Open research questions in Gestational Diabetes Research and Management

What the literature leaves open

• Pre-pregnancy body mass index (ppBMI) and gestational weight gain (GWG) are modifiable maternal factors, but their joint impact on LGA in sub-high-altitude, multiethnic populations remain unclear.

  Associations of pre-pregnancy BMI and gestational weight gain with large-for-gestational-age in women with gestational diabetes mellitus in Southwestern China · 2026 · DOI

• The 95% CI widened beyond 600 g/day due to sparse data, and these inflection points should be interpreted as exploratory descriptive features rather than precise thresholds.

  Association between the dietary obesity-prevention score and risk of gestational diabetes mellitus: an exploratory subgroup analysis · 2026 · DOI

• Although risk factors for GDM such as a family history of diabetes, obesity, and advanced maternal age are well recognized, the underlying pathophysiological mechanisms are complex and remain incompletely understood.

  Advancing Personalized Medicine in Gestational Diabetes Mellitus Management Through Gut Microbiota Insights · 2026 · DOI

• Future research should explore later gestational windows or more specific sonographic biomarkers such as fetal pancreatic circumference and liver volume to enable truly early warning of GDM.

  Predictive value of routine ultrasound indicators in the second trimester for gestational diabetes mellitus: a retrospective cohort study · 2026 · DOI

• This systematic review’s protocol has been developed in accordance with the PRISMA-P framework. This review will provide an understanding of the current landscape of remote care within the antenatal pathway for pregnant women with diabetes, evaluate its impact on outcomes compared to standard in-person care, and identify areas across the six IoM domains of healthcare quality where remote care is effective and where further improvement is needed. The search strategy was designed and optimised in collaboration with an experienced librarian, and multiple databases will be systematically searched. No language or country restrictions will be applied, enhancing the global relevance of the findings. Outcomes have been aligned with IoM domains of healthcare quality, providing a structured and internationally recognised framework for evaluating remote care for pregnant women with diabetes. A framework that has been previously used to assess the impact of remote care on specific disease populations (13,14). There are several potential limitations to be considered. Firstly, the amount and heterogeneity of the available evidence may pose challenges for synthesis, particularly given the variation in the types of remote care interventions assessed. Remote care encompasses a range of interventions, including telephone and video consultations, app-based interactions, and remote monitoring via connected devices. This introduces variability and limits the ability to make direct, like-for-like comparisons across studies. In addition, many interventions may be part of a multicomponent approach, which would make it difficult to assess the impact on quality of care due to the remote care alone. Secondly, heterogeneity in the methods used to collect and reported outcomes across studies may limit the ability to conduct a robust meta- analysis and draw definitive conclusions. Thirdly, a wide range of digital and telemedicine interventions are included necessitating subgroup analyses, as these interventions are not directly comparable and cannot be meaningfully aggregated. Thus, a potential limitation is ARTICLE IN PRESSACCEPTED MANUSCRIPTARTICLE IN PRESS that there may be an insufficient number of studies per each intervention type to support robust subgroup analyses.

  The impact of remote care on the quality of care of pregnant women with diabetes: a systematic review protocol · 2026 · DOI

• In summary, while the study by Yajnik et al [1] advances understanding of glycae- mic tracking across the life course, the current evidence remains insufficient to define GDM as a definitive expres- sion of lifelong dysglycaemia, particularly in the context of the extensive existing literature on GDM.

  Reappraising gestational hyperglycaemia: a manifestation of lifelong glycaemia or a correlation of glycaemic indices across life stages · 2026 · DOI

• Introduction and Objective: Human milk (HM) fatty acids (FA), which are critical for infant development, show wide inter-individual variability, but the impact of maternal metabolic health on HM FA profiles remain unclear.

  2140-P: Differential Fatty Acids in Human Milk from Individuals with Gestational Diabetes · 2026 · DOI

• Objective The majority of LGA occurs in normoglycemic pregnancies, yet prediction models integrating lipid metabolism for this population are lacking.

  Lipid metabolism-based machine learning models for predicting large for gestational age in non-diabetic pregnancies · 2026 · DOI

• This review provides the first nationally pooled estimates of GWG and determinants in Ethiopia. The inclusion of a large participant cohort (N = 6,099), which exceeds the sample sizes of individual studies, enhances the statistical power of the analysis. However, the high heterogeneity observed for categorical GWG outcomes and reliance on observational studies limit causal inference. Recall bias in pre-pregnancy weight estimation and variability in measurement timing should be considered when interpreting findings. Future research should prioritize longitudinal studies with standardized GWG assessment protocols, deeper investigations into the quality of ANC nutritional counseling, and mixed-methods studies to understand the sociocultural drivers of dietary choices during pregnancy. Finally, the absence of PROSPERO registration may limit transparency. However, measures such as adherence to PRISMA guidelines, the use of independent reviewers, and searching the PROSPERO database ( h t t p s: / / w w w. c r d. y o r k. a c. u k / p r o s p e r o /) to identify any recently p u b l i s h e d, completed, or ongoing projects on this topic were applied to minimize bias. No relevant registered or ongoing reviews were found.

  Magnitude and determinants of gestational weight gain in Ethiopia: a systematic review and meta-analysis · 2026 · DOI

• Firstly, the relatively broad age range (min 22.77 years, maximum 40 years), varying total sample (24 to 1075 par- ticipants), variety of digital tools and varied study durations across the maternal stages, along with lack of consistent measurements of GWG and self-reported weight data, should be taken into consideration for the reliability of out- comes. Furthermore, follow-ups were short-term (4 weeks to 9 months), and protocols were highly variable. The risk of bias constitutes also another limitation, rated mostly as high (n = 23/31). Finally, the absence of thorough and con- sistent reporting of engagement measures makes it difficult to identify adherence via user engagement data.

  Effectiveness of Digital Tools on Lifestyle & Health-Related Outcomes Across the Full Continuum of the Maternal Journey: A Systematic Review · 2026 · DOI

• Future research should focus on high-quality, large-scale, and long-term RCTs with further follow-up timepoints to measure sustained effects, that includes elements related to both contextual (e.g. cultural norms) and socio-demo- graphic diversity. Whilst no geographic or individual demo- graphic limitations were applied in the present study, only a minority (19.4%, 6/31) of the interventions involved par- ticipants Low Income and Lower Middle Income Coun- tries (LLMICs) and also merely 3% recruited male partners [59]-in spite of the evidence of partner’s positive influence on women’s intake [71], highlighting an existing literature gap. This review identified that mHealth lifestyle interven- tions across the preconception period remain in infancy, constituted of a restrained number of studies, with high heterogeneity, and a significant lack of sleep outcome data, necessitating further examination to maximise their effec- tiveness in advancing healthy lifestyle behaviours. Also, future studies need to prioritize the inclusion of smoking and alcohol consumption data, as these variables were nota- bly underreported in the current review. Lastly, across the entire maternal continuum, inconsistencies in definitions (e.g., GWG) and measurement methods (e.g., PPAQ ver- sus device-measured PA) narrow comparability, thus com- pelling the conduct of implementation-oriented research to achieve equitable, human-centred digital maternal care. Future research should prioritize harmonized engagement definitions, validated measurement tools, and hybrid deliv- ery strategies to optimize adherence and clinical impact [71]. Moreover, future studies should leverage co-design in the development of these interventions since the involvement of end-users can improve relevance, usability and engagement Page 11 of 15 45 particularly across diverse cultural, socioeconomic, and geographic contexts. It is also important to acknowledge the need for interventions that can be feasibly scaled within rou- tine maternity care. A meaningful distinction exists between primary prevention strategies targeting all women and more tailored programs designed for women with overweight or obesity; in these contexts, stand-alone digital interventions, in-person approaches, and hybrid models may each serve distinct and complementary roles. Moreover, interventions incorporating substantial digital components may support more efficient allocation of clinical resources by enabling clinicians to focus intensive support on those with greater need.

  Effectiveness of Digital Tools on Lifestyle & Health-Related Outcomes Across the Full Continuum of the Maternal Journey: A Systematic Review · 2026 · DOI

• The potential health benefits of probiotics and exercise cannot be overlooked in the management and prevention of Amirani E, Asemi Z, Taghizadeh M. 2022. The effects of selenium plus probiotics supplementation on glycemic status and serum lipoproteins in patients with gestational diabetes mellitus: a randomized, double-blind, placebo-controlled trial. Clin Nutr ESPEN. 48:56–62. https://doi.org/10.1016/j.clnesp.2022.02.010 Artal R, Catanzaro RB, Gavard JA, Mostello DJ, Friganza JC. 2007. A lifestyle intervention of weight-gain restriction: diet and exercise in obese women with gestational diabetes mellitus. Appl Physiol Nutr Metab. 32(3):596–601. https://doi.org/10.1139/H07-024 Asgharian H, Homayouni-Rad A, Mirghafourvand M, Mohammad- Alizadeh-Charandabi S. 2020. Effect of probiotic yoghurt on plasma glucose in overweight and obese pregnant women: a randomized controlled clinical trial. Eur J Nutr. 59(1):205–215. https://doi.org/ 10.1007/s00394-019-01900-1 Barakat R, Cordero Y, Coteron J, Luaces M, Montejo R. 2012. Exercise during pregnancy improves maternal glucose screen at 24–28 weeks: a randomised controlled trial. Br J Sports Med. 46(9):656–661. https://doi.org/10.1136/bjsports-2011-090009 Barakat R, Refoyo I, Coteron J, Franco E. 2019. Exercise during pregnancy has a preventative effect on excessive maternal weight gain and gestational diabetes. A randomized controlled trial. Braz J Phys Ther. 23(2):148–155. https://doi.org/10.1016/j.bjpt.2018.11.005 Callaway LK et al. 2019. Probiotics for the prevention of gestational diabetes mellitus in overweight and obese women: findings from the SPRING double-blind randomized controlled trial. Diabetes Care. 42(3):364–371. https://doi.org/10.2337/dc18-2248 Campaniello D et al. 2022. How diet and physical activity modulate gut microbiota: evidence, and perspectives. Nutrients. 14(12):2456. https://doi.org/10.3390/nu14122456 8 M. c. GRANt Chen J et al. 2022. Relationship between gut microbiome characteristics and the effect of nutritional therapy on glycemic control in pregnant women with gestational diabetes mellitus. PLoS One. 17(4):e0267045. https://doi.org/10.1371/journal.pone.0267045 Colberg SR, Castorino K, Jovanovič L. 2013. Prescribing physical activity to prevent and manage gestational diabetes. World J Diab. 4(6):256–262. https://doi.org/10.4239/wjd.v4.i6.256 Cordero Y, Mottola MF, Vargas J, Blanco M, Barakat R. 2015. Exercise is associated with a reduction in gestational diabetes mellitus. Med Sci Sports Exerc. 47(7):1328–1333. https://doi.org/ 10.1249/MSS.0000000000000547 de Barros MC, Lopes MA, Francisco RP, Sapienza AD, Zugaib M. 2010.

  The impact of exercise and probiotic supplementation on the gut microbiota and management of gestational diabetes mellitus: a critical review · 2026 · DOI

• The effect of specific probiotic strains should be investigated to under- stand better the influence the have on the gut microbiota. The relationship between the gut microbiota, exercise, probi- otics and glycemic control also needs to be explored in depth. In particular the mechanisms whereby Bifidobacterium, Lactobacillus, and Bacteroides influence the gut microbiota warrant further investigation as these have been found to be reduced during pregnancy (Chen et al.

  The impact of exercise and probiotic supplementation on the gut microbiota and management of gestational diabetes mellitus: a critical review · 2026 · DOI

• By contrast, exact gestational week was not standardized or broadly available in the primary NHANES discovery cohort, which remains an important limitation despite our supportive analyses in currently pregnant participants.

  Comparative predictive value of the cholesterol-high-density lipoprotein-glucose index versus the triglyceride-glucose index for gestational dysglycemia: a two-cohort study · 2026 · DOI

• these This study had some limitations that should be considered. First, we did not track the usage of the exercise or confirm that they were being performed correctly, which may variably influence the effect individuals. exercises had on that Additionally, we did not assess activity levels or interest in participating in exercise programs, which may impact patients’ desires to participate in exercise. Further, the control groups are composed of different individuals for the week 6 and week 12 comparison adding variability to our cohort. Third, showing that even among women who self-selected into the exercise education treatment, there was less than 50% compliance to the exercise program after 12 weeks. These findings highlight a persistent challenge in maternal health: even motivated individuals may struggle to maintain consistent exercise behaviors. ACOG maintains these physical activity guidelines regardless of maternal age. Multiple studies including pregnant women of all ages have found positive impacts of exercise. A RCT performed in Spain randomized three sessions of aerobic exercise a week to pregnant women with a mean age of 31.04 (+/- 3.78) and found significant decrease in risk of excessive maternal weight gain and gestational diabetes.2 In an observational paper by Haakstad et al., pregnant women older than 35 times a week two or more who exercised experienced positive outcomes such as lower gestational weight gain, but fewer pregnant women older than 35 reported exercising at all compared to those less than 35 in the study.25 We observed very low dropout rates among women ≥35. Thus, possibly among women ≥35 educational online content promotes consistency in current exercise habits. However, these results were not statistically significant, and no definitive conclusions can be drawn from these findings.

  Safety and efficacy of a video-based physical education program during pregnancy: A pilot study · 2026 · DOI

• A strength is that we included all available prognostic models on T2D following GDM, regardless of the set- ting and modelling methods. We summarise character- istics of developed or evaluated models, with particular focus on calibration, overall measure, and clinical utility measures - areas not addressed in earlier reviews [30, 33]. Additionally, this review is the first to apply up-to- date best-practice guides such as the PROBAST + AI for the prediction field to assess regression- and machine learning-based prognostic models. We did not undertake meta-analysis because experts agree that this is inappro- priate when less than five external validation studies not available and assumptions violate for pooling [10, 29]. Meta-analysis when done on inappropriately selected or non-comparable samples enables inappropriate compari- sons to be made, thereby not assisting the discipline to progress using the correct available evidence. Optimistic but non-generalisable performance of the models due to high concern for applicability might arise from overfitting risk due to imbalance of events per pre- dictor, validation choice, missing data handling, and underreporting of key calibration and clinical utility measures. Specifying the exact gestational week would strengthen clarity, however, the original studies included in our review typically reported BMI as measured in “early pregnancy” or “at the start of pregnancy” without providing a specific gestational age. As such, we were unable to extract an exact gestational week. In general, early-pregnancy BMI is commonly assessed during the first trimester, typically around or before 12 weeks’ ges- tation, consistent with how this is defined in the litera- ture [73]. Although planned, we were unable to conduct a meta-analysis of performance metrics due to the lack of external validations and considerable heterogeneity between the studies. No examples of models integrating Yimer et al. Diagnostic and Prognostic Research (2026) 10:12 AI and ML with causal inference approaches were found, however, these new approaches have been proposed to enhance interpretability and warrant future research.

  Traditional statistics and artificial intelligence-based prognostic models for predicting type 2 diabetes mellitus after gestational diabetes: a systematic review · 2026 · DOI

• The machine learning model integrating multidimensional indicators (lipids, TP, RDW, clinical parameters) should be prospectively evaluated in a second-trimester pregnant cohort to assess its earlier risk identification capability and validate whether the identified lipid biomarkers enable clinical intervention before GDM diagnosis.

  Lipidomics and machine learning revealing dysregulation of specific triacylglycerol and phosphatidylglycerol as hub lipids associated with fetal growth in gestational diabetes mellitus · 2026 · DOI

• The role of elevated TP (total protein) levels as a marker of metabolic disturbance in GDM pathogenesis needs mechanistic investigation to clarify how TP relates to the identified lipid dysregulation and whether TP-guided interventions could complement lipid-based risk stratification strategies.

  Lipidomics and machine learning revealing dysregulation of specific triacylglycerol and phosphatidylglycerol as hub lipids associated with fetal growth in gestational diabetes mellitus · 2026 · DOI

• The identified 12 serum lipid metabolites associated with GDM risk require confirmation using self-built external databases and comparable lipidomic datasets from other research teams to validate the TG and PG metabolic pathway findings across different populations and analytical platforms.

  Lipidomics and machine learning revealing dysregulation of specific triacylglycerol and phosphatidylglycerol as hub lipids associated with fetal growth in gestational diabetes mellitus · 2026 · DOI

• The precise underlying mechanisms linking RDW (red cell distribution width) to microvascular dysfunction and inflammation in GDM pathogenesis remain to be fully elucidated; mechanistic studies are needed to establish the causal relationship between RDW elevation and the 12 identified triacylglycerol and phosphatidylglycerol dysregulation.

  Lipidomics and machine learning revealing dysregulation of specific triacylglycerol and phosphatidylglycerol as hub lipids associated with fetal growth in gestational diabetes mellitus · 2026 · DOI

• The LogitBoost-based lipidomic signature model (AUC = 0.904) for GDM detection requires validation in larger, independent cohorts beyond the current dataset to establish generalizability and diagnostic accuracy across diverse pregnant populations and different geographic regions.

  Lipidomics and machine learning revealing dysregulation of specific triacylglycerol and phosphatidylglycerol as hub lipids associated with fetal growth in gestational diabetes mellitus · 2026 · DOI

• The paper states future work will include maternal and fetal health, immunology, and other aspects in extensive analysis, but does not specify which immunological markers, fetal health parameters, or comorbidities will be incorporated into the machine learning model. The integration strategy and feature engineering approach for these expanded domains remains undefined.

  Maternal Health Risk Prediction using Machine Learning · 2026 · DOI

• The XAI explanations using LIME and SHAP show feature importance rankings (Blood Glucose Level, Systolic Blood Pressure, age), but the paper does not address whether these explanations are clinically validated or compared against expert physician interpretations. The clinical actionability and reliability of LIME and SHAP explanations for maternal health decision-making remains unvalidated.

  Maternal Health Risk Prediction using Machine Learning · 2026 · DOI

• The paper mentions evaluating large amounts of data and developing a hybrid algorithm or new algorithm for maternal health risk prediction, but does not specify the dataset size, data sources, or geographic populations to be included. The current evaluation lacks clarity on whether the model will be validated across different maternal health datasets, demographic groups, or healthcare systems.

  Maternal Health Risk Prediction using Machine Learning · 2026 · DOI

• The role of the adipokines retinol binding protein-4, resistin and nesfatin-1 in the development of GDM is relatively poorly understood, but their role in glucose metabolism is suspected and their use as early markers to predict the development of GDM is being sought.

  Adipokine Levels of RBP4, Resistin and Nesfatin-1 in Women Diagnosed With Gestational Diabetes · 2024 · DOI

Most-cited papers in Gestational Diabetes Research and Management

• Association of Gestational Weight Gain With Maternal and Infant Outcomes · JAMA · 2017 · 1,434 citations
• International Small for Gestational Age Advisory Board Consensus Development Conference Statement: Management of Short Children Born Small for Gestational Age, April 24–October 1, 2001 · PEDIATRICS · 2003 · 464 citations
• Benefits of Physical Activity during Pregnancy and Postpartum: An Umbrella Review · Medicine & Science in Sports & Exercise · 2019 · 388 citations
• Risk of development of diabetes mellitus after diagnosis of gestational diabetes · Canadian Medical Association Journal · 2008 · 346 citations
• Maternal lipid levels during pregnancy and gestational diabetes: a systematic review and meta-analysis · BJOG An International Journal of Obstetrics & Gynaecology · 2015 · 337 citations
• The role of postpartum weight retention in obesity among women: A review of the evidence · Annals of Behavioral Medicine · 2003 · 308 citations
• Prevalence and Changes in Preexisting Diabetes and Gestational Diabetes Among Women Who Had a Live Birth — United States, 2012–2016 · MMWR Morbidity and Mortality Weekly Report · 2018 · 304 citations
• An internet-based prospective study of body size and time-to-pregnancy · Human Reproduction · 2009 · 233 citations
• Care of Women with Obesity in Pregnancy · BJOG An International Journal of Obstetrics & Gynaecology · 2018 · 212 citations
• Antenatal interventions for overweight or obese pregnant women: a systematic review of randomised trials · BJOG An International Journal of Obstetrics & Gynaecology · 2010 · 211 citations

Most recent work

• Gestational diabetes: from pathogenesis to therapeutic intervention · Diabetology & Metabolic Syndrome · 2026
• Reappraising gestational hyperglycaemia: a manifestation of lifelong glycaemia or a correlation of glycaemic indices across life stages · Diabetologia · 2026
• Bridging the confidence gap in maternity services: midwives and the rollout of diabetes technology · British Journal of Midwifery · 2026
• How yoga affects blood glucose levels in pregnancies affected by gestational diabetes mellitus · British Journal of Midwifery · 2026
• Effects of a cash-plus intervention combining conditional cash transfer with social and behaviour-change communication on pregnancy weight and birth weight in India · Nature Health · 2026
• Fetal Growth Abnormalities in Overweight and Obese Pregnant Women: A Study Among Bulgarian Pregnant Women · Acta Medica Bulgarica · 2026
• Trimester-specific associations of maternal prenatal dietary patterns with fetal growth: a prospective pre-birth cohort study · European Journal of Nutrition · 2026
• Pregnancy and delivery outcomes in fetal macrosomia (birth weight exceeding 5,000 g) · Journal of Perinatal Medicine · 2026
• Gestational weight gain and adverse perinatal outcomes among individuals with gestational diabetes · Communications Medicine · 2026
• The transformative role of artificial intelligence in gestational diabetes mellitus: advancements in screening, management, and long-term outcome prediction · Journal of Diabetes & Metabolic Disorders · 2026

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