Association between maternal education level and gestational diabetes mellitus: a meta-analysis

Temporal Trends in Gestational Diabetes Mellitus and Associated Risk Factors in Ontario, Canada, 2012-2020: A Population-Based Cohort Study

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Maternal Education Level Among People with Diabetes and Associated Adverse Outcomes

OBJECTIVE

The aim of the study is to determine the relation between education and adverse outcomes in individuals with pregestational or gestational diabetes.

STUDY DESIGN

This population-based cohort study, using the U.S. vital statistics datasets, evaluated individuals with pregestational or gestational diabetes who delivered between 2016 and 2019. The primary outcome was composite neonatal adverse outcome including any of the following: large for gestational age (LGA), Apgar's score 6 hours, neonatal seizure, or neonatal death. The secondary outcome was composite maternal adverse outcomes including any of the following: admission to intensive care unit, transfusion, uterine rupture, or unplanned hysterectomy. Multivariable analysis was used to estimate adjusted relative risks (aRR) and 95% confidence intervals (CIs).

RESULTS

Of 15,390,962 live births in the United States, 858,934 (5.6%) were eligible for this analysis. Compared with individuals with a college education and above, the risk of composite neonatal adverse outcome was higher in individuals with some college (aRR = 1.08, 95% CI = 1.07-1.09), high school (aRR = 1.06, 95% CI = 1.04-1.07), and less than high school (aRR = 1.05, 95% CI = 1.03-1.07) education. The components of composite neonatal adverse outcome that differed significantly between the groups were LGA, Apgar's score 6 hours. Infant death differed when stratified by education level. An increased risk of composite maternal adverse outcome was also found with a lower level of education.

CONCLUSION

Among individuals with diabetes, lower education was associated with a modestly higher risk of adverse neonatal and maternal outcomes.

KEY POINTS

· Education levels were associated with adverse outcomes among individuals with diabetes.. · Lower education is associated with multiple neonatal complications, including infant death.. · Individuals with varying levels of education are at higher risk for adverse maternal outcomes..

Mediating Factors in the Association of Maternal Educational Level With Pregnancy Outcomes: A Mendelian Randomization Study

Importance

Lower educational attainment is associated with increased risk of adverse pregnancy outcomes, but it is unclear which pathways mediate this association.

Objective

To investigate the association between educational attainment and pregnancy outcomes and the proportion of this association that is mediated through modifiable cardiometabolic risk factors.

Design, Setting, and Participants

In this 2-sample mendelian randomization (MR) cohort study, uncorrelated (R2 < 0.01) single-nucleotide variants (formerly single-nucleotide polymorphisms) associated with the exposure (P < 5 × 10-8) and mediators and genetic associations with the pregnancy outcomes from genome-wide association studies were extracted. All participants were of European ancestry and were largely from Finland, Iceland, the United Kingdom, or the US. The inverse variance-weighted method was used in the main analysis, and the weighted median, weighted mode, and MR Egger regression were used in sensitivity analyses. In mediation analyses, the direct effect of educational attainment estimated in multivariable MR was compared with the total effect estimated in the main univariable MR analysis. Data were extracted between December 1, 2022, and April 30, 2023.

Exposure

Genetically estimated educational attainment. The mediators considered were genetically estimated type 2 diabetes, body mass index, smoking, high-density lipoprotein cholesterol level, and systolic blood pressure.

Main Outcomes and Measures

Ectopic pregnancy, hyperemesis gravidarum, gestational diabetes, preeclampsia, preterm birth, and offspring birth weight.

Results

The analyses included 3 037 499 individuals with data on educational attainment, and those included in studies on pregnancy outcomes ranged from 141 014 for ectopic pregnancy to 270 002 with data on offspring birth weight. Each SD increase in genetically estimated educational attainment (ie, 3.4 years) was associated with an increased birth weight of 42 (95% CI, 28-56) g and an odds ratio ranging from 0.53 (95% CI, 0.46-0.60) for ectopic pregnancy to 0.81 (95% CI, 0.71-0.93) for preeclampsia. The combined proportion of the association that was mediated by the 5 cardiometabolic risk factors ranged from -17% (95% CI, -46% to 26%) for hyperemesis gravidarum to 78% (95% CI, 10%-208%) for preeclampsia. Sensitivity analyses accounting for pleiotropy were consistent with the main analyses.

Conclusions and Relevance

In this MR cohort study, intervening for type 2 diabetes, body mass index, smoking, high-density lipoprotein cholesterol level, and systolic blood pressure may lead to reductions in several adverse pregnancy outcomes associated with lower levels of education. Such public health interventions would serve to reduce health disparities attributable to social inequalities.

Gestational glucose intolerance (GGI) and gestational diabetes mellitus (GDM) among antenatal women attending urban community health centers of Lucknow: A cross-sectional study

Background

Gestational diabetes mellitus (GDM) is an emerging public health concern in India, which has detrimental effects on both the mother and the baby. The data on prevalence of GDM was unavailable at secondary urban health facilities, from where a majority of pregnant women seek antenatal services, and the following study identifies this burden.

Methods

A cross-sectional study was conducted from May 2019 to June 2020 among pregnant women attending the antenatal outpatient department (OPD) at secondary level health facilities in urban Lucknow. A semi-structured interview schedule was administered to the study subjects for collecting the relevant information and 75 g of oral glucose tolerance test was performed irrespective of the meal. The cut-off points taken for the diagnosis of GDM and gestational glucose intolerance (GGI) was as per the guidelines of the Ministry of Health and Family Welfare for diagnosis of GGI/GDM.

Results

The overall prevalence of GDM and GGI in the study was 11.6% and 16.8%, respectively. Three-fourth of the women (22/29) were diagnosed with GDM in the second trimester of pregnancy. The prevalence of GDM (16.7%) was significantly higher in pregnant women aged more than 25 years and in those who were overweight. Mean birth weight (3.2 ± 8.1 kg) of the babies was significantly higher in the women with GDM. Among the fetal complications was respiratory distress observed among 28 pregnant women and 31% of them had GDM and this was statistically significant.

Conclusion

The prevalence of GGI and GDM was found 16.8% and 11.6%, respectively. Gestational age, pre-pregnancy weight, pre-pregnancy BMI, weight gain during the pregnancy, family history of diabetes. PCOS, macrosomia and GDM in prior pregnancies was found to significant with GDM in the study.

Analysis of an Association between Preterm Birth and Parental Educational Level in Japan Using National Data

Preterm birth rate depending on parental educational level in recent years has not been surveyed in Japan. In this study, we showed the trend in preterm birth rate depending on parental educational level from 2000 to 2020 by linking data from the Census regarding individuals' educational level and parents in birth data of the vital statistics. Four types of parental educational level, namely junior high school, high school, technical school or junior college, and university or graduate school, were compared. Slope and relative indexes of inequality for preterm birth by educational level were computed by binomial models. Data on 3,148,711 births and 381,129,294 people were used in the analysis, and data on 782,536 singleton births were used after data linkage. The preterm birth rate (%) for junior high school graduate mothers and fathers was 5.09 and 5.20 in 2020, respectively. Contrarily, the preterm birth rate (%) for parents who graduated from a university or graduate school was 4.24 for mothers and 4.39 for fathers, and the rate tended to increase as educational level decreased, irrespective of parental gender. Results of inequality indexes showed that a statistically significant inequality by parental educational level persisted from 2000 to 2020.

Maternal Early-Life Risk Factors and Later Gestational Diabetes Mellitus: A Cross-Sectional Analysis of the UAE Healthy Future Study (UAEHFS)

Limited studies have focused on maternal early-life risk factors and the later development of gestational diabetes mellitus (GDM). We aimed to estimate the GDM prevalence and examine the associations of maternal early-life risk factors, namely: maternal birthweight, parental smoking at birth, childhood urbanicity, ever-breastfed, parental education attainment, parental history of diabetes, childhood overall health, childhood body size, and childhood height, with later GDM. This was a retrospective cross-sectional study using the UAE Healthy Future Study (UAEHFS) baseline data (February 2016 to April 2022) on 702 ever-married women aged 18 to 67 years. We fitted a Poisson regression to estimate the risk ratio (RR) for later GDM and its 95% confidence interval (CI). The GDM prevalence was 5.1%. In the fully adjusted model, females with low birthweight were four times more likely (RR 4.04, 95% CI 1.36-12.0) and females with a parental history of diabetes were nearly three times more likely (RR 2.86, 95% CI 1.10-7.43) to report later GDM. In conclusion, maternal birthweight and parental history of diabetes were significantly associated with later GDM. Close glucose monitoring during pregnancy among females with either a low birth weight and/or parental history of diabetes might help to prevent GDM among this high-risk group.

An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus

Gestational Diabetes Mellitus (GDM), a common pregnancy complication associated with many maternal and neonatal consequences, is increased in mothers with overweight and obesity. Interventions initiated early in pregnancy can reduce the rate of GDM in these women, however, untargeted interventions can be costly and time-consuming. We have developed an explainable machine learning-based clinical decision support system (CDSS) to identify at-risk women in need of targeted pregnancy intervention. Maternal characteristics and blood biomarkers at baseline from the PEARS study were used. After appropriate data preparation, synthetic minority oversampling technique and feature selection, five machine learning algorithms were applied with five-fold cross-validated grid search optimising the balanced accuracy. Our models were explained with Shapley additive explanations to increase the trustworthiness and acceptability of the system. We developed multiple models for different use cases: theoretical (AUC-PR 0.485, AUC-ROC 0.792), GDM screening during a normal antenatal visit (AUC-PR 0.208, AUC-ROC 0.659), and remote GDM risk assessment (AUC-PR 0.199, AUC-ROC 0.656). Our models have been implemented as a web server that is publicly available for academic use. Our explainable CDSS demonstrates the potential to assist clinicians in screening at risk patients who may benefit from early pregnancy GDM prevention strategies.