research topics on gestational diabetes

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 05 October 2023

Precision gestational diabetes treatment: a systematic review and meta-analyses

• Jamie L. Benham 1   na1 ,
• Véronique Gingras 2 , 3   na1 ,
• Niamh-Maire McLennan 4 , 5   na1 ,
• Jasper Most   ORCID: orcid.org/0000-0001-8591-5629 6   na1 ,
• Jennifer M. Yamamoto 7   na1 ,
• Catherine E. Aiken 8 , 9   na1 ,
• Susan E. Ozanne   ORCID: orcid.org/0000-0001-8753-5144 10   na2 ,
• Rebecca M. Reynolds   ORCID: orcid.org/0000-0001-6226-8270 4 , 5   na2 &

ADA/EASD PMDI

Communications Medicine volume  3 , Article number:  135 ( 2023 ) Cite this article

3661 Accesses

3 Citations

20 Altmetric

Metrics details

• Combination drug therapy
• Gestational diabetes
• Predictive markers

Gestational Diabetes Mellitus (GDM) affects approximately 1 in 7 pregnancies globally. It is associated with short- and long-term risks for both mother and baby. Therefore, optimizing treatment to effectively treat the condition has wide-ranging beneficial effects. However, despite the known heterogeneity in GDM, treatment guidelines and approaches are generally standardized. We hypothesized that a precision medicine approach could be a tool for risk-stratification of women to streamline successful GDM management. With the relatively short timeframe available to treat GDM, commencing effective therapy earlier, with more rapid normalization of hyperglycaemia, could have benefits for both mother and fetus.

We conducted two systematic reviews, to identify precision markers that may predict effective lifestyle and pharmacological interventions.

There was a paucity of studies examining precision lifestyle-based interventions for GDM highlighting the pressing need for further research in this area. We found a number of precision markers identified from routine clinical measures that may enable earlier identification of those requiring escalation of pharmacological therapy (to metformin, sulphonylureas or insulin). This included previous history of GDM, Body Mass Index and blood glucose concentrations at diagnosis.

Conclusions

Clinical measurements at diagnosis could potentially be used as precision markers in the treatment of GDM. Whether there are other sensitive markers that could be identified using more complex individual-level data, such as omics, and if these can feasibly be implemented in clinical practice remains unknown. These will be important to consider in future studies.

Plain language summary

Gestational diabetes (GDM) is high blood sugar first detected during pregnancy. Normalizing blood sugar levels quickly is important to avoid pregnancy complications. Many women achieve this with lifestyle changes, such as to diet, but some need to inject insulin or take tablets. We did two thorough reviews of existing research to see if we could predict which women need medication. Firstly we looked for ways to identify the characteristics of women who benefit most from changing their lifestyles to treat GDM, but found very limited research on this topic. We secondly searched for characteristics that help identify women who need medication to treat GDM. We found some useful characteristics that are obtained during routine pregnancy care. Further studies are needed to test if additional information could provide even better information about how we could make GDM treatment more tailored for individuals during pregnancy.

Similar content being viewed by others

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

Effectiveness of pre-pregnancy lifestyle in preventing gestational diabetes mellitus—a systematic review and meta-analysis of 257,876 pregnancies

Gestational diabetes in Germany—prevalence, trend during the past decade and utilization of follow-up care: an observational study

Introduction.

Gestational diabetes (GDM) is the most common pregnancy complication, occurring in 3–25% of pregnancies globally 1 . GDM is associated with short- and long-term risks to both mothers and babies, including adverse perinatal outcomes, future obesity, type 2 diabetes and cardiovascular disease 1 , 2 , 3 . The landmark Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) demonstrated that effective treatment of GDM reduces serious perinatal morbidity 4 .

Current treatment guidelines for management of GDM assume homogeneous treatment requirements and responses, despite the known heterogeneity of GDM aetiology 5 , 6 , 7 , 8 . Standard care includes diet and lifestyle advice at a multi-disciplinary clinic, home blood glucose monitoring at least four times per day, clinic reviews every 2 to 4 weeks, and then progression to pharmacological treatment with metformin, glyburide and/or insulin if glucose targets are not met. Around a third of women cannot maintain euglycaemia with lifestyle measures alone and require treatment escalation to a pharmacological agent 3 . Yet current treatment pathways often take 4–8 weeks to achieve glucose targets. This delay resulting in continued exposure to hyperglycaemia poses a risk of accelerated foetal growth 9 , 10 . Previous research has suggested that maternal characteristics including body mass index (BMI) ≥ 30 kg/m 2 , family history of type 2 diabetes, prior history of GDM and higher glycated haemoglobin (HbA1c) increase the likelihood of need for insulin treatment in GDM 11 , indicating the potential for risk-stratification of women to streamline successful GDM management. There is emerging evidence that precision biomarkers predict treatment response in type 2 diabetes, which has similar heterogeneity to GDM 12 , 13 and thus gives rationale to investigate whether a similar precision approach could be successful in optimising outcomes in GDM.

To address this knowledge gap, we conducted two systematic reviews of the available evidence for precision markers of GDM treatment. We aimed to determine which patient-level characteristics are precision markers for predicting (i) responses to personalised diet and lifestyle interventions delivered in addition to standard of care (ii) requirement for escalation of treatment in women treated with diet and lifestyle alone, and in women receiving pharmacological agents for the treatment of GDM. For both reviews we considered whether the precision markers predicted achieving glucose targets, as well as maternal and neonatal outcomes. The Precision Medicine in Diabetes Initiative (PMDI) was established in 2018 by the American Diabetes Association (ADA) in partnership with the European Association for the Study of Diabetes (EASD). The ADA/EASD PMDI includes global thought leaders in precision diabetes medicine who are working to address the burgeoning need for better diabetes prevention and care through precision medicine 14 . This systematic review is written on behalf of the ADA/EASD PMDI as part of a comprehensive evidence evaluation in support of the 2nd International Consensus Report on Precision Diabetes Medicine 15 .

We find a paucity of studies examining precision lifestyle-based interventions for GDM highlighting the pressing need for further research in this area. We find a number of precision markers identified from routine clinical measures that may enable earlier identification of those requiring escalation of pharmacological therapy (to metformin, sulphonylureas or insulin). These findings suggest that clinical measurements at diagnosis could potentially be used as precision markers in the treatment of GDM. Whether there are other sensitive markers that could be identified using more complex individual-level data, such as omics, and if these can feasibly be implemented in clinical practice remains unknown and will be important to consider in future studies.

The systematic reviews and meta-analyses were performed as outlined a priori in the registered protocols (PROSPERO registration IDs CRD42022299288 and CRD42022299402). The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines 16 were followed. Ethical approval was not required as these were secondary studies using published data.

Literature searches, search strategies and eligibility criteria

Search strategies for both reviews were developed based on relevant keywords in partnership with scientific librarians (see Supplementary Note  1 for full search strategies). We searched two databases (MEDLINE and EMBASE) for studies published from inception until January 1st, 2022. We also scanned the references of included manuscripts for inclusion as well as relevant reviews and meta-analyses published within the past two years for additional citations.

For both systematic reviews we included studies (randomised or non-randomised trials and observational studies) published in English and including women ≥16 years old with diagnosed GDM, as defined by the study authors. For the first systematic review (precision diet and lifestyle interventions), we included studies with any behavioural intervention using any approach (e.g., specific exercise, dietary interventions, motivational interviewing) that examined precision markers that could tailor a lifestyle intervention in a more precise way compared to a control group receiving standard care only. For the second systematic review (precision markers for escalation of pharmacological interventions to achieve glucose targets), we included studies investigating women with GDM that required escalation of pharmacological therapy (e.g., insulin, metformin, sulphonylurea) compared to women with GDM that achieved glucose targets with diet and lifestyle measures only, or women with GDM treated with oral agents that required progression to insulin to achieve glucose targets. For both reviews, we included any relevant reported outcomes; maternal (e.g., treatment adherence, hypertensive disorders of pregnancy, gestational weight gain, mode of birth), neonatal (e.g., birthweight, macrosomia, shoulder dystocia, preterm birth, neonatal hypoglycaemia, neonatal death), cost efficiency or acceptability. We excluded studies with a total sample size <50 participants to ensure sufficient data to interpret the effect of precision markers. We also excluded studies published before or during 2004, in order to consider studies with standard care similar to ACHOIS 4 .

Study selection and data extraction

The results of our two searches were imported separately into Covidence software (Veritas Health Innovation, Australia, available at www.covidence.org ) and duplicates were removed. Two reviewers independently reviewed identified studies. First, they screened titles and abstracts of all references identified from the initial search. In a second step, the full-text articles of potentially relevant publications were scrutinised in detail and inclusion criteria were applied to select eligible articles. Reason for exclusion at the full-text review stage was documented. Disagreement between reviewers was resolved through consensus by discussion with the group of authors.

Two reviewers independently extracted relevant information from each eligible study, using a pre-specified standardised extraction form. Any disagreement between reviewers was resolved as outlined above.

Data extracted included first author name, year of publication, country, study design, type and details of the intervention when applicable, number of cases/controls or cohort groups, total number of participants and diagnostic criteria used for GDM. Extracted data elements also included outcomes measures, size of the association (Odds Ratio (OR), Relative Risk (RR) or Hazard Ratio (HR)) with corresponding 95% Confidence Interval (CI) and factors adjusted for, confounding factors taken into consideration and methods used to control covariates. We prioritised adjusted values where both raw and adjusted data were available. Details of precision markers (mean (standard deviation) for continuous variables or N (%) for categorical variables) including BMI (pre-pregnancy or during pregnancy), ethnicity, age, smoking status, comorbidities, parity, glycaemic variables (e.g., oral glucose tolerance test (OGTT) diagnostic values, HbA1c), timing of GDM diagnosis, history of diabetes or of GDM, and season were also extracted.

Quality assessment (risk of bias and GRADE assessments)

We first assessed the quality and risk of bias of each individual study using the Joanna Briggs Institute (JBI) critical appraisal tools 17 . A Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach was then used to review the total evidence for each precision marker, and the quality of the included studies to assign a GRADE certainty to this body of evidence (high, moderate, low and/or very low) 18 . Quality assessment was performed in duplicate and conflicts were resolved through consensus.

Statistical analysis

Where possible, meta-analyses were conducted using random effects models for each precision marker available. The pooled effect size (mean difference for continuous outcomes and ORs for categorical outcomes) with the corresponding 95% CI was computed. The heterogeneity of the studies was quantified using I 2 statistics, where I 2  > 50% represents moderate and I 2  > 75% represents substantial heterogeneity across studies. Publication bias was assessed with visual assessment of funnel plots. Statistical analyses were performed using Review Manager software [RevMan, Version 5.4.1, The Cochrane Collaboration, Copenhagen, Denmark].

As part of the diabetes scientific community, we are sensitive in using inclusive language, especially in relation to gender. However, the vast majority of original studies that the GDM precision medicine working groups reviewed used women as their terminology to describe their population, as GDM per definition occurs in pregnancy which can only occur in individuals that are female at birth. To be consistent with the original studies defined populations, we use the word ‘women’ in our summary of the evidence, current gaps and future perspectives, but fully acknowledge that not all individuals who experienced a pregnancy may self-identify as women at all times over their life course.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Study selection and study characteristics

PRISMA flow charts (Figs.  1 and 2 ) summarise both searches and study selection processes.

The PRISMA flow diagram details the search and selection process applied in the review.

For the first systematic review (precision approaches to diet and lifestyle interventions), we identified 2 eligible studies ( n  = 2354 participants), which were randomised trials from USA and Singapore (Supplementary Data  1 ) 19 , 20 .

For the second systematic review (precision markers for escalation of pharmacological interventions to achieve target glucose levels), we identified 48 eligible studies ( n  = 25,724 participants) (Supplementary Data  2 ) 21 , 22 , 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 . There were 34 studies ( n  = 23,831 participants) investigating precision markers for escalation to pharmacological agent(s) in addition to standard care with diet and lifestyle advice. Of these, 29 studies ( n  = 20,486) reported escalation to insulin as the only option 21 , 22 , 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 and 5 ( n  = 3345) reported escalation to any medication (metformin, glyburide and/or insulin) 50 , 51 , 52 , 53 , 54 . There were 12 studies ( n  = 1836 participants) investigating precision markers for escalation to insulin when treatment with oral agents was not adequate to achieve target glucose levels. Initial treatment was with glyburide in 6 of these studies ( n  = 527) 55 , 56 , 57 , 58 , 59 , 60 and metformin in the other 6 studies ( n  = 1142) 61 , 62 , 63 , 64 , 65 , 66 . A further 2 eligible studies reported maternal genetic predictors of need for supplementary insulin after glyburide ( n  = 117 participants) 67 and maternal lipidome responses to metformin and insulin ( n  = 217 participants) 68 .

The majority of included studies were observational in design. Most studies reported outcomes of singleton pregnancies. The studies were from a range of geographical locations: Europe (Belgium, Finland, France, Italy, Netherlands, Poland, Portugal, Spain, Sweden), Switzerland, Middle East (Israel, Qatar, United Arab Emirates), Australasia (Australia, New Zealand), North America/Latin America (Canada, USA and Brazil) and Asia (China, Malaysia, Japan). There were a range of approaches to GDM screening, choice of diagnostic test and diagnostic glucose thresholds.

Quality assessment

Study quality assessment is presented as an overall risk of bias for the studies included in the meta-analyses in Fig.  3 and as a heat map for quality assessment for each included study in Fig.  4 . Most of the studies were rated as low risk of bias, as they adequately described how a diagnosis of GDM was assigned, defining inclusion and exclusion criteria, and reported the protocol for initiation of pharmacological therapy. Not all studies reported whether women received diet and lifestyle advice as standard care. Few studies reported whether the precision marker was measured in a valid and reliable way. Using the GRADE approach, the majority of precision markers were classified as having a low certainty of evidence with some classified as very low certainty (Tables  1 and 2 ). No publication bias (as ascertained by funnel plot analyses) was detected.

Green circle with + sign, Yes, Red circle with – sign, No, Blank – not described.

Green – low risk of bias, Grey – unclear risk of bias, Red – high risk of bias.

Precision diet and lifestyle interventions in GDM

Two studies examining different precision approaches to behavioural interventions were included in the first systematic review, so we present a narrative synthesis of the findings. Neither study examined whether a precision approach to specific lifestyle interventions facilitated achievement of glucose targets during pregnancy or improved outcomes that reflect glycaemic control during pregnancy such as macrosomia, large for gestational age, or neonatal hypoglycaemia.

In one study of women with GDM 19 , the intervention was distribution of a tailored letter based on electronic health record data detailing gestational weight gain (GWG) recommendations (as defined by the Institute of Medicine). Receipt of this tailored letter increased the likelihood of meeting the end-of-pregnancy weight goal among women with normal pre-pregnancy BMI, but not among women with overweight or obese pre-pregnancy BMI. This study identified normal pre-pregnancy BMI as a precision marker for intervention success.

The second study 20 used a Web/Smart phone lifestyle coaching programme in women with GDM. Pre-intervention excessive GWG was evaluated as a potential precision marker for the response to the Web/Smart phone lifestyle coaching programme in preventing excess GWG. There was no difference between study arms with respect to either excess GWG or absolute GWG by the end of pregnancy indicating that early GWG is not a useful precision marker with respect to this intervention.

Precision markers for escalation of pharmacological interventions to achieve glucose targets in GDM

Of the 34 studies of precision markers for escalation to pharmacological therapy to achieve glucose targets in addition to standard care with diet and lifestyle advice, 23 studies ( n  = 19,112 participants) were included in the meta-analysis 21 , 22 , 23 , 25 , 26 , 31 , 32 , 33 , 34 , 35 , 36 , 38 , 40 , 41 , 43 , 44 , 45 , 46 , 48 , 50 , 51 , 52 , 53 and 11 studies ( n  = 7158 participants) in the narrative synthesis 24 , 27 , 28 , 29 , 30 , 37 , 39 , 42 , 47 , 49 , 54 .

Table  1 and Supplementary Figs.  1 – 13 show that precision markers for GDM to be adequately managed with lifestyle measures were lower maternal age, nulliparity, lower BMI, no previous history of GDM, lower HbA1c, lower glucose values at the diagnostic OGTT (fasting, 1 h, 2 and/or 3 h glucose), no family history of diabetes, later gestation of diagnosis of GDM and no macrosomia in previous pregnancies. There was a similar pattern for not smoking but this did not reach statistical significance.

Twelve studies ( n  = 1836 participants) of precision markers for escalation to insulin to achieve glucose targets in addition to oral agents were included in the meta-analysis 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 .

Table  2 and Supplementary Figs.  14 – 25 show that precision markers for achieving glucose targets with oral agents only were nulliparity, lower BMI, no previous history of GDM, lower HbA1c, lower glucose values at the diagnostic OGTT (fasting, 1 h, and/or 2 h glucose), later gestation of diagnosis of GDM and later gestation at initiation of the oral agent. In sensitivity analyses, there were no differences in the precision markers predicting response to metformin versus glyburide (Supplementary Data  3 ).

Similar precision markers for escalation to pharmacotherapy to achieve glucose targets were observed in the 11 studies ( n  = 7158 participants) that were not included in the meta-analysis 24 , 27 , 28 , 29 , 30 , 37 , 39 , 42 , 47 , 49 , 54 (Supplementary Data  4 ). Additional precision markers including foetal sex 28 , ethnicity 30 , 47 and season of birth 37 were evaluated in some studies but there was insufficient data to draw conclusions.

There was a paucity of data in examining other precision markers with only weak evidence that the maternal lipidome 68 or genetics 67 hold potential as precision markers for escalation of pharmacological treatment (Supplementary Data  4 ).

As the factors contributing to the development of GDM and its aetiology are heterogeneous 5 , 6 , 7 , 8 , it is plausible that the most effective treatment strategies may also be variable among women with GDM. A precision medicine approach resulting in more rapid normalisation of hyperglycaemia could have substantial benefits for both mother and foetus. By synthesising the evidence from two systematic reviews, we sought to identify key precision markers that may predict effective lifestyle and pharmacological interventions. There were a paucity of studies examining precision approaches to better target lifestyle-based interventions for GDM treatment highlighting the pressing need for further research in this area. However, we found a number of precision markers to enable earlier identification of those requiring escalation of pharmacological therapy. These included characteristics such as BMI, that are easily and routinely measured in clinical practice, and thus have potential to be integrated into prediction models with the aim of achieving rapid glycaemic control. With the relatively short timeframe available to treat GDM, commencing effective therapy earlier, and thus reducing excess foetal growth, is an important target to improve outcomes. Basing treatment decisions closely on precision markers could also avoid over-medicalisation of women who are likely to achieve glucose targets with dietary counselling alone.

In our first systematic review, we identified only two studies addressing precision markers in lifestyle-based interventions for GDM, over and above the usual lifestyle intervention as standard care 19 , 20 . In both studies, precision markers were examined as secondary analyses of the trials and only two precision markers (communication of GWG goals according to pre-pregnancy BMI; and early GWG as a precision marker for the efficacy of technological enhancement to a behavioural intervention) were assessed; it is thus not possible to conclusively identify any precision marker in lifestyle-based interventions for GDM. This gap in the literature highlights the need for more research, as also echoed by patients and healthcare professionals participating in the 2020 James Lind Alliance (JLA) Priority Setting Partnership (PSP) 69 .

Our second systematic review extends the observations of a previous systematic review reporting maternal characteristics associated with the need for insulin treatment in GDM 11 . We identified a number of additional precision markers of successful GDM treatment with lifestyle measures alone, without need for additional pharmacological therapy. The same set of predictors identified women requiring additional insulin after treatment with glyburide as with metformin, despite their different mechanisms of action. However, the numbers of women included in most studies were relatively low and most studies with data in relation to need to escalation to insulin in addition to glyburide were over 10 years old 55 , 56 , 58 , 59 , 60 . We acknowledge that there are also differences in diagnostic criteria, clinical practices, and preferences for choice of which drug to start as first pharmacological agent in various global regions which may limit the generalisability of our findings.

Notably, many of the identified precision markers are routinely measured in clinical practice and so could be incorporated into prediction models of need for pharmacological treatment 70 , 71 . By identifying those who require escalation of pharmacological therapy earlier, better allocation of resources can be achieved. Additionally, some of the precision markers identified, such as BMI, are potentially modifiable. This raises the question of how women can be helped to better prepare for pregnancy 72 . Implementing interventions prior to pregnancy could help understand if these precision markers are on the causal pathway, thus providing an opportunity for prevention and improving health outcomes.

Importantly, there was a lack of data on other potential precision treatment biomarkers, with only two eligible low-quality studies reporting maternal genetic and metabolomic findings 67 , 68 . In the non-pregnancy literature, efficacy of dietary interventions has been reported to differ for patients with distinct metabolic profiles, for example high fasting glucose versus high fasting insulin, or insulin resistance versus low insulin secretion 73 , 74 , 75 . More recent evidence from appropriately designed, prospective dietary intervention studies has confirmed that dietary interventions tailored towards specific metabolic profiles have more beneficial effects than interventions not specifically designed towards a patient’s metabolic profile 76 , 77 , 78 , 79 . Ongoing studies such as the Westlake Precision Birth Cohort (WeBirth) in China (NCT04060056) and the USA Hoosier Moms Cohort (NCT03696368) are collecting additional biomarkers which will enhance knowledge in this field. However, implementing such measures in clinical practice, if they prove informative, could be complex and expensive and thus not suitable for use in all global contexts.

Our study has several limitations: Our reviews primarily relied on secondary analyses from observational studies that were not specifically designed to address the question of precision medicine in GDM treatment and were not powered for many of the comparisons made. Prior to introduction in clinical practice, any marker would have to be rigorously and prospectively tested with respect to sensitivity and specificity to predict treatment needs. The majority of data were extracted from clinical records leading to a lack of detail, such as the precise timing of BMI measurements, and limited information about whether BMI was self-reported or clinician measured. There was marked variation in approaches to GDM screening methods, choice of glucose challenge test and diagnostic thresholds as well as heterogeneity in glucose targets or criteria met to warrant escalation in treatment. Whilst we included studies from a range of geographical settings, the majority of studies were from high income settings, and therefore our findings may not be applicable to low- and middle-income countries. Pregnancy outcomes of precision medicine strategies for GDM also remain unknown, underscoring the need for tailored interventions that account for patient perspective and diverse patient populations.

Despite these limitations, our study has several strengths. We used robust methods to identify a broad range of precision markers, many of which are routinely measured and can be easily translated into prediction models. We excluded studies where the choice of drug was decided by the clinician based on participant characteristics to avoid bias. Our study also highlights the need for further research in this area, particularly in exploring whether there are more sensitive markers that could be identified through omics approaches.

In conclusion, our findings suggest that precision medicine for GDM treatment holds promise as a tool to stream-line individuals towards the most effective and potentially cost-effective care. Whether this will impact on short-term pregnancy outcomes and longer term health outcomes for both mother and baby is not known. More research is urgently needed to identify precision lifestyle interventions and to explore whether more sensitive markers could be identified. Prospective studies, appropriately powered and designed to allow assessment of discriminative abilities (sensitivity, specificity), and (external) validation studies are urgently needed to understand the utility and generalisability of our findings to under-represented populations. This is an area of active research with findings from ongoing studies (NCT04187521, NCT03029702, NCT05932251) eagerly awaited. Consideration of how identified markers can be implemented feasibly and cost effectively in clinical practice is also required. Such efforts will be critical for realising the full potential of precision medicine and empowering patients and their health care providers to optimise short and long-term health outcomes for both mother and child.

Data availability

The included studies are detailed in Supplementary Data  1 and 2 . The data underlying Tables  1 and 2 are in Supplementary Figs.  1 – 13 and 14 – 25 , respectively. Additional information is available via contact with the corresponding author.

Saravanan, P. Gestational diabetes: opportunities for improving maternal and child health. Lancet Diabetes Endocrinol. 8 , 793–800 (2020).

PubMed   Google Scholar  

Vounzoulaki, E. et al. Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis. BMJ 369 , m1361 (2020).

PubMed   PubMed Central   Google Scholar  

Metzger, B. E. et al. Hyperglycemia and adverse pregnancy outcomes. N. Engl. J. Med. 358 , 1991–2002 (2008).

Crowther, C. A. et al. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N. Engl. J. Med. 352 , 2477–2486 (2005).

CAS   PubMed   Google Scholar  

Powe, C. E., Hivert, M. F. & Udler, M. S. Defining heterogeneity among women with gestational diabetes mellitus. Diabetes 69 , 2064–2074 (2020).

CAS   PubMed   PubMed Central   Google Scholar  

Powe, C. E. et al. Heterogeneous contribution of insulin sensitivity and secretion defects to gestational diabetes mellitus. Diabetes Care 39 , 1052–1055 (2016).

Benhalima, K. et al. Characteristics and pregnancy outcomes across gestational diabetes mellitus subtypes based on insulin resistance. Diabetologia 62 , 2118–2128 (2019).

Madsen, L. R. et al. Do variations in insulin sensitivity and insulin secretion in pregnancy predict differences in obstetric and neonatal outcomes? Diabetologia 64 , 304–312 (2021).

Harrison, R. K., Cruz, M., Wong, A., Davitt, C. & Palatnik, A. The timing of initiation of pharmacotherapy for women with gestational diabetes mellitus. BMC Preg, Childbirth 20 , 773 (2020).

CAS   Google Scholar  

Tisi, D. K., Burns, D. H., Luskey, G. W. & Koski, K. G. Fetal exposure to altered amniotic fluid glucose, insulin, and insulin-like growth factor-binding protein 1 occurs before screening for gestational diabetes mellitus. Diabetes Care 34 , 139–144 (2011).

Alvarez-Silvares, E., Bermúdez-González, M., Vilouta-Romero, M., García-Lavandeira, S. & Seoane-Pillado, T. Prediction of insulin therapy in women with gestational diabetes: a systematic review and meta-analysis of observational studies. J. Perinat. Med. 50 , 608–619 (2022).

Dennis, J. M., Shields, B. M., Henley, W. E., Jones, A. G. & Hattersley, A. T. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 7 , 442–451 (2019).

Dawed, A. Y. et al. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials. Lancet Diabetes Endocrinol. 11 , 33–41 (2023).

Nolan, J. J. et al. ADA/EASD Precision Medicine in Diabetes Initiative: an international perspective and future vision for precision medicine in diabetes. Diabetes Care 45 , 261–266 (2022).

Tobias, D. K., Merino, J., Ahmad, A. & PMDI, A. E. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat. Med. (in press), https://doi.org/10.1038/s41591-023-02502-5 . (2023)

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & Group, P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151 , 264–269 (2009).

Joanna Briggs Institute (JBI) critical appraisal tools https://jbi.global/critical-appraisal-tools . Accessed 15 April 2023.

Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) https://guidelines.diabetes.ca/cpg/chapter2 . Accessed 15 April 2023.

Hedderson, M. M. et al. A tailored letter based on electronic health record data improves gestational weight gain among women with gestational diabetes mellitus: the Gestational Diabetes’ Effects on Moms (GEM) cluster-randomized controlled trial. Diabetes Care 41 , 1370–1377 (2018).

Yew, T. W. et al. A randomized controlled trial to evaluate the effects of a smartphone application-based lifestyle coaching program on gestational weight gain, glycemic control, and maternal and neonatal outcomes in women with gestational diabetes mellitus: the SMART-GDM study. Diabetes Care 44 , 456–463 (2021).

Ares, J. et al. Gestational Diabetes Mellitus (GDM): relationship between higher cutoff values for 100g Oral Glucose Tolerance Test (OGTT) and insulin requirement during pregnancy. Matern. Child Health J. 21 , 1488–1492 (2017).

Barnes, R. A. et al. Predictors of large and small for gestational age birthweight in offspring of women with gestational diabetes mellitus. Diabet. Med. 30 , 1040–1046 (2013).

Benhalima, K. et al. Differences in pregnancy outcomes and characteristics between insulin- and diet-treated women with gestational diabetes. BMC Preg. Childbirth 15 , 271 (2015).

Google Scholar  

Berg, M., Adlerberth, A., Sultan, B., Wennergren, M. & Wallin, G. Early random capillary glucose level screening and multidisciplinary antenatal teamwork to improve outcome in gestational diabetes mellitus. Acta Obstetr. Gynecol, Scand. 86 , 283–290 (2007).

Ducarme, G. et al. Predictive factors of subsequent insulin requirement for glycemic control during pregnancy at diagnosis of gestational diabetes mellitus. Int. J. Gynaecol. Obstetr. 144 , 265–270 (2019).

Durnwald, C. P. et al. Glycemic characteristics and neonatal outcomes of women treated for mild gestational diabetes. Obstetr. Gynecol. 117 , 819–827 (2011).

Elnour, A. A. Antenatal oral glucose-tolerance test values and pregnancy outcomes. Int. J. Pharm Pract. 16 , 189–197 (2008).

Giannubilo, S. R., Pasculli, A., Ballatori, C., Biagini, A. & Ciavattini, A. Fetal sex, need for insulin, and perinatal outcomes in gestational diabetes mellitus: an observational cohort study. Clin. Ther. 40 , 587–592 (2018).

Gibson, K. S., Waters, T. P. & Catalano, P. M. Maternal weight gain in women who develop gestational diabetes mellitus. Obstetr. Gynecol. 119 , 560–565 (2012).

Hillier, T. A., Ogasawara, K. K., Pedula, K. L. & Vesco, K. K. Markedly different rates of incident insulin treatment based on universal gestational diabetes mellitus screening in a diverse HMO population. Am. J. Obstetr. Gynecol. 209 , 440.e441–449 (2013).

Ikenoue, S. et al. Clinical impact of women with gestational diabetes mellitus by the new consensus criteria: two year experience in a single institution in Japan. Endocr. J. 61 , 353–358 (2014).

Ito, Y. et al. Indicators of the need for insulin treatment and the effect of treatment for gestational diabetes on pregnancy outcomes in Japan. Endocr. J. 63 , 231–237 (2016).

Kalok, A. et al. Correlation between oral glucose tolerance test abnormalities and adverse pregnancy outcomes in gestational diabetes: a cross-sectional study. Int. J. Environ. Res. Public Health 17 , 6990 (2020).

Koning, S. H. et al. Risk stratification for healthcare planning in women with gestational diabetes mellitus. Netherlands J. Med. 74 , 262–269 (2016).

Mecacci, F. et al. Different gestational diabetes phenotypes: which insulin regimen fits better? Front. Endocrinol. 12 , 630903 (2021).

Meghelli, L., Vambergue, A., Drumez, E. & Deruelle, P. Complications of pregnancy in morbidly obese patients: What is the impact of gestational diabetes mellitus? J. Gynecol. Obstetr. Hum. Reprod. 49 , 101628 (2020).

Molina-Vega, M. et al. Relationship between environmental temperature and the diagnosis and treatment of gestational diabetes mellitus: an observational retrospective study. Sci. Total Environ. 744 , 140994 (2020).

Ng, A., Liu, A. & Nanan, R. Association between insulin and post-caesarean resuscitation rates in infants of women with GDM: a retrospective study. J. Diabetes 12 , 151–157 (2020).

Nguyen, T. H., Yang, J. W., Mahone, M. & Godbout, A. Are there benefits for gestational diabetes mellitus in treating lower levels of hyperglycemia than standard recommendations? Can. J. Diabetes 40 , 548–554 (2016).

Nishikawa, T. et al. One-hour oral glucose tolerance test plasma glucose at gestational diabetes diagnosis is a common predictor of the need for insulin therapy in pregnancy and postpartum impaired glucose tolerance. J. Diabetes Investig. 9 , 1370–1377 (2018).

Ouzounian, J. G. et al. One-hour post-glucola results and pre-pregnancy body mass index are associated with the need for insulin therapy in women with gestational diabetes. J. Matern.-Fetal Neonatal Med. 24 , 718–722 (2011).

Parrettini, S. et al. Gestational diabetes: a link between OGTT, maternal-fetal outcomes and maternal glucose tolerance after childbirth. Nutr. Metab. Cardiovasc. Dis. 30 , 2389–2397 (2020).

Silva, J. K., Kaholokula, J. K., Ratner, R. & Mau, M. Ethnic differences in perinatal outcome of gestational diabetes mellitus. Diabetes Care 29 , 2058–2063 (2006).

Souza, A. et al. Can we stratify the risk for insulin need in women diagnosed early with gestational diabetes by fasting blood glucose? J. Matern-Fetal Neonatal Med. 32 , 2036–2041 (2019).

Suhonen, L., Hiilesmaa, V., Kaaja, R. & Teramo, K. Detection of pregnancies with high risk of fetal macrosomia among women with gestational diabetes mellitus. Acta Obstetr. Gynecol. Scand. 87 , 940–945 (2008).

Sun, T. et al. The effects of insulin therapy on maternal blood pressure and weight in women with gestational diabetes mellitus. BMC Preg. Childbirth 21 , 657 (2021).

Wong, V. W. Gestational diabetes mellitus in five ethnic groups: a comparison of their clinical characteristics. Diabet. Med. 29 , 366–371 (2012).

Wong, V. W. & Jalaludin, B. Gestational diabetes mellitus: who requires insulin therapy? Aust. N.Z. J. Obstetr. Gynaecol. 51 , 432–436 (2011).

Zawiejska, A., Wender-Ozegowska, E., Radzicka, S. & Brazert, J. Maternal hyperglycemia according to IADPSG criteria as a predictor of perinatal complications in women with gestational diabetes: a retrospective observational study. J. Matern.-Fetal Neonatal Med. 27 , 1526–1530 (2014).

Bashir, M. et al. Metformin-treated-GDM has lower risk of macrosomia compared to diet-treated GDM- a retrospective cohort study. J. Matern.-Fetal Neonatal Med. 33 , 2366–2371 (2020).

Gilbert, L. et al. Mental health and its associations with glucose-lowering medication in women with gestational diabetes mellitus. A prospective clinical cohort study. Psychoneuroendocrinology 124 , 105095 (2021).

Krispin, E., Ashkenazi Katz, A., Shmuel, E., Toledano, Y. & Hadar, E. Characterization of women with gestational diabetes who failed to achieve glycemic control by lifestyle modifications. Arch. Gynecol. Obstetr. 303 , 677–683 (2021).

Meshel, S. et al. Can we predict the need for pharmacological treatment according to demographic and clinical characteristics in gestational diabetes? J. Matern.-Fetal Neonatal Med. 29 , 2062–2066 (2016).

Zhu, S., Meehan, T., Veerasingham, M. & Sivanesan, K. COVID-19 pandemic gestational diabetes screening guidelines: a retrospective study in Australian women. Diabetes & Metabolic Syndrome 15 , 391–395 (2021).

Chmait, R., Dinise, T. & Moore, T. Prospective observational study to establish predictors of glyburide success in women with gestational diabetes mellitus. J. Perinatol. 24 , 617–622 (2004).

Conway, D. L., Gonzales, O. & Skiver, D. Use of glyburide for the treatment of gestational diabetes: the San Antonio experience. J. Matern.-Fetal Neonatal Med. 15 , 51–55 (2004).

Harper, L. M., Glover, A. V., Biggio, J. R. & Tita, A. Predicting failure of glyburide therapy in gestational diabetes. J. Perinatol. 36 , 347–351 (2016).

Kahn, B. F., Davies, J. K., Lynch, A. M., Reynolds, R. M. & Barbour, L. A. Predictors of glyburide failure in the treatment of gestational diabetes. Obstetr. Gynecol. 107 , 1303–1309 (2006).

Rochon, M., Rand, L., Roth, L. & Gaddipati, S. Glyburide for the management of gestational diabetes: risk factors predictive of failure and associated pregnancy outcomes. Am. J. Obstetr. Gynecol. 195 , 1090–1094 (2006).

Yogev, Y. et al. Glyburide in gestational diabetes–prediction of treatment failure. J. Matern.-Fetal Neonatal Med. 24 , 842–846 (2011).

Gante, I., Melo, L., Dores, J., Ruas, L. & Almeida, M. D. C. Metformin in gestational diabetes mellitus: predictors of poor response. Eur. J. Endocrinol. 178 , 129–135 (2018).

Khin, M. O., Gates, S. & Saravanan, P. Predictors of metformin failure in gestational diabetes mellitus (GDM). Diabetes Metab. Syndr. 12 , 405–410 (2018).

McGrath, R. T., Glastras, S. J., Hocking, S. & Fulcher, G. R. Use of metformin earlier in pregnancy predicts supplemental insulin therapy in women with gestational diabetes. Diabetes Res. Clin. Pract. 116 , 96–99 (2016).

Picón-César, M. J. et al. Metformin for gestational diabetes study: metformin vs insulin in gestational diabetes: glycemic control and obstetrical and perinatal outcomes: randomized prospective trial. Am. J. Obstetr. Gynecol. 225 , 517.e511–517.e517 (2021).

Rowan, J. A., Hague, W. M., Gao, W., Battin, M. R. & Moore, M. P. Metformin versus insulin for the treatment of gestational diabetes. N. Engl J. Med. 358 , 2003–2015 (2008).

Tertti, K., Ekblad, U., Koskinen, P., Vahlberg, T. & Rönnemaa, T. Metformin vs. insulin in gestational diabetes. A randomized study characterizing metformin patients needing additional insulin. Diabetes Obes. Metab. 15 , 246–251 (2013).

Bouchghoul, H. et al. Hypoglycemia and glycemic control with glyburide in women with gestational diabetes and genetic variants of cytochrome P450 2C9 and/or OATP1B3. Clin. Pharmacol. Ther. 110 , 141–148 (2021).

Huhtala, M. S., Tertti, K. & Rönnemaa, T. Serum lipids and their association with birth weight in metformin and insulin-treated patients with gestational diabetes. Diabetes Res. Clin. Pract. 170 , 108456 (2020).

Ayman, G. et al. The top 10 research priorities in diabetes and pregnancy according to women, support networks and healthcare professionals. Diabet. Med. 38 , e14588 (2021).

Cooray, S. D. et al. Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes: the PeRSonal GDM model. EClinicalMedicine 52 , 101637 (2022).

Liao, L. D. et al. Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study. BMC Med. 20 , 307 (2022).

Cassinelli, E. H. et al. Preconception health and care policies and guidelines in the UK and Ireland: a scoping review. Lancet 400 , S61 (2022).

Hjorth, M. F. et al. Pretreatment Fasting glucose and insulin as determinants of weight loss on diets varying in macronutrients and dietary fibers-the POUNDS LOST study. Nutrients 11 , 586 (2019).

Hjorth, M. F. et al. Pretreatment fasting plasma glucose and insulin modify dietary weight loss success: results from 3 randomized clinical trials. Am. J. Clin. Nutr. 106 , 499–505 (2017).

Hjorth, M. F., Due, A., Larsen, T. M. & Astrup, A. Pretreatment fasting plasma glucose modifies dietary weight loss maintenance success: results from a stratified RCT. Obesity 25 , 2045–2048 (2017).

Bergia, R. E. et al. Differential glycemic effects of low- versus high-glycemic index Mediterranean-style eating patterns in adults at risk for type 2 diabetes: the MEDGI-Carb randomized controlled trial. Nutrients 14 , 706 (2022).

Aldubayan, M. A. et al. A double-blinded, randomized, parallel intervention to evaluate biomarker-based nutrition plans for weight loss: the PREVENTOMICS study. Clin. Nutr. 41 , 1834–1844 (2022).

Trouwborst, I. et al. Cardiometabolic health improvements upon dietary intervention are driven by tissue-specific insulin resistance phenotype: a precision nutrition trial. Cell Metab. 35 , 71–83.e75 (2023).

Cifuentes, L. et al. Phenotype tailored lifestyle intervention on weight loss and cardiometabolic risk factors in adults with obesity: a single-centre, non-randomised, proof-of-concept study. EClinicalMedicine 58 , 101923 (2023).

Download references

Acknowledgements

The ADA/EASD Precision Diabetes Medicine Initiative, within which this work was conducted, has received the following support: The Covidence license was funded by Lund University (Sweden) for which technical support was provided by Maria Björklund and Krister Aronsson (Faculty of Medicine Library, Lund University, Sweden). Administrative support was provided by Lund University (Malmö, Sweden), University of Chicago (IL, USA), and the American Diabetes Association (Washington D.C., USA). The Novo Nordisk Foundation (Hellerup, Denmark) provided grant support for in-person writing group meetings (PI: L Phillipson, University of Chicago, IL). J.M.M. acknowledges the support of the Henry Friesen Professorship in Endocrinology, University of Manitoba, Canada. N.-M.M. and R.M.R. acknowledge the support of the British Heart Foundation (RE/18/5/34216). S.E.O. is supported by the Medical Research Council (MC_UU_00014/4) and British Heart Foundation (RG/17/12/33167).

Author information

These authors contributed equally: Jamie L. Benham, Véronique Gingras, Niamh-Maire McLennan, Jasper Most, Jennifer M. Yamamoto, Catherine E. Aiken.

These authors jointly supervised this work: Susan E. Ozanne, Rebecca M. Reynolds.

Authors and Affiliations

Department of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

Jamie L. Benham

Department of Nutrition, Université de Montréal, Montreal, QC, Canada

Véronique Gingras

Research Center, Sainte-Justine University Hospital Center, Montreal, QC, Canada

MRC Centre for Reproductive Health, Queens’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Niamh-Maire McLennan & Rebecca M. Reynolds

Centre for Cardiovascular Science, Queens’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Department of Orthopedics, Zuyderland Medical Center, Sittard-Geleen, The Netherlands

Jasper Most

Internal Medicine, University of Manitoba, Winnipeg, MB, Canada

Jennifer M. Yamamoto

Department of Obstetrics and Gynaecology, the Rosie Hospital, Cambridge, UK

Catherine E. Aiken & Catherine Aiken

NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK

University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK

Susan E. Ozanne

Division of Preventative Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Deirdre K. Tobias & Vanessa Santhakumar

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Deirdre K. Tobias, Zhila Semnani-Azad, Marta Guasch-Ferré & Paul W. Franks

Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Jordi Merino, Anne Cathrine B. Thuesen, Mette K. Andersen, Christoffer Clemmensen, Torben Hansen, Mariam Nakabuye & Ruth J. F. Loos

Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA

Jordi Merino, Sara J. Cromer, Raymond J. Kreienkamp, Aaron Leong, Camille E. Powe, Jose C. Florez, Marie-France Hivert & Miriam S. Udler

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Jordi Merino, Raymond J. Kreienkamp, Aaron J. Deutsch, Jose C. Florez & Miriam S. Udler

Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden

Abrar Ahmad, Monika Dudenhöffer-Pfeifer, Hugo Fitipaldi, Hugo Pomares-Millan, Maria F. Gomez & Paul W. Franks

Department of Molecular Genetics, Madras Diabetes Research Foundation, Chennai, India

Dhanasekaran Bodhini

Division of Pediatric Endocrinology, Department of Pediatrics, Saint Louis University School of Medicine, SSM Health Cardinal Glennon Children’s Hospital, St. Louis, MO, USA

Amy L. Clark

Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, Devon, UK

Kevin Colclough, Alice Hughes, Kashyap Amratlal Patel, Katherine Young, Angus G. Jones, Elisa de Franco, Sarah E. Flanagan, Andrew McGovern, John M. Dennis, Andrew T. Hattersley & Richard Oram

CIBER-BBN, ISCIII, Madrid, Spain

Rosa Corcoy

Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain

Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain

Program in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA

Sara J. Cromer, Raymond J. Kreienkamp, Magdalena Sevilla-Gonzalez, Aaron J. Deutsch, Camille E. Powe, Jose C. Florez & Miriam S. Udler

Department of Medicine, Harvard Medical School, Boston, MA, USA

Sara J. Cromer, Magdalena Sevilla-Gonzalez, Tinashe Chikowore, Aaron J. Deutsch, Aaron Leong, Camille E. Powe, Jose C. Florez, James B. Meigs & Miriam S. Udler

Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA

Jamie L. Felton, Linda A. DiMeglio, Carmella Evans-Molina, Arianna Harris-Kawano, Heba M. Ismail, Dianna Perez, Gabriela S. F. Monaco & Emily K. Sims

Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA

Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA

Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA

• Ellen C. Francis

University Hospital Leuven, Leuven, Belgium

Pieter Gillard & Chantal Mathieu

Department of Pediatrics, Erasmus Medical Center, Rotterdam, The Netherlands

Romy Gaillard

Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK

Eram Haider, Robert Massey, Adem Y. Dawed & Ewan R. Pearson

Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA

Jennifer M. Ikle & Anna L. Gloyn

Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA

University of Florida, Gainesville, FL, USA

Laura M. Jacobsen

Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Anna R. Kahkoska

Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland

Jarno L. T. Kettunen & Tiinamaija Tuomi

Folkhalsan Research Center, Helsinki, Finland

Jarno L. T. Kettunen

Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland

Department of Pediatrics, Division of Endocrinology, Boston Children’s Hospital, Boston, MA, USA

Raymond J. Kreienkamp

Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Lee-Ling Lim

Asia Diabetes Foundation, Hong Kong SAR, China

Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China

Lee-Ling Lim, Claudia Ha-ting Tam, Chuiguo Huang, Gechang Yu, Yingchai Zhang & Ronald C. W. Ma

Department of Pediatrics and Clinical Genetics, Kuopio University Hospital, Kuopio, Finland

Jonna M. E. Männistö

Department of Medicine, University of Eastern Finland, Kuopio, Finland

Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Niamh-Maire Mclennan, Rebecca M. Reynolds & Robert K. Semple

Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA

Rachel G. Miller & Tina Costacou

Metabolic Disease Unit, University Hospital of Padova, Padova, Italy

Mario Luca Morieri

Department of Medicine, University of Padova, Padova, Italy

Department of Pediatrics and Medicine, University of Chicago, Chicago, IL, USA

Rochelle N. Naylor

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Bige Ozkan, Mary R. Rooney, Amelia S. Wallace & Elizabeth Selvin

Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA

Department of Medicine, Johns Hopkins University, Baltimore, MD, USA

Scott J. Pilla, Sarah Kanbour, Sudipa Sarkar & Nestoras Mathioudakis

Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA

Scott J. Pilla

Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m Hennekamp 65, 40225, Düsseldorf, Germany

Katsiaryna Prystupa, Martin Schön & Robert Wagner

German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany

Katsiaryna Prystupa, Martin Schön, Norbert Stefan & Robert Wagner

Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA

Sridharan Raghavan

Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Mary R. Rooney, Amelia S. Wallace, Caroline C. Wang, Debashree Ray & Elizabeth Selvin

Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia

Martin Schön

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA

Magdalena Sevilla-Gonzalez

Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway

Pernille Svalastoga, Ingvild Aukrust, Janne Molnes & Pål Rasmus Njølstad

Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway

Pernille Svalastoga & Pål Rasmus Njølstad

Eastern Health Clinical School, Monash University, Melbourne, VIC, Australia

Wubet Worku Takele, Gebresilasea Gendisha Ukke & Siew S. Lim

Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China

Claudia Ha-ting Tam, Chuiguo Huang, Gechang Yu, Yingchai Zhang & Ronald C. W. Ma

Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China

Claudia Ha-ting Tam & Ronald C. W. Ma

Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA

Mustafa Tosur & Maria J. Redondo

Division of Pediatric Diabetes and Endocrinology, Texas Children’s Hospital, Houston, TX, USA

Mustafa Tosur, Marzhan Urazbayeva & Maria J. Redondo

Children’s Nutrition Research Center, USDA/ARS, Houston, TX, USA

Mustafa Tosur

Stanford University School of Medicine, Stanford, CA, USA

Jessie J. Wong & Korey K. Hood

Department of Diabetology, APHP, Paris, France

Chloé Amouyal

Sorbonne Université, INSERM, NutriOmic team, Paris, France

Department of Nutrition, Dietetics and Food, Monash University, Melbourne, VIC, Australia

Maxine P. Bonham & Gloria K. W. Leung

Monash Centre for Health Research and Implementation, Monash University, Clayton, VIC, Australia

Mingling Chen

Health Management Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China

Feifei Cheng

MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Tinashe Chikowore

Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Department of Women and Children’s health, King’s College London, London, UK

Sian C. Chivers & Sara L. White

Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Dana Dabelea, Kristen Boyle & Wei Perng

Section of Adult and Pediatric Endocrinology, Diabetes and Metabolism, Kovler Diabetes Center, University of Chicago, Chicago, USA

Laura T. Dickens

Department of Pediatrics, Riley Hospital for Children, Indiana University School of Medicine, Indianapolis, IN, USA

Linda A. DiMeglio

Richard L. Roudebush VAMC, Indianapolis, IN, USA

Carmella Evans-Molina

Biomedical Research Institute Girona, IdIBGi, Girona, Spain

María Mercè Fernández-Balsells

Diabetes, Endocrinology and Nutrition Unit, Girona, University Hospital Dr Josep Trueta, Girona, Spain

Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA

Stephanie L. Fitzpatrick

Department of Pediatrics, Diabetes Center, University of California at San Francisco, San Francisco, CA, USA

Stephen E. Gitelman

Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Mark O. Goodarzi

Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia

Jessica A. Grieger, Nahal Habibi, Kai Liu, Maleesa Pathirana & Alejandra Quinteros

Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia

Jessica A. Grieger, Nahal Habibi, Maleesa Pathirana & Shao J. Zhou

Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 1014, Copenhagen, Denmark

Marta Guasch-Ferré

Division of Endocrinology and Diabetes, Department of Pediatrics, Sanford Children’s Hospital, Sioux Falls, SD, USA

Benjamin Hoag

University of South Dakota School of Medicine, E Clark St, Vermillion, SD, USA

Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Randi K. Johnson & Maggie A. Stanislawski

Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA

Randi K. Johnson

Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK

Angus G. Jones, Andrew T. Hattersley & Richard Oram

Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK

Robert W. Koivula, Katharine R. Owen & Paul W. Franks

Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA

Aaron Leong & James B. Meigs

UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA

Ingrid M. Libman

Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA

S. Alice Long

Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

William L. Lowe Jr.

Department of Pathology & Molecular Medicine, McMaster University, Hamilton, ON, Canada

Robert W. Morton

Population Health Research Institute, Hamilton, ON, Canada

Robert W. Morton, Russell de Souza & Diana Sherifali

Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Tuborg Havnevej 19, 2900, Hellerup, Denmark

Robert W. Morton & Paul W. Franks

Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa

Ayesha A. Motala

Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

Suna Onengut-Gumuscu & Stephen S. Rich

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA

James S. Pankow

Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium

Sofia Pazmino, Nele Steenackers & Bart Van der Schueren

School of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

John R. Petrie

Department of Obstetrics, Gynecology, and Reproductive Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

• Camille E. Powe

Sanford Children’s Specialty Clinic, Sioux Falls, SD, USA

Rashmi Jain

Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA

Rashmi Jain & Kurt Griffin

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Debashree Ray

Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark

Mathias Ried-Larsen

Institute for Sports and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark

Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA

AMAN Hospital, Doha, Qatar

Sarah Kanbour

Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Denise M. Scholtens

Institute of Molecular and Genomic Medicine, National Health Research Institutes, Taipei City, Taiwan, ROC

Wayne Huey-Herng Sheu

Divsion of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan, ROC

Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan, ROC

Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA

Cate Speake

Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Andrea K. Steck & Peter A. Gottlieb

University Hospital of Tübingen, Tübingen, Germany

Norbert Stefan

Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany

Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark

University of Newcastle, Newcastle upon Tyne, UK

Rachael Taylor

Section on Genetics and Epidemiology, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA

Sok Cin Tye

Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, The Netherlands

Gastroenterology, Baylor College of Medicine, Houston, TX, USA

Marzhan Urazbayeva

Department of Endocrinology, University Hospitals Leuven, Leuven, Belgium

Bart Van der Schueren

Sorbonne University, Inserm U938, Saint-Antoine Research Centre, Institute of Cardiometabolism and Nutrition, Paris, 75012, France

Camille Vatier

Department of Endocrinology, Diabetology and Reproductive Endocrinology, Assistance Publique-Hôpitaux de Paris, Saint-Antoine University Hospital, National Reference Center for Rare Diseases of Insulin Secretion and Insulin Sensitivity (PRISIS), Paris, France

Royal Melbourne Hospital Department of Diabetes and Endocrinology, Parkville, VIC, Australia

John M. Wentworth

Walter and Eliza Hall Institute, Parkville, VIC, Australia

John M. Wentworth & Tiinamaija Tuomi

University of Melbourne Department of Medicine, Parkville, VIC, Australia

Deakin University, Melbourne, VIC, Australia

Wesley Hannah

Department of Epidemiology, Madras Diabetes Research Foundation, Chennai, India

Department of Diabetes and Endocrinology, Guy’s and St Thomas’ Hospitals NHS Foundation Trust, London, UK

Sara L. White

School of Agriculture, Food and Wine, University of Adelaide, Adelaide, SA, Australia

Shao J. Zhou

Institut Cochin, Inserm U 10116, Paris, France

Jacques Beltrand & Michel Polak

Pediatric endocrinology and diabetes, Hopital Necker Enfants Malades, APHP Centre, université de Paris, Paris, France

Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway

Ingvild Aukrust & Janne Molnes

Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

Kristin A. Maloney & Toni I. Pollin

Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA

Hugo Pomares-Millan

Nephrology, Dialysis and Renal Transplant Unit, IRCCS—Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, Bologna, Italy

Michele Provenzano

Department of Medical Genetics, AP-HP Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France

Cécile Saint-Martin

Global Center for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

Cuilin Zhang

Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

Kaiser Permanente Northern California Division of Research, Oakland, CA, USA

Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA

National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA

Sungyoung Auh & Rebecca J. Brown

Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

Russell de Souza

Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Andrea J. Fawcett & Jami L. Josefson

Department of Clinical and Organizational Development, Chicago, IL, USA

Andrea J. Fawcett

American Diabetes Association, Arlington, VA, USA

Chandra Gruber

College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

Eskedar Getie Mekonnen

Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, 2160, Antwerp, Belgium

Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA

Emily Mixter & Louis H. Philipson

School of Nursing, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

Diana Sherifali

Division of Endocrinology, Metabolism, Diabetes, University of Colorado, Boulder, CO, USA

Robert H. Eckel

Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Dublin, Ireland, UK

John J. Nolan

Department of Endocrinology, Wexford General Hospital, Wexford, Ireland, UK

Division of Endocrinology, NorthShore University HealthSystem, Skokie, IL, USA

Liana K. Billings

Department of Medicine, Prtizker School of Medicine, University of Chicago, Chicago, IL, USA

Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, CA, USA

Anna L. Gloyn

Faculty of Health, Aarhus University, Aarhus, Denmark

Maria F. Gomez

Department of Pediatrics and Medicine and Kovler Diabetes Center, University of Chicago, Chicago, USA

Siri Atma W. Greeley

Sanford Research, Sioux Falls, SD, USA

Kurt Griffin

University of Washington School of Medicine, Seattle, WA, USA

Irl B. Hirsch

Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA

Marie-France Hivert

Department of Medicine, Universite de Sherbrooke, Sherbrooke, QC, Canada

Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea

Soo Heon Kwak

Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA

Lori M. Laffel

Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Ruth J. F. Loos

Broad Institute, Cambridge, MA, USA

James B. Meigs

Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Shivani Misra

Department of Diabetes & Endocrinology, Imperial College Healthcare NHS Trust, London, UK

Department of Diabetology, Madras Diabetes Research Foundation & Dr. Mohan’s Diabetes Specialities Centre, Chennai, India

Viswanathan Mohan

Department of Medicine, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand

Rinki Murphy

Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Auckland, New Zealand

Medical Bariatric Service, Te Whatu Ora Counties, Health New Zealand, Auckland, New Zealand

Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK

Katharine R. Owen

University of Cambridge, Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK

Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA

Toni I. Pollin

Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI, USA

Rodica Pop-Busui

AdventHealth Translational Research Institute, Orlando, FL, USA

Richard E. Pratley

Pennington Biomedical Research Center, Baton Rouge, LA, USA

Leanne M. Redman

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

Robert K. Semple

Yale School of Medicine, New Haven, CT, USA

Jennifer L. Sherr

Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

Arianne Sweeting

Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, NSW, Australia

Kaiser Permanente Northwest, Kaiser Permanente Center for Health Research, Portland, OR, USA

Kimberly K. Vesco

Clinial Research, Steno Diabetes Center Copenhagen, Herlev, Denmark

Tina Vilsbøll

Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany

Robert Wagner

You can also search for this author in PubMed   Google Scholar

• Deirdre K. Tobias
• , Jordi Merino
• , Abrar Ahmad
• , Catherine Aiken
• , Jamie L. Benham
• , Dhanasekaran Bodhini
• , Amy L. Clark
• , Kevin Colclough
• , Rosa Corcoy
• , Sara J. Cromer
• , Daisy Duan
• , Jamie L. Felton
• , Ellen C. Francis
• , Pieter Gillard
• , Véronique Gingras
• , Romy Gaillard
• , Eram Haider
• , Alice Hughes
• , Jennifer M. Ikle
• , Laura M. Jacobsen
• , Anna R. Kahkoska
• , Jarno L. T. Kettunen
• , Raymond J. Kreienkamp
• , Lee-Ling Lim
• , Jonna M. E. Männistö
• , Robert Massey
• , Niamh-Maire Mclennan
• , Rachel G. Miller
• , Mario Luca Morieri
• , Jasper Most
• , Rochelle N. Naylor
• , Bige Ozkan
• , Kashyap Amratlal Patel
• , Scott J. Pilla
• , Katsiaryna Prystupa
• , Sridharan Raghavan
• , Mary R. Rooney
• , Martin Schön
• , Zhila Semnani-Azad
• , Magdalena Sevilla-Gonzalez
• , Pernille Svalastoga
• , Wubet Worku Takele
• , Claudia Ha-ting Tam
• , Anne Cathrine B. Thuesen
• , Mustafa Tosur
• , Amelia S. Wallace
• , Caroline C. Wang
• , Jessie J. Wong
• , Jennifer M. Yamamoto
• , Katherine Young
• , Chloé Amouyal
• , Mette K. Andersen
• , Maxine P. Bonham
• , Mingling Chen
• , Feifei Cheng
• , Tinashe Chikowore
• , Sian C. Chivers
• , Christoffer Clemmensen
• , Dana Dabelea
• , Adem Y. Dawed
• , Aaron J. Deutsch
• , Laura T. Dickens
• , Linda A. DiMeglio
• , Monika Dudenhöffer-Pfeifer
• , Carmella Evans-Molina
• , María Mercè Fernández-Balsells
• , Hugo Fitipaldi
• , Stephanie L. Fitzpatrick
• , Stephen E. Gitelman
• , Mark O. Goodarzi
• , Jessica A. Grieger
• , Marta Guasch-Ferré
• , Nahal Habibi
• , Torben Hansen
• , Chuiguo Huang
• , Arianna Harris-Kawano
• , Heba M. Ismail
• , Benjamin Hoag
• , Randi K. Johnson
• , Angus G. Jones
• , Robert W. Koivula
• , Aaron Leong
• , Gloria K. W. Leung
• , Ingrid M. Libman
• , S. Alice Long
• , William L. Lowe Jr.
• , Robert W. Morton
• , Ayesha A. Motala
• , Suna Onengut-Gumuscu
• , James S. Pankow
• , Maleesa Pathirana
• , Sofia Pazmino
• , Dianna Perez
• , John R. Petrie
• , Camille E. Powe
• , Alejandra Quinteros
• , Rashmi Jain
• , Debashree Ray
• , Mathias Ried-Larsen
• , Zeb Saeed
• , Vanessa Santhakumar
• , Sarah Kanbour
• , Sudipa Sarkar
• , Gabriela S. F. Monaco
• , Denise M. Scholtens
• , Elizabeth Selvin
• , Wayne Huey-Herng Sheu
• , Cate Speake
• , Maggie A. Stanislawski
• , Nele Steenackers
• , Andrea K. Steck
• , Norbert Stefan
• , Julie Støy
• , Rachael Taylor
• , Sok Cin Tye
• , Gebresilasea Gendisha Ukke
• , Marzhan Urazbayeva
• , Bart Van der Schueren
• , Camille Vatier
• , John M. Wentworth
• , Wesley Hannah
• , Sara L. White
• , Gechang Yu
• , Yingchai Zhang
• , Shao J. Zhou
• , Jacques Beltrand
• , Michel Polak
• , Ingvild Aukrust
• , Elisa de Franco
• , Sarah E. Flanagan
• , Kristin A. Maloney
• , Andrew McGovern
• , Janne Molnes
• , Mariam Nakabuye
• , Pål Rasmus Njølstad
• , Hugo Pomares-Millan
• , Michele Provenzano
• , Cécile Saint-Martin
• , Cuilin Zhang
• , Sungyoung Auh
• , Russell de Souza
• , Andrea J. Fawcett
• , Chandra Gruber
• , Eskedar Getie Mekonnen
• , Emily Mixter
• , Diana Sherifali
• , Robert H. Eckel
• , John J. Nolan
• , Louis H. Philipson
• , Rebecca J. Brown
• , Liana K. Billings
• , Kristen Boyle
• , Tina Costacou
• , John M. Dennis
• , Jose C. Florez
• , Anna L. Gloyn
• , Maria F. Gomez
• , Peter A. Gottlieb
• , Siri Atma W. Greeley
• , Kurt Griffin
• , Andrew T. Hattersley
• , Irl B. Hirsch
• , Marie-France Hivert
• , Korey K. Hood
• , Jami L. Josefson
• , Soo Heon Kwak
• , Lori M. Laffel
• , Siew S. Lim
• , Ruth J. F. Loos
• , Ronald C. W. Ma
• , Chantal Mathieu
• , Nestoras Mathioudakis
• , James B. Meigs
• , Shivani Misra
• , Viswanathan Mohan
• , Rinki Murphy
• , Richard Oram
• , Katharine R. Owen
• , Susan E. Ozanne
• , Ewan R. Pearson
• , Wei Perng
• , Toni I. Pollin
• , Rodica Pop-Busui
• , Richard E. Pratley
• , Leanne M. Redman
• , Maria J. Redondo
• , Rebecca M. Reynolds
• , Robert K. Semple
• , Jennifer L. Sherr
• , Emily K. Sims
• , Arianne Sweeting
• , Tiinamaija Tuomi
• , Miriam S. Udler
• , Kimberly K. Vesco
• , Tina Vilsbøll
• , Robert Wagner
• , Stephen S. Rich
•  & Paul W. Franks

Contributions

All authors J.L.B., V.G., N.-M.M., J.M., J.M.Y., C.E.A., S.E.O. and R.M.R. contributed to the design of the research questions, study selection, extraction of data, data analyses, quality assessment and data interpretation. The ADA/EASD PMDI consortium members provided feedback on methodology and reporting guidelines. RMR wrote the first draft of the manuscript. All authors edited the manuscript and all approved the final version.

Corresponding author

Correspondence to Rebecca M. Reynolds .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Communications Medicine thanks Rochan Agha-Jaffar and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, supplementary data 1, supplementary data 2, supplementary data 3, supplementary data 4, peer review file, description of additional supplementary files, reporting summary, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Benham, J.L., Gingras, V., McLennan, NM. et al. Precision gestational diabetes treatment: a systematic review and meta-analyses. Commun Med 3 , 135 (2023). https://doi.org/10.1038/s43856-023-00371-0

Download citation

Received : 08 May 2023

Accepted : 25 September 2023

Published : 05 October 2023

DOI : https://doi.org/10.1038/s43856-023-00371-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine.

• Jordi Merino
• Paul W. Franks

Nature Medicine (2023)

Communications Medicine (2023)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• - Google Chrome

Intended for healthcare professionals

• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Gestational diabetes...

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.

• Download figure
• Open in new tab
• Download powerpoint

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

• View inline

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/ .

• Saravanan P ,
• Diabetes in Pregnancy Working Group ,
• Maternal Medicine Clinical Study Group ,
• Royal College of Obstetricians and Gynaecologists, UK
• O’Sullivan JB ,
• Hartling L ,
• Dryden DM ,
• Guthrie A ,
• Vandermeer B ,
• McIntyre HD ,
• Catalano P ,
• Mathiesen ER ,
• Metzger BE ,
• HAPO Study Cooperative Research Group
• Persson M ,
• Balsells M ,
• García-Patterson A ,
• Simmonds M ,
• Murphy HR ,
• Roland JM ,
• East Anglia Study Group for Improving Pregnancy Outcomes in Women with Diabetes (EASIPOD)
• Magnuson A ,
• Simmons D ,
• Sumeksri P ,
• Wongyai S ,
• González González NL ,
• Bellart J ,
• Guillén MA ,
• Herranz L ,
• Barquiel B ,
• Hillman N ,
• Burgos MA ,
• Pallardo LF
• Higgins JP ,
• Thompson SG ,
• Davey Smith G ,
• Schneider M ,
• Sweeting MJ ,
• Sutton AJ ,
• Hartung J ,
• IntHout J ,
• Ioannidis JP ,
• Alberico S ,
• Montico M ,
• Barresi V ,
• Multicentre Study Group on Mode of Delivery in Friuli Venezia Giulia
• Alfadhli EM ,
• Anderberg E ,
• Ardawi MS ,
• Nasrat HA ,
• Al-Sagaaf HM ,
• Kenealy T ,
• Barakat MN ,
• Youssef RM ,
• Al-Lawati JA
• Ibrahim I ,
• Eltaher F ,
• Benhalima K ,
• Hanssens M ,
• Devlieger R ,
• Verhaeghe J ,
• Berggren EK ,
• Boggess KA ,
• Stuebe AM ,
• Jonsson Funk M
• Korucuoglu U ,
• Aksakal N ,
• Himmetoglu O
• Bodmer-Roy S ,
• Cousineau J ,
• Broekman BFP ,
• GUSTO study group
• Catalano PM ,
• Cruickshank JK ,
• Chanprapaph P ,
• Cheung NW ,
• Lopez-Rodo V ,
• Rodriguez-Vaca D ,
• Benchimol M ,
• Carbillon L ,
• Benbara A ,
• Pharisien I ,
• Scifres CM ,
• Gorban de Lapertosa S ,
• Salzberg S ,
• DPSG-SAD Group
• Zijlmans AB ,
• Rademaker D ,
• Djelmis J ,
• Mulliqi Kotori V ,
• Pavlić Renar I ,
• Ivanisevic M ,
• Oreskovic S
• Domanski G ,
• Ittermann T ,
• Donovan LE ,
• Edwards AL ,
• Torrejón MJ ,
• Ekeroma AJ ,
• Chandran GS ,
• McCowan L ,
• Eagleton C ,
• Erjavec K ,
• Poljičanin T ,
• Matijević R
• Ethridge JK Jr . ,
• Feleke BE ,
• Feleke TE ,
• Forsbach G ,
• Cantú-Diaz C ,
• Vázquez-Lara J ,
• Villanueva-Cuellar MA ,
• Alvarez y García C ,
• Rodríguez-Ramírez E
• Gonçalves E ,
• Gortazar L ,
• Flores-Le Roux JA ,
• Benaiges D ,
• Gruendhammer M ,
• Brezinka C ,
• Lechleitner M
• Hedderson MM ,
• Ferrara A ,
• Hillier TA ,
• Pedula KL ,
• Morris JM ,
• Hossein-Nezhad A ,
• Maghbooli Z ,
• Vassigh AR ,
• Massaro N ,
• Streckeisen S ,
• Ikenoue S ,
• Miyakoshi K ,
• Raghav SK ,
• Jensen DM ,
• Sørensen B ,
• Rich-Edwards JW ,
• Kreisman S ,
• Tildesley H
• Kachhwaha CP ,
• Kautzky-Willer A ,
• Bancher-Todesca D ,
• Weitgasser R ,
• Keikkala E ,
• Mustaniemi S ,
• Koivunen S ,
• Keshavarz M ,
• Babaee GR ,
• Moghadam HK ,
• Kgosidialwa O ,
• Carmody L ,
• Gunning P ,
• Kieffer EC ,
• Carman WJ ,
• Sanborn CZ ,
• Viljakainen M ,
• Männistö T ,
• Kachhawa G ,
• Laafira A ,
• Griffin CJ ,
• Johnson JA ,
• Lapolla A ,
• Dalfrà MG ,
• Ragazzi E ,
• De Cata AP ,
• Norwitz E ,
• Leybovitz-Haleluya N ,
• Wainstock T ,
• Hinkle SN ,
• Grantz KL ,
• Lopez-de-Andres A ,
• Carrasco-Garrido P ,
• Gil-de-Miguel A ,
• Hernandez-Barrera V ,
• Jiménez-García R
• Luengmettakul J ,
• Sunsaneevithayakul P ,
• Talungchit P
• Macaulay S ,
• Munthali RJ ,
• Dunger DB ,
• Makwana M ,
• Bhimwal RK ,
• El Mallah KO ,
• Kulaylat NA ,
• Melamed N ,
• Vandenberghe H ,
• Jensen RC ,
• Kibusi SM ,
• Munyogwa MJ ,
• Patient C ,
• Miailhe G ,
• Legardeur H ,
• Mandelbrot L
• Minsart AF ,
• N’guyen TS ,
• Ratsimandresy R ,
• Ali Hadji R
• Matsumoto T ,
• Morikawa M ,
• Sugiyama T ,
• Knights SJ ,
• Olayemi OO ,
• Mwanri AW ,
• Ramaiya K ,
• Abalkhail B ,
• Nguyen TH ,
• Nguyen CL ,
• Minh Pham N ,
• Nicolosi BF ,
• Vernini JM ,
• Kragelund Nielsen K ,
• Andersen GS ,
• Nybo Andersen AM
• Ogonowski J ,
• Miazgowski T ,
• Czeszyńska MB ,
• Kuczyńska M ,
• Miazgowski T
• Morrish DW ,
• O’Sullivan EP ,
• O’Reilly M ,
• Dennedy MC ,
• Gaffney G ,
• Atlantic DIP collaborators
• Ovesen PG ,
• Rasmussen S ,
• Kesmodel US
• Ozumba BC ,
• Premuzic V ,
• Zovak Pavic A ,
• Bevanda M ,
• Mihaljevic S ,
• Ramachandran A ,
• Snehalatha C ,
• Clementina M ,
• Sasikala R ,
• Redman LM ,
• LIFE-Moms Research Group
• Spanish Group for the Study of the Impact of Carpenter and Coustan GDM thresholds
• Bowker SL ,
• Montoro MN ,
• Lawrence JM
• Kamalanathan S ,
• Saldana TM ,
• Siega-Riz AM ,
• Savitz DA ,
• Thorp JM Jr .
• Savona-Ventura C ,
• Schwartz ML ,
• Lubarsky SL
• Segregur J ,
• Buković D ,
• Milinović D ,
• Cavkaytar S ,
• Shahbazian H ,
• Nouhjah S ,
• Shahbazian N ,
• McElduff A ,
• Sheffield JS ,
• Butler-Koster EL ,
• McIntire DD ,
• Saigusa Y ,
• Nakanishi S ,
• Templeton A ,
• Sirimarco MP ,
• Guerra HM ,
• Lisboa EG ,
• Sletner L ,
• Yajnik CS ,
• Soliman A ,
• Al Rifai H ,
• Soonthornpun S ,
• Soonthornpun K ,
• Aksonteing J ,
• Thamprasit A
• Srichumchit S ,
• Sugiyama MS ,
• Roseveare C ,
• Basilius K ,
• Madraisau S
• Hansen BB ,
• Mølsted-Pedersen L
• Caswell A ,
• Holliday E ,
• Petrović O ,
• Crnčević Orlić Ž ,
• Vambergue A ,
• Nuttens MC ,
• Goeusse P ,
• Biausque S ,
• van Hoorn J ,
• von Katterfeld B ,
• McNamara B ,
• Langridge AT
• Wahabi HA ,
• Esmaeil SA ,
• Alzeidan RA
• Esmaeil S ,
• Mamdouh H ,
• Wahlberg J ,
• Nyström L ,
• Persson B ,
• Arnqvist HJ
• Nankervis A ,
• Weijers RN ,
• Bekedam DJ ,
• Smulders YM
• Bleicher K ,
• Saunders LD ,
• Demianczuk NN
• Homer CSE ,
• Sullivan EA
• American Diabetes Association
• U.S. Preventive Services Task Force
• Tabrizi R ,
• Lankarani KB ,
• Leung-Pineda V ,
• Gronowski AM
• Bryson CL ,
• Ioannou GN ,
• Rulyak SJ ,
• Critchlow C

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Global Research Trends in Gestational Diabetes Mellitus from 2000 to 2020: A Bibliometric Study

Affiliations.

• 1 School of Nursing, Guangdong Pharmaceutical University, Guangzhou, China.
• 2 Central Laboratory, Longgang District Maternity & Child Healthcare Hospital, Shenzhen, China.
• PMID: 35276736
• DOI: 10.1055/a-1756-5518

Aims: This study analyzed major trends and topics in the field of gestational diabetes mellitus research between 2000 and 2020.

Methods: Studies that investigated gestational diabetes mellitus published between 2000 and 2020 were retrieved from the Web of Science Core Collection database. Data from the identified studies were analyzed using CiteSpace software.

Results: A total of 22,713 publications were retrieved, among which 21,722 publications were included in this scientometric analysis. Clustering analysis revealed 13 themes across all fields. Physical activity is an emerging trend. Co-word analysis showed that subject high-frequency keywords were: risk factor, obesity, insulin resistance, prevalence, and association. Centrality indices identified the most influential keywords to be: body mass index, risk factors, gestational weight gain, and obesity. Burst keywords revealed that there were six research frontier subtopics: i) prediction of adverse neonatal outcomes in gestational diabetes mellitus; ii) postpartum period research - blood glucose levels and insulin resistance; iii) meta-analysis - understanding the best evidence in pregnancy gestational diabetes mellitus; iv) gene expression profiles and DNA methylation in gestational diabetes mellitus; v) biomarkers for predicting higher birth and children weights; and vi) discussion on diagnostic criteria for gestational diabetes mellitus classification.

Conclusion: The number of studies on gestational diabetes mellitus is increasing. For two decades, the United States has been the global leader in the number of published studies. Studies on gestational diabetes mellitus are mainly from developed countries, with a few of them being from developing countries. An emerging field of research aims at elucidating the association between physical activity and gestational diabetes mellitus.

Thieme. All rights reserved.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflict of interest.

Similar articles

• Research trends in gestational diabetes mellitus in Southeast Asia: A bibliometric analysis (1975-2020). Tantengco OAG, Aquino IMC, Asis JLB, Tan JJE, Uy MNAR, Pacheco EP. Tantengco OAG, et al. Diabetes Metab Syndr. 2021 Jul-Aug;15(4):102202. doi: 10.1016/j.dsx.2021.102202. Epub 2021 Jul 8. Diabetes Metab Syndr. 2021. PMID: 34265490
• Evidence in support of the International Association of Diabetes in Pregnancy study groups' criteria for diagnosing gestational diabetes mellitus worldwide in 2019. Hod M, Kapur A, McIntyre HD; FIGO Working Group on Hyperglycemia in Pregnancy; FIGO Pregnancy and Prevention of early NCD Committee. Hod M, et al. Am J Obstet Gynecol. 2019 Aug;221(2):109-116. doi: 10.1016/j.ajog.2019.01.206. Epub 2019 Jan 22. Am J Obstet Gynecol. 2019. PMID: 30682358
• Global architecture of gestational diabetes research: density-equalizing mapping studies and gender analysis. Brüggmann D, Richter T, Klingelhöfer D, Gerber A, Bundschuh M, Jaque J, Groneberg DA. Brüggmann D, et al. Nutr J. 2016 Apr 4;15:36. doi: 10.1186/s12937-016-0154-0. Nutr J. 2016. PMID: 27044432 Free PMC article. Review.
• Screening and diagnosing gestational diabetes mellitus. Hartling L, Dryden DM, Guthrie A, Muise M, Vandermeer B, Aktary WM, Pasichnyk D, Seida JC, Donovan L. Hartling L, et al. Evid Rep Technol Assess (Full Rep). 2012 Oct;(210):1-327. Evid Rep Technol Assess (Full Rep). 2012. PMID: 24423035 Free PMC article. Review.
• Metabolic syndrome in childhood: association with birth weight, maternal obesity, and gestational diabetes mellitus. Boney CM, Verma A, Tucker R, Vohr BR. Boney CM, et al. Pediatrics. 2005 Mar;115(3):e290-6. doi: 10.1542/peds.2004-1808. Pediatrics. 2005. PMID: 15741354

Publication types

• Search in MeSH

Related information

Linkout - more resources, full text sources.

• Georg Thieme Verlag Stuttgart, New York
• Genetic Alliance

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

• Research article
• Open access
• Published: 07 February 2020

Women’s experiences of a diagnosis of gestational diabetes mellitus: a systematic review

• Louise Craig 1 ,
• Rebecca Sims 1 ,
• Paul Glasziou 1 &
• Rae Thomas   ORCID: orcid.org/0000-0002-2165-5917 1  

BMC Pregnancy and Childbirth volume  20 , Article number:  76 ( 2020 ) Cite this article

28k Accesses

69 Citations

46 Altmetric

Metrics details

Gestational diabetes mellitus (GDM) - a transitory form of diabetes induced by pregnancy - has potentially important short and long-term health consequences for both the mother and her baby. There is no globally agreed definition of GDM, but definition changes have increased the incidence in some countries in recent years, with some research suggesting minimal clinical improvement in outcomes. The aim of this qualitative systematic review was to identify the psychosocial experiences a diagnosis of GDM has on women during pregnancy and the postpartum period.

We searched CINAHL, EMBASE, MEDLINE and PsycINFO databases for studies that provided qualitative data on the psychosocial experiences of a diagnosis of GDM on women across any stage of pregnancy and/or the postpartum period. We appraised the methodological quality of the included studies using the Critical Appraisal Skills Programme Checklist for Qualitative Studies and used thematic analysis to synthesis the data.

Of 840 studies identified, 41 studies of diverse populations met the selection criteria. The synthesis revealed eight key themes: initial psychological impact; communicating the diagnosis; knowledge of GDM; risk perception; management of GDM; burden of GDM; social support; and gaining control. The identified benefits of a GDM diagnosis were largely behavioural and included an opportunity to make healthy eating changes. The identified harms were emotional, financial and cultural. Women commented about the added responsibility (eating regimens, appointments), financial constraints (expensive food, medical bills) and conflicts with their cultural practices (alternative eating, lack of information about traditional food). Some women reported living in fear of risking the health of their baby and conducted extreme behaviours such as purging and starving themselves.

A diagnosis of GDM has wide reaching consequences that are common to a diverse group of women. Threshold cut-offs for blood glucose levels have been determined using the risk of physiological harms to mother and baby. It may also be advantageous to consider the harms and benefits from a psychosocial and a physiological perspective. This may avoid unnecessary burden to an already vulnerable population.

Peer Review reports

Gestational diabetes mellitus (GDM) is diagnosed by elevated blood glucose in pregnancy though the definition has changed repeatedly since its first description in the 1960’s [ 1 , 2 ]. The most frequently reported perinatal consequence of GDM is macrosomia (usually defined as a neonate weighing over 4 kg) which can increase the risk of caesarean section and shoulder dystocia. For the mother, there are also potential longer-term consequences including an increased risk of type 2 diabetes post-pregnancy and/or in later life [ 3 ]. The investigators of a large international Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study aimed to identify a cut-point in the continuum to decide the blood glucose level (BGL) thresholds that should be used to define GDM [ 4 ]. However, a definitive cut-point was not identified and using the HAPO data the International Association of the Diabetes and Pregnancy Study Groups (IADSPG) consensus panel recommended a BGL threshold associated with the risk of adverse infant outcomes (such as risk of macrosomia, excess infant adiposity and neonatal hyperinsulinemia) [ 5 ]. This change was controversial, and there is currently a lack of an agreed standard for diagnosing high blood glucose in pregnancy.

Pregnancy can be a vulnerable period when a woman is adapting and responding to changes in body perceptions, such as loss of strength or fitness, which can result in reduced self-esteem and depression [ 6 ]. Many women report depression and anxiety during pregnancy which often includes worry for the baby’s wellbeing [ 7 , 8 ]. A diagnosis of a health condition such as GDM could have a detrimental effect on a pregnant woman’s quality of life due to fears that the illness may affect her and/or her baby [ 9 ]. This has potential to convert pregnancy, a natural process, into one associated with risks, ill-health and increased surveillance [ 10 ]. Understanding a women’s response to the GDM diagnosis and its psychological impact has emerged as an important issue [ 11 ]. Some studies report women describing the initial response as one of ‘shock’ [ 12 , 13 ], ‘sadness’ and ‘guilt’ [ 13 ]. A women’s acceptance of risk and fear of complications is likely to influence the acceptability of various interventions. Therefore, it is imperative to amalgamate the findings of these studies to synthesise the array of potential psychosocial consequences of a diagnosis of GDM.

In many countries the prevalence of GDM is rising [ 14 , 15 , 16 ]. Some of this is due to the increasing age at which women are becoming pregnant, an increase in obesity amongst women, more testing during pregnancy, and better recording during pregnancy. However, much of the rise has occurred since 2013 when some countries adopted the new IADPSG criteria and testing regimen for gestational diabetes. This resulted in the anomalous position that two women in two countries with exactly the same glucose levels may or may not be diagnosed with GDM depending on the country’s definition. Caution had been previously raised that the new IADPSG definition would increase prevalence of women diagnosed with GDM by two-to-three-fold [ 17 ].

Despite a significant increase in prevalence of GDM after the introduction of the new IADPSG criteria [ 15 , 16 ], some pre-post studies suggest negligible clinical improvement in the adverse outcomes measured [ 17 , 18 ]. Findings from a qualitative study of 19 women of different cultural backgrounds investigating women’s experiences of a GDM diagnosis reported that the diagnostic criteria itself was viewed as ‘confusing’ by some women and treatment for their ‘borderline’ condition unnecessary [ 19 ].

Although multiple studies have considered the impact of a diagnosis of GDM, a systematic review to synthesise the evidence around the emotional impact of a diagnosis at different stages, i.e. time of diagnosis, after diagnosis, at the delivery of the baby, and post-delivery, is lacking. The findings could inform healthcare clinicians of women’s attitudes and the consequences of a diagnosis and illuminate potential opportunities to provide support and advise. Therefore, in this systematic review, we aim to synthesise the evidence of the psychosocial experiences a diagnosis of GDM has on women during pregnancy and the postpartum period.

We followed the Enhancing Transparency in Reporting the Synthesis of Qualitative Research Guidelines (ENTREQ; Additional file  1 : Table S1) [ 20 ]. We included primary studies published in peer-review journals that:

included pregnant women with a current diagnosis or women with a history of GDM;

provided qualitative data on the psychosocial experiences of a diagnosis of GDM on women across any stage of pregnancy and/or the postpartum period; and

where participants have provided an account of their experience or perspective of living with GDM

No restrictions were placed on country, written language, or year of publication.

Studies were excluded, if:

the primary aim was to identify barriers and/or facilitators to service as these focused on the management of GDM rather than the GDM diagnosis; or

participants were women diagnosed with diabetes before pregnancy

Abstracts, letters, editorials and commentaries were also excluded.

Search methods for identification of studies

The search strategy (MEDLINE version provided in the Additional file  1 ) was developed using a combination of Medical Subject Headings terms centred around three key areas: i) gestational diabetes mellitus ii) diagnostic testing for gestational diabetes mellitus and iii) patient experiences. The Systematic Review Accelerator software was used to translate the search strategy for each of the different databases and to remove duplicated articles [ 21 ]. We searched CINAHL, EMBASE, MEDLINE and PsycINFO databases from inception to April 2018. Forward and backward citation searching of included studies was conducted.

Selection process

A single reviewer (LC) screened the titles and abstracts of retrieved references using Endnote Version X7.7.1. Potentially eligible full-texts were independently reviewed by LC and RS with conflicts resolved via discussion. Two full-text studies published in Portuguese were first translated using Google Translate and then validated by a researcher with both spoken and written Portuguese language skills located within our research network.

Data extraction

All data labelled as results or findings including themes, categories, theories were extracted and imported into NVivo Version 12 by LC. Study characteristics were extracted by LC which included study location, reported research aims, study design, methodology and the analytical approach. Information about the diagnostic criteria used to determine GDM in women was also extracted.

Data synthesis and analysis

To synthesise the findings, we used a thematic synthesis described by Thomas and Harden [ 22 ]. Thematic synthesis has the potential for conclusions to be drawn based on common aspects across otherwise heterogeneous studies and produce findings that directly inform health practitioners [ 22 , 23 ]. Coding was inductive, with codes derived from the data. First, extracted text relevant to patient experiences and perspectives was coded line by line. A subset of studies ( n  = 5) were coded independently by LC and RS to develop a coding framework. Disagreements were resolved by discussion. LC and RS coded a further subset ( n  = 4) and established an inter-rater reliability of Kappa = 0.87. Following this, LC applied the coding framework to the remaining studies. New codes were iteratively developed as new concepts arose.

Second, relationships between the codes were identified by LC to form the basis of descriptive themes across the studies. Similar codes were grouped to generate themes and less frequently used codes were classified into sub-themes. In the final stage, analytical themes were developed to ‘go beyond’ the primary studies to amalgamate and interpret the findings. The relevant quotes to support each theme were tabulated.

Quality assessment

As recommended by the Cochrane Qualitative Research Methods Group, we assessed the quality of the included studies using the Critical Appraisal Skills Programme Qualitative Checklist (CASP). This tool uses a systematic approach to appraise three key areas: study validity, an evaluation of methodological quality, and an assessment of external validity [ 24 ]. Critical appraisal was conducted by one reviewer (LC) for all studies, with second reviewer appraisal (RS) for a sub-set of included papers. The findings from the two reviewers were compared and any contrasting items were discussed and re-reviewed.

The search identified 840 studies. After deduplication and screening of titles and abstracts 88 full-text articles were assessed (Fig.  1 ). Seven further articles were identified through citation searching. Data were extracted from 41 studies meeting eligibility criteria and were included in the review [ 11 , 12 , 13 , 19 , 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 ].

Prima flow diagram

Study characteristics

The studies reflected a variety of sampling methods and data collection methods. For example, interviews were conducted in 34 studies [ 10 , 12 , 13 , 25 , 27 , 28 , 30 , 31 , 32 , 34 , 35 , 36 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 60 , 61 ], focus group methods were used in three [ 19 , 32 , 37 ], and interviews and focus groups were used in two studies [ 29 , 51 ]. Two studies used a mixed method approach [ 26 , 59 ]. The sample sizes ranged from 6 to 57 women. Eighteen studies were conducted in Europe, 10 in Australia, 9 in North America, and 2 studies each in Asia and South America. Table  1 details the characteristics of the included studies.

Quality appraisal

Most studies were assessed as high quality (Additional file  1 : Table S2). Study aims were stated in all but one study [ 47 ]. As the purpose of all included studies was to explore or gain knowledge, opinions or attitudes about GDM, the qualitative methods employed in all the studies were appropriate. Different study designs were used and in some cases the lack of reporting details made judgments of the appropriateness of study methods difficult. Data were collected in a way that addressed the research issue, however, a few authors did not discuss or report details such as saturation of data [ 31 , 47 , 56 , 59 ]. The relationship between researcher and participants was considered in only two studies [ 51 , 61 ]. Appropriateness of data analysis was assessed as “unclear” when there was a lack of details about how themes were derived.

Thematic analyses

Eight themes were generated from the data synthesis: 1. initial psychological impact; 2. communicating the diagnosis; 3. knowledge of GDM; 4. risk perception; 5. management of GDM; 6. burden of GDM; 7. social support; and 8. gaining control. The relevant quotes to support each theme are presented in Table  2 .

Initial psychological impact

When initially diagnosed with GDM, most women reported reactions such as self-blame, failure, fear, sadness, concern and confusion. Women often focused on the uncertainty of diagnostic prognosis and some considered it to be a life-altering experience. Some women felt lost and unsure what to do next. Often women felt an overwhelming sense of vulnerability and guilt. In some cases, the diagnosis was received positively and was viewed as an opportunity for lifestyle improvements. For example, some women viewed the diagnosis as a ‘ wake up’ call and were grateful for the chance to intervene and potentially prevent adverse outcomes for their babies and themselves. Some women viewed gaining less weight than expected during their pregnancy as a benefit of having a GDM diagnosis.

Communicating the diagnosis

Communication with healthcare professionals (HCPs) and their families was a common theme throughout the findings of the included studies. Generally, the level and quality of communication with HCPs was mixed – with some women reporting positive and informative encounters, while others described brief encounters with overly technical language and unsupportive consultations. The main issues were limited time available to spend with the HCP, lack of continuity of care and lack of understanding about the role of the HCP at follow-up. In some instances, women felt that GDM was not a topic that HCPs were keen to discuss - ‘the nurses, they never talked to me about my gestational diabetes’. [ 23 ] The level and quality of information provided was often conflicting, confusing or insufficient. Areas of contention were appropriate foods and the dietary changes that should be made.

Some women formed a dependency on HCPs to know what to do and on the electronic reminders for follow-up appointments and monitoring. Often women reported having no choice in treatment resulting in them feeling threatened and frustrated. Often women were automatically booked in for a caesarean section without consultation or lived in fear of this occurring. One woman referred to GDM as being over medicalised. Receiving limited information also prompted women to independently seek information about the impact and management of GDM from other sources such as the internet. However, some women found the internet limited for specific information or confusing.

Knowledge of GDM

Women had varying levels of understanding of GDM which impacted on their initial reaction to the diagnosis. Those who were able to explain the cause of GDM were able to process and accept the diagnosis more readily than those who had little understanding of GDM, or were confused as to how GDM occurred. Lack of knowledge also extended to and impacted on relatives. Some women stated that they would have preferred to be more prepared to receive the diagnosis by having early knowledge about the testing for diabetes. Women reported being on a steep learning curve, especially the onerous approach of dietary trial and error whereby women learnt what foods would increase their blood glucose level (BGL) and what food to avoid. Women also reported challenges in adopting new habits to manage their GDM, including understanding food labels and nutritional values of food. Often this required a trial and error approach. There was also a lack of understanding about the impact of GDM on their baby with some women believing it would be transmitted to their baby via breastmilk.

Risk perception

Women’s perception of risk were reported before the diagnosis of GDM, after they were diagnosed in pregnancy, and after the delivery. Some women attempted to understand their level of risk in context of family history. Some were very surprised by the diagnosis, especially if they were asymptomatic; and some women found it difficult to come to terms with the diagnosis. There was uncertainty about the severity of the condition. Some women considered the condition to be mild, downplaying the disease and believing that too much ‘ fuss’ was being made about GDM and other women doubted the diagnosis and its seriousness. Women often discussed: the adverse effects that GDM would have on her baby; frustration that the focus was on risks to the baby and less so them; their worry about the consequences for the future; and questioned the impact of insulin on the baby. Women worried that their diet was too restrictive for their growing baby and would not provide the nutrients that the baby required. Some women held the view that GDM was a temporary condition and would disappear once the baby was born, and many women reverted to old eating habits after the baby’s birth. Often women referred to the birth as a ‘ moment of truth ’ or as an endpoint to their GDM. This was also reflected in the level of care that the women received after the birth of their baby.

Managing GDM

Dietary management-related stress was commonly reported amongst interviewed women and was experienced by both insulin and non-insulin users. Stress and frustrations often occurred as a consequence of an unexpected abnormal blood glucose reading following strict adherence to dietary advice. Maintaining stable BGL was an ongoing struggle and in some cases the burden proved too much, with a few women ceasing employment. Insulin users described the process as a ‘ roller coaster ’ as well as the emotional and physical discomfort of injecting, while non-insulin users often became obsessed with a well-controlled diet, with some women viewing this as a way to avoid the use of insulin. Conversely, some women felt relieved when they were transitioned onto insulin, as it reduced the need for dietary restriction.

Making lifestyle changes was considered stringent and restrictive by the majority of women, and for some required ‘ major restructuring’ to their diet and daily routines to incorporate exercise. Some women reported extreme behaviours, including falsifying blood glucose readings, self-starvation and hiding their condition, including from family members. Often the impact of non-adherence to lifestyle changes resulted in guilt and belief that the baby would know they have cheated. Other pregnancy related ailments and the need to care for other children interfered with the ability to make the required changes. Women who had a specific culture-related diet discussed the impact and difficulty of applying or tailoring the dietary recommendations to their diet.

The key motivator to making required lifestyle changes, despite the associated hardships, was to minimise the risks to their unborn baby. Women prioritised the health of the baby over their own health and were willing to do anything to ensure that the health of their baby was not compromised. Over time, management of the GDM became a part of their normal routine for many women. However, some women expressed a desire to have a ‘ normal’ pregnancy similar to their friends, discussing that a diagnosis of GDM made them feel as though their pregnancy was atypical, leading to defining their pregnancy as ‘ abnormal ’ or as an ‘ illness ’. For one woman, it made her feel like an ‘ illegal’ person.

Burden of GDM

Women reported that a diagnosis of GDM came with extra responsibility, which added pressure whilst trying to juggle life commitments such as work, childcare, and daily living responsibilities. Monitoring and treating GDM placed burden on women’s daily routines and most woman agreed that taking BGL measurements was time consuming and disruptive. There was a constant need to prudently plan meals and co-ordinate the attendance at additional hospital appointments, all of which were considered time intensive, especially with travel and wait times. Women expressed that GDM consumed a lot of their thinking time e.g., ‘ I think about diabetes everyday’ and felt that they had to acknowledge GDM all the time and became ‘ super-conscious’ . In some instances, women reported a GDM diagnosis took away some of the ‘ joy of pregnancy ’ . One woman described her pregnancy as a ‘ misfortune’ . Women mentioned the financial burden of buying healthier food – ‘it would take lots of money just because it is so expensive to eat healthy’. [ 25 ] Women also considered the physical burden of GDM such as fatigue and the side effects of treatment such as insulin. There was a longer-term impact on family planning, where in some cases women decided not to have another child because they were fearful of enduring a similar restrictive and stressful pregnancy due to GDM.

Social support

Social support, including family and HCP support, was an important aspect for women during their experience of a GDM diagnosis. Changes in lifestyle often had an overflow effect, with other family members adopting healthier lifestyles. Women not in their country of birth, and without family, often reported feeling isolated and lonely. Disappointment and isolation were also expressed by some women when they perceived a lack of healthcare system support. This often occurred postnatally when the expectations of postpartum care were high, however, in reality, support was absent. In some cases, women were stigmatised by their families and in a few cases received undesirable feedback that they were not doing enough to protect their unborn child.

Gaining control

Control was a frequently used word when women described living with and managing a GDM diagnosis. Initially women reported a lack of control especially over their emotions, however, over time women transitioned from feeling like a victim of diabetes, to being active agents in controlling their GDM. The terms ‘ balance’ and ‘ adjustment’ were used to describe how some women tried to offset the strict compliance and active self-management with reducing their risk to their unborn baby and their own future risk of developing diabetes after pregnancy. Some women reported feeling empowered as their pregnancies progressed, especially when they gained more knowledge about GDM and what action they could take to accept and make sense of the diagnosis. Taking control included realising the changes that were required to their lifestyle, self-initiated care, and self-education. Often investigating alternative options, such as natural remedies outside those recommended by HCPs, provided women with some autonomy in managing their condition and some believed that it was a safer option to medication.

Summary of main findings

This synthesis of the qualitative evidence of women’s experiences of being diagnosed with GDM highlighted the psychosocial consequences a diagnosis of GDM can have on women. The purported benefits of a GDM diagnosis identified from our review, were largely behavioural and included an opportunity to improve health, prevent excessive weight gain, control weight during pregnancy, and prompts to make healthy eating changes. However, the purported harms included the added responsibility (eating regimens, appointments), financial constraints (expensive food, medical bills), and conflicts with their cultural practices (alternative eating, lack of information about traditional food). The psychosocial consequences were wide reaching and often resulted in significant social isolation with women only sharing their diagnosis with partners. Furthermore, there were a few reports of over-medicalisation due to a GDM diagnosis, with the perception that HCPs were often authoritarian, focusing on physiological aspects, with little attempt to involve women in decision making. This is noteworthy considering a non-GDM pregnancy has already come under scrutiny as being over-medicalised with increasing levels of unnecessary intervention [ 62 ].

Women from studies included in our review frequently reported inconsistent information provision. Limited GDM information provision has been identified in another systematic review regarding healthcare seeking for GDM during the postpartum period [ 63 ]. In contrast, findings from another study which aimed to evaluate satisfaction with obtaining a diagnosis of GDM concluded that the majority of women were satisfied with their experience of being diagnosed [ 64 ]. Further, women in the latter study associated poor GDM control with perinatal complications and an increased risk of type 2 diabetes following pregnancy [ 64 ].

Another key finding from this review was low awareness of the potential risks of GDM, particularly in the long-term. Low health literacy levels could be one factor to explain knowledge deficits and understanding of GDM, especially given the sociodemographic diverse population included in this review. One study found that low literacy among disadvantaged women had a significant impact on their understanding of GDM information [ 65 ]. Other research found that women who live in an English-speaking country but primarily speak a non-English language, have lower rates of dietary awareness compared with their English speaking counterparts, and this may affect compliance to dietary interventions [ 66 ]. Therefore, it is important that new educational interventions are developed to target those with lower health literacy as well as cultural factors when diagnosing and managing multi-ethnic populations with GDM [ 66 ].

Interestingly, women with a borderline diagnosis of GDM did not seem as concerned as other women and in some cases were dismissive of the diagnosis and the potential consequences. Similarly, in a study which specifically included women with a borderline diagnosis of GDM, the majority of women reported that they were not worried by the diagnosis [ 67 ]. For some women, the potential transitory nature of GDM was emphasised and some reported that it didn’t seem like a real illness. The diagnostic criteria for GDM has previously been compared with the established criteria used to classify a condition as a disease. This comparison revealed disparity which Goer, in 1996, used to suggest that GDM did not pose a serious health risk, was neither easily nor accurately diagnosed, was not treated effectively and that treatment outweighed the risks of the condition [ 68 ]. Therefore, the levels of heightened psychological distress as reported by the women in our review, may actually be unnecessary and others have gone as far as saying that GDM is an example of ‘obstetric iatrogenesis’ [ 69 ].

The findings of this review did underline a few unmet service needs with recurring themes around the lack of individualised care and its continuity, lack of choice regarding important aspects of care such as birthing options, and the scarcity of comprehensive follow-up. There was a sense of abandonment amongst women after delivery in that they had experienced intensive intervention and then nothing. This could be viewed as a missed opportunity to capitalise on the motivation to make changes during pregnancy. Researchers have previously highlighted that adherence to postpartum screening and continued lifestyle modifications to prevent future diabetes seems to dissipate after birth, possibly because the driver to protect their unborn child is no longer there [ 70 ].

The studies included in our review had participants of varying cultures sampled from countries with different GDM definitions. However, there appeared no difference in the qualitative outcomes between studies/countries. In our review, the experiences of women diagnosed with GDM suggest psychosocial harms appear to outweigh the qualitative benefits. Quantitative studies [ 14 , 15 ] that report prevalence increases in GDM after the IADSPG [ 71 ] definition changed, also report minimal improvements to maternal and infant physical outcomes.

This synthesis of women’s experiences of a GDM diagnosis could be used to inform the content of communication materials both before and after a GDM diagnosis. For example, an awareness of GDM testing and basic information including cultural adaptations to dietary guidelines and addressing misconceptions around breastfeeding. There is also an opportunity for HCPs to use teachable moments with women who have been identified at risk of developing type 2 diabetes post-pregnancy and offer supportive, effective advice about lifestyle changes. This is particularly relevant considering a previous review highlighted a significant time is spent in sedentary behaviour during pregnancy [ 72 ]. A study which examined HCPs views of healthcare provision to women with GDM showed that HCPs themselves perceived that there was a shortfall in GDM education [ 73 ]. There are also signals for service improvement and potential for service redesign, such as increasing community-delivered care for women diagnosed with GDM. This would assist in alleviating the burden on women to attend hospital appointments and potentially offer flexible appointment times. Follow-up appointments post-pregnancy could be made with consideration of other appointments such as maternal and child health milestones and breastfeeding weaning classes, and could also focused on healthy eating for both mother and baby.

Strengths and limitations

This systematic review included studies with women of different demographic characteristics and multicultural samples. The themes identified were represented in the majority of studies which increased the internal validity. The relatively high participation rate in the included studies, and that most studies were conducted during pregnancy or shortly after delivery, contributes to the external validity of our study. Although some participants were interviewed antenatally and some postnatally, this distribution over different gestational stages assists the generalisability of the study findings.

The comparison of coding between authors, discussion of the results and reaching consensus was a robust approach to improve the credibility of the results. Overall, the quality of most studies was good, however, a third of the studies used convenience methods to recruit participants which could contribute to sampling bias and limit the external validity of our findings. Only two studies adequately described the facilitator’s prior experience and the relationship between the participants and the facilitator/researcher. Unfortunately, this review did not capture the perception of HCPs which might be used to explain some of the behaviours and attitudes of the women, particularly in relation to communication of the diagnosis and information provision. Finally, although the data were collected from diverse populations, the majority of the countries in which research were conducted in were high-income countries, which could be considered to have more established and evidence-based healthcare systems than low-income countries.

Further research

A previous study has suggested the need for more research on the benefits and harms of alternative treatment choices for women with GDM [ 33 ]. The findings from this review suggest a need for more investigation around the psychosocial benefits and harms of a diagnosis of GDM. Given some women viewed treatment of ‘borderline GDM’ as unimportant, a new model of care based on stratification or individual level of risk for pregnancy and birth complications could be further explored. This may reduce the need for all women to be labelled as having GDM and negate unnecessary anxiety and burden for those at the lower ‘borderline’ threshold. This would then potentially offer tailored treatment options, improve shared-decision making, and improve women’s knowledge about how a diagnosis of GDM might affect them.

Consequences of a GDM diagnosis are multidimensional and highly contextual. Despite the psychosocial challenges frequently experienced, many women (driven by the innate response to safeguard their unborn baby) were able to gradually adapt to the required lifestyle changes and monitoring regimens. Perhaps a question is whether some of them should have to. There is opportunity to improve lifestyle and to assist the prevention of diabetes after pregnancy, however, this needs to be managed alongside the potential harms of a GDM diagnosis such as the negative psychological impact and social isolation. In the context of rising prevalence [ 14 , 15 , 16 , 17 ], potential minimal clinical [ 14 , 15 , 16 ] improvements, and the wide range of psychosocial experiences identified in this study, the findings of this review highlight the need for HCPs to consider the implications that a GDM diagnosis may have on women. It is essential that women diagnosed with GDM receive consistent evidence-based information and ongoing psychological and social support.

Availability of data and materials

The datasets generated during the current systematic review are available from the lead author upon request.

Abbreviations

Blood glucose level

Critical Appraisal Skills Programme Checklist (Qualitative)

Enhancing Transparency in Reporting the Synthesis of Qualitative Research

• Gestational diabetes mellitus

Hyperglycemia and Adverse Pregnancy Outcomes

Health care professional

International Association of the Diabetes and Pregnancy Study Groups

O'Sullivan JB, Mahan CM. Criteria for the oral glucose tolerance test in pregnancy. Diabetes. 1964;13:278–85.

CAS   PubMed   Google Scholar  

Mishra S, Rao CR, Shetty A. Trends in the diagnosis of gestational diabetes mellitus. Scientifica (Cairo). 2016;2016:5489015.

Google Scholar  

Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes. Syst Rev. 2002;25(10):1862–8. https://doi.org/10.2337/diacare.25.10.1862 .

Article   Google Scholar  

HAPO Study Cooperative Research Group. Hyperglycaemia and adverse pregnancy outcomes. N Engl J Med. 2008;358:1991–2002.

McIntyre HD, Gibbons KS, Lowe J, Oats JJN. Development of a risk engine relating maternal glycemia and body mass index to pregnancy outcomes. Diabetes Res Clin Pract. 2018;139:331–8.

Article   PubMed   Google Scholar  

Kamysheva E, Skouteris H, Wertheim EH, et al. Examination of a multi-factorial model of body-related experiences during pregnancy: The relationships among physical symptoms, sleep quality, depression, self-esteem, and negative body attitudes. Body Image. 2008;5(2):152–63 https://doi.org/10.1016/j.bodyim.2007.12.005 .

Carolan-Olah M, Barry M. Antenatal stress: an Irish case study. Midwifery. 2014;30(3):310–6. https://doi.org/10.1016/j.midw.2013.03.014 [published Online First: 2013/05/21].

Ferreira CR, Orsini MC, Vieira CR, et al. Prevalence of anxiety symptoms and depression in the third gestational trimester. Arch Gynecol Obstet. 2015;291(5):999–1003. https://doi.org/10.1007/s00404-014-3508-x [published Online First: 2014/10/15].

Dalfra MG, Nicolucci A, Bisson T, et al. Quality of life in pregnancy and post-partum: a study in diabetic patients. Qual Life Res. 2012;21(2):291–8. https://doi.org/10.1007/s11136-011-9940-5 .

Article   CAS   PubMed   Google Scholar  

Evans MK, O’Brien B. Gestational diabetes: the meaning of an at-risk pregnancy. Qual Health Res. 2005;15(1):66–81.

Devsam BU, Bogossian FE, Peacock AS. An interpretive review of women's experiences of gestational diabetes mellitus: proposing a framework to enhance midwifery assessment. Women Birth. 2013;26(2):E69–76.

Carolan-Olah M, Duarte-Gardea M, Lechuga J, et al. The experience of gestational diabetes mellitus (GDM) among Hispanic women in a U.S. border region. Sex Reprod Healthc. 2017;12:16–23. https://doi.org/10.1016/j.srhc.2016.11.003 .

Persson M, Winkvist A, Mogren I. ‘From stun to gradual balance’—women’s experiences of living with gestational diabetes mellitus. Scand J Caring Sci. 2010;24(3):454–62. https://doi.org/10.1111/j.1471-6712.2009.00735.x .

Cade TJ, Polyakov A, Brennecke SP. Implications of the introduction of new criteria for the diagnosis of gestational diabetes: a health outcome and cost of care analysis. BMJ Open. 2019;9(1):e023293–e93. https://doi.org/10.1136/bmjopen-2018-023293 .

Article   PubMed   PubMed Central   Google Scholar  

Sexton H, Heal C, Banks J, et al. Impact of new diagnostic criteria for gestational diabetes. J Obstet Gynaecol Res. 2018;44(3):425–31. https://doi.org/10.1111/jog.13544 [published Online First: 2018/01/13].

Erjavec K, Poljičanin T, Matijević R. Impact of the implementation of new WHO diagnostic criteria for gestational diabetes mellitus on prevalence and perinatal outcomes: a population-based study. J Pregnancy. 2016;2016:2670912. https://doi.org/10.1155/2016/2670912 .

Feldman RK, Tieu RS, Yasumura L. Gestational diabetes screening the international association of the diabetes and pregnancy study groups compared with carpenter-coustan screening. Obstet Gynecol. 2016;127(1):10–7. https://doi.org/10.1097/aog.0000000000001132 .

Pocobelli G, Yu O, Fuller S, et al. One-step approach to identifying gestational diabetes mellitus: association with perinatal outcomes. Obstet Gynecol. 2018;132(4):859–67. https://doi.org/10.1097/aog.0000000000002780 [published Online First: 2018/08/22].

Draffin CR, Alderdice FA, McCance DR, et al. Exploring the needs, concerns and knowledge of women diagnosed with gestational diabetes: a qualitative study. Midwifery. 2016;40:141–7. https://doi.org/10.1016/j.midw.2016.06.019 .

Tong A, Flemming K, McInnes E, et al. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Med Res Methodol. 2012;12(1):181. https://doi.org/10.1186/1471-2288-12-181 .

Rathbone J, Carter M, Hoffmann T, et al. Better duplicate detection for systematic reviewers: evaluation of systematic review assistant-deduplication module. Syst Rev. 2015;4:6. https://doi.org/10.1186/2046-4053-4-6 [published Online First: 2015/01/16].

Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008;8(1):45. https://doi.org/10.1186/1471-2288-8-45 .

Lucas PJ, Baird J, Arai L, et al. Worked examples of alternative methods for the synthesis of qualitative and quantitative research in systematic reviews. BMC Med Res Methodol. 2007;7(1):4. https://doi.org/10.1186/1471-2288-7-4 .

Group CCQM. Chapter 4: critical appraisal of qualitative research. In: Noyes J, Booth A, Hannes K, Harden A, Harris J, Lewin S, Lockwood C, editors. Supplementary guidance for inclusion of qualitative research in cochrane systematic reviews of interventions. Version 1; 2011.

Abraham K, Wilk N. Living with gestational diabetes in a rural community. MCN Am J Matern Child Nurs. 2014;39(4):239–45.

Araujo MF, Pessoa SM, Damasceno MM, et al. Gestational diabetes from the perspective of hospitalized pregnant women. Rev Bras Enferm. 2013;66(2):222–7.

Bandyopadhyay M, Small R, Davey MA. Attendance for postpartum glucose tolerance testing following gestational diabetes among south Asian women in Australia: a qualitative study. Int J Gynecol Obstet. 2015;131:E149.

Bandyopadhyay M, Small R, Davey MA, et al. Lived experience of gestational diabetes mellitus among immigrant South Asian women in Australia. Aust N Z J Obstet Gynaecol. 2011;51(4):360–4. https://doi.org/10.1111/j.1479-828X.2011.01322.x .

Carolan M. Women’s experiences of gestational diabetes self-management: a qualitative study. Midwifery. 2013;29(6):637–45. https://doi.org/10.1016/j.midw.2012.05.013 .

Doran F. Gestational diabetes mellitus: perspectives on lifestyle changes during pregnancy and post-partum, physical activity and the prevention of future type 2 diabetes. Aust J Prim Health. 2008;14(3):85–92.

Doran F, Davis K. Gestational diabetes mellitus in Tonga: insights from healthcare professionals and women who experienced gestational diabetes mellitus. N Z Med J. 2010;123(1326):59–67.

PubMed   Google Scholar  

Eades CE, France EF, Evans JMM. Postnatal experiences, knowledge and perceptions of women with gestational diabetes. Diabet Med. 2018;35(4):519–29.

Figueroa Gray M, Hsu C, Kiel L, et al. “It's a very big burden on me”: Women’s experiences using insulin for gestational diabetes. Matern Child Health J. 2017;21(8):1678–85.

Ge L, Albin B, Hadziabdic E, et al. Beliefs about health and illness and health-related behavior among urban women with gestational diabetes mellitus in the south east of China. J Transcult Nurs. 2016;27(6):593–602.

Ge L, Wikby K, Rask M. ‘Is gestational diabetes a severe illness?’ Exploring beliefs and self-care behaviour among women with gestational diabetes living in a rural area of the south east of China. Aust J Rural Health. 2016;24(6):378–84. https://doi.org/10.1111/ajr.12292 .

Han S, Middleton PF, Bubner TK, et al. Women’s views on their diagnosis and management for borderline gestational diabetes mellitus. J Diabetes Res. 2015;2015:209215. https://doi.org/10.1155/2015/209215 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Hirst JE, Tran TS, Do MAT, et al. Women with gestational diabetes in Vietnam: a qualitative study to determine attitudes and health behaviours. BMC Pregnancy Childbirth. 2012;12:81.

Hjelm K, Bard K, Apelqvist J. Gestational diabetes: prospective interview-study of the developing beliefs about health, illness and health care in migrant women. J Clin Nurs. 2012;21(21–22):3244–56.

Hjelm K, Bard K, Apelqvist J. A qualitative study of developing beliefs about health, illness and healthcare in migrant African women with gestational diabetes living in Sweden. BMC Womens Health. 2018;18(1):34.

Hjelm K, Bard K, Berntorp K, et al. Beliefs about health and illness postpartum in women born in Sweden and the Middle East. Midwifery. 2009;25(5):564–75.

Hjelm K, Bard K, Nyberg P, et al. Swedish and middle-eastern-born women’s beliefs about gestational diabetes. Midwifery. 2005;21(1):44–60.

Hjelm K, Berntorp K, Apelqvist J. Beliefs about health and illness in Swedish and African-born women with gestational diabetes living in Sweden. J Clin Nurs. 2012;21(9–10):1374–86. https://doi.org/10.1111/j.1365-2702.2011.03834.x .

Hjelm K, Berntorp K, Frid A, et al. Beliefs about health and illness in women managed for gestational diabetes in two organisations. Midwifery. 2008;24(2):168–82.

Hui AL, Sevenhuysen G, Harvey D, et al. Stress and anxiety in women with gestational diabetes during dietary management. Diabetes Educ. 2014;40(5):668–77.

Kaptein S, Evans M, McTavish S, et al. The subjective impact of a diagnosis of gestational diabetes among ethnically diverse pregnant women: a qualitative study. Can. 2015;39(2):117–22. https://doi.org/10.1016/j.jcjd.2014.09.005 .

Kilgour C, Bogossian FE, Callaway L, et al. Postnatal gestational diabetes mellitus follow-up: Australian women’s experiences. Women Birth. 2015;28(4):285–92. https://doi.org/10.1016/j.wombi.2015.06.004 .

Lawson EJ, Rajaram S. A transformed pregnacy – the psychological consequences of gestational diabetes. Sociol Health Illn. 1994;16(4):536–62.

Lie MLS, Hayes L, Lewis-Barned NJ, et al. Preventing type 2 diabetes after gestational diabetes: women’s experiences and implications for diabetes prevention interventions. Diabet Med. 2013;30(8):986–93.

Neufeld HT. Food perceptions and concerns of aboriginal women coping with gestational diabetes in Winnipeg, Manitoba. J Nutr Educ Behav. 2011;43(6):482–91. https://doi.org/10.1016/j.jneb.2011.05.017 .

Nielsen JH, Olesen CR, Kristiansen TM, et al. Reasons for women’s non-participation in follow-up screening after gestational diabetes. Women Birth. 2015;28(4):e157–63. https://doi.org/10.1016/j.wombi.2015.04.006 .

Parsons J, Sparrow K, Ismail K, et al. Experiences of gestational diabetes and gestational diabetes care: a focus group and interview study. BMC Pregnancy Childbirth. 2018;18:25.

Pennington AVR, O’Reilly SL, Young D, et al. Improving follow-up care for women with a history of gestational diabetes: perspectives of GPs and patients. Aust J Prim Health. 2017;23(1):66–74.

Rafii F, Vasegh Rahimparvar SF, Keramat A, et al. Procrastination as a key factor in postpartum screening for diabetes: A qualitative study of Iranian women with recent gestational diabetes. Iran Red Crescent Med J. 2017;19(5). https://doi.org/10.5812/ircmj.44833 .

Razee H, van der Ploeg HP, Blignault I, et al. Beliefs, barriers, social support, and environmental influences related to diabetes risk behaviours among women with a history of gestational diabetes. Health Promot J Austr. 2010;21(2):130–7.

Salomon IMM, Soares SM. Understanding the impact of gestational diabetes diagnosis. Revista Mineira de Enfermagem. 2004;8(3):349–57.

Svensson L, Nielsen KK, Maindal HT. What is the postpartum experience of Danish women following gestational diabetes? A qualitative exploration. Scand J Caring Sci. 2018;32(2):756–64.

Tang JW, Foster KE, Pumarino J, et al. Perspectives on prevention of type 2 diabetes after gestational diabetes: a qualitative study of Hispanic, African-American and White women. Matern Child Health J. 2015;19(7):1526–34. https://doi.org/10.1007/s10995-014-1657-y .

Tierney M, O'Dea A, Danyliv A, et al. Factors influencing lifestyle behaviours during and after a gestational diabetes mellitus pregnancy. Health Psychol Behav Med. 2015;3(1):204–16. https://doi.org/10.1080/21642850.2015.1073111 .

Trutnovsky G, Panzitt T, Magnet E, et al. Gestational diabetes: women's concerns, mood state, quality of life and treatment satisfaction. J Matern Fetal Neonatal Med. 2012;25(11):2464–6. https://doi.org/10.3109/14767058.2012.683900 .

Wah YYE, McGill M, Wong J, et al. Self-management of gestational diabetes among Chinese migrants: a qualitative study. Women Birth. 2019;32(1):e17–e23.

Whitty-Rogers J, Caine V, Cameron B. Aboriginal women’s experiences with gestational diabetes mellitus: a participatory study with mi'kmaq women in Canada. ANS Adv Nurs Sci. 2016;39(2):181–98. https://doi.org/10.1097/ANS.0000000000000115 .

Johanson R, Newburn M, Macfarlane A. Has the medicalisation of childbirth gone too far? BMJ. 2002;324(7342):892–5.

Van Ryswyk E, Middleton P, Shute E, et al. Women's views and knowledge regarding healthcare seeking for gestational diabetes in the postpartum period: a systematic review of qualitative/survey studies. Diabetes Res Clin Pract. 2015;110(2):109–22.

Goldstein RF, Gibson-Helm ME, Boyle JA, et al. Satisfaction with diagnosis process for gestational diabetes mellitus and risk perception among Australian women. Int J Gynaecol Obstet. 2015;129(1):46–9.

Carolan M. Diabetes nurse educators’ experiences of providing care for women, with gestational diabetes mellitus, from disadvantaged backgrounds. J Clin Nurs. 2014;23(9–10):1374–84. https://doi.org/10.1111/jocn.12421 .

Yuen L, Wong VW. Gestational diabetes mellitus: challenges for different ethnic groups. World J Diabetes. 2015;6(8):1024–32. https://doi.org/10.4239/wjd.v6.i8.1024 [published Online First: 2015/07/25].

Carolan M, Steele C, Margetts H. Knowledge of gestational diabetes among a multi-ethnic cohort in Australia. Midwifery. 2010;26(6):579–88. https://doi.org/10.1016/j.midw.2009.01.006 .

Goer H. Gestational diabetes: the emperor has no clothes. The Birth Gazette Summertown: Second Foundation; 1996. p. 32–5.

Han S, Bubner T, Middleton PF, et al. A qualitative study of women's views on diagnosis and management for borderline gestational diabetes. J Paediatr Child Health. 2013;49:128. https://doi.org/10.1111/jpc.12133 .

Nielsen KK, Kapur A, Damm P, et al. From screening to postpartum follow-up - the determinants and barriers for gestational diabetes mellitus (GDM) services, a systematic review. BMC Pregnancy Childbirth. 2014;14:41. https://doi.org/10.1186/1471-2393-14-41 .

International Association of Diabetes and Pregnancy Study Groups Consensus Panel. International Association of Diabetes and Pregnancy Study Groups Recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–82.

Article   PubMed Central   Google Scholar  

Fazzi C, Saunders DH, Linton K, et al. Sedentary behaviours during pregnancy: a systematic review. Int J Behav Nutr Phys Act. 2017;14:32. https://doi.org/10.1186/s12966-017-0485-z .

Van Ryswyk E, Middleton P, Hague W, et al. Clinician views and knowledge regarding healthcare provision in the postpartum period for women with recent gestational diabetes: A systematic review of qualitative/survey studies. Diabetes Res Clin Pract. 2014;106(3):401–11 https://doi.org/10.1016/j.diabres.2014.09.001 .

Download references

Acknowledgements

Not applicable.

LC is supported by a National Health and Medical Research Council Partnership Centre for Health System Sustainability grant (#9100002). RS and RT are supported by a National Health and Medical Research Council Program grant (#1106452) and PG is supported by a NHMRC Research Fellowship (#1080042). The funders had no role in design, data collection, analysis, interpretation or writing of the manuscript.

Author information

Authors and affiliations.

Institute for Evidence-Based Healthcare, Bond University, Gold Coast, Australia

Louise Craig, Rebecca Sims, Paul Glasziou & Rae Thomas

You can also search for this author in PubMed   Google Scholar

Contributions

PG, LC and RT conceived the project design, LC conducted the search, extracted, analysed and interpreted the data assisted by RS. RT assisted in data interpretation. All authors contributed to the drafting of the manuscript and approve the final version.

Corresponding author

Correspondence to Rae Thomas .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1:.

Table S1. Enhancing Transparency in Reporting the Synthesis of Qualitative Research Guidelines Checklist. Table S2. Assessment of quality of included studies using the CASP tool.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Craig, L., Sims, R., Glasziou, P. et al. Women’s experiences of a diagnosis of gestational diabetes mellitus: a systematic review. BMC Pregnancy Childbirth 20 , 76 (2020). https://doi.org/10.1186/s12884-020-2745-1

Download citation

Received : 24 September 2019

Accepted : 15 January 2020

Published : 07 February 2020

DOI : https://doi.org/10.1186/s12884-020-2745-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Systematic review
• Qualitative
• Diagnostic impacts

BMC Pregnancy and Childbirth

ISSN: 1471-2393

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Frontiers in Clinical Diabetes and Healthcare
• Diabetes and Pregnancy
• Research Topics

Gestational Diabetes: Where Are We and Where Are We Going?

Total Downloads

Total Views and Downloads

About this Research Topic

Gestational diabetes mellitus (GDM) is a temporary form of diabetes that occurs during pregnancy, frequently from the 24th to 28th week of gestation. The incidence of this type of diabetes is increasing worldwide, leading to significant morbidity for both the mother and the offspring. Indeed, GDM is ...

Keywords : Gestational diabetes, Pregnancy complications, Obstetric Medicine

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic Editors

Topic coordinators, recent articles, submission deadlines.

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

total views

• Demographics

No records found

total views article views downloads topic views

Top countries

Top referring sites, about frontiers research topics.

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

The American Diabetes Association Kicks Off the 84th Scientific Sessions with Breakthrough Diabetes Research

Leading Diabetes Scientific Sessions Meeting Will Present Groundbreaking Data on Key Topics Including: Obesity, Technology, AI Health and the Latest in Diabetes Innovation     

From June 21–24, the American Diabetes Association® (ADA) will host the 84th Scientific Sessions in Orlando, FL. The ADA’s Scientific Sessions is the world’s largest diabetes meeting, convening more than 11,000 leading physicians, scientists, researchers and health care professionals from around the globe. The hybrid meeting will feature the latest scientific findings in diabetes, including more than 200 sessions and 2,000 original research presentations at the Orange County Convention Center.  

Diabetes is among the top 10 leading causes of death in the United States, with more than 1.4 million new cases diagnosed each year . Over the past 20 years, the number of adults diagnosed with diabetes has more than doubled due to factors like increased obesity, weight gain and aging. During the annual meeting, the ADA is highlighting the latest cutting-edge advances in diabetes research and care.  

Among the key themes, you can expect: 

• Obesity/weight-loss drugs: Obesity impacts 42% of American adults and contributes to  up to 53% of the new cases of diabetes each year. This comes amid a surge of increased use of weight-loss drugs, with almost two million people in the U.S. taking semaglutide medications in 2021 —more than three times as many as in 2019. 

This year’s meetings will feature several clinical studies addressing obesity treatment including groundbreaking data on glucagon-like peptide-1 (GLP-1) drugs and their impact on not only obesity and diabetes, but sleep apnea, kidney outcomes, and  more. This will include data on Food and Drug Administration (FDA)-approved GLP-1 drugs such as Tirzepatide and a large number of GLP-1 drug candidates at various stages in the research pipeline, including Retatrutide, Pemvidutide and oral Ecnoglutide.  

• Technology and AI: Innovative technologies and solutions are transforming diabetes management, offering new possibilities for improved patient outcomes and more personalized care. Studies will highlight advancements on solutions such as automated insulin delivery (AID) systems and new research on continuous glucose monitors (CGMs).    
• Innovation: On Saturday, June 22 from 4:30-6:00 p.m. ET, the Innovation Challenge, which debuted in 2023, will once again invite emerging companies to pitch novel ideas to improve the lives of people living with diabetes. A panel of judges, with input from a live audience, determine which contestants will earn a private audience with potential funders.  

“As we commence the 84th Scientific Sessions, we are proud of our program that underscores our commitment to addressing the multifaceted challenges of diabetes," said Robert Gabbay, MD, PhD, the ADA’s chief scientific and medical officer. “This year’s program reflects our dedication to advancing research, fostering innovation, and ultimately improving the lives of those affected by diabetes. We look forward to an engaging and impactful event that will inspire and drive the future of diabetes care, at a pivotal time of pharmacological and technological advances in the field." 

Other notable topics and themes highlighted in the presentations at the 84th Scientific Sessions include eye health, health disparities, diabetes and obesity related cancers, and early risk monitoring. Most sessions, in addition to the 60-plus livestreamed sessions, will be available for on-demand viewing for attendees following the meeting through August 26. 

“This year’s Scientific Sessions is set to highlight the latest and greatest advances in diabetes research with an exceptional, data-driven program,” said Alice Y.Y. Cheng, MD, FRCPC, chair of the 84th Scientific Sessions Planning Committee. “Whether you’re joining us in-person or virtually, you'll have the opportunity to engage with leading experts, participate in dynamic discussions, and gain valuable insights that will drive the future of diabetes care.” 

Learn more about the 84th Scientific Sessions. For access to program navigation, educational session information, news updates, abstracts, and exhibitor information, the 2024 ADA Scientific Sessions mobile app and the online planner are your go-to meeting resources. 

For more information, please contact the ADA Scientific Sessions media at [email protected] . 

Embargo Policy:  

Oral presentations:  Oral presentations are embargoed from the time of submission until the start of their presentation at the 84th Scientific Sessions. 

Symposiums (including late-breaking symposiums):  Symposiums and presentations within symposiums are embargoed until the beginning of the Symposium session at the 84th Scientific Sessions..  

Poster presentations (including late-breaking posters):  Poster presentations are embargoed from the time of submission until Friday, June 21, 2024, at 6:30 p.m. ET. 

About the American Diabetes Association’s Scientific Sessions   The ADA's 84th Scientific Sessions, the world's largest scientific meeting focused on diabetes research, prevention, and care, will be held in Orlando, FL on June 21-24. More than 11,000 leading physicians, scientists, and health care professionals from around the world are expected to convene both in person and virtually to unveil cutting-edge research, treatment recommendations, and advances toward a cure for diabetes. Attendees will receive exclusive access to thousands of original research presentations and take part in provocative and engaging exchanges with leading diabetes experts. Join the Scientific Sessions conversation on social media using #ADAScientificSessions.  

About the American Diabetes Association   The American Diabetes Association (ADA) is the nation’s leading voluntary health organization fighting to bend the curve on the diabetes epidemic and help people living with diabetes thrive. For 83 years, the ADA has driven discovery and research to treat, manage, and prevent diabetes while working relentlessly for a cure. Through advocacy, program development, and education we aim to improve the quality of life for the over 136 million Americans living with diabetes or prediabetes. Diabetes has brought us together. What we do next will make us Connected for Life®. To learn more or to get involved, visit us at diabetes.org or call 1-800-DIABETES (1-800-342-2383). Join the fight with us on Facebook ( American Diabetes Association ), Spanish Facebook ( Asociación Americana de la Diabetes ), LinkedIn ( American Diabetes Association ), Twitter ( @AmDiabetesAssn ), and Instagram ( @AmDiabetesAssn ).   

Contact Virginia Cramer for press-related questions.

Your support helps drive lifesaving research, advocacy, and programs to help improve the lives of people living with diabetes.

Donate Now and DOUBLE your impact.

A .gov website belongs to an official government organization in the United States.

A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Risk Factors
• Providing Care
• Living with Diabetes
• Clinical Guidance
• DSMES for Health Care Providers
• Prevent Type 2 Diabetes: Talking to Your Patients About Lifestyle Change
• Employers and Insurers
• Community-based Organizations (CBOs)
• Toolkits for Diabetes Educators and Community Health Workers
• National Diabetes Statistics Report
• Reports and Publications
• Data and Statistics
• Current Research Projects

Related Topics:

• Show All Home
• National Diabetes Prevention Program
• State, Local, and National Partner Diabetes Programs for Public Health
• Diabetes Self-Management Education and Support (DSMES) Toolkit

What to know

The lifestyle change program that is part of the CDC-led National Diabetes Prevention Program is proven to help prevent or delay type 2 diabetes.

Additional formats

• Print Ready [PDF – 159 KB]
• Larger version [JPG]

Graphic text

Prevent type 2 diabetes.

Talking to your patients about lifestyle change.

Threat of Prediabetes

About 98 million American adults— more than 1 in 3 —have prediabetes.

More than 8 in 10 adults with prediabetes don't know they have it.

Prediabetes increases the risk of:

• Type 2 Diabetes
• Heart Disease

If your patients have prediabetes, losing weight by eating healthy and being more active can cut their risk of getting type 2 diabetes in half.

Lifestyle Change Program

The lifestyle change program that is part of the CDC-led National Diabetes Prevention Program is proven to help prevent or delay type 2 diabetes . It is based on research that showed:

• 58% lower incidence of type 2 diabetes after weight loss of 5 to 7% of body weight achieved by reducing calories and increasing physical activity to at least 150 minutes per week.
• 71% reduced incidence of type 2 diabetes for people 60 and older.
• 27% lower incidence of type 2 diabetes in lifestyle change program participants after 15 years.

The lifestyle change program provides:

• A trained lifestyle coach
• CDC-approved curriculum
• Group support over the course of a year
• A full year of in-person or online meetings

Your patients will learn to make achievable and realistic life changes

• Eat healthy
• Manage stress
• Incorporate physical activity into their daily routine
• Solve problems that get in the way of healthy changes

Patient Eligibility

• 18 years or older, and
• Overweight, and
• Diagnosed with prediabetes or previously diagnosed with gestational diabetes

How you can help your patients

• Test your at-risk patients for prediabetes.
• Refer your patients with prediabetes to a CDC-approved lifestyle change program.

Learn more from CDC and find an approved lifestyle change program at: www.cdc.gov/diabetes-prevention

Diabetes is a chronic disease that affects how your body turns food into energy. About 1 in 10 Americans has diabetes.

For Everyone

Health care providers, public health.

Gestational Diabetes

You May Also Like

Planning visitors during hospital stay, you’re pregnant how these moms reacted, jump to your week of pregnancy, trending on what to expect, moms share home remedies for pregnancy morning sickness, 8 expensive products moms say are worth the money, ⚠️ you can't see this cool content because you have ad block enabled., 14 moms on what labor really feels like, what are your go-to healthy snacks, things they don't tell you about: mom edition, pregnancy brain moments let's have a laugh, help keep our community safe, to create a safe place, please, on our end, we will.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Womens Health (Larchmt)

High Priority Research Needs for Gestational Diabetes Mellitus

Wendy l. bennett.

1 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.

2 Department of Population, Family, Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Karen A. Robinson

3 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Ian J. Saldanha

Lisa m. wilson, wanda k. nicholson.

4 Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, North Carolina.

Identification of unanswered research questions about the management of gestational diabetes mellitus (GDM) is necessary to focus future research endeavors. We developed a process for elucidating the highest priority research questions on GDM.

Using a systematic review on GDM as a starting point, we developed an eight-step process: (1) identification of research gaps, (2) feedback from the review's authors, (3) translation of gaps into researchable questions using population, intervention, comparators, outcomes, setting (PICOS) framework, (4) local institutions' stakeholders' refinement of research questions, (5) national stakeholders' use of Delphi method to develop consensus on the importance of research questions, (6) prioritization of outcomes, (7) conceptual framework, and (8) evaluation.

We identified 15 high priority research questions for GDM. The research questions focused on medication management of GDM (e.g., various oral agents vs. insulin), delivery management for women with GDM (e.g., induction vs. expectant management), and identification of risk factors for, prevention of, and screening for type 2 diabetes in women with prior GDM. Stakeholders rated the development of chronic diseases in offspring, cesarean delivery, and birth trauma as high priority outcomes to measure in future studies.

Conclusions

We developed an eight-step process using a multidisciplinary group of stakeholders to identify 15 research questions of high clinical importance. Researchers, policymakers, and funders can use this list to direct research efforts and resources to the highest priority areas to improve care for women with GDM.

Introduction

G estational diabetes mellitus (GDM) is a common pregnancy complication, affecting about 7% of pregnancies in the United States, and its prevalence has been increasing. 1 , 2 GDM is associated with both perinatal and longer-term maternal and offspring risks, such as cesarean delivery, 3 , 4 fetal macrosomia, 3 , 5 development of type 2 diabetes in the mother, 6 and obesity in the offspring. 7 Because of these risks and the potential implications of treatment, 8 , 9 GDM is an important, emerging area for clinical, epidemiologic, and basic research. Notably, between 2001 and 2010, MEDLINE included >3000 citations indexed as “gestational diabetes mellitus” compared with <1700 citations in the prior decade. In addition, the majority of clinical trials on GDM have been published in the last 10 years.

In 2008, we completed an Agency for Healthcare Research and Quality (AHRQ)-funded systematic review on specific aspects of management of GDM. 10 The review addressed 4 questions proposed by the American College of Obstetricians and Gynecologists (ACOG) because of their high clinical relevance: (I) What are the risks and benefits of an oral diabetes agent (e.g., glyburide), as compared with all types of insulin, for GDM? (II) What is the evidence that elective labor induction, cesarean delivery, or timing of induction is associated with benefits or harm to the mother and neonate? (III) What risk factors are associated with the development of type 2 diabetes after a pregnancy with GDM? (IV) What are the performance characteristics of diagnostic tests for type 2 diabetes in women with prior GDM? 10 We identified 11,400 unique citations, independently reviewed titles, abstracts, and full articles and included 45 articles, which included 9 randomized controlled trials (RCT) that applied to review questions I and II. 10 We graded the evidence as either insufficient or low strength for addressing the 4 review questions, suggesting widespread deficiencies in the field and the need for higher-quality studies to address the gaps. 10 Although the review synthesized and graded the existing evidence, the next step of identifying and prioritizing research gaps was descriptive and not systematic, as few frameworks currently exist to inform this final process. 11 AHRQ recognized that relying on systematic reviews to identify and report research needs was not sufficient and, thus, has funded various pilot studies, including the one reported here, to develop standard methods.

The primary objective of this study was to identify clinically important research questions for the management GDM using a process that involved stakeholder input and the 2008 systematic review's findings as a starting point. The secondary objectives were to prioritize outcomes to measure in future trials and to highlight feasibility and study design challenges related to the identified research questions. Ultimately, the goal was to guide future research endeavors on GDM management.

Materials and Methods

We developed and completed an eight-step process to identify research needs for GDM. We describe the process that began with the 2008 systematic review's 4 original questions, followed by the identification of research gaps and 17 research questions, and ending with 15 final questions, which multidisciplinary stakeholders deemed to have the highest clinical impact and potential benefit. We also described the methods in more detail in the final report prepared for AHRQ 12 and in an article focused on the methods. 13

Steps 1, 2, and 3: Identification of research gaps from review and formulation of 17 research questions

In step 1, two investigators independently abstracted statements about research gaps from the published AHRQ evidence report 10 and five articles based on findings from the review. 14 – 18 The two investigators compared and combined the lists using a consensus process. In step 2, we sought feedback from the eight authors of the 2008 systematic review via electronic communication. The authors provided free-text feedback about existing gaps identified within the review and suggested additional deficiencies, including challenges in the design of future studies. Readers are referred to the final report for a tabulation of all identified gaps. 12 For step 3, our research team organized the list of gaps into the population, intervention, comparators, outcomes, setting (PICOS) framework. We then translated these gaps into 17 new research questions.

Step 4: Refinement of the 17 research questions by institutionally based local stakeholders

For step 4, we invited six stakeholders from our own institution (local stakeholders) with expertise in GDM research or patient care to provide feedback on the 17 research questions. A list of the local stakeholders is available in the final AHRQ report. 12 This multidisciplinary group included two academic obstetricians, one dietitian whose clinical practice is focused on GDM, one epidemiologist whose research has focused on diabetes, and two members with insight into the patient perspective (a social worker and the director of a Medicaid case management program for women with complicated pregnancies), but no patients. Stakeholders first completed an online questionnaire in which we listed the 17 draft research questions and asked them to comment on (1) the clarity of the questions (or suggest alternate wording), (2) the clinical benefit and importance of addressing the questions, using a 9-point Likert scale, where 9 indicated highly clinically important/high clinical benefit and 1 indicated unlikely to have clinical importance or benefit, and (3) the feasibility for researchers to conduct a study that would address the research questions, using a 9-point scale, where 9 indicated highly feasible and 1 indicated unlikely to be feasible. We refined these 17 research questions based on the online feedback. The online questionnaire was developed using SurveyGizmo™ (Widgix LLC, Boulder, CO).

We invited the six local stakeholders to an interactive in-person meeting to solicit further feedback about missed research gaps and develop informal consensus on the wording, content of the research questions, and missed gaps. Finally, we asked the local stakeholders to consider study design needs and challenges for each of the research questions. Following step 4, we refined each question and added questions suggested by the stakeholders to yield 19 research questions.

Step 5: Consensus development by national stakeholders about research questions' clinical benefit/importance, leading to 15 final questions

We identified national leaders and experts in GDM and invited nine of them to be stakeholders for this project (national stakeholders). They represented the areas of obstetrics and gynecology, nutrition, epidemiology, research funders, and consumers. A list of the national stakeholders is available in the final AHRQ report. 12 We used the Delphi method for consensus development using an online form. The Delphi method involves iterative rounds of responses by group members, providing aggregated feedback about other members' responses until consensus is reached. 19 A priori we determined the maximum number of rounds to be three, because we believed, based on prior experience with the Delphi method, that consensus would be reached by then and to minimize stakeholder burden. For each round, we used an online instrument (SurveyGizmo) to gain feedback on the list of the research questions. As with the local stakeholders, we asked the national stakeholders to comment on the clarity of each question (or to suggest alternate wording) and the clinical benefit and importance of addressing each question, using the same 9-point Likert scale. We categorized clinical benefit/importance scores into high (between 7 and 9), medium (between 4 and 6), and low (between 1 and 3) clinical benefit/importance. We defined achievement of consensus for each research question as ≥75% (i.e., 7 of 9) of external stakeholders' ratings within the same single category. We refined all research questions based on comments. The research questions without consensus were retained and entered into the next round. In Delphi rounds 2 and 3, we also provided stakeholders with information about how the other stakeholders had scored the questions in the prior Delphi round (the mean and range of the clinical benefit/importance scores) as well as a brief synopsis of comments and changes we made to the questions. Following completion of step 5, we eliminated 4 of the 19 research questions, as 3 had failed to achieve consensus and 1 had achieved consensus but as having medium clinical benefit/importance. 12

During Delphi round 1, national stakeholders additionally commented on the study needs, challenges, and feasibility issues that had been identified from the local stakeholders' meeting. Comments on study needs, challenges, and feasibility were collated with the feedback from the local stakeholder meeting and organized into the PICOS framework. We did not require consensus on these topics.

Step 6: Prioritization by national stakeholders of outcomes for the two questions on medication and delivery management

During Delphi round 3, national stakeholders completed step 6 of the process by prioritizing the maternal and fetal outcomes related to the research questions on medication and delivery management (which corresponded to the original review's questions I and II). We focused on these research questions because they had high potential for clinical benefit, were the most amenable to the clinical trial design, and had a long list of outcomes necessitating priority ranking. From a list of >20 possible outcomes that had been suggested in steps 1 through 4, each stakeholder ranked their top three outcomes that would be most important to include in a clinical trial that assessed medication and delivery management.

Steps 7 and 8: Refinement of 15 questions and evaluation of process by all participants

Step 7 involved the final refinement of the questions after Delphi round 3 and the development of a conceptual framework to display the results of the process, which included high priority questions and outcomes.

In step 8, we developed an online evaluation tool (using SurveyGizmo) and invited all systematic review authors (except the three who were involved with this project) and local and national stakeholders to evaluate the process. We asked them to comment on whether they had adequate information to participate effectively, which mode of participation they would have preferred (i.e., web-based survey, phone, in-person), whether they believed that we had accomplished our objective, whether the representation of the local and national stakeholder groups was sufficiently comprehensive, and whether we needed each of the eight steps in the process to accomplish our aim.

Identification of 15 research questions with high clinical importance and benefit

We developed an eight-step process, using a systematic review on GDM 10 as a starting point and incorporating feedback from multidisciplinary stakeholders to identify 15 high priority research questions on GDM. Table 1 displays the 15 questions and the mean and range of the clinical importance/benefit scores from the Delphi round where each achieved consensus. We organized the research questions by the 2008 review's questions' topics. Based on feedback from the local stakeholders, we added questions to address the prevention of type 2 diabetes in women with GDM and adherence to recommendations on postpartum screening for type 2 diabetes.

Final 15 Research Questions on Gestational Diabetes Mellitus with High Clinical Importance/Benefit

The 4 research questions excluded and reason in parentheses:

1. What is the evidence that the interconception interval is associated with the risk of developing type 2 diabetes or glucose intolerance/impaired fasting glucose following GDM? (Consensus of medium level of importance)

2. What is the evidence that maternal psychosocial factors (e.g., mood disorders, substance use disorders, eating disorders, stress) are associated with the risk of developing type 2 diabetes or glucose intolerance/impaired fasting glucose following GDM? (No consensus achieved)

3. What is the evidence that contraceptive method (e.g., progestin-only) is associated with the risk of developing type 2 diabetes or glucose intolerance/impaired fasting glucose following GDM? (No consensus achieved)

4. What is the comparative effectiveness of health information technology interventions to track postpartum screening for the development of type 2 diabetes and glucose intolerance/impaired fasting glucose in women with a history of GDM? (No consensus achieved)

DPP-4, Dipeptidyl-peptidase-4; HbA1c, hemoglobin A1c.

Regarding medication management of GDM, both local and national stakeholders agreed that the effectiveness and safety of oral hypoglycemic agents in pregnancy have not clearly been established, even though they are commonly used in clinical practice. Stakeholders rated comparisons of sulfonylureas with insulin as having the highest clinical benefit/importance, with a mean score of 8.2 on a scale of 1 (lowest) to 9 (highest). National stakeholders were also interested in examining the long-term effects of treatment on offspring, particularly metformin as an insulin sensitizer ( Table 1 ).

National stakeholders reached consensus that identifying risk factors for type 2 diabetes in women with GDM was of high clinical importance and benefit, particularly to guide future preventive interventions. Research that addresses maternal health behaviors (e.g., breastfeeding, physical activity, diet) received the second highest clinical benefit/importance score out of the 15 questions (mean score 8.1). In Delphi round 1, national stakeholders also highlighted that future research on genetics, including the gene-environment interaction, would have high potential benefit. Although the local stakeholders with input into the patient perspective had suggested examining psychosocial factors (e.g., anxiety and postpartum depression) as risk factors for the development of type 2 diabetes, national stakeholders rated these questions with low clinical importance/benefit; thus, they were excluded from the final list ( Table 1 ).

The two research questions addressing delivery management for women with GDM achieved consensus as having high clinical benefit and importance because of a dearth of evidence in this clinically important area. Both local and national stakeholders emphasized the importance of including patient-oriented outcomes for these questions, such as satisfaction with delivery care.

Finally, stakeholders generally agreed that patient and provider adherence to postpartum testing recommendations has higher clinical importance than assessment of the performance characteristics of the various screening tests.

Prioritization of outcomes for future studies of medication or delivery management

Table 2 lists the highest priority maternal and offspring outcomes, defined as appearing in the top 3 list of two or more of the nine national stakeholders. These outcomes were identified as being high priority for future studies assessing the impact of medication or delivery management on GDM. When assessing the impact of medication management, four of nine stakeholders ranked the long-term offspring outcome of chronic diseases (e.g., obesity, type 2 diabetes) as one of their top 3, making it the highest rated outcome. The next most highly rated outcomes were the short-term maternal outcomes of hypertensive disorders of pregnancy (e.g., gestational hypertension, preeclampsia) and medication adherence and the neonatal outcomes of large for gestational age and macrosomia.

Highest Priority Outcomes to Assess Impact of Medication or Labor Management in Gestational Diabetes Mellitus

Defined as≥two of nine national stakeholders ranked it among top 3 outcomes to measure in future studies.

The outcomes that were not ranked in the top 3 by≥two of nine stakeholders were:

Medication management of GDM—glycemic control, patient-reported outcomes (e.g., patient treatment preference, quality of life), cesarean delivery and indication, complications of cesarean delivery (e.g., wound infection), other obstetric complications (e.g., birth trauma, shoulder dystocia, perineal lacerations, postpartum hemorrhage), neonatal complications (e.g., hypoxia/anoxia, hypoglycemia, respiratory distress syndrome, hyperbilirubinemia), peripartum mortality, birth weight, postpartum weight retention, longer-term infant, and child growth.

Delivery management of GDM—resource utilization (e.g., cost of care, length of stay), peripartum mortality, birth weight (and large or small for gestational age, macrosomia), other obstetric complications (e.g., perineal lacerations, postpartum hemorrhage, pulmonary embolism), other neonatal complications (e.g., neonatal hypoglycemia, perinatal mortality).

When assessing the impact of delivery management, the highest priority outcome, for which six of nine stakeholders ranked as one of the top 3 outcomes, was cesarean delivery, including primary and repeat cesarean delivery as well as the indications for cesarean delivery (e.g., suspected macrosomia, birth weight). Cesarean delivery was followed by the neonatal outcomes of birth trauma, highly ranked by four of nine stakeholders, and neonatal intensive care unit admission, highly ranked by three of nine stakeholders.

Conceptual framework

Figure 1 displays the conceptual framework to illustrate the results of the process. The framework displays the highly clinically important research questions addressing the management of GDM during pregnancy, delivery, and the postpartum period, as well as the high priority outcomes.

Conceptual framework displaying high priority research needs (in italics) addressing pregnancy, delivery, and postpartum management of gestational diabetes mellitus (GDM), with examples of high priority outcomes. NICU, neonatal intensive care unit.

Feasibility and study design challenges

At each step of the process, report authors, local stakeholders, and national stakeholders commented on the feasibility of addressing the research gaps, study design needs, and potential challenges. Where possible, we organized and presented these comments in the PICOS framework. Overall, stakeholders concurred that it would be feasible to design studies to address the research gaps in GDM. In terms of selecting a population, stakeholders noted the need for racial diversity and applying standard methods for the diagnosis of GDM. In terms of interventions, stakeholders noted a need for pharmacologic and pharmacokinetic studies, including the development of animal models, to examine the effects of oral medications on the fetus and neonate. When considering comparators of interest, stakeholders noted challenges with designing an RCT to address the comparative effectiveness of delivery management strategies. They discussed the potential risk of high crossover between arms because of patient and provider preference for delivery mode ( Table 1 ). To address medication comparisons, they suggested a need for large, well-designed prospective observational studies in large medical centers (or collaborating centers), with stratification by GDM therapies (e.g., diet vs. insulin). Such studies could be completed in a shorter period of time than a trial, thus mitigating the impact of changing practice patterns, and could lead to long-term prospective cohorts to examine both high priority maternal and offspring outcomes ( Table 2 ). Finally, stakeholders highlighted the need for future studies to apply standard outcome definitions, such as for hypoglycemia, and to aim for complete, consistent outcome ascertainment to improve the ability to make comparisons between studies.

Evaluation of the eight-step process

All 20 contributors, including 5 authors of the original systematic review, 6 local stakeholders and 9 national stakeholders, completed evaluations on the eight-step process and their involvement. Saldanha et al. 13 included a complete description of the evaluation results. After review of the final list of 15 research questions and a summary of the eight-step process, evaluators responded that they had “adequate information to effectively participate” in the process and that our research team “had accomplished the objective of identifying important research questions for GDM.”

Using a systematic review on GDM as a starting point, we developed an eight-step process, which included stakeholder feedback to identify and prioritize 15 clinically important research questions in GDM. The research questions focused on medication management of GDM, delivery management for patients with GDM, identification of risk factors for type 2 diabetes in women with prior GDM, prevention of type 2 diabetes and other chronic diseases in women with prior GDM, and postpartum screening for type 2 diabetes. Stakeholders expressed high levels of interest in examining medication comparisons, particularly sulfonylureas vs. insulin, and the influence of maternal lifestyle factors in the subsequent development of type 2 diabetes. In addition, stakeholders ranked long-term offspring chronic disease, cesarean delivery, and birth trauma as high priority outcomes to include when designing clinical trials and proposed key study design elements to increase research yield.

To our knowledge, ours is the first study to develop a method for identification of high priority research questions in the field of GDM. Prior research on identification of research needs in other disease areas has focused on the prioritization 20 or the presentation of research needs. 21 Recently, members of our team (K.A.R., I.J.S.) developed other frameworks to identify research needs using evidence-based clinical practice guidelines 22 and systematic reviews 11 but did not involve stakeholders. Our process for identifying high priority research questions has implications for a broader application beyond the field of GDM by using systematic reviews to identify future research needs and formulate researchable questions with stakeholder input. We invited committed, multidisciplinary stakeholders to comment on and refine the questions to ensure a balanced and broad perspective on research needs in GDM. Stakeholders were able to efficiently use the Delphi method to share comments and classify the clinical benefit of the questions to come to consensus.

We believe that these 15 high priority research questions will be useful to GDM researchers, funders, and policymakers. Based on the low and insufficient quality of evidence in the published systematic review, 10 substantial gaps in the evidence exist. GDM continues to be a very active area of research, with several major treatment trials published, 8 , 23 , 24 as well as ongoing innovative trials on diabetes prevention in women with recent GDM. 25 In addition, policymakers are particularly interested in boosting funding in GDM. In 2010, the U.S. House of Representatives passed the Gestational Diabetes Act (GEDI Act) to increase funding to the Centers for Disease Control and Prevention (CDC) for research in GDM. 26 The 15 questions we identified can provide a tool for researchers and funders planning high impact grants to target areas with existing gaps and where results are likely to be quickly translated into clinical practice. Of particular interest, with high potential for clinical benefit, are trials comparing medications for treatment of GDM, with attention to outcomes that are not traditionally measured, such as medication adherence, comorbid hypertensive disorders in pregnancy, and longer-term offspring outcomes. Studies examining the role of maternal lifestyle factors in the development of type 2 diabetes will also have high clinical impact, if funded.

Several limitations to our study deserve mention. First, we invited nonresearch-oriented clinicians who work closely with patients, but their feedback may have been limited by the complexity of the research in the project and amount of information presented from the 2008 review. Although we did not include patients in the process we reported here, developing methods that involve patients in identifying and prioritizing research gaps is extremely important. The local stakeholders' group proposed questions about psychosocial factors, which would have provided insight from the patients' perspectives. Unfortunately, these questions were not deemed of high clinical importance in subsequent Delphi rounds, highlighting the necessity for a systematic and effective way to elicit and incorporate patients' perspectives without risking their input being superseded by other stakeholders. Second, the eight steps were resource intense and may not be feasible or practical to conduct following all systematic reviews. Third, there may be additional useful steps to refine these research needs and questions, as well as to identify possible study designs for future research. For example, additional steps may include an update of the literature search in the 2008 systematic review, a search for ongoing studies, or more systematic discussion about appropriate study designs and the feasibility of addressing the 15 questions with involved stakeholders. Fourth, because we used the 2008 systematic review as a starting point, our project had a similar scope and, thus, did not assess research needs related to screening or diagnostic criteria for GDM, which are important, actively debated topics and will be addressed in an upcoming AHRQ evidence report and NIH consensus panel in the fall of 2012. 27 We believe our findings to be useful and significant, however, as the topics addressed in the 2008 review were suggested by the ACOG because of their high clinical relevance, and our process engaged multiple stakeholders beyond the results of the 2008 review.

Using a comprehensive systematic review on GDM as a starting point, we developed an eight-step process and identified 15 research questions on GDM management considered of high clinical importance by a multidisciplinary group of stakeholders. We prioritized outcomes to be examined in future studies on medication and delivery management. We anticipate that our process could be used as is or modified as necessary as a model for taking the results of other systematic reviews to the next stage, toward identification of researchable questions in areas of highest clinical importance. These 15 questions can be used by researchers and funders to target areas of highest need for future research in GDM to make a significant clinical impact in this rapidly growing field.

Acknowledgments

This project was funded under Contract No. HHSA 290-2007-10061-I from the Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services. The authors of this report are responsible for its content. Statements in the report should not be construed as endorsement by the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services.

Disclosure Statement

The authors have no conflicts of interest to report.

• Search the site GO Please fill out this field.
• Newsletters
• Reproductive Health

13 Medications You Should Avoid While Pregnant

Lindsay Curtis is a freelance health & medical writer in South Florida. Prior to becoming a freelancer, she worked as a communications professional for health nonprofits and the University of Toronto’s Faculty of Medicine and Faculty of Nursing.

• Unsafe Medications

Other Therapies To Avoid

• Safety Considerations

damircudic / Getty Images

Taking medications to treat new or existing health conditions during pregnancy is common—about 90% of pregnant people take over-the-counter (OTC) medications, and 70% take prescription medications. Many medicines are safe during pregnancy, but some can pose serious risks for you or the fetus, such as pregnancy loss, preterm birth, and congenital disorders (previously known as birth defects).  

Knowing which medications to avoid while pregnant is essential for protecting your and your baby's health and well-being. Talk to a healthcare provider before taking any medications, including prescription and OTC. They can discuss the safest and most effective medication options for managing your health conditions or concerns during pregnancy.

Nonsteroidal Anti-Inflammatory Drugs (NSAIDs)

Nonsteroidal anti-inflammatory drugs (NSAIDs)—such as Advil (ibuprofen), Aleve (naproxen), and Ecotrin (aspirin)—help reduce pain, inflammation, and fever. NSAIDs can cause serious kidney problems in a fetus, especially after 20 weeks (5 months) gestation. After 20 weeks, the fetus's kidneys produce most of the protective amniotic fluid surrounding the fetus, which is essential for developing the lungs, muscles, and digestive system.  

While NSAID use is most dangerous after 20 weeks of pregnancy, you may want to avoid the drugs throughout your entire pregnancy. Some evidence suggests NSAIDs increase the risk of miscarriage and birth defects when taken in early pregnancy.  

Statins are prescription medications that lower blood cholesterol levels in people with high cholesterol. They work by reducing cholesterol production in the liver and helping the liver remove low-density lipoprotein (LDL)—bad cholesterol—from the blood.  

In 2021, the U.S. Food and Drug Administration (FDA) removed the strongest warning against statin use during pregnancy after acknowledging research evidence that shows statins do not cause birth defects. However, statins are associated with other risks during pregnancy, including a higher risk of low birth weight and preterm birth.  

Retinoids, such as Zenatane (isotretinoin), are prescription acne medications associated with a high risk of severe birth defects. These medications affect how cells grow and divide, disrupting the normal development of an embryo or fetus. Retinoid use during pregnancy is associated with malformations (e.g., cleft lip or palate; abnormal head shape), heart defects, intellectual disabilities, and developmental delays.  

Although oral retinoids are particularly dangerous for a fetus, there is not enough research to determine whether topical retinoids (creams and lotions) are safe. Some experts recommend avoiding them entirely during pregnancy.

Warfarin (Blood Thinner)

Blood thinners , or anticoagulants, are prescription medications that help prevent blood clots. Coumadin (warfarin) is a blood thinner that passes through the placenta to the fetus and increases the risk of miscarriage, stillbirth, and congenital disorders.

Warfarin use in the first trimester is associated with fetal warfarin syndrome (FWS), which causes skeletal problems such as shortened limbs, skull and facial differences, intellectual disability, and heart defects.

In the second and third trimesters, warfarin use increases the risk of hemorrhage (excessive bleeding) and stillbirth. Fortunately, there are blood thinners that are safe to take during pregnancy, such as heparin, that do not pose health risks to a developing fetus.

Anti-Seizure Medications 

Anti-seizure medications (ACMs) help reduce the frequency of seizures in people with seizure disorders, such as epilepsy .

Some ACMs, particularly Valproic (valproic acid), increase the risk of congenital disorders. Valproic acid exposure in the first trimester is associated with a high risk of babies born with neural tube defects, such as spina bifida, cleft lip, and heart defects. Exposure in the first trimester is also associated with an increased risk of intellectual disabilities, autism spectrum disorder , facial differences, and limb defects.

The decision of whether or not to take ACMs during pregnancy involves weighing the risks of uncontrolled seizures for both the pregnant parent and fetus against the potential birth risks. Your healthcare provider can advise you based on your specific health needs.

Tetracycline Antibiotics 

Tetracyclines, such as Monodox (doxycycline), are antibiotics that treat many bacterial infections, including respiratory tract infections and skin, eye, intestinal, and urinary tract infections.

During pregnancy, these medications can affect bone growth and cause permanent staining of a baby's teeth. While less common, tetracycline may slow bone growth in the developing fetus. However, this effect appears to be reversible once the pregnant parent stops taking the medication.  

High-dose tetracyclines also pose a health risk for pregnant people, increasing the risk of acute fatty liver in pregnancy (AFLP), a rare but possibly life-threatening disorder. Healthcare providers can prescribe safer antibiotics to treat bacterial infections during pregnancy.  

Benzodiazepines

Benzodiazepines (like Valium) are sedative medications that treat anxiety and insomnia. Some studies suggest a possible link between first-trimester exposure to benzodiazepines and an increased risk of heart problems. The risk may be dose-dependent, meaning a higher dose could lead to a greater risk.

Long-term use of benzodiazepines throughout pregnancy may affect the developing fetus's central nervous system and increase the risk of preterm birth and low birth weight. Newborns exposed to benzodiazepines may experience respiratory distress, low muscle tone, and poor feeding and require admission to the neonatal intensive care unit (NICU).

Decongestants

Decongestant medications containing Sudafed (pseudoephedrine) can help relieve nasal congestion from allergic rhinitis (hay fever).

Some evidence suggests there is a link between pseudoephedrine use in the first trimester and an increased risk of congenital disorders, including gastroschisis (an opening in the abdominal wall), small intestinal atresia (underdeveloped small intestine), and hemifacial microsomia (smaller than usual facial features).   

Pseudoephedrine acts as a vasoconstrictor, meaning it narrows blood vessels. This may reduce blood flow to the uterus and fetus, especially during the first trimester when organ formation occurs.

ACE Inhibitors

Angiotensin-converting enzyme (ACE) inhibitors, such as Lotensin (benazepril) and Zestril (lisinopril), are prescription medications that treat hypertension (high blood pressure), heart disease, and kidney problems.  

These medications pose potentially serious risks for a fetus, particularly in the second and third trimesters. ACE inhibitors can cause low levels of amniotic fluid in the womb, increasing the risk of congenital disorders, including lung and kidney problems, skull deformities, and, in severe cases, fetal death.

Methotrexate

Trexall (methotrexate) is a medication that blocks the growth of cells and decreases immune system activity to treat autoimmune conditions like rheumatoid arthritis and certain types of cancer. Methotrexate poses serious risks during pregnancy. Exposure in the first trimester is associated with congenital anomalies such as craniofacial (face and skull), finger and toe, and spinal defects.

Exposure to methotrexate in the first trimester also increases the risk of lung, heart, and kidney problems. Studies suggest that low-dose methotrexate may increase the risk of miscarriage, preterm birth, or intrauterine growth restriction (IUGR) that may stunt growth through infancy and childhood.

Cannabis (marijuana) is a plant that some people use to treat anxiety, insomnia, nausea, and pain. Cannabis contains tetrahydrocannabinol (THC), a psychoactive compound that crosses the placenta and affects fetal development.

Smoking, vaping , or ingesting cannabis edibles during pregnancy can lead to adverse outcomes such as low birth weight, preterm birth, and developmental delays. Some studies suggest the effects may continue throughout childhood—some children whose gestational parents used cannabis while pregnant have behavioral and learning problems.

Lithium, a mood-stabilizing prescription medication for treating bipolar disorder, poses potential risks during pregnancy. First-trimester lithium exposure increases the risk of heart defects in developing fetuses, such as Ebstein anomaly, which affects the positioning and function of a heart valve.

Some studies suggest lithium use during pregnancy may increase the risk of preterm birth and low birth weight, though the risk appears to be small. However, lithium use is associated with neonatal complications, such as respiratory distress, poor feeding, and low muscle tone, requiring longer hospital stays after birth.

Many people with bipolar disorder who take lithium during pregnancy deliver healthy babies. However, your healthcare provider may recommend lowering your lithium dose to prevent adverse outcomes. Talk to your provider before stopping or reducing your medication.

Opioids, such as buprenorphine, codeine, and oxycodone, are powerful pain medications that help manage moderate to severe pain. Opioid use during pregnancy is associated with an increased risk of miscarriage, stillbirth, and congenital disorders, including heart defects, neural tube defects (e.g., spina bifida), and eye problems like glaucoma and blindness.

Opioid exposure can lead to neonatal abstinence syndrome (NAS) when a baby goes through withdrawal from the drug after birth. Babies with NAS may cry excessively, have body shakes or seizures, breathing problems, diarrhea, fever, or have trouble eating and gaining weight.

While many natural remedies and supplements are generally safe, some can pose significant risks during pregnancy.

Herbs to avoid during pregnancy include:  

• Black and blue cohosh: These herbs can cause uterine contractions and induce preterm labor. High doses of blue cohosh herb are associated with stroke, heart attack, and respiratory distress in infants.
• Dong quai: This increases the risk of miscarriage or preterm birth due to its blood-thinning properties and ability to stimulate uterine contractions.
• Goldenseal: This contains a chemical called berberine, which can cross through the placenta and may cause brain damage called kernicterus in infants exposed to goldenseal during pregnancy.

Essential Oils

Essential oils are popular for aromatherapy and topical use, but some can be unsafe during pregnancy. Essential oils to avoid include:

• Wintergreen

High Doses of Vitamins

Prenatal vitamins are important for a healthy pregnancy, but excessive intake of certain vitamins may be unsafe during pregnancy, especially in high doses. For example, excessive intake of vitamin A during pregnancy increases the risk of birth defects and miscarriage.

Also, high doses of vitamin E during pregnancy are associated with an increased risk of intrauterine growth restriction, premature rupture of membranes (water breaking early), placental abruption, preterm birth, and stillbirth.

How To Know If a Medication Is Safe To Take

Navigating medications and their safety during pregnancy can be confusing. In the United States, prescription and over-the-counter (OTC) medications have information on the packaging or medication insert regarding the medicine's safety during pregnancy. This information includes:  

• Risk summary: A summary of research findings about the potential risks of exposure to the medication during pregnancy, including known birth defects and the risk of miscarriage
• Clinical considerations: Additional information on the risk-to-benefit ratio and dose adjustments during pregnancy
• Data: A brief summary of the evidence supporting the information included in the risk summary section

Always consult your healthcare provider before taking any medications, including OTC drugs and supplements, while pregnant. Your provider will consider your medical history, the medication's purpose, and the stage of your pregnancy to determine if a medication is safe. 

A Quick Review

Pregnancy requires extra attention and consideration before taking medications and supplements. In the first trimester, certain medications can cause congenital disorders or lead to miscarriage. Other medications may be harmful in the second and third trimesters, increasing the risk of preterm birth, low birth weight, or developmental delays in infants and children.

When it comes to your pregnancy and the health of your developing baby, prioritize erring on the side of caution and avoid taking any medications without your healthcare provider's approval. 

Centers for Disease Control and Prevention. Medicine and pregnancy: An overview .

U.S. Food and Drug Administration. FDA recommends avoiding use of NSAIDs in pregnancy at 20 weeks or later because they can result in low amniotic fluid .

Dos Santos CS, Silva PV, Castelo R, Tiago J. Premature closure of ductus arteriosus after a single dose of diclofenac during pregnancy . BMJ Case Rep . 2021;14(6):e243485. doi:10.1136/bcr-2021-243485

U.S. Food and Drug Administration. FDA requests removal of strongest warning against using cholesterol-lowering statins during pregnancy; still advises most pregnant patients should stop taking statins .

Poornima IG, Pulipati VP, Brinton EA, Wild RA. Update on statin use in pregnancy . Am J Med . 2023;136(1):12-14. doi:10.1016/j.amjmed.2022.08.029

National Center for Advancing Translational Sciences: Genetic and Rare Diseases Information Center. Fetal retinoid syndrome .

Mother To Baby: Fact Sheets. [Internet] Topical Tretinoin . Brentwood, TN. Organization of Teratology Information Specialists; 2022.

American College of Cardiology. Anticoagulation during pregnancy: Evolving strategies .

National Center for Advancing Translational Sciences: Genetic and Rare Diseases Information Center. Vitamin K antagnoist embryofetopathy .

Bates SM, Middeldorp S, Rodger M, James AH, Greer I. Guidance for the treatment and prevention of obstetric-associated venous thromboembolism . J Thromb Thrombolysis . 2016;41(1):92-128. doi:10.1007/s11239-015-1309-0

National Cancer Institute. Anti-seizure medication .

National Center for Advancing Translational Sciences: Genetic and Rare Disease Information Center. Fetal valproate spectrum disorder .

Epilepsy Foundation. Pregnancy and seizures .

MedlinePlus. Tetracycline .

Hadi Y, Kupec J. Fatty Liver in Pregnancy . In: StatPearls . StatPearls Publishing; 2024.

U.S. Food and Drug Administration. Doxycycline use by pregnant and lactating women .

Noh Y, Lee H, Choi A, et al. First-trimester exposure to benzodiazepines and risk of congenital malformations in offspring: A population-based cohort study in South Korea . PLoS Med . 2022;19(3):e1003945. doi:10.1371/journal.pmed.1003945

Huitfeldt A, Sundbakk LM, Skurtveit S, Handal M, Nordeng H. Associations of maternal use of benzodiazepines or benzodiazepine-like hypnotics during pregnancy with immediate pregnancy outcomes in Norway . JAMA Netw Open . 2020;3(6):e205860. doi:10.1001/jamanetworkopen.2020.5860

Grigoriadis S, Graves L, Peer M, et al. Pregnancy and delivery outcomes following benzodiazepine exposure: A systematic review and meta-analysis . Can J Psychiatry . 2020;65(12):821-834. doi:10.1177/0706743720904860

Mother To Baby: Fact Sheets. [Internet] Pseudoephedrine . Brentwood, TN. Organization of Teratology Information Specialists; 2022.

Głowacka K, Wiela-Hojeńska A. Pseudoephedrine-benefits and risks . Int J Mol Sci . 2021;22(10):5146. doi:10.3390/ijms22105146

MedlinePlus. ACE inhibitors .

Bateman BT, Patorno E, Desai RJ, et al. Angiotensin-Converting Enzyme Inhibitors and the Risk of Congenital Malformations . Obstet Gynecol . 2017;129(1):174-184. doi:10.1097/AOG.0000000000001775

MedlinePlus. Methotrexate . 

Wentzell N, Kollhorst B, Reinold J, Haug U. Use of Methotrexate in Girls and Women of Childbearing Age, Occurrence of Methotrexate-Exposed Pregnancies and Their Outcomes in Germany: A Claims Data Analysis . Clin Drug Investig . 2023;43(2):109-117. doi:10.1007/s40261-022-01227-6

MedlinePlus. Medical marijuana .

American College of Obstetrics and Gynecologists. Marijuana and pregnancy .

MedlinePlus. Ebstein anomaly .

Poels EMP, Bijma HH, Galbally M, Bergink V. Lithium during pregnancy and after delivery: a review . Int J Bipolar Disord . 2018;6(1):26. doi:10.1186/s40345-018

Centers for Disease Control and Prevention. About opioid use during pregnancy .

Yazdy MM, Desai RJ, Brogly SB. Prescription Opioids in Pregnancy and Birth Outcomes: A Review of the Literature . J Pediatr Genet . 2015;4(2):56-70. doi:10.1055/s-0035-1556740

Anbalagan S, Falkowitz DM, Mendez MD. Neonatal Abstinence Syndrome . In: StatPearls. StatPearls Publishing; 2024.

American Pregnancy Association. Herbs and pregnancy .

Society for Birth Defects Research and Prevention. Is herb use during pregnancy a cause for concern? .

Abebe W. Review of herbal medications with the potential to cause bleeding: dental implications, and risk prediction and prevention avenues . EPMA J . 2019;10(1):51-64. doi:10.1007/s13167-018-0158-2

MedlinePlus. Berberine

Dosoky NS, Setzer WN. Maternal Reproductive Toxicity of Some Essential Oils and Their Constituents . Int J Mol Sci . 2021;22(5):2380. doi:10.3390/ijms22052380

Bastos Maia S, Rolland Souza AS, Costa Caminha MF, et al. Vitamin A and pregnancy: A narrative review . Nutrients . 2019;11(3):681. doi:10.3390/nu11030681

Van Leer P. Vitamin E in pregnancy . Am Fam Physician . 2017;95(7):.

Society for Birth Defects Research and Prevention. How are prescription medications labeled for pregnancy and lactation? .

Related Articles