Mobile-Based Lifestyle Intervention in Women with Glucose Intolerance after Gestational Diabetes Mellitus (MELINDA), A Multicenter Randomized Controlled Trial: Methodology and Design

Postpartum Glucose Follow-up Screening Among Women With Gestational Diabetes Mellitus: A Retrospective Cohort Study

Objective

To evaluate the impact of pregestational and gestational characteristics on postpartum glucose follow-up screening (PGFS) compliance in women diagnosed with gestational diabetes mellitus (GDM) in southwest China.

Methods

This retrospective cohort study was conducted in West China Second Hospital, Sichuan University. Pregestational and gestational factors were extracted from hospital records and compared between women who completed PGFS and those who did not. The screening method chosen was the 75 g oral glucose tolerance test (OGTT), performed 4-12 weeks postpartum. Univariate analysis, logistic regression analysis, and Cochran-Armitage test were used to assess associations between maternal characteristics and PGFS compliance.

Results

A total of 3047 women with GDM were included, with a PGFS completion rate of 47.2%. Of those who completed PGFS, 430 women (29.9%) presented abnormal results: 1.8% with impaired fasting glucose (IFG), 24.1% with impaired glucose tolerance (IGT), 2.2% with both IFG and IGT, and 1.8% with suspected diabetes. Independent factors associated with non-compliance to PGFS included higher pregestational BMI (odds ratio (OR): 0.952; 95% confidence interval (CI): 0.922, 0.984), multipara (OR: 0.721; 95% CI: 0.593, 0.877), use of assisted reproduction technology (ART) (OR: 1.427; 95% CI: 1.080, 1.885), excessive gestational weight gain (OR: 0.956; 95% CI: 0.936, 0.977), elevated fasting plasma glucose (FPG) prior to delivery (OR: 0.909; 95% CI: 0.835, 0.988), and undergoing cesarean section (OR: 1.232; 95% CI: 1.017, 1.492). PGFS completion rates significantly decreased with increasing pregestational BMI and earlier gestational age (P < 0.001).

Conclusion

Establishing dedicated postpartum follow-up teams and targeting women with higher pregestational BMI, multiparity, ART use, excessive gestational weight gain, elevated pre-delivery FPG, and those undergoing cesarean section are critical to improving postpartum GDM management.

One-year postpartum weight retention and glucose intolerance in women with prediabetes after gestational diabetes

AIMS

To determine risk factors for 1-year postpartum weight retention (PPWR) and glucose intolerance (prediabetes + diabetes) in women with a previous history of gestational diabetes (GDM) and prediabetes in early postpartum.

METHODS

In this exploratory analysis of the MELINDA randomized controlled trial, we report data of 167 women with prediabetes at the 6-16 weeks (early) postpartum oral glucose tolerance test after a recent history of GDM.

RESULTS

Of all participants, 45% (75) had PPWR >0 kg at 1-year postpartum. Compared to women without PPWR, women with PPWR had higher gestational weight gain [10.5 ± 6.4 vs. 6.5 ± 4.5 kg, p < 0.001], higher BMI (p < 0.01) and a worse metabolic profile (higher waist circumference, worse lipid profile and more insulin resistance) (all p < 0.05) both in early and late postpartum. Of all women with PPWR, 40.0% developed metabolic syndrome, compared to 18.9% of women without late PPWR (p = 0.003). The only independent predictor for late PPWR was weight retention in early postpartum (p < 0.001). Of all participants, 55.1% (92) had glucose intolerance (84 prediabetes, 8 diabetes) 1-year postpartum. Independent predictors for late postpartum glucose intolerance were lower gestational age at start insulin therapy in pregnancy and delivery by caesarean section (resp. p = 0.044 and 0.014).

CONCLUSIONS

In women with a previous history of GDM and prediabetes in early postpartum, PPWR in early postpartum was a strong independent predictor for late PPWR, while earlier start of insulin therapy during pregnancy and delivery by caesarean section were independent predictors of glucose intolerance in late postpartum.

Preventing Type 2 Diabetes after Gestational Diabetes: A Systematic Review Mapping Physical Activity Components using the Socio-Ecological Model

OBJECTIVES

Gestational diabetes commonly occurs during pregnancy and increases lifetime risk of type 2 diabetes following pregnancy. Engaging in physical activity postnatally can reduce this subsequent risk. Interventions aiming to increase physical activity after gestational diabetes may not address the wide range of post-pregnancy barriers. A socio-ecological approach highlights the need to include multi-level factors such as social, community and organisational factors. The aim of the review was to map intervention components to prevent type 2 diabetes after gestational diabetes using the socio-ecological model as a framework and investigate how physical activity changes align with different intervention components utilised.

METHODS

Eligible studies included any study type within 5 years of a gestational diabetes diagnosis and targeted physical activity. A systematic search of MEDLINE, Cochrane Library, Web of Science, CINAHL Complete, and Scopus was conducted in October 2022. Results were categorised based on whether findings demonstrated no increases, non-statistically significant increases or statistically significant increases in physical activity.

RESULTS

Forty-eight studies were included (37 different interventions). Thirty-eight studies were assessed as "adequate" quality, only two studies were "good" quality, and the remaining were limited quality. Mixed physical activity outcomes were observed across components used at the intrapersonal level, with components across other levels of the socio-ecological model showing more increases in physical activity. Intervention components within the social and organisational levels, for example childcare provision, providing group-based sessions and offering remote delivery, were more often present in interventions with physical activity increases.

CONCLUSIONS FOR PRACTICE

Future interventions targeting physical activity after gestational diabetes should aim to include social and organisational-level components in their intervention design. This systematic review was registered in PROSPERO (ID: CRD42021272044).

The efficacy of interventions to prevent type 2 diabetes among women with recent gestational diabetes mellitus-A living systematic review and meta-analysis

BACKGROUND

While previously considered a transient condition, with no lasting adverse impact, gestational diabetes mellitus (GDM) is now a well-established risk factor for developing type 2 diabetes mellitus (T2DM). The risk of developing T2DM appears to be particularly high in the first few years after childbirth, providing a compelling case for early intervention. This review provides an up-to-date systematic review and meta-analysis to assess the effectiveness of interventions to reduce incidence of T2DM in women with a recent history of GDM.

METHODS

The search was conducted on October 20, 2023 with an annual surveillance planned for the next 5 years to maintain a living systematic review. The inclusion criteria were randomized controlled trials of any type in women within 5 years of GDM-complicated pregnancy that reported outcomes of T2DM diagnosis or measures of dysglycemia with a follow-up of at least 12 months.

RESULTS

Seventeen studies met our inclusion criteria and have been included in this review. There were 3 pharmacological and 14 lifestyle interventions. Intervention was not associated with significant reduction in the primary outcome of T2DM (risk ratio, 0.78; 95% confidence interval [CI]: 0.43-1.41; p = 0.41; I2 = 79%) compared with the control group (placebo or usual care). However, meta-analysis of the four studies reporting hazard ratios suggested a reduction in diabetes incidence (hazard ratio, 0.68; 95% CI: 0.48-0.97; p = 0.03; I2 = 31%).

CONCLUSION

This review provides equivocal evidence about the efficacy of interventions to reduce the risk of T2DM in women within 5 years of GDM-complicated pregnancy and highlights the need for further studies, including pharmacotherapy.

Current Use and Discrepancies in the Adoption of Health-Related Internet of Things and Apps Among Working Women in Japan: Large-Scale, Internet-Based, Cross-Sectional Survey

BACKGROUND

Demographic changes and a low birth rate have led to a workforce shortage in Japan. To address this issue, the government has promoted engagement of female employment. However, increased female employment can impact women's health. Using Internet of Things (IoT) and apps to manage women's health has gained attention, but few studies have focused on working women.

OBJECTIVE

This study aimed to clarify the current situation of working women and their use of IoT or apps to manage their health.

METHODS

A large-scale, nationwide internet survey was conducted among 10,000 female participants aged from 20 years to 64 years in Japan. Participants were recruited from a marketing research company's active survey panel of 5.24 million members. The survey included questions about health status, sociodemographic factors, psychological characteristics, and the use of IoT or apps for health management. We compared perceived health status and reasons for current IoT use using t tests and assessed participant characteristics that predicted IoT use using the C5.0 decision tree algorithm. Ethical approval was granted by St. Luke's International University.

RESULTS

Among participants, 14.6% (1455/10,000) currently used IoT or apps, 7% (695/10,000) used them previously, and 78.5% (7850/10,000) had never used them. Current users (42.7 years old) were older than past users (39.7 years old). Discrepancies were observed between participants' perceived health problems and the purpose for using IoT or apps, with 21.3% (2130/10,000) of all women reporting they experienced menstrual symptoms or disorders but only 3.5% (347/10,000) used IoT or apps to manage the same symptom. On the other hand, current users were more likely to use IoT or apps to manage nutrition-related problems such as underweight or obesity (405/1455, 27.8%). Device use was highest among current users, with 87.3% (1270/1455) using smartphones, 19.7% (287/1455) using smartwatches, and 13.3% (194/1455) using PCs. Decision tree analysis identified 6 clusters, the largest consisting of 81.6% (5323/6523) of non-IoT users who did not exercise regularly, while pregnant women were more likely to use IoT or apps.

CONCLUSIONS

Our findings highlight the idea that woman with particular health problems (ie, menstrual symptoms or disorders and premenstrual syndrome) have lower use of IoT or apps, suggesting an unmet need for IoT and apps in specific areas.

Impact of breastfeeding on risk of glucose intolerance in early postpartum after gestational diabetes

Aims

To determine the impact of breastfeeding on the risk of postpartum glucose intolerance in women with gestational diabetes.

Methods

Sub-analysis of two multi-centric prospective cohort studies (BEDIP-N and MELINDA) in 1008 women with gestational diabetes. Data were collected during pregnancy and at a mean of 12 weeks postpartum. Multivariate logistic regression was used to estimate the effect of breastfeeding on glucose intolerance, with adjustment for ethnicity, education, income, professional activity and BMI.

Results

Of all participants, 56.3% (567) breastfed exclusively, 10.1% (102) gave mixed milk feeding and 33.6% (339) did not breastfeed. Mean breastfeeding duration was 3.8 ± 2.4 and 3.7 ± 2.1 months in the breastfeeding and mixed milk feeding groups (p=0.496). The rate of glucose intolerance was lower in both the breastfeeding [22.3% (126)] and mixed milk feeding [25.5% (26)] groups compared to the no breastfeeding group [29.5% (100)], with an adjusted OR of 0.7 (95% CI 0.5-1.0) for glucose intolerance in the breastfeeding group compared to no breastfeeding group and an adjusted OR of 0.7 (95% CI 0.4-1.2) for the mixed milk feeding group compared to the no breastfeeding group. Postpartum, breastfeeding women had a lower BMI, less often postpartum weight retention, lower fasting triglycerides, less insulin resistance and a higher insulin secretion-sensitivity index-2 than the mixed milk feeding and no breastfeeding group. The mixed milk feeding group was more often from an non-White background, had a lower blood pressure and lower fasting triglycerides compared to the no breastfeeding group.

Conclusions

Breastfeeding (exclusive and mixed milk feeding) is associated with less glucose intolerance and a better metabolic profile in early postpartum in women with gestational diabetes.

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/.

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes

Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes - a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings ("virtual ward" and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.

ENDOCRINOLOGY IN PREGNANCY: Targeting metabolic health promotion to optimise maternal and offspring health

There is an increase in maternal metabolic burden due to the rise in pregnancies complicated by obesity, gestational diabetes, type 2 diabetes and polycystic ovary syndrome. Metabolic dysfunction during pregnancy is associated with increased risks of long-term morbidity and mortality for women and their offspring. Lifestyle interventions in pregnancy in women at risk of metabolic dysfunction have demonstrated short-term improvements such as reduced gestational weight gain and lowered risk of gestational diabetes. It is not known whether these interventions lead to sustained improvements in the metabolic health of the mother and baby. Pharmacological interventions have also shown benefits for the mother and baby in pregnancy, including improvements in glycaemic control, reduction in gestational weight gain and reduction in large for gestational age infants; however, there remains uncertainty over long-term outcomes for mother and child. Existing studies on interventions targeting metabolic health are limited to selected populations in the preconception and postpartum periods and lack follow-up beyond delivery of the intervention. The COVID-19 pandemic has refocused our attention on the effects of maternal metabolic ill-health that play a role in contributing to premature morbidity and mortality. There is an urgent need for strategies to accurately identify the growing number of women and offspring at risk of long-term adverse metabolic health. Strategies which focus on early identification and risk stratification using individualised risk scores in the pre and inter-conception periods must take priority if we are to target and improve the metabolic health of women and their offspring who are at highest risk.

Recent Advances in Gestational Diabetes Mellitus

The incidence of gestational diabetes mellitus (GDM) and overt diabetes in pregnancy is rising globally [...].