Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. We aimed to evaluate GDM-predictive AI-based models using birth cohort data with a wide range of information and to explore factors contributing to GDM development. This investigation was conducted as a part of the Japan Environment and Children's Study. In total, 82,698 pregnant mothers who provided data on lifestyle, anthropometry, and socioeconomic status before pregnancy and the first trimester were included in the study. We employed machine learning methods as AI algorithms, such as random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM), along with logistic regression (LR) as a reference. GBDT displayed the highest accuracy, followed by LR, RF, and SVM. Exploratory analysis of the JECS data revealed that health-related quality of life in early pregnancy and maternal birthweight, which were rarely reported to be associated with GDM, were found along with variables that were reported to be associated with GDM. The results of decision tree-based algorithms, such as GBDT, have shown high accuracy, interpretability, and superiority for predicting GDM using birth cohort data.

Maternal exposure to polychlorinated biphenyls (PCBs) during pregnancy is associated with a low birth weight; however, the congener-specific effects of PCB congeners are not well defined. In this study, we used maternal serum samples from the Chiba Study of Mother and Child Health (C-MACH) cohort, collected at 32 weeks of gestational age, to analyze the effects of PCB congener exposure on birth weight by examining the relationship between newborn birth weight and individual PCB congener levels in maternal serum (n = 291). The median total PCB level in the serum of mothers of male and female newborns at approximately 32 weeks of gestation was 39 and 37 ng g⁻¹ lipid wt, respectively. The effect of the total PCB levels and the effects of PCB congener mixtures were analyzed using a linear regression model and a generalized weighted quantile sum regression model (gWQS). The birth weight of newborns was significantly associated with maternal exposure to PCB mixtures in the gWQS model. The results suggest that exposure to PCB mixtures results in low newborn birth weight. However, specific impacts of individual PCB congeners could not be related to newborn birth weight.

Maternal exposure to polychlorinated biphenyls (PCBs) results in abnormal fetal development, possibly because of epigenetic alterations. However, the association between PCB levels in cord serum with fetal DNA methylation status in cord tissue is unclear. This study aims to identify alterations in DNA methylation in cord tissue potentially associated with PCB levels in cord serum from a birth cohort in Chiba, Japan (male neonates = 32, female neonates = 43). Methylation array analysis identified five sites for female neonates (cg09878117, cg06154002, cg06289566, cg12838902, cg01083397) and one site for male neonates (cg13368805) that demonstrated a change in the methylation degree. This result was validated by pyrosequencing analysis, showing that cg06154002 (tudor domain containing 9: TDRD9) in cord tissue from female neonates is significantly correlated with total PCB levels in cord serum. These results indicate that exposure to PCBs may alter TDRD9 methylation levels, although this hypothesis requires further validation using data obtained from female neonates. However, since the present cohort is small, further studies with larger cohorts are required to obtain more data on the effects of PCB exposure and to identify corresponding biomarkers.

A fetal growth restriction is related to adverse child outcomes. We investigated risk ratios and population-attributable fractions (PAF) of small-for-gestational-age (SGA) infants in the Japanese population. Among 28,838 infants from five ongoing prospective birth cohort studies under the Japan Birth Cohort Consortium, two-stage individual-participant data meta-analyses were conducted to calculate risk ratios and PAFs for SGA in advanced maternal age, pre-pregnancy underweight, and smoking and alcohol consumption during pregnancy. Risk ratio was calculated using modified Poisson analyses with robust variance and PAF was calculated in each cohort, following common analyses protocols. Then, results from each cohort study were combined by meta-analyses using random-effects models to obtain the overall estimate for the Japanese population. In this meta-analysis, an increased risk (risk ratio, [95% confidence interval of SGA]) was significantly associated with pre-pregnancy underweight (1.72 [1.42–2.09]), gestational weight gain (1.95 [1.61–2.38]), and continued smoking during pregnancy (1.59 [1.01–2.50]). PAF of underweight, inadequate gestational weight gain, and continued smoking during pregnancy was 10.0% [4.6–15.1%], 31.4% [22.1–39.6%], and 3.2% [−4.8–10.5%], respectively. In conclusion, maternal weight status was a major contributor to SGA births in Japan. Improving maternal weight status should be prioritized to prevent fetal growth restriction.

Environmental exposure to persistent organic pollutants during pregnancy has potential adverse health effects on the fetus. One of the environmental pollutants is polychlorinated biphenyl (PCB). Earlier, we reported the presence of PCBs in fetal tissues such as the umbilical cord. Telomere length (TL) is a biomarker of aging because it shortens with each cell division. According to the Developmental Origins of Health and Disease hypothesis, fetal exposure to environmental pollutants during pregnancy affects the occurrence of non-communicable diseases in later life. In the current study, we investigated the association between cord tissue TL and serum levels of PCBs. The subjects were 114 mother-child pairs participating in a birth cohort study, the Chiba Study of Mother and Child Health (C-MACH). Maternal serum was collected during pregnancy, and cord serum and tissue were obtained at birth. TL was assessed by qPCR using genomic DNA extracted from the cord tissue. Maternal and cord serum PCB congener levels were assessed using gas chromatography and negative ion chemical ionization qMS. In male fetuses, serum levels of PCB74 in the cord blood were significantly associated with TL following covariate adjustment, but no significant association was found in female fetuses. These data suggest that the TL of the umbilical cord is affected by fetal exposure to PCBs.

Purpose: Epidemiological studies have reported that environmental factors from fetal period to early childhood can influence the risk of non-communicable diseases in adulthood. This concept has been termed the developmental origins of health and disease (DOHaD). The Chiba study of Mother and Child Health (C-MACH) is a DOHaD concept-based birth cohort study which started in 2014. This study aims to investigate the effects of genetic and environmental factors, particularly fetal and postnatal living environment, on children’s health. We also aim to identify candidate biomarkers for their health status. Moreover, the second phase study of C-MACH which was initiated in 2021 aimed at expanding the sample size, especially for gut microbiota and epigenomic analysis; it also aimed at clarifying the impact of the coronavirus disease 2019 (COVID-19) pandemic on children’s health. Participants: This study consists of four hospital-based cohorts. Women who were <13 weeks pregnant and their partners were enrolled in the study. All data and biological samples will be stored in the Chiba University Centre for Preventive Medical Sciences. Findings to date: A total of 561 women and their partners provided their consent to participate in this study. Of these women, 505 completed the questionnaire during the early gestational period. The mean age of the 505 women at enrolment was 33.0 (SD, 4.5) years. The mean prepregnancy body mass index (BMI) was 21.7 (SD, 3.6) kg/m², with 74.5% of the women having a BMI of 18.5–24.9 kg/m². About 5.2% of the women smoked cigarettes during the early stages of pregnancy. Future plans: The primary study outcomes are allergies, obesity, endocrine and metabolic disorders and developmental difficulties in children. Variables related to genome, metabolome, epigenome, gut microbiota and exposome will be evaluated as health-related factors. The relationships between these outcomes and the health-related factors will be analysed.