There is increasing interest in the implementation of precision medicine in diabetes. Gender is an important determinant of health, and there are numerous characteristics of diabetes in women that can be taken into account for the implementation of precision medicine in diabetes. Furthermore, better appreciation of the unique clinical characteristics of diabetes in women, especially in relation to the lifecourse perspective of a woman's life, would greatly enhance public health efforts to address diabetes and its associated risk factors, and to improve the lives of women and the next generation. These include the unique challenges and opportunities linked with polycystic ovary syndrome, gestational diabetes, other pregnancy complications including preeclampsia, and the potential impact of these diagnoses in the offspring of affected women. In this review article, we will summarize some of these key aspects of how diabetes intersects with women's health, and how a holistic, integrative, and lifecourse approach to diabetes and women's health can have a significant impact well beyond just the benefits of achieving better glucose control for the women living with diabetes, but more importantly, toward improved prevention and control of noncommunicable diseases on a population level.

Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication associated with adverse short-term and long-term health outcomes for both mother and child. This study aimed to investigate the association between plasma amino acid concentrations and the incidence of GDM from 2019 to 2021.

Plasma levels of 37 amino acids were precisely measured using triple quadrupole mass spectrometry. The principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models identified metabolic differences between GDM and non-GDM groups. Conditional logistic regression, generalized linear model, and quantile g-computation were employed to assess the associations between individual or combined amino acids and GDM risk/blood glucose levels. The discriminatory power of various factors associated with the risk of GDM was evaluated using the area under the receiver operating characteristic curve (AUC-ROC).

A total of 969 pregnant women were included in this case-control study. OPLS-DA model identified 16 biomarkers that differentiated the GDM and non-GDM groups. After adjusting for potential covariates and correcting for multiple testing, conditional logistic regression analysis revealed that certain key amino acids, such as valine and isoleucine, were positively associated with the incidence of GDM, while glycine and serine were negatively associated with GDM risk (OR = 0.753-1.671, Pfdr = <0.001-0.001). Generalized linear model analysis showed that specific amino acids, including alpha-aminoadipic acid and arginine, were positively associated with blood glucose levels, while glycine and serine were negatively associated (β = -0.211-0.365, Pfdr = <0.001-0.045). Additionally, mixtures of the identified amino acids were significantly associated with an increased risk of GDM (P < 0.001). The combination of selected amino acids showed the highest ability to identify GDM in comparison with traditional risk factors and specific amino acids (AUC-ROC = 0.761, 95 % CI: 0.729-0.792). The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified two metabolic pathways related to GDM risk: "Glycine, Serine, and Threonine Metabolism" and "Arginine biosynthesis".

The overall amino acid profile, rather than disturbances in specific amino acids, may serve as a more important prevention or therapeutic target for GDM.

Obesity during pregnancy is related to fetal overgrowth. Effective interventions that can mitigate this risk are needed.

This study aimed to investigate the effect of a lifestyle intervention for pregnant women with obesity on fetal growth trajectories.

In the DALI trial, pregnant women with a body mass index ≥29.0 kg/m2 and without gestational diabetes at baseline were randomized to counselling on physical activity (PA), healthy eating (HE) or a combination (PA + HE), or to usual care (UC). Fetal growth trajectories were modelled based on a combination of estimated fetal weight (EFW) from repeated ultrasound scans and weight measured at birth. Differences in fetal growth trajectories between groups were assessed.

Three hundred eighty-four women were included. Those in the PA + HE intervention had slower EFW gain from 32 weeks onwards, with differences (PA + HE vs. UC) at 32, 36 and 40 weeks of -54.1 g (-146.7 to 38.9 g), -84.9 g (-194.0 to 24.7 g), and -99.8 g (-227.1 to 28.1 g), respectively. Effects appeared stronger in males, with a difference at 40 weeks of -185.8 g (-362.5 g to -9.2 g) versus -23.4 g (-190.4 g to 143.5 g) in females.

A lifestyle intervention for pregnant women with obesity resulted in attenuated fetal growth, which only reached significance in male offspring. Future larger trials are needed to confirm these findings and elucidate underlying pathways.

Gestational diabetes mellitus (GDM) affects millions of females and their children. Effective dietary strategies for early prevention are controversial.

This study aims to investigate whether an individualized nutritional intervention reduced GDM incidence in high-risk females.

A randomized trial was conducted at 3 tertiary hospitals in Shanghai, China. We randomly assigned (1:1) pregnant females in the first trimester who were identified to be at high risk of GDM by a prediction model to either an individualized nutritional intervention or usual care. The intervention consisted of 3 dietary consultations by dietitians based on Chinese dietary guidelines before the oral glucose tolerance test (OGTT) at 24-28 wk of gestation. The control group received usual care. All participants provided 3-d food records at each follow-up. The primary outcome was GDM incidence using the International Association of Diabetes and Pregnancy Study Group criteria. Key secondary outcomes were dietary alterations, gestational weight gain (GWG), maternal metabolic profile, perinatal and pregnancy outcomes. Intention-to-treat analyses were conducted.

A total of 519 females were enrolled, of whom 261 were assigned to the intervention and 258 to usual care. GDM was diagnosed in 85/245 (34.7%) females in the intervention group compared with 89/244 (36.5%) in the control group [adjusted relative risk 0.91 (95% confidence interval: 0.73, 1.15), P = 0.44]. More females in the intervention group had an appropriate GWG than the control group [1.38 (1.06, 1.79)] and lower levels of fasting and 2-h insulin during the OGTT (P < 0.001). We observed a reduction in the rate of small for gestational age in the intervention group compared with the control group [0.11 (0.01, 0.80)] and neonatal hypoglycemia [0.14 (0.04, 0.57)].

Among females at risk of GDM, an individualized nutritional intervention based on Chinese dietary guidelines provided before the OGTT did not prevent GDM but helped to manage GWG appropriately and improved pregnancy outcomes. This trial was registered on 27 October, 2019, with initial participant enrollment on 5 May, 2020 at ChiCTR as 1900026963 (https://www.chictr.org.cn/searchproj.html?regno=1900026963).

To develop a clinically applicable and promotable prediction model for assessing the risk of gestational diabetes mellitus (GDM) within the context of primary healthcare institutions.

The construction and the internal validation of the prediction model involved a cohort of 6,216 pregnant women observed from January 2019 to June 2019 in a Class A tertiary hospital in western China. External validation was subsequently conducted with 443 pregnant women from October 2020 to June 2021. Core characteristics were identified and the model was established using the least absolute shrinkage and selection operator (LASSO) regression. Internal validation was performed using the Bootstrap method. Model evaluation included discrimination and calibration tests, decision curve analysis (DCA), and the clinical impact curve. Visualization of the model was achieved through a static nomogram and a risk-scoring model.

The simplified prediction model possessed seven variables, including age, prepregnancy body mass index (BMI), polycystic ovary syndrome (PCOS), history of GDM, family history of diabetes, fasting plasma glucose (FPG), and urine glucose. This model exhibited a predictive accuracy, as reflected by a C-index of 0.736 (95% CI: 0.720 ~ 0.753) in the training set. The C-indexes were 0.735 and 0.694 in the internal and external testing set. Well-fitted calibration curves, the DCA curve, and the clinical impact curve demonstrated the feasibility of the simplified prediction model. For enhanced clinical application, the static nomogram and the risk-scoring model were employed to visualize the model.

This study developed a prediction model for assessing the risk of GDM among women from 8 to 14 weeks of gestation in western China. The model demonstrated moderate discriminatory ability, well-fitted calibration, and convenient visualization, suggesting its suitability for implementation and widespread adoption, particularly within the context of primary healthcare institutions.

In recent decades, clinical research has seen significant advancements, both in the generation and synthesis of evidence through meta-analyses. Despite these methodological advancements, there is a growing concern about the accumulation of repetitive and redundant literature, potentially contributing to research waste. This highlights the necessity for a mechanism to determine when a meta-analysis has conclusively addressed a research question, signaling no further need for additional studies-a concept we term an "exit" meta-analysis.

We introduced a convergence index, the Doi-Abdulmajeed Trial Stability (DAts) index, and a convergence plot to determine the exit status of a meta-analysis. The performance of DAts was examined through simulation and applied to two real-world meta-analyses.

The DAts index and convergence plot demonstrate highly effective discriminative ability across varying study scenarios. This represents the first attempt to define an exit meta-analysis using a quantitative measurement of stability (as opposed to sufficiency) and its corresponding plot. The application to real-world scenarios further validated the utility of DAts and the convergence plot in identifying a conclusive (exit) meta-analysis.

The new development of DAts and the convergence plot provide a promising tool for investigating the conclusiveness of meta-analyses. By identifying an exit status for meta-analysis, the scientific community may be equipped to make better-informed decisions on the continuation of research on a specific topic, thereby preventing research waste and focusing efforts on areas with unresolved questions.

This study aimed to identify and evaluate risk factors associated with gestational diabetes mellitus (GDM) in mainland China.

Eight electronic databases were searched for literature published from January 2010 until December 2023. Heterogeneity was quantified using I2. Data were pooled by fixed or random effects models and expressed as odds ratio and 95% confidence intervals.

A total of 69 observational studies with an overall sample size of 2,138,032 Chinese women and 219,303 patients with GDM were included in the analysis. After adjusting confounders, older maternal age (OR = 1.12, 95% CI: 1.09-1.15), maternal age ≥35 years (OR = 1.96, 95% CI: 1.74-2.21), higher pre-pregnancy body mass index (OR = 1.24, 95% CI: 1.17-1.32), pre-pregnancy overweight (OR = 1.78, 95% CI: 1.64-1.92) or obesity (OR 2.52, 95% CI: 2.06-3.08), family history of diabetes (OR = 1.85, 95% CI: 1.58-2.17), history of GDM (OR = 4.09, 95% CI: 2.13-7.82), and elevated levels of fasting plasma glucose (OR = 2.54, 95% CI: 2.13-3.01), hemoglobin (OR = 1.47, 95% CI: 1.14-1.89) and serum triglycerides (OR = 1.69, 95% CI: 1.31-2.16) in early pregnancy were associated with an increased risk of GDM in mainland China. But gravidity ≥2 (OR = 1.06, 95% CI: 0.89-1.27), conception by assisted reproductive technology analyses (OR = 1.54, 95% CI: 0.95-2.51) were not associated with GDM, and parity ≥1 (OR = 0.88, 95% CI: 0.82-0.94) was related to lower risk of GDM. In available unadjusted studies, history of abortion (OR = 1.34, 95% CI: 1.31-1.37) increased risk of GDM, non-Han ethnicity (OR = 0.78, 95% CI: 0.59-1.03) and high school or lower education level (OR1.09, 95% CI: 0.94-1.26) showed no correlation with GDM.

The key risk factors for GDM in mainland China included older maternal age, maternal age ≥35 years, pre-pregnancy overweight or obesity, family history of diabetes, history of GDM, elevated levels of FPG, Hb, and serum TG in early pregnancy. Early identification and intervention for women at high risk should be performed to prevent the development of GDM.

Processed foods play an important role in achieving both food and nutrition security. However, in recent years, there has been increased concern about the health effects of food processing, in large part because of the emergence of the Nova food classification system. Nova classifies all foods into one of four groups purportedly based entirely on the extent to which they have been processed. Recommendations to limit intake of ultra-processed foods (UPF) (group 4) are based primarily on observational studies showing that their intake is associated with a range of adverse outcomes. Nearly all plant milks and the entire new generation of plant-based meat alternatives (PBMAs), which are made using concentrated sources of plant protein, are classified as UPFs. This classification may deter the public from consuming and health professionals from recommending these products even though they represent a convenient way to increase plant protein intake in high-income countries, which is recommended by health authorities. However, although total UPF intake is associated with adverse health outcomes, this is not the case for many subcategories of UPFs. Furthermore, in many instances, clinical research shows that PBMAs and plant milks have beneficial effects relative to their animal-based counterparts (Group 1). Collectively, the evidence leads to two conclusions. First, PBMAs represent a viable approach for lowering the dietary animal to plant protein ratio. Second, Nova paints with too broad a brush and is insufficiently nuanced to serve as a public guide for food purchasing decisions and may distract consumers from focusing on the importance of nutrient content.

There is no evidence to suggest that an association exists between the remnant cholesterol (RC) to high-density lipoprotein cholesterol (HDL-C) ratio and gestational diabetes mellitus (GDM). In this study, the RC/HDL-C ratio during the first trimester was examined as a potential indicator of the onset of GDM during the second trimester.

This was a secondary analysis of data from a Korea-based prospective cohort study. The study involved 582 women within 14 weeks of pregnancy who were examined between November 2014 and July 2016 at two Korean hospitals. RC was calculated as total cholesterol (TC) minus the sum of low-density lipoprotein cholesterol (LDL-C) and HDL-C. The RC/HDL-C ratio was determined by dividing the RC content by the HDL-C content. The RC/HDL-C ratio and GDM occurrence were investigated utilizing a binary logistic regression model, various sensitivity analyses, and subgroup analyses. Additionally, the RC/HDL-C ratio was evaluated using receiver operating characteristic (ROC) analysis.

The average age of the pregnant women was 32.07 ± 3.78 years, and the RC/HDL-C ratio had a median value of 0.39. The prevalence of GDM was 6.01%. There was a positive association between the RC/HDL-C ratio and the incidence of GDM after adjusting for potential confounding variables (odds ratio: 21.78, 95% confidence interval [CI]: 3.55-133.73, P < 0.001). Furthermore, this association was validated by subgroup and sensitivity analyses. The results indicated that the RC/HDL-C ratio was a robust predictor of GDM, with an area under the ROC curve of 0.795 (95% CI: 0.723-0.868). The optimal threshold value was 0.45, with a sensitivity of 71.4% and a specificity of 75.3%. Compared with traditional lipid markers, including LDL-C, HDL-C, triglycerides, TC, and the emerging marker RC, the RC/HDL-C exhibited higher diagnostic efficacy.

There is an increased risk of GDM associated with higher levels of the RC/HDL-C ratio between 12 and 14 weeks of gestation, independent of traditional risk factors. The RC/HDL-C ratio is more effective in diagnosing GDM than traditional lipid markers.

The relationship between economic disadvantages and the risk of developing gestational diabetes mellitus (GDM), as well as its impact on birth outcomes, remains uncertain.

From the Taiwan Maternal and Child Health Database, we identified 984,712 pregnant women between 1 January 2007 and 31 December 2018. Using propensity score matching, we selected 5,068 pairs of women across four income levels: very low, low, middle and high. We used a multivariable Cox regression model to assess the risk of GDM in these pregnant women and analyzed the birth outcomes.

The mean age of the pregnant women was 30.89 years. We found no significant difference in GDM risk among pregnant women with different family income. However, newborns of women with GDM and very low-income were at higher risk for several adverse conditions, such as small for gestational age (adjusted odds ratio (aOR) 1.17, 95% confidence interval (CI) 1.04-1.31), large for gestational age (aOR 1.27, 95% CI 1.08-1.51), hypoxic-ischemic encephalopathy (aOR 3.19, 95% CI 1.15-8.86), respiratory distress (aOR 1.58, 95% CI 1.14-2. 19), congenital anomalies (aOR 1.32, 95% CI 1.08-1.62), jaundice requiring phototherapy or exchange transfusion (aOR 1.14, 95% CI 1.05-1.24) and so on.

This study found that low family income alone was not associated with GDM development. However, for a GDM pregnancy, pregnant women with lower income had worse birth outcomes. Improving maternal health and nutrition among low-income pregnant women with GDM might be critical to improving birth outcomes.

To compare pregnancy outcomes among women with a normal oral glucose tolerance test (OGTT) before 20 weeks' gestation (early) and at 24-28 weeks' gestation (late) (no gestational diabetes mellitus, or No-GDM), those with early GDM randomized to observation with a subsequent normal OGTT (GDM-Regression), and those with GDM on both occasions (GDM-Maintained).

Women at <20 weeks' gestation with GDM risk factors who were recruited for a randomized controlled early GDM treatment trial were included. Women with treated early GDM and late GDM (according to the World Health Organization's 2013 criteria) were excluded from this analysis. Logistic regression compared pregnancy outcomes.

GDM-Regression (n = 121) group risk factor profiles and OGTT results generally fell between the No-GDM (n = 2,218) and GDM-Maintained (n = 254) groups, with adjusted incidences of pregnancy complications similar between the GDM-Regression and No-GDM groups.

Women with early GDM but normal OGTT at 24-28 weeks' gestation had pregnancy outcomes that were similar to those of individuals without GDM. Identifying early GDM likely to regress would allow treatment to be avoided.

Steroid hormones (SH) during pregnancy are associated with the development of gestational diabetes mellitus (GDM). Early and mid-Down syndrome screening is used to assess the risk of Down syndrome in the fetus. It is unclear whether changes in SH during this period can be used as an early predictor of GDM.

This study was a multicenter, longitudinal cohort study. GDM is diagnosed by an oral glucose tolerance test (OGTT) between 24 and 28 weeks of gestation. We measured SH levels at early and mid-Down syndrome screening, respectively. Based on the SH changes, logistic regression analysis was used to construct a prediction model for GDM. Finally, evaluated the model's predictive performance by creating a receiver operating characteristic curve (ROC) and performing external validation.

This study enrolled 193 pregnant women (discovery cohort, n = 157; validation cohort, n = 36). SH changes occur dynamically after pregnancy. At early Down syndrome screening, only cortisol (F) (p < 0.05, 95 % CI 4780.95-46083.68) was elevated in GDM. At mid-Down syndrome screening, free testosterone (FT) (p < 0.01, 95 % CI 0.10-0.55) and estradiol (E2) (p < 0.05, 95 % CI 203.55-1784.78) were also significantly elevated. There were significant differences in the rates of change in E2 (Fold change (FC) = 1.3425, p = 0.0072), albumin (ALB) (FC=1.5759, p = 0.0117), and dihydrotestosterone (DHT) (FC=-2.1234, p = 0.0165) between GDM and no-GDM. Stepwise logistic regression analysis resulted in the best predictive model, including six variables (Δweight, ΔF, Δcortisone (E), ΔE2, Δprogesterone (P), ΔDHT). The area under the curve for this model was 0.791, and for the external validation cohort, it was 0.799.

A GDM prediction model can be constructed using SH measures during early and mid-Down syndrome screening.

Gestational diabetes mellitus (GDM) is a common and costly condition of pregnancy. The Healthy Gut Diet for Preventing Gestational Diabetes study is a novel randomised controlled trial that aims to prevent GDM through a diet that modulates the gut microbiota for pregnant women with GDM risk factors. Despite increasing interest in co-designing interventions with consumers (lived experience experts), co-design methods and outcomes are often poorly reported. The present study aims to report on the co-design process used to develop The Healthy Gut Diet intervention.

Co-design occurred across three online workshops with consumer participants (women with a lived experience of GDM, n = 11), researchers (n = 6) and workshop co-facilitators (including a consumer co-facilitator, n = 2). The workshops explored women's preferences for the mode and length of education sessions, as well as the types of information and supportive resources women wanted to receive, and undertook a "behaviour diagnosis" to understand barriers and enablers to the target behaviours (eating for gut health). The final intervention is reported according to the Template for Intervention Description and Replication.

A co-designed dietary intervention (The Healthy Gut Diet), delivered via telehealth, with a suite of educational and supportive resources that integrates published behaviour change techniques, was developed. Generally, the co-design process was reported as a positive experience based on participant feedback and evidenced by no participant dropouts over the 3-month study period.

Co-design is recognised as a process that creates a partnership between lived experience experts and researchers who can engage and empower research recipients and improve health behaviours.

This study aimed to explore associations of serum cluster of differentiation 44 (CD44) levels and its genetic variants in early pregnancy with gestational diabetes mellitus (GDM). We conducted a 1:1 case-control study (n = 414) nested in a prospective cohort of 22,302 pregnant women recruited from 2010 to 2012 in Tianjin, China. Blood samples were collected at the first antenatal care visit (at a median of 10th gestational week). Binary conditional logistic regressions were performed to examine associations of serum CD44 levels and its genetic variants with increased risk of GDM. In this study, we found that serum CD44 levels in early pregnancy was associated with GDM risk in a U-shaped manner. High serum CD44 levels and its genetic risk score in early pregnancy were associated with markedly increased risk of GDM after adjustment for traditional confounders (OR: 1.95, 95%CI: 1.12-3.40 & 1.95, 1.05-3.61). Furthermore, after adjustment for serum CD44 levels, the OR of CD44 genetic risk score for GDM was slightly attenuated but not significant (1.84, 0.98-3.48). In conclusion, serum CD44 levels and its genetic variants in early pregnancy were associated with GDM risk in Chinese pregnant women, with the effect of CD44 genetic variants being accounted for by serum CD44. SIGNIFICANCE: Recent studies suggested that pregnant women with GDM may have abnormal levels of CD44 and abnormal expression of CD44 gene, but it is uncertain whether abnormal CD44 plays a causal role in occurrence of GDM. Specifically, it remains unknown whether serum CD44 levels in early pregnancy and its genetic variants can predict the later occurrence of GDM. In this study, we found that high serum CD44 levels in early pregnancy and its genetic variants were associated with markedly increased risk of GDM in Chinese pregnant women, with the effect of CD44 genetic variants being largely accounted for by serum CD44 levels. Our study is the first reporting that serum CD44 levels and its genetic variants were associated with markedly increased risk of GDM. These multi-omics risk markers may be useful for identification of women at high risk of GDM in early pregnancy. Our findings also provide new insights into the disease mechanisms.

Hyperglycemia in pregnancy due to pre-existing Type 2 diabetes mellitus (T2DM) and gestational diabetes mellitus (GDM) is rising globally with increasing rates of risk factors for metabolic disease. This review summarizes current evidence and recommendations from national and international guidelines for diagnosis and management of T2DM and GDM to optimize maternal and neonatal outcomes.

We aimed to develop novel artificial intelligence (AI) models based on early pregnancy features to forecast the likelihood of recurrent gestational diabetes mellitus (GDM) before 14 weeks of gestation in subsequent pregnancies.

This study involved a cohort of 588 women who had two consecutive singleton deliveries and were diagnosed with GDM during the index pregnancy. The least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. 5 AI algorithms, namely support vector machine (SVM), extreme gradient boosting (XGB), light gradient boosting (LGB), decision tree classifier (DTC), and random forest (RF) classifier, and traditional multivariate logistic regression (LR) model, were employed to construct predictive models for recurrent GDM.

326 (55.4%) experienced GDM recurrence in subsequent pregnancy. In the training set (67% of the study sample), 13 features were selected for AI models construction. In the testing set (33% of the study sample), the AI models (LGB, RF, and XGB) exhibited outstanding discrimination, with AUROC values of 0.942, 0.936, and 0.924, respectively. The traditional LR model showed moderate discrimination (AUROC = 0.696). LGB, RF, and XGB models also demonstrated excellent calibration, while other models indicated a lack of fit. All AI models showed superior overall net benefits, with LGB, RF, and XGB outperforming the others.

The proposed LGB model demonstrated exceptional accuracy, excellent calibration, and superior overall net benefits. These advancements have the potential to assist healthcare professionals in advising women with a history of GDM and in developing preventive strategies to mitigate the adverse effects on maternal and fetal well-being.

This study examined the influence of a pregnancy and postnatal multicomponent lifestyle intervention for women with gestational diabetes mellitus (GDM) and overweight/obesity from 6 weeks to 12 months postnatal. The primary outcome was weight at 12 months. Secondary outcomes included change in body mass index (BMI), waist circumference (WC) and fasting plasma glucose (FPG).

The study involved 235 pregnant women with GDM and BMI ≥ 25 kg/m2 during pregnancy. Intervention components included an educational session, activity tracker (Fitbit), monthly phone calls, weekly motivational text messages, 12-week voucher for a commercial weight management organization and anthropometric, biochemical, and clinical measurements taken at 6 weeks, 6 months, and 12 months postnatal. The control group received routine local maternity care.

A mean weight change of -2.0 (SD 7.1) kg was observed in the intervention group compared with -0.6 (SD 8.0) kg in the control group, difference -1.4 (95% CI -4.4, 1.5) kg from 6 weeks to 12 months postnatal, but this was not statistically significant (P = .34). Neither were significant differences obtained for any secondary outcomes: BMI -0.6 (-1.6, 0.5) kg/m2, WC -1.0 (-5.1, 3.2) cm and FPG 0.07 (-0.15, 0.29) mmol/L.

This lifestyle intervention among women with overweight/obesity and GDM resulted in a statistically nonsignificant 1.4 kg greater weight loss compared with routine care at 12 months postnatal. Further research is needed to understand how the different components of this lifestyle intervention might be better applied to elicit more successful results.

To examine long-term risk of overweight in offspring of women with gestational diabetes mellitus (GDM) defined by the International Association of Diabetes and Pregnancy Study Group (IADPSG)'s criteria but not by the 1999 World Health Organization (WHO)'s criteria.

We followed up 1681 mother-child pairs for 8 years in Tianjin, China. Overweight in children aged 1-5 and 6-8 were respectively defined as body mass index-for-age and -sex above the 2 z-score and 1 z-score curves of the WHO's child growth standards. Logistic regression was performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs) of hyperglycemia indices at oral glucose tolerance test and GDMs defined by different criteria for offspring overweight at different ages.

Offspring of women with fasting plasma glucose ≥5.1 mmol/L were at increased risk of overweight at 6-8 years old (OR:1.45, 95% CI: 1.09-1.93). GDM defined by the IADPSG's criteria only was associated with increased risk of childhood overweight at 6-8 years old (1.65, 1.13-2.40), as compared with non-GDM by either of the two sets of criteria.

Newly defined GDM by the IADPSG's criteria increased the risk of offspring overweight aged 6-8 years.

The prevalence of gestational diabetes mellitus (GDM) has been increasing globally over recent decades; however, underlying reasons for the increase remain unclear. We analyzed trends in GDM rates and evaluated risk factors associated with the observed trends in Ontario, Canada.

We conducted a retrospective population-based cohort study using the Better Outcomes Registry and Network Ontario, linked with the Canadian Institute for Health Information Discharge Abstract Database. All pregnant individuals who had a singleton hospital delivery from 1 April 2012 to 31 March 2020 were included. We calculated rates and 95% CIs for GDM by year of delivery and contrasted fiscal year 2019/20 with 2012/13. Temporal trends in GDM were quantified using crude and adjusted risk ratios by modified Poisson regression. We further quantified the temporal increase attributable to changes in maternal characteristics by decomposition analysis.

Among 1 044 258 pregnant individuals, 82 896 (7.9%) were diagnosed with GDM over the 8 years. GDM rate rose from 6.1 to 10.4 per 100 deliveries between fiscal years 2012/13 and 2019/20. The risk of GDM in 2019/20 was 1.53 times (95% CI 1.50-1.56) higher compared with 2012/13. 27% of the increase in GDM was due to changes in maternal age, 8 BMI, and Asian ethnicity.

The GDM rate has been consistently increasing in Ontario, Canada. The contribution of increasing maternal age, pre-pregnancy obesity, and Asian ethnicity to the recent increase in GDM is notable. Further investigation is required to better understand the contributors to increasing GDM.

Gestational diabetes remains the most common medical disorder in pregnancy, with short-term and long-term consequences for mothers and offspring. New insights into pathophysiology and management suggest that the current gestational diabetes treatment approach should expand from a focus on late gestational diabetes to a personalised, integrated life course approach from preconception to postpartum and beyond. Early pregnancy lifestyle intervention could prevent late gestational diabetes. Early gestational diabetes diagnosis and treatment has been shown to be beneficial, especially when identified before 14 weeks of gestation. Early gestational diabetes screening now requires strategies for integration into routine antenatal care, alongside efforts to reduce variation in gestational diabetes care, across settings that differ between, and within, countries. Following gestational diabetes, an oral glucose tolerance test should be performed 6-12 weeks postpartum to assess the glycaemic state. Subsequent regular screening for both dysglycaemia and cardiometabolic disease is recommended, which can be incorporated alongside other family health activities. Diabetes prevention programmes for women with previous gestational diabetes might be enhanced using shared decision making and precision medicine. At all stages in this life course approach, across both high-resource and low-resource settings, a more systematic process for identifying and overcoming barriers to preventative care and treatment is needed to reduce the current global burden of gestational diabetes.

Objective: This study is a Narrative Review that aims at investigating the implications of obesity, excessive gestational weight gain (GWG) and gestational diabetes mellitus (GDM). Additionally, this Review seeks to explore the effectiveness of nutrition, and/or exercise interventions during pregnancy on reducing GWG and preventing GDM. Materials and Methods: The search in literature included studies that identified obesity, GWG, GDM and associated risks during pregnancy. Also, SR and MA focusing on interventions including diet, or physical activity (PA), or combined (i.e., lifestyle interventions) and their impact on metabolic risk during pregnancy, were identified through searches in PubMed, Cochrane Database of Systematic Reviews (CDSRs), and Scopus. Results: The study findings suggest that lifestyle interventions during pregnancy may be effective in reducing excessive GWG. Regarding the prevention of GDM, results from studies evaluating lifestyle interventions vary. However, significant and less controversial results were reported from studies assessing the efficacy of exercise interventions, particularly in high-risk pregnant women. Conclusions: Lifestyle interventions during pregnancy may reduce excessive GWG. Exercise during pregnancy may prevent GDM, especially in high-risk pregnant women. Future research is warranted to tailor lifestyle interventions for optimal effectiveness during pregnancy.

Health professionals and their patients should understand the importance of evidence. In the case of gestational diabetes mellitus, which is often associated with an abnormally high body mass index, the immediate and long-term outcome of women and their offspring depends in part on advice and implementation of lifestyle changes before, during and after pregnancy.

Up to September 2023, MEDLINE, CENTRAL, and WEB OF SCIENCE were used to identify systematic reviews and meta-analyses on the prevention of gestational diabetes. The ROBIS and AMSTAR criteria were analyzed for all systematic reviews.

A total of 36 systematic reviews were identified. Dietary interventions, physical activity or a combined approach all reduced adverse pregnancy outcomes such as gestational diabetes, pregnancy-induced hypertension and related morbidities. Within the randomized controlled trials included in the 36 systematic reviews, the type, intensity and frequency of interventions varied widely. The primary outcomes, reporting and methodological quality of the 36 systematic reviews and meta-analyses also varied.The meta-analysis with the highest ROBIS and AMSTAR-2 scores was selected to design an icon array based on a fact box simulating 100 patients.

We propose a methodology for selecting the best evidence and transforming it into a format that illustrates the benefits and harms in a way that can be understood by lay patients, even if they cannot read. This model can be applied to counselling for expectant mothers in low and high-income countries, regardless of socioeconomic status, provided that women have access to appropriately trained healthcare providers.

Lifestyle choices, metformin, and dietary supplements may prevent GDM, but the effect of intervention characteristics has not been identified. This review evaluated intervention characteristics to inform the implementation of GDM prevention interventions.

Ovid, MEDLINE/PubMed, and EMBASE databases were searched. The Template for Intervention Description and Replication (TIDieR) framework was used to examine intervention characteristics (who, what, when, where, and how). Subgroup analysis was performed by intervention characteristics.

116 studies involving 40,940 participants are included. Group-based physical activity interventions (RR 0.66; 95% CI 0.46, 0.95) reduce the incidence of GDM compared with individual or mixed (individual and group) delivery format (subgroup p-value = 0.04). Physical activity interventions delivered at healthcare facilities reduce the risk of GDM (RR 0.59; 95% CI 0.49, 0.72) compared with home-based interventions (subgroup p-value = 0.03). No other intervention characteristics impact the effectiveness of all other interventions.

Dietary, physical activity, diet plus physical activity, metformin, and myoinositol interventions reduce the incidence of GDM compared with control interventions. Group and healthcare facility-based physical activity interventions show better effectiveness in preventing GDM than individual and community-based interventions. Other intervention characteristics (e.g. utilization of e-health) don't impact the effectiveness of lifestyle interventions, and thus, interventions may require consideration of the local context.

To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.

To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.

An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

Per- and polyfluoroalkyl substances (PFAS) are persistent chemicals that have been linked to increased risk of gestational diabetes mellitus (GDM) and may affect glucose metabolisms during pregnancy. We examined the associations between maternal PFAS exposure and maternal glucose metabolisms and GDM risk among 1601 mothers who joined the Hyperglycaemia-and-Adverse-Pregnancy-Outcome (HAPO) Study in Hong Kong in 2001-2006. All mothers underwent a 75 g-oral-glucose-tolerance test at 24-32 weeks of gestation. We measured serum concentrations of six PFAS biomarkers using high-performance liquid-chromatography-coupled-with-tandem-mass-spectrometry (LC-MS-MS). We fitted conventional and advanced models (quantile-g-computation [qgcomp] and Bayesian-kernel machine regression [BKMR]) to assess the associations of individual and a mixture of PFAS with glycaemic traits. Subgroup analyses were performed based on the enrollment period by the severe-acute-respiratory-syndrome (SARS) epidemic periods in Hong Kong between March 2003 and May 2004. PFOS and PFOA were the main components of PFAS mixture among 1601 pregnant women in the Hong Kong HAPO study, with significantly higher median PFOS concentrations (19.09 ng/mL), compared to Chinese pregnant women (9.40 ng/mL) and US women (5.27 ng/mL). Maternal exposure to PFAS mixture was associated with higher HbA1c in the qgcomp (β = 0.04, 95 % CI: 0.01-0.06) model. We did not observe significant associations of PFAS mixture with fasting plasma glucose (PG), 1-h and 2-h PG in either model, except for 2-h PG in the qgcmop model (β = 0.074, 95 % CI: 0.01-0.15). PFOS was the primary contributor to the overall positive effects on HbA1c. Epidemic-specific analyses showed specific associations between PFAS exposure and the odds of GDM in the pre-SARS epidemic period. The median concentration of PFOS was highest during the peri-SARS epidemic (21.2 [14.5-43.6] ng/mL) compared with the pre-SARS (12.3 [9.2-19.9] ng/mL) and post-SARS (20.3 [14.2-46.3] ng/mL) epidemic periods. Potential interactions and exposure-response relationships between PFOA and PFNA with elevated HbA1c were observed in the peri-SARS period in BKMR model. Maternal exposure to PFAS mixture was associated with altered glucose metabolism during pregnancy. SARS epidemic-specific associations call for further studies on its long-term adverse health effects, especially potential modified associations by lifestyle changes during the COVID-19 pandemic.

The standardised pooled prevalence of gestational diabetes mellitus (GDM) globally is approximately 14 %, a reflection of increasing rates of obesity in women of childbearing age. Lifestyle interventions to reduce GDM and subsequent type 2 diabetes (T2D) have been deemed a research priority but are challenging to perform and have variable success rates. The PAIGE2 study was a pragmatic lifestyle randomised controlled trial for women with GDM and body mass index ≥25 kg/m2, which began during pregnancy and continued for one year postnatally. The primary outcome was weight loss 12 months postnatally compared with mothers receiving standard maternity care. Qualitative results are presented from end of study focus groups conducted amongst intervention mothers to gather feedback and determine acceptability of the PAIGE2 intervention. In total, 19 mothers participated in five virtual focus groups. Content analysis explored general study experience, longer term changes to lifestyle and suggested improvements of intervention components including monthly phone calls, motivational text messages, Fitbit experience, Slimming World, and study contact timings. Overall, most mothers found the individual PAIGE2 intervention components enjoyable, although opinions differed as to which were the most effective. Several mothers claimed the intervention helped them make long-term changes to their behaviours. A common suggested improvement was the establishment of a local group where mothers could share their experiences. In conclusion, most mothers deemed the intervention acceptable, and felt that with minor enhancements, it could be utilised as an effective tool to support weight loss after pregnancy and reduce future risk of obesity and T2D.

Gestational diabetes (GDM) changes the maternal metabolic and uterine environment, thus increasing the risk of short- and long-term adverse outcomes for both mother and child. Children of mothers who have GDM during their pregnancy are more likely to develop Type 2 Diabetes (T2D), early-onset cardiovascular disease and GDM when they themselves become pregnant, perpetuating a multigenerational increased risk of metabolic disease. The negative effect of GDM is exacerbated by maternal obesity, which induces a greater derangement of fetal adipogenesis and growth. Multiple factors, including genetic, epigenetic and metabolic, which interact with lifestyle factors and the environment, are likely to contribute to the development of GDM. Genetic factors are particularly important, with 30% of women with GDM having at least one parent with T2D. Fetal epigenetic modifications occur in response to maternal GDM, and may mediate both multi- and transgenerational risk. Changes to the maternal metabolome in GDM are primarily related to fatty acid oxidation, inflammation and insulin resistance. These might be effective early biomarkers allowing the identification of women at risk of GDM prior to the development of hyperglycaemia. The impact of the intra-uterine environment on the developing fetus, "developmental programming", has a multisystem effect, but its influence on adipogenesis is particularly important as it will determine baseline insulin sensitivity, and the response to future metabolic challenges. Identifying the critical window of metabolic development and developing effective interventions are key to our ability to improve population metabolic health.

To investigate the association of healthy lifestyle factors before pregnancy (body mass index [BMI] 18.5-24.9, nonsmoking, 150 min/wk or more of moderate-to-vigorous physical activity, healthy eating [top 40% of Dietary Approaches to Stop Hypertension score], no or low-to-moderate alcohol intake [less than 15 g/d], and use of multivitamins) with risk of adverse pregnancy outcomes.

We conducted a secondary analysis of prospectively collected data for women without chronic diseases who are participating in an ongoing cohort in the United States (the NHSII [Nurses' Health Study II]). Healthy lifestyle factors preceding pregnancy were prospectively assessed every 2-4 years from 1991 to 2009 with validated measures. Reproductive history was self-reported in 2001 and 2009. A composite outcome of adverse pregnancy outcomes that included miscarriage, ectopic pregnancy, gestational diabetes, gestational hypertension, preeclampsia, preterm birth, stillbirth, or low birth weight was assessed.

Overall, 15,509 women with 27,135 pregnancies were included. The mean maternal age was 35.1±4.2 years. Approximately one in three pregnancies (n=9,702, 35.8%) was complicated by one or more adverse pregnancy outcomes. The combination of six low-risk factors was inversely associated with risk of adverse pregnancy outcomes in a dose-dependent manner ( P for trend <.001). Compared with women who had zero or one healthy lifestyle factor, those with six had a 37% lower risk of adverse pregnancy outcomes (relative risk 0.63, 95% CI 0.55-0.72), driven primarily by lower risks of gestational diabetes, gestational hypertension, and low birth weight. All prepregnancy healthy lifestyle factors, except avoiding harmful alcohol consumption and regular physical activity, were independently associated with lower risk of adverse pregnancy outcomes after mutual adjustment for each other. Healthy BMI, high-quality diet, and multivitamin supplementation showed the strongest inverse associations with adverse pregnancy outcomes. If the observed relationships were causal, 19% of adverse pregnancy outcomes could have been prevented by the adoption of all six healthy lifestyle factors (population attributable risk 19%, 95% CI 13-26%).

Prepregnancy healthy lifestyle is associated with a substantially lower risk of adverse pregnancy outcomes and could be an effective intervention for the prevention of adverse pregnancy outcomes.

To examine the association of a combined healthy lifestyle in early pregnancy with gestational diabetes mellitus (GDM) risk.

A Chinese prospective cohort study with 6980 pregnant women.

Individual modifiable lifestyle factors were assessed in early pregnancy and a combined lifestyle score was derived from the sum of the lifestyle factors, with a higher score indicating a healthier lifestyle. The association of a combined healthy lifestyle with GDM risk was examined.

Gestational diabetes mellitus was diagnosed in middle pregnancy according to the International Association of Diabetes and Pregnancy Study Group criteria or diagnoses in medical records.

Overall, 501 (7.2%) pregnant women were diagnosed with GDM. Being physically active (total energy expenditure in upper three quintiles, i.e. ≥100.1 metabolic equivalent of task [MET]-hours/week; odds ratio [OR] 0.76, 95% confidence interval [CI] 0.63-0.92), healthy diet (total intake of vegetables and fruits ≥5 times/day; OR 0.74, 95% CI 0.59-0.94), sufficient sleep (night-time sleep duration ≥7 hours/night; OR 0.66, 95% CI 0.48-0.90) and healthy weight (early-pregnancy BMI <24.0 kg/m2 ; OR 0.57, 95% CI 0.46-0.71) were associated with lower GDM risk. The GDM risk decreased linearly across the combined lifestyle score (Ptrend  <0.001): women with 2, 3 and 4 lifestyle factors compared with those with 0-1 factor had 38% (OR 0.62, 95% CI 0.46-0.84), 57% (OR 0.43, 95% CI 0.31-0.58) and 66% (OR 0.34, 95% CI 0.22-0.52) lower risks of GDM, respectively.

A healthy lifestyle in early pregnancy was associated with a substantially lower GDM risk.

Previous studies have demonstrated that excessive gestational weight gain (GWG) increases the risk of gestational diabetes mellitus (GDM). This study aimed to determine the effect of using health coaching on the prevention of GDM in overweight pregnant women.

In this quasi-experimental study, 64 eligible overweight women at 12-14 gestational weeks were randomly divided into 2 groups: the coaching group and the control group (usual care group). The intervention group received 8 weeks of the phone coaching program, which integrated GWG and physical activity to reduce the incidence of GDM. The Pregnancy Physical Activity Questionnaire (PPAQ) was used to assess physical activity during pregnancy. The occurrence of gestational diabetes was determined based on the 75-g 2-hour oral glucose tolerance test (OGTT) at 24-28 weeks of gestation. The primary outcome was the incidence of GDM, and the secondary outcomes included physical activity, GWG, and neonatal and maternal birth outcomes.

The incidence of GDM in the control and intervention groups was 24.1% and 22.6%, respectively. The relative risk (RR) was 0.93 (95% CI, 0.37-2.34; P = 0.887). The post survey results indicated that GWG decreased more considerably in the coaching than in the control group between pre-trial (T0) and post-trial (T1), (MD; -2.49 with 95% CI, -4.38 to -0.60; P < 0.011). Moreover, the total GWG (between pre-pregnancy and birth) diminished more remarkably in the coaching than in the control group, (MD; -2.83 with 95% CI, -5.08 to -0.58; P < 0.014). However, the score of self-efficacy and concern about PPAQ Metabolic Equivalent of Task (METs) did not differ between the coaching and control groups.

The findings and implications of this research could significantly contribute to maternal health and gestational diabetes prevention. Additional support from a midwife coach resulted in better GWG. More studies are needed to assess the impact of health coaching as a component of usual care and its long-term effect on maternal and neonatal outcomes.

Gestational Diabetes Mellitus (GDM) is hyperglycaemia first detected during pregnancy. Globally, GDM affects around 1 in 6 live births (up to 1 in 4 in low- and middle-income countries- LMICs), thus, urgent measures are needed to prevent this public health threat.

To determine the effectiveness of pre-pregnancy lifestyle in preventing GDM.

We searched MEDLINE, Web of science, Embase and Cochrane central register of controlled trials. Randomized control trials (RCTs), case-control studies, and cohort studies that assessed the effect of pre-pregnancy lifestyle (diet and/or physical activity based) in preventing GDM were included. Random effects model was used to calculate odds ratio (OR) with 95% confidence interval. The Cochrane ROB-2 and the Newcastle-Ottawa Scale were used for assessing the risk of bias. The protocol was registered in PROSPERO (ID: CRD42020189574) RESULTS: Database search identified 7935 studies, of which 30 studies with 257,876 pregnancies were included. Meta-analysis of the RCTs (N = 5; n = 2471) in women who received pre-pregnancy lifestyle intervention showed non-significant reduction of the risk of developing GDM (OR 0.76, 95% CI: 0.50-1.17, p = 0.21). Meta-analysis of cohort studies showed that women who were physically active pre-pregnancy (N = 4; n = 23263), those who followed a low carbohydrate/low sugar diet (N = 4; n = 25739) and those women with higher quality diet scores were 29%, 14% and 28% less likely to develop GDM respectively (OR 0.71, 95% CI: 0.57, 0.88, p = 0.002, OR 0.86, 95% CI: 0.68, 1.09, p = 0.22 and OR 0.72, 95% CI 0.60-0.87, p = 0.0006).

This study highlights that some components of pre-pregnancy lifestyle interventions/exposures such as diet/physical activity-based preparation/counseling, intake of vegetables, fruits, low carbohydrate/low sugar diet, higher quality diet scores and high physical activity can reduce the risk of developing gestational diabetes. Evidence from RCTs globally and the number of studies in LMICs are limited, highlighting the need for carefully designed RCTs that combine the different aspects of the lifestyle and are personalized to achieve better clinical and cost effectiveness.

In contrast to overt diabetes mellitus (DM), gestational DM (GDM) is defined as impaired glucose tolerance induced by pregnancy, which may arise from exaggerated physiologic changes in glucose metabolism. GDM prevalence is reported to be as high as 20% among pregnancies depending on the screening method, gestational age, and the population studied. Maternal and fetal effects of uncontrolled GDM include stillbirth, macrosomia, neonatal diabetes, birth trauma, and subsequent postpartum hemorrhage. Therefore, it is essential to find the potential target population and associated predictive and preventive measures for future intensive peripartum care.

To review studies that explored the cellular and molecular mechanisms of GDM as well as predictive measures and prevention strategies.

The search was performed in the Medline and PubMed databases using the terms "gestational diabetes mellitus," "overt diabetes mellitus," and "insulin resistance." In the literature, only full-text articles were considered for inclusion (237 articles). Furthermore, articles published before 1997 and duplicate articles were excluded. After a final review by two experts, all studies (1997-2023) included in the review met the search terms and search strategy (identification from the database, screening of the studies, selection of potential articles, and final inclusion).

Finally, a total of 79 articles were collected for review. Reported risk factors for GDM included maternal obesity or overweight, pre-existing DM, and polycystic ovary syndrome. The pathophysiology of GDM involves genetic variants responsible for insulin secretion and glycemic control, pancreatic β cell depletion or dysfunction, aggravated insulin resistance due to failure in the plasma membrane translocation of glucose transporter 4, and the effects of chronic, low-grade inflammation. Currently, many antepartum measurements including adipokines (leptin), body mass ratio (waist circumference and waist-to-hip ratio], and biomarkers (microRNA in extracellular vesicles) have been studied and confirmed to be useful markers for predicting GDM. For preventing GDM, physical activity and dietary approaches are effective interventions to control body weight, improve glycemic control, and reduce insulin resistance.

This review explored the possible factors that influence GDM and the underlying molecular and cellular mechanisms of GDM and provided predictive measures and prevention strategies based on results of clinical studies.

Until now, it is uncertain whether lifestyle interventions during pregnancy can prevent gestational diabetes mellites (GDM) in high-risk pregnant women.

This study aims at investigating the effectiveness of dietary interventions and/or exercise interventions during pregnancy for preventing GDM in high-risk pregnant women.

Eligible randomized controlled trials (RCTs) were selected after a search in CENTRAL, Scopus, and PubMed. Synthesis was performed for the outcome of GDM in women with any identified GDM risk factor. Separate meta-analyses (MA) were performed to assess the efficacy of either nutrition or physical activity (PA) interventions or both combined compared with standard prenatal care for preventing GDM. Subgroup and sensitivity analyses, as well as meta-regressions against OR, were performed to assess potentional heterogeneity. Overall quality, the quality of RCTs, and publication bias were also evaluated.

A total of 13,524 participants comprising high-risk pregnant women in 41 eligible RCTs were analyzed for GDM. Women receiving only a nutrition intervention during pregnancy were less likely to experience GDM compared with women following standard prenatal care. Among 3109 high-risk pregnant women undergoing only dietary intervention for preventing GDM, 553 (17.8%) developed GDM; however, the result of the MA was marginally not significant (OR 0.73, 95%CI 0.51, 1.03; p-value 0.07), (Q 21.29, p-value 0.01; I2 58% (95%CI 10, 78%)). Subgroup analyses demonstrated an effect for studies that were conducted in Great Britain (OR 0.65, 95%CI 0.49, 0.81; p-value 0.003), and in Spain (OR 0.50, 95%CI 0.27, 0.94; p-value 0.03), for studies with forms of the Mediterranean diet as the intervention's component (OR 0.61; 95%CI 0.46, 0.81; p-value 0.0005), and for studies including a motivation arm in the intervention (OR 0.71, 95%CI 0.58, 0.87; p-value 0.0008). Among 2742 high-risk pregnant women being analyzed for GDM outcome after receiving only an exercise intervention, 461 (16.8%) were diagnosed with GDM. Women after receiving PA intervention were less likely to develop GDM (OR 0.64, 95%CI 0.51, 0.80; p-value < 0.0001), (Q 11.27, p-value 0.51; I2 0% (95%CI 0, 99%)). Finally, 1308 (17%) cases of GDM were diagnosed among 7673 high-risk pregnant women undergoing both diet and PA intervention. Women in the group of mixed lifestyle intervention had a significant reduction in incidence of GDM (OR 0.70, 95%CI 0.55, 0.90; p-value 0.005), (Q 50.32, p-value < 0.0001, I2 66%, (95% CI 44, 79%)).

The results of this study support the efficacy of lifestyle interventions during pregnancy for preventing GDM in high-risk women if an exercise component is included in the intervention arm, either alone, or combined with diet. A combined lifestyle intervention including physical exercise and a Mediterranean diet accompanied by motivation support may be considered the most effective way to prevent GDM among high-risk women during pregnancy. Future research is needed to strengthen these findings.

Around 14% of pregnancies globally are affected by gestational diabetes mellitus (GDM), making it one of the most common disorders experienced by women in pregnancy. While dietary, physical activity and supplement interventions have been implemented to prevent GDM, with varying levels of success, altering the gut microbiota through diet is a promising strategy for prevention. Several studies have demonstrated that women with GDM likely have a different gut microbiota to pregnant women without GDM, demonstrating that the gut microbiota may play a part in glycemic control and the development of GDM. To date, there have been no randomized controlled trials using diet to alter the gut microbiota in pregnancy with the aim of preventing GDM. Here, we present the study protocol for a single-blind randomized controlled trial which aims to determine the effectiveness of the Healthy Gut Diet on reducing the diagnosis of GDM in pregnant women with one or more risk factors. Consenting women will be randomized into either the Healthy Gut Diet intervention group or the usual care (control) group after 11 weeks gestation. The women in the intervention group will receive three telehealth counseling appointments with an Accredited Practicing Dietitian with the aim of educating and empowering these women to build a healthy gut microbiota through their diet. The intervention was co-designed with women who have lived experience of GDM and incorporates published behavior change techniques. The control group will receive the usual care and will also be shown a brief (3 min) video on general healthy eating in pregnancy. The primary outcome is the diagnosis of GDM at any stage of the pregnancy. Secondary outcomes include changes to gut microbiota composition and diversity; gestational weight gain; maternal and infant outcomes; management of GDM (where relevant); dietary quality and intake; physical activity; and depression scoring. We aim to recruit 120 women over 16 months. Recruitment commenced in January 2023. The trial has been registered with the Australian New Zealand Clinical Trials Registry (ACTRN12622001285741).