pressure, as well as preeclampsia — a serious complication of pregnancy that causes high blood pressure and other symptoms that can threaten both your life and your baby's life.
• Having a surgical delivery (C-section). You're more likely to have a C-section if you have gestational diabetes.
• Future diabetes. If you have gestational diabetes, you're more likely to get it again during a future pregnancy. You also have a higher risk of developing type 2 diabetes as you get older.

There are no guarantees when it comes to preventing gestational diabetes — but the more healthy habits you can adopt before pregnancy, the better. If you've had gestational diabetes, these healthy choices may also reduce your risk of having it again in future pregnancies or developing type 2 diabetes in the future.

• Eat healthy foods. Choose foods high in fiber and low in fat and calories. Focus on fruits, vegetables and whole grains. Strive for variety to help you achieve your goals without compromising taste or nutrition. Watch portion sizes.
• Keep active. Exercising before and during pregnancy can help protect you from developing gestational diabetes. Aim for 30 minutes of moderate activity on most days of the week. Take a brisk daily walk. Ride your bike. Swim laps. Short bursts of activity — such as parking further away from the store when you run errands or taking a short walk break — all add up.
• Start pregnancy at a healthy weight. If you're planning to get pregnant, losing extra weight beforehand may help you have a healthier pregnancy. Focus on making lasting changes to your eating habits that can help you through pregnancy, such as eating more vegetables and fruits.
• Don't gain more weight than recommended. Gaining some weight during pregnancy is typical and healthy. But gaining too much weight too quickly can increase your risk of gestational diabetes. Ask your health care provider what a reasonable amount of weight gain is for you.

Apr 09, 2022

• American College of Obstetricians and Gynecologists. Practice Bulletin No.190: Gestational diabetes mellitus. Obstetrics & Gynecology. 2018; doi:10.1097/AOG.0000000000002501.
• Diabetes and Pregnancy: Gestational diabetes. Centers for Disease Control and Prevention. https://www.cdc.gov/pregnancy/diabetes-gestational.html. Accessed Dec. 31, 2019.
• Gestational diabetes. National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/diabetes/overview/what-is-diabetes/gestational/all-content. Accessed Dec. 31, 2019.
• AskMayoExpert. Gestational diabetes mellitus. Mayo Clinic; 2021.
• Durnwald C. Gestational diabetes mellitus: Screening, diagnosis, and prevention. https://www.uptodate.com/contents/search. Accessed Nov. 30, 2021.
• American Diabetes Association. Standards of medical care in diabetes — 2021. Diabetes Care. 2021. https://care.diabetesjournals.org/content/44/Supplement_1. Accessed Nov. 11, 2021.
• Mack LR, et al. Gestational diabetes — Diagnosis, classification, and clinical care. Obstetrics and Gynecology Clinics of North America. 2017; doi:10.1016/j.ogc.2017.02.002.
• Tsirou E, et al. Guidelines for medical nutrition therapy in gestational diabetes mellitus: Systematic review and critical appraisal. Journal of the Academy of Nutrition and Dietetics. 2019; doi:10.1016/j.jand.2019.04.002.
• Rasmussen L, et al. Diet and healthy lifestyle in the management of gestational diabetes mellitus. Nutrients. 2020; doi:10.3390/nu12103050.
• Caughey AB. Gestational diabetes mellitus: Obstetric issues and management. https://www.uptodate.com/contents/search. Accessed Nov. 30, 2021.
• Castro MR (expert opinion). Mayo Clinic. Jan. 14, 2022.
• Diseases & Conditions
• Gestational diabetes symptoms & causes

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• Glucose tolerance test
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Blood sugar level at gestational diabetes diagnosis linked to harmful outcomes for mothers and babies: Study

The higher the blood sugar level in pregnant women when first diagnosed with diabetes, the higher the risk of complications around and after birth, according to research presented at the 26th European Congress of Endocrinology in Stockholm.

For every 5mg/L above the diagnosis threshold, the risk of newborns having low blood sugar levels, or a large birth weight, rises by 9% and 6%, accordingly, while mothers have a 31% higher risk of diabetes after birth. The findings suggest that high-risk women with gestational diabetes should be classified further to limit these complications for both mothers and newborns.

Gestational diabetes — a condition in which women have elevated blood sugar, or glucose, levels during pregnancy — affects around 20 million pregnancies worldwide and poses increased health risks for both mothers and their babies. For example, mothers are more likely to develop type 2 diabetes and to give birth to especially large babies who face a high risk of birth injuries or even obesity later in life. Women are diagnosed with gestational diabetes if their fasting (pre-meal) blood glucose levels are above 92 mg/dL in the first trimester or their 2-hour oral post-meal glucose levels (OGTT) in the second trimester is above 153 mg/dL.

In this study, researchers from the Tâmega e Sousa Hospital Center in Portugal analysed data on blood sugar levels and birth complications of 6,927 pregnant women, aged 30-37 years old, who carried one child and were diagnosed with gestational diabetes between 2012 and 2017. The researchers found that for every 5mg/L increase in their blood sugar levels, there was a 9% higher risk of low blood sugar (hypoglycemia) and a 6% higher risk of large birth weight (large for gestational age) in newborns and a 31% higher risk of maternal high blood levels (hyperglycemia) after birth.

“While it is not surprising that high glucose levels are associated with these adverse outcomes in mothers and newborns, our study shows for the first time how much increase in risk there is with 5 mg/dL of increase in the mother’s blood glucose levels when first diagnosed with gestational diabetes,” said co-lead researcher Dr Catarina Cidade-Rodrigues.

Dr Cidade-Rodrigues continued: “The magnitude of elevated risk can be calculated with our measurements and, in practice, could be used to identify and stratify women at higher risk of developing these complications.”

“We now want to evaluate if there is a benefit in further stratifying these high-risk women with gestational diabetes, who will need to be more closely monitored and to whom pharmacological interventions can be carried out appropriately. This may help reduce complications during labour and in newborns and prevent future diabetes in these women.”

Blood sugar level at gestational diabetes diagnosis linked to harmful outcomes for mothers and babies

European Congress of Endocrinology

Dr Kamal Kant Kohli-MBBS, DTCD- a chest specialist with more than 30 years of practice and a flair for writing clinical articles, Dr Kamal Kant Kohli joined Medical Dialogues as a Chief Editor of Medical News. Besides writing articles, as an editor, he proofreads and verifies all the medical content published on Medical Dialogues including those coming from journals, studies,medical conferences,guidelines etc. Email: [email protected]. Contact no. 011-43720751

Gestational Diabetes in Pregnancy Research Paper

An increase in blood sugar that first appears during pregnancy but is not severe enough to be diagnosed as diabetes mellitus is called gestational diabetes. These are covert carbohydrate metabolism disorders that pose a risk of progressing to diabetes mellitus. The production of large amounts of steroid hormones is increased during pregnancy, which causes hormonal changes. Some of them, like cortisol and progesterone, have an important impact on cell receptors and raise insulin resistance in those receptors (Davidson et al., 2021). As a result, the pancreas must produce significantly more insulin, which raises blood glucose levels. When the pancreas’ capacity for compensating is insufficient, sugar metabolism spirals out of control, leading to the development of gestational diabetes.

I propose educating women and medical staff to prevent the occurrence of gestational diabetes in pregnancy. People should be aware that the first step in managing gestational diabetes is continuously checking their blood sugar levels. Using a portable blood glucose meter, women can perform it independently if they know the importance of it (Davidson et al., 2021). The main strategies include holding special training sessions for women and medical professionals and displaying informational posters about gestational diabetes during pregnancy. Gestational diabetes symptoms appear in pregnant women during the first trimester, but they frequently go unnoticed due to their lack of awareness. Therefore, everybody should understand that monitoring blood glucose levels is crucial, beginning with the first positive pregnancy test.

The current state of gestational diabetes in pregnancy can be evaluated with the help of some information that qualitative research methods can offer. Qualitative research involves collecting and analyzing data from primary sources (Aspers & Corte, 2019). The most common qualitative research methods are focus groups and in-depth interviews. These studies will provide evidence and insights into the knowledge that medical professionals lack and the prejudices that are prevalent among the general public.

The research’s findings will undoubtedly shape my efforts to identify the precise points that should be emphasized during training planning. It will be necessary to conduct research in two directions: with medical professionals, pregnant women, and people planning to have a baby. Which course is prioritized—one about the causes of gestational diabetes, prevention, or treatment—will be determined by a focus group of health professionals. An in-depth interview will show the causes of the lack of understanding in some disease-related areas. The average level of public knowledge about gestational diabetes will be revealed by the same studies conducted with pregnant women and those who plan to become parents. An example of how effective the research can be is when the number of pregnant women with gestational diabetes has increased in a particular city. In this case, qualitative research must be done to comprehend what led to this situation. Fewer cases will result from this practice and help address the problem’s root.

In conclusion, gestational diabetes in pregnancy is a fairly common disease among women of all ages. Early detection of diabetes during pregnancy is possible, but medical professionals frequently overlook the condition and do not conduct testing. The same is true for women who mistake toxicosis for diabetes symptoms. Applying the custom of healthcare professionals and the general populace is required to remedy this situation. Conducting research with qualitative techniques, such as in-depth interviews and focus groups, is necessary to make training effective before it is planned.

Aspers, P., & Corte, U. (2019). What is Qualitative in Qualitative Research. Qualitative Sociology , 42 (2), 139–160. Web.

Davidson, K. W., Barry, M. J., Mangione, C. M., Cabana, M., Caughey, A. B., Davis, E. M., Donahue, K. E., Doubeni, C. A., Kubik, M., Li, L., Ogedegbe, G., Pbert, L., Silverstein, M., Stevermer, J., Tseng, C. W., & Wong, J. B. (2021). Screening for Gestational Diabetes. JAMA , 326 (6), 531. Web.

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What are ‘Ozempic babies’? Can the drug really increase your chance of pregnancy?

We’ve heard a lot about the impacts of Ozempic recently, from rapid weight loss and lowered blood pressure, to persistent vomiting and “Ozempic face”.

Now we’re seeing a rise in stories about “ Ozempic babies ”, where women who use drugs like Ozempic (semaglutide) report unexpected pregnancies.

But does semaglutide (also sold as Wegovy) improve fertility? And if so, how? Here’s what we know so far.

Remind me, what is Ozempic?

Ozempic and related drugs (glucagon-like peptide-1 receptor agonists or GLP-1-RAs) were developed to help control blood glucose levels in people with type 2 diabetes.

But the reason for Ozempic’s huge popularity worldwide is that it promotes weight loss by slowing stomach emptying and reducing appetite.

Ozempic is prescribed in Australia as a diabetes treatment. It’s not currently approved to treat obesity , but some doctors prescribe it “off label” to help people lose weight. Wegovy (a higher dose of semaglutide) is approved for use in Australia to treat obesity, but it’s not yet available.

Read more: Ozempic isn’t approved for weight loss in Australia. So how are people accessing it?

How does obesity affect fertility?

Obesity affects the fine-tuned hormonal balance that regulates the menstrual cycle.

Women with a body mass index (BMI) above 27 are three times more likely than women in the normal weight range to be unable to conceive because they’re less likely to ovulate.

The metabolic conditions of type 2 diabetes and polycystic ovary syndrome (PCOS) are both linked to obesity and fertility difficulties.

Women with type 2 diabetes are more likely than other women to have obesity, and to experience fertility difficulties and miscarriage .

Similarly, women with PCOS are more likely to have obesity and trouble conceiving than other women because of hormonal imbalances that cause irregular menstrual cycles.

Read more: I have PCOS and I want to have a baby – what do I need to know?

In men, obesity, diabetes and metabolic syndrome (a cluster of conditions that increase the risk of heart disease and stroke) have negative effects on fertility.

Low testosterone levels caused by obesity or type 2 diabetes can affect the quality of sperm.

Ozempic babies: Women claim weight-loss drugs are making them more fertile and experts agree https://t.co/rISMGHyjTx — Fox News (@FoxNews) April 16, 2024

So how might Ozempic affect fertility?

Weight loss is recommended for people with obesity to reduce the risk of health problems. As weight loss can improve menstrual irregularities, it may also increase the chance of pregnancy in women with obesity.

This is why weight loss and metabolic improvement are the most likely reasons why women who use Ozempic report unexpected pregnancies.

But unexpected pregnancies have also been reported by women who use Ozempic and the contraceptive pill. This has led some experts to suggest that some GLP-1-RAs might affect the absorption of the pill and make it less effective.

However, it’s uncertain whether there’s a connection between Ozempic and contraceptive failure.

In men with type 2 diabetes, obesity and low testosterone, drugs like Ozempic have shown promising results for weight loss and increasing testosterone levels.

Avoid Ozempic if you’re trying to conceive

It’s unclear if semaglutide can be harmful in pregnancy. But data from animal studies suggests it should not be used in pregnancy due to potential risks of foetal abnormalities.

That’s why the Therapeutic Goods Administration recommends women of childbearing potential use contraception when taking semaglutide.

Similarly, PCOS guidelines state health professionals should ensure women with PCOS who use Ozempic have effective contraception.

Guidelines recommended stopping semaglutide at least two months before planning pregnancy .

For women who use Ozempic to manage diabetes, it’s important to seek advice on other options to control blood glucose levels when trying for pregnancy.

What if you get pregnant while taking Ozempic?

For those who conceive while using Ozempic, deciding what to do can be difficult. This decision may be even more complicated considering the unknown potential effects of the drug on the foetus.

While there’s little scientific data available, the findings of an observational study of pregnant women with type 2 diabetes who were on diabetes medication, including GLP-1-RAs, are reassuring. This study did not indicate a large increased risk of major congenital malformations in the babies born.

Women considering or currently using semaglutide before, during, or after pregnancy should consult with a health provider about how to best manage their condition.

When pregnancies are planned , women can take steps to improve their baby’s health, such as taking folic acid before conception to reduce the risk of neural tube defects, and stopping smoking and consuming alcohol.

While unexpected pregnancies and “Ozempic babies” may be welcomed, their mothers haven’t had the opportunity to take these steps and give them the best start in life.

This article was co-authored by Robert Norman, Emeritus Professor of Reproductive and Periconceptual Medicine, The Robinson Research Institute,  University of Adelaide .

This story originally appeared on The Conversation .

• Ozempic and pregnancy
• Ozempic and weight loss
• Ozempic and fertility
• Ozempic and diabetes
• PCOS guidelines
• semaglutide
• polycystic ovary syndromw

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Senior Research Fellow, School of Public Health and Preventive Medicine

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Carbohydrates and Blood Sugar

When people eat a food containing carbohydrates, the digestive system breaks down the digestible ones into sugar, which enters the blood.

• As blood sugar levels rise, the pancreas produces insulin, a hormone that prompts cells to absorb blood sugar for energy or storage.
• As cells absorb blood sugar, levels in the bloodstream begin to fall.
• When this happens, the pancreas start making glucagon, a hormone that signals the liver to start releasing stored sugar.
• This interplay of insulin and glucagon ensure that cells throughout the body, and especially in the brain, have a steady supply of blood sugar.

Carbohydrate metabolism is important in the development of type 2 diabetes , which occurs when the body can’t make enough insulin or can’t properly use the insulin it makes.

• Type 2 diabetes usually develops gradually over a number of years, beginning when muscle and other cells stop responding to insulin. This condition, known as insulin resistance, causes blood sugar and insulin levels to stay high long after eating. Over time, the heavy demands made on the insulin-making cells wears them out, and insulin production eventually stops.

Glycemic index

In the past, carbohydrates were commonly classified as being either “simple” or “complex,” and described as follows:

Simple carbohydrates:

These carbohydrates are composed of sugars (such as fructose and glucose) which have simple chemical structures composed of only one sugar (monosaccharides) or two sugars (disaccharides). Simple carbohydrates are easily and quickly utilized for energy by the body because of their simple chemical structure, often leading to a faster rise in blood sugar and insulin secretion from the pancreas – which can have negative health effects.

Complex carbohydrates:

These carbohydrates have more complex chemical structures, with three or more sugars linked together (known as oligosaccharides and polysaccharides).  Many complex carbohydrate foods contain fiber, vitamins and minerals, and they take longer to digest – which means they have less of an immediate impact on blood sugar, causing it to rise more slowly. But other so called complex carbohydrate foods such as white bread and white potatoes contain mostly starch but little fiber or other beneficial nutrients.

Dividing carbohydrates into simple and complex, however, does not account for the effect of carbohydrates on blood sugar and chronic diseases. To explain how different kinds of carbohydrate-rich foods directly affect blood sugar, the glycemic index was developed and is considered a better way of categorizing carbohydrates, especially starchy foods.

The glycemic index ranks carbohydrates on a scale from 0 to 100 based on how quickly and how much they raise blood sugar levels after eating. Foods with a high glycemic index, like white bread, are rapidly digested and cause substantial fluctuations in blood sugar. Foods with a low glycemic index, like whole oats, are digested more slowly, prompting a more gradual rise in blood sugar.

• Low-glycemic foods have a rating of 55 or less, and foods rated 70-100 are considered high-glycemic foods. Medium-level foods have a glycemic index of 56-69.
• Eating many high-glycemic-index foods – which cause powerful spikes in blood sugar – can lead to an increased risk for type 2 diabetes, ( 2 ) heart disease, ( 3 ), ( 4 ) and overweight, ( 5 , 6 ) ( 7 ). There is also preliminary work linking high-glycemic diets to age-related macular degeneration, ( 8 ) ovulatory infertility, ( 9 ) and colorectal cancer. ( 10 )
• Foods with a low glycemic index have been shown to help control type 2 diabetes and improve weight loss.
• A 2014 review of studies researching carbohydrate quality and chronic disease risk showed that low-glycemic-index diets may offer anti-inflammatory benefits. ( 16 )
• The University of Sydney in Australia maintains a searchable database of foods and their corresponding glycemic indices.

Many factors can affect a food’s glycemic index, including the following:

• Processing : Grains that have been milled and refined—removing the bran and the germ—have a higher glycemic index than minimally processed whole grains.
• Physical form : Finely ground grain is more rapidly digested than coarsely ground grain. This is why eating whole grains in their “whole form” like brown rice or oats can be healthier than eating highly processed whole grain bread.
• Fiber content : High-fiber foods don’t contain as much digestible carbohydrate, so it slows the rate of digestion and causes a more gradual and lower rise in blood sugar. ( 17 )
• Ripeness : Ripe fruits and vegetables tend to have a higher glycemic index than un-ripened fruit.
• Fat content and acid content : Meals with fat or acid are converted more slowly into sugar.

Numerous epidemiologic studies have shown a positive association between higher dietary glycemic index and increased risk of type 2 diabetes and coronary heart disease. However, the relationship between glycemic index and body weight is less well studied and remains controversial.

Glycemic load

One thing that a food’s glycemic index does not tell us is how much digestible carbohydrate – the total amount of carbohydrates excluding  fiber – it delivers. That’s why researchers developed a related way to classify foods that takes into account both the amount of carbohydrate in the food in relation to its impact on blood sugar levels. This measure is called the glycemic load. ( 11 , 12 ) A food’s glycemic load is determined by multiplying its glycemic index by the amount of carbohydrate the food contains. In general, a glycemic load of 20 or more is high, 11 to 19 is medium, and 10 or under is low.

The glycemic load has been used to study whether or not high-glycemic load diets are associated with increased risks for type 2 diabetes risk and cardiac events. In a large meta-analysis of 24 prospective cohort studies, researchers concluded that people who consumed lower-glycemic load diets were at a lower risk of developing type 2 diabetes than those who ate a diet of higher-glycemic load foods. ( 13 ) A similar type of meta-analysis concluded that higher-glycemic load diets were also associated with an increased risk for coronary heart disease events. ( 14 )

Here is a listing of low, medium, and high glycemic load foods. For good health, choose foods that have a low or medium glycemic load, and limit foods that have a high glycemic load.

Low glycemic load (10 or under)

• Bran cereals
• Kidney beans
• Black beans
• Wheat tortilla

Medium glycemic load (11-19)

• Pearled barley: 1 cup cooked
• Brown rice: 3/4 cup cooked
• Oatmeal: 1 cup cooked
• Bulgur: 3/4 cup cooked
• Rice cakes: 3 cakes
• Whole grain breads: 1 slice
• Whole-grain pasta: 1 1/4 cup cooked

High glycemic load (20+)

• Baked potato
• French fries
• Refined breakfast cereal: 1 oz
• Sugar-sweetened beverages: 12 oz
• Candy bars: 1 2-oz bar or 3 mini bars
• Couscous: 1 cup cooked
• White basmati rice: 1 cup cooked
• White-flour pasta: 1 1/4 cup cooked ( 15 )

Here’s a list of the glycemic index and glycemic load for the most common foods.

2. de Munter JS, Hu FB, Spiegelman D, Franz M, van Dam RM. Whole grain, bran, and germ intake and risk of type 2 diabetes: a prospective cohort study and systematic review.  PLoS Med . 2007;4:e261.

3. Beulens JW, de Bruijne LM, Stolk RP, et al. High dietary glycemic load and glycemic index increase risk of cardiovascular disease among middle-aged women: a population-based follow-up study.  J Am Coll Cardiol . 2007;50:14-21.

4. Halton TL, Willett WC, Liu S, et al. Low-carbohydrate-diet score and the risk of coronary heart disease in women.  N Engl J Med . 2006;355:1991-2002.

5. Anderson JW, Randles KM, Kendall CW, Jenkins DJ. Carbohydrate and fiber recommendations for individuals with diabetes: a quantitative assessment and meta-analysis of the evidence.  J Am Coll Nutr . 2004;23:5-17.

6. Ebbeling CB, Leidig MM, Feldman HA, Lovesky MM, Ludwig DS. Effects of a low-glycemic load vs low-fat diet in obese young adults: a randomized trial.   JAMA . 2007;297:2092-102.

7. Maki KC, Rains TM, Kaden VN, Raneri KR, Davidson MH. Effects of a reduced-glycemic-load diet on body weight, body composition, and cardiovascular disease risk markers in overweight and obese adults.  Am J Clin Nutr . 2007;85:724-34.

8. Chiu CJ, Hubbard LD, Armstrong J, et al. Dietary glycemic index and carbohydrate in relation to early age-related macular degeneration.  Am J Clin Nutr . 2006;83:880-6.

9. Chavarro JE, Rich-Edwards JW, Rosner BA, Willett WC. A prospective study of dietary carbohydrate quantity and quality in relation to risk of ovulatory infertility.  Eur J Clin Nutr . 2009;63:78-86.

10. Higginbotham S, Zhang ZF, Lee IM, et al. Dietary glycemic load and risk of colorectal cancer in the Women’s Health Study.  J Natl Cancer Inst . 2004;96:229-33.

11. Liu S, Willett WC. Dietary glycemic load and atherothrombotic risk.  Curr Atheroscler Rep . 2002;4:454-61.

12. Willett W, Manson J, Liu S. Glycemic index, glycemic load, and risk of type 2 diabetes.  Am J Clin Nutr . 2002;76:274S-80S.

13. Livesey G, Taylor R, Livesey H, Liu S. Is there a dose-response relation of dietary glycemic load to risk of type 2 diabetes? Meta-analysis of prospective cohort studies.  Am J Clin Nutr . 2013;97:584-96.

14. Mirrahimi A, de Souza RJ, Chiavaroli L, et al. Associations of glycemic index and load with coronary heart disease events: a systematic review and meta-analysis of prospective cohorts.  J Am Heart Assoc . 2012;1:e000752.

15. Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002.  Am J Clin Nutr . 2002;76:5-56.

16. Buyken, AE, Goletzke, J, Joslowski, G, Felbick, A, Cheng, G, Herder, C, Brand-Miller, JC. Association between carbohydrate quality and inflammatory markers: systematic review of observational and interventional studies. The American Journal of Clinical Nutrition Am J Clin Nutr . 99(4): 2014;813-33.

17. AlEssa H, Bupathiraju S, Malik V, Wedick N, Campos H, Rosner B, Willett W, Hu FB. Carbohydrate quality measured using multiple quality metrics is negatively associated with type 2 diabetes. Circulation . 2015; 1-31:A:20.

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