Exercise During the First Trimester of Pregnancy and the Risks of Abnormal Screening and Gestational Diabetes Mellitus

Gestational Diabetes Mellitus and Subsequent Risks of Diabetes and Cardiovascular Diseases: the Life Course Perspective and Implications of Racial Disparities

PURPOSE OF REVIEW

Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications worldwide and the prevalence is continuously rising globally. Importantly, GDM is not an isolated complication of pregnancy. Growing evidence suggests that individuals with GDM, compared to those without GDM, have an increased risk of subsequent type 2 diabetes (T2D) and cardiovascular diseases (CVD). Substantial racial and ethnic disparities exist in the risk of GDM. However, the role of race and ethnicity in the progression from GDM to T2D and CVD remains unclear. The purpose of the current review is to summarize recent research about GDM and its life-course impacts on cardiometabolic health, including 1) the peak time of developing T2D and CVD risks after GDM, 2) the racial and ethnic disparities in the risk cardiometabolic diseases after GDM, 3) the biological plausibility and underlying mechanisms, and 4) recommendations for screening and prevention of cardiometabolic diseases among individuals with GDM, collectively to provide an updated review to guide future research.

RECENT FINDINGS

Growing evidence has indicated that individuals with GDM had greater risks of T2D (7.4 to 9.6 times), hypertension (78% higher), and CDV events (74% higher) after GDM than their non-GDM counterparts. More recently, a few studies also suggested that GDM could slightly increase the risk of mortality. Available evidence suggests that key CVD risk factors such as blood pressure, plasma glucose, and lipids levels are all elevated as early as < 1 year postpartum in individuals with GDM. The risk of T2D and hypertension is likely to reach a peak between 3-6 years after the index pregnancy with GDM compared to normal glycemia pregnancy. Cumulative evidence also suggests that the risk of cardiometabolic diseases including T2D, hypertension, and CVD events after GDM varies by race and ethnicity. However, whether the risk is higher in certain racial and ethnic groups and whether the pattern may vary by the postpartum cardiometabolic outcome of interest remain unclear. The underlying mechanisms linking GDM and subsequent T2D and CVD are complex, often involving multiple pathways and their interactions, with the specific mechanisms varying by individuals of different racial and ethnic backgrounds. Diabetes and CVD risk screening among individuals with GDM should be initiated early during postpartum and continue, if possible, frequently. Unfortunately, adherence to postpartum glucose testing with either obstetrician or primary care providers remained poor among individuals with GDM. A life-course perspective may provide critical information to address clinical and public health gaps in postpartum screening and interventions for preventing T2D and CVD risks in individuals with GDM. Future research investigating the racial- and ethnic-specific risk of progression from GDM to cardiometabolic diseases and the role of multi-domain factors including lifestyle, biological, and socio-contextual factors are warranted to inform tailored and culture-appropriate interventions for high-risk subpopulations. Further, examining the barriers to postpartum glucose testing among individuals with GDM is crucial for the effective prevention of cardiometabolic diseases and for enhancing life-long health.

Handling missing data when estimating causal effects with targeted maximum likelihood estimation

Targeted maximum likelihood estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on data (1992-1998) from the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate 8 missing-data methods in this context: complete-case analysis, extended TMLE incorporating an outcome-missingness model, the missing covariate missing indicator method, and 5 multiple imputation (MI) approaches using parametric or machine-learning models. We considered 6 scenarios that varied in terms of exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/nonlinear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a nonlinear term. When choosing a method for handling missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and nonlinearities is expected to perform well.

Subsidized gestational diabetes mellitus screening and management program in rural China: a pragmatic multicenter, randomized controlled trial

BACKGROUND

The increasing prevalence of gestational diabetes mellitus (GDM) is a major challenge, particularly in rural areas of China where control rates are suboptimal. This study aimed to evaluate the effectiveness of a GDM subsidy program in promoting GDM screening and management in these underserved regions.

METHODS

This multicenter, randomized controlled trial (RCT) was conducted in obstetric clinics of six rural hospitals located in three provinces in China. Eligible participants were pregnant women in 24-28 weeks' gestation, without overt diabetes, with a singleton pregnancy, access to a telephone, and provided informed consent. Participants were randomly assigned in a 1:1 ratio to either the intervention or control groups using an internet-based, computer-generated randomization system. The intervention group received subsidized care for GDM, which included screening, blood glucose retesting, and lifestyle management, with financial assistance provided to health care providers. In contrast, the control group received usual care. The primary outcomes of this study were the combined maternal and neonatal complications associated with GDM, as defined by the occurrence of at least one pre-defined complication in either the mother or newborn. The secondary outcomes included the GDM screening rate, rates of glucose retesting for pregnant women diagnosed with GDM, dietary patterns, physical activity levels, gestational weight gain, and antenatal visit frequency for exploratory purposes. Primary and secondary outcomes were obtained for all participants with and without GDM. Binary outcomes were analyzed by the generalized linear model with a link of logistic, and odds ratios (OR) with 95% confidence intervals (CIs) were reported. Count outcomes were analyzed by Poisson regression, and incidence rate ratios with 95% CIs were reported.

RESULTS

A total of 3294 pregnant women were randomly assigned to either the intervention group (n = 1649) or the control group (n = 1645) between 15 September 2018 and 30 September 2019. The proportion of pregnant women in the intervention group who suffered from combined maternal and/or neonatal complications was lower than in the control group with adjusted OR = 0.86 (0.80 to 0.94, P = 0.001), and a more significant difference was observed in the GDM subgroup (adjusted OR = 0.66, 95% CI 0.47 to 0.95, P = 0.025). No predefined safety or adverse events of ketosis or ketoacidosis associated with GDM management were detected in this study. Both the intervention and control groups had high GDM screening rates (intervention: 97.2% [1602/1649]; control: 94.5% [1555/1645], P < 0.001). Moreover, The intervention group showed a healthier lifestyle, with lower energy intake and more walking minutes (P values < 0.05), and more frequent blood glucose testing (1.5 vs. 0.4 visits; P = 0.001) compared to the control group.

CONCLUSION

In rural China, a GDM care program that provided incentives for both pregnant women and healthcare providers resulted in improved maternal and neonatal health outcomes. Public health subsidy programs in China should consider incorporating GDM screening and management to further enhance reproductive health.

TRIAL REGISTRATION

China Clinical Trials Registry ChiCTR1800017488. https://www.chictr.org.cn/.

Investigating the Potential Mechanisms of Ferroptosis and Autophagy in the Pathogenesis of Gestational Diabetes

Ferroptosis and autophagy are two different cellular processes that have recently been highlighted for their potential roles in the pathogenesis and progression of gestational diabetes (GD). This research sought to uncover the crucial genes tied to ferroptosis and autophagy in GD, further investigating their mechanisms. Differentially expressed genes (DEGs) linked to ferroptosis and autophagy in GD were identified using publicly available data. Pathway enrichment, protein interactions, correlation with immune cell infiltration, and diagnostic value of DEGs were analyzed. HTR-8/SVneo cells were subjected to varying glucose levels to evaluate cell viability and the expression of markers related to ferroptosis and proteins associated with autophagy. Crucial DEGs were validated in vitro. A total of 12 DEGs associated with ferroptosis and autophagy in GD were identified, enriched in the PI3K-AKT signaling pathway. These genes exhibited significant correlations with monocyte infiltration, resting CD4 memory T cells, and follicular helper T cells. They exhibited high diagnostic value for GD (AUC: 0.77-0.97). High glucose treatment inhibited cell viability, induced ferroptosis, and activated autophagy in HTR-8/SVneo cells. Validation confirmed altered expression of SNCA, MTDH, HMGB1, TLR4, SOX2, SESN2, and HMOX1 after glucose treatments. In conclusion, ferroptosis and autophagy may play a role in GD development through key genes (e.g., TLR4, SOX2, SNCA, HMOX1, HMGB1). These genes could serve as promising biomarkers for GD diagnosis.

Effects of COVID-19 home quarantine on pregnancy outcomes of patients with gestational diabetes mellitus: a retrospective cohort study

OBJECTIVE

This study aimed to evaluate the effects of the home quarantine on pregnancy outcomes of gestational diabetes mellitus (GDM) patients during the COVID-19 outbreak.

METHODS

The complete electronic medical records of patients with GDM with home quarantine history were collected and classified into the home quarantine group from 24 February 2020 to 24 November 2020. The same period of patients with GDM without home quarantine history were included in the control group from 2018 to 2019. The pregnant outcomes of the home quarantine and control groups were systematically compared, such as neonatal weight, head circumference, body length, one-minute Apgar score, fetal macrosomia, and pre-term delivery.

RESULTS

A total of 1358 patients with GDM were included in the analysis, including 484 in 2018, 468 in 2019, and 406 in 2020. Patients with GDM with home quarantine in 2020 had higher glycemic levels and adverse pregnancy outcomes than in 2018 and 2019, including higher cesarean section rates, lower Apgar scores, and higher incidence of macrosomia and umbilical cord around the neck. More importantly, the second trimester of home quarantine had brought a broader impact on pregnant women and fetuses.

CONCLUSION

Home quarantine has aggravated the condition of GDM pregnant women and brought more adverse pregnancy outcomes during the COVID-19 outbreak. Therefore, we suggested governments and hospitals strengthen lifestyle guidance, glucose management, and antenatal care for patients with GDM with home quarantine during public health emergencies.

Intrinsic motivation for physical activity, healthy eating, and self-weighing in association with corresponding behaviors in early pregnancy

Healthy lifestyle behaviors influence maternal cardiovascular health, but motivation for them in pregnancy is poorly understood. We examined whether intrinsic motivation (assessed on 5-point scales for each behavior) is associated with three lifestyle behaviors in early pregnancy: physical activity, by intensity level; healthy eating, quantified with the Alternate Healthy Eating Index for Pregnancy (AHEI-P); and weight self-monitoring, a standard weight management technique. Participants in the Northern California Pregnancy, Lifestyle and Environment Study (PETALS) population-based cohort completed validated surveys in early pregnancy (2017-18; N = 472; 22 % Asian, 6 % Black, 30 % Hispanic, 13 % multiracial, 30 % White). Cross-sectional data were analyzed in 2021-22. Overall, 40.7 % (n = 192) met United States national physical activity guidelines; the average AHEI-P score was 62.3 out of 130 (SD 11.4); and 36.9 % reported regular self-weighing (≥once/week; n = 174). In models adjusted for participant characteristics, 1-unit increases in intrinsic motivation were associated with increased likelihood of meeting physical activity guidelines (risk ratio [95 % CI]: 1.66 [1.48, 1.86], p < 0.0001); meeting sample-specific 75th percentiles for vigorous physical activity (1.70 [1.44, 1.99], p < 0.0001) and AHEI-P (1.75 [1.33, 2.31], p < 0.0001); and regular self-weighing (2.13 [1.92, 2.37], p < 0.0001). A 1-unit increase in intrinsic motivation lowered the risk of meeting the 75th percentile for sedentary behavior (0.79 [0.67, 0.92], p < 0.003). Intrinsic motivation was not associated with reaching 75th percentiles for total, light, or moderate activity. Intrinsic motivation is associated with physical activity, healthy eating, and self-weighing among diverse individuals in early pregnancy. Results can inform intervention design to promote maternal health via increased enjoyment of lifestyle behaviors.

PFAS concentrations in early and mid-pregnancy and risk of gestational diabetes mellitus in a nested case-control study within the ethnically and racially diverse PETALS cohort

BACKGROUND

Per- and polyfluoroalkyl substances (PFAS) are persistent synthetic chemicals and are commonly found in everyday items. PFAS have been linked to disrupting glucose homeostasis, however, whether they are associated with gestational diabetes mellitus (GDM) risk remains inconclusive. We examined prospective associations of PFAS concentrations measured twice in pregnancy with GDM risk.

METHODS

In the PETALS pregnancy cohort, a nested case-control study which included 41 GDM cases and 87 controls was conducted. PFAS analytes were measured in blood serum collected in both early and mid-pregnancy (mean [SD]: 13.9 [2.2] and 20.2 [2.2] gestational weeks, respectively), with cumulative exposure calculated by the area-under-the-curve (AUC) to integrate both the PFAS concentration and the timing of the exposure. Individual adjusted weighted unconditional logistic regression models examined seven PFAS in association with GDM risk. P-values were corrected using the false-discovery-rate (FDR). Mixture models were analyzed with Bayesian kernel machine regression (BKMR).

RESULTS

PFDA, PFNA and PFOA were individually associated with higher GDM risk per interquartile range (IQR) in early pregnancy (OR [95% CI]: 1.23 [1.09, 1.38]), 1.40 [1.24, 1.58]), and 1.15 [1.04, 1.27], respectively), mid-pregnancy (1.28 [1.15, 1.43], 1.16 [1.05, 1.28], and 1.20 [1.09, 1.33], respectively), and with cumulative exposure (1.23 [1.09, 1.38], 1.21 [1.07, 1.37], and 1.19 [1.09, 1.31], respectively). PFOS in mid-pregnancy and with cumulative exposure was associated with increased GDM risk (1.41 [1.17, 1.71] and 1.33 [1.06, 1.58], respectively). PFUnDA in early pregnancy was associated with lower GDM risk (0.79 [0.64, 0.98]), whereas mid-pregnancy levels were associated with higher risk (1.49 [1.18, 1.89]). PFHxS was associated with decreased GDM risk in early and mid-pregnancy (0.48 [0.38, 0.60] and 0.48 [0.37, 0.63], respectively) and with cumulative exposure (0.49 [0.38,0.63]). PFPeA was not associated with GDM. Similar conclusions were observed in BKMR models; however, overall associations in these models were not statistically significant.

CONCLUSIONS

Higher risk of GDM was consistently observed in association with PFDA, PFNA, and PFOA exposure in both early and mid-pregnancy. Results should be corroborated in larger population-based cohorts and individuals of reproductive age should potentially avoid known sources of PFAS.

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Introduction

Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.

Aim

To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.

Methodology

Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

Current state

ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.

Future challenges

To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.

Conclusion

The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Physical Activity during Preconception Impacts Some Maternal Outcomes-A Cross-Sectional Study on a Population of Polish Women

BACKGROUND

Physical activity is an element of a healthy lifestyle and is safe in most pregnancies. The aim of this study was to assess the impact of physical activity levels before and during pregnancy on pregnancy outcomes for both the mother and child.

METHODS

A cross-sectional survey was conducted on a population of Polish women. An anonymous questionnaire was distributed electronically via maternity and parental Facebook groups.

RESULTS

The final research group included 961 women. The analysis showed that physical activity 6 months before pregnancy was associated with a lower risk of gestational diabetes mellitus (GDM), but physical activity during pregnancy showed no such association. In all, 37.8% of women with low activity in the first trimester, in comparison to 29.4% of adequately active women, gained an excessive amount of weight during pregnancy (p = 0.0306). The results showed no association between activity level and pregnancy duration, type of delivery or newborn birth weight.

CONCLUSIONS

Our study indicates that physical activity during the preconception period is crucial to GDM occurrence.

Urinary Phenols in Early to Midpregnancy and Risk of Gestational Diabetes Mellitus: A Longitudinal Study in a Multiracial Cohort

Environmental phenols are ubiquitous endocrine disruptors and putatively diabetogenic. However, data during pregnancy are scant. We investigated the prospective associations between pregnancy phenol concentrations and gestational diabetes mellitus (GDM) risk. In a nested matched case-control study of 111 individuals with GDM and 222 individuals without GDM within the prospective PETALS cohort, urinary bisphenol A (BPA), BPA substitutes (bisphenol F and bisphenol S [BPS]), benzophenone-3, and triclosan were quantified during the first and second trimesters. Cumulative concentrations across the two times were calculated using the area under the curve (AUC). Multivariable conditional logistic regression examined the association of individual phenols with GDM risk. We conducted mixture analysis using Bayesian kernel machine regression. We a priori examined effect modification by Asian/Pacific Islander (A/PI) race/ethnicity resulting from the case-control matching and highest GDM prevalence among A/PIs. Overall, first-trimester urinary BPS was positively associated with increased risk of GDM (adjusted odds ratio comparing highest vs. lowest tertile [aORT3 vs. T1] 2.12 [95% CI 1.00-4.50]). We identified associations among non-A/Ps, who had higher phenol concentrations than A/PIs. Among non-A/PIs, first-trimester BPA, BPS, and triclosan were positively associated with GDM risk (aORT3 vs. T1 2.91 [95% CI 1.05-8.02], 4.60 [1.55-13.70], and 2.88 [1.11-7.45], respectively). Triclosan in the second trimester and AUC were positively associated with GDM risk among non-A/PIs (P < 0.05). In mixture analysis, triclosan was significantly associated with GDM risk. Urinary BPS among all and BPA, BPS, and triclosan among non-A/PIs were associated with GDM risk. Pregnant individuals should be aware of these phenols' potential adverse health effects.

Sex-specific mediating effect of gestational weight gain between pre-pregnancy body mass index and gestational diabetes mellitus

Background

Inappropriate weight gain may increase the risk of gestational diabetes mellitus (GDM). However, the relationship between pre-pregnancy body mass index (BMI), weight gain, and GDM has not been precisely quantified. This study aimed to explore whether gestational weight gain played a mediating role between pre-pregnancy BMI and GDM and whether the mediating effect was sex specific.

Methods

This study established a population-based observational cohort to assess weight gain in pregnant women. Mediation analyses were performed to quantify whether weight gain mediated the association between pre-pregnancy BMI and GDM.

Results

A total of 67,777 pregnant women were included in the final analysis, among whom 6751 (10.0%) were diagnosed with GDM. We verified that both pre-pregnancy BMI and weight gain were associated with GDM, and that BMI negatively contributed to weight gain. We also found that weight gain had a significant mediating effect on the relationship between pre-pregnancy BMI and GDM (Z_a × Z_b confidence intervals [CIs] 0.00234–0.00618). Furthermore, the effect was sex-specific, in that it was only significant in overweight women carrying female fetuses (Z_a × Z_b CIs 0.00422–0.01977), but not male fetuses (Z_a × Z_b CIs −0.00085 to 0.01236).

Conclusions

Weight gain during pregnancy had a fetal sex-specific mediating effect between pre-pregnancy BMI and GDM.

Research Advances in the Roles of Circular RNAs in Pathophysiology and Early Diagnosis of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) refers to different degrees of glucose tolerance abnormalities that occur during pregnancy or are discovered for the first time, which can have a serious impact on the mother and the offspring. The screening of GDM mainly relies on the oral glucose tolerance test (OGTT) at 24–28 weeks of gestation. The early diagnosis and intervention of GDM can greatly improve adverse pregnancy outcomes. However, molecular markers for early prediction and diagnosis of GDM are currently lacking. Therefore, looking for GDM-specific early diagnostic markers has important clinical significance for the prevention and treatment of GDM and the management of subsequent maternal health. Circular RNA (circRNA) is a new type of non-coding RNA. Recent studies have found that circRNAs were involved in the occurrence and development of malignant tumors, metabolic diseases, cardiovascular and cerebrovascular diseases, etc., and could be used as the molecular marker for early diagnosis. Our previous research showed that circRNAs are differentially expressed in serum of GDM pregnant women in the second and third trimester, placental tissues during cesarean delivery, and cord blood. However, the mechanism of circular RNA in GDM still remains unclear. This article focuses on related circRNAs involved in insulin resistance and β-cell dysfunction, speculating on the possible role of circRNAs in the pathophysiology of GDM under the current research context, and has the potential to serve as early molecular markers for the diagnosis of GDM.

Views and needs of people who at high-risk of gestational diabetes mellitus for the development of mobile health applications: A descriptive qualitative research (Preprint)

Background

Early prevention of gestational diabetes mellitus (GDM) can reduce the incidence of not only GDM, but also adverse perinatal pregnancy outcomes. Moreover, it is of great significance to prevent or reduce the occurrence of type 2 diabetes. Mobile health (mHealth) apps can help pregnant women effectively prevent GDM by providing risk prediction, lifestyle support, peer support, professional support, and other functions. Before designing mHealth apps, developers must understand the views and needs of pregnant women, and closely combine users’ needs to develop app functions, in order to better improve user experience and increase the usage rate of these apps in the future.

Objective

The objective of this study was to understand the views of the high-risk population of gestational diabetes mellitus on the development of mobile health apps and the demand for app functions, so as to provide a basis for the development of gestational diabetes mellitus prevention apps.

Methods

Fifteen pregnant women with at least one risk factor for gestational diabetes were recruited from July to September 2021, and were interviewed via a semistructured interview using the purpose sampling method. The transcribed data were analyzed by the traditional content analysis method, and themes were extracted.

Results

Respondents wanted to develop user-friendly and fully functional mobile apps for the prevention of gestational diabetes mellitus. Pregnant women's requirements for app function development include: personalized customization, accurate information support, interactive design, practical tool support, visual presentation, convenient professional support, peer support, reasonable reminder function, appropriate maternal and infant auxiliary function, and differentiated incentive function.These function settings can encourage pregnant women to improve or maintain healthy living habits during their use of the app

Conclusions

This study discusses the functional requirements of target users for gestational diabetes mellitus prevention apps, which can provide reference for the development of future applications.

Exercise as a Therapeutic Intervention in Gestational Diabetes Mellitus

Gestational Diabetes Mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. Regular exercise is important for a healthy pregnancy and can lower the risk of developing GDM. For women with GDM, exercise is safe and can affect the pregnancy outcomes beneficially. A single exercise bout increases skeletal muscle glucose uptake, minimizing hyperglycemia. Regular exercise training promotes mitochondrial biogenesis, improves oxidative capacity, enhances insulin sensitivity and vascular function, and reduces systemic inflammation. Exercise may also aid in lowering the insulin dose in insulin-treated pregnant women. Despite these benefits, women with GDM are usually inactive or have poor participation in exercise training. Attractive individualized exercise programs that will increase adherence and result in optimal maternal and offspring benefits are needed. However, as women with GDM have a unique physiology, more attention is required during exercise prescription. This review (i) summarizes the cardiovascular and metabolic adaptations due to pregnancy and outlines the mechanisms through which exercise can improve glycemic control and overall health in insulin resistance states, (ii) presents the pathophysiological alterations induced by GDM that affect exercise responses, and (iii) highlights cardinal points of an exercise program for women with GDM.