江口 哲史

Postpartum depressive symptoms (PDS) are a common mental health condition among women after delivery. Although various causative factors have been reported, PDS remains a challenging condition to predict and prevent. The disruption of the gut microbiota due to antibiotic exposure has been reported to affect psychiatric conditions. Similarly, previous research suggests that antibiotic exposure during pregnancy could be related to PDS. Therefore, this prospective study examines the association between antibiotic exposure during pregnancy and PDS for 6 months after delivery. Data were obtained from 65,272 mothers from the Japan environment and children's study, a prospective birth cohort study. The ratios of maternal PDS at 1 and 6 months after delivery were 12.3% and 10.1%, respectively. During pregnancy, 10.7% of women took antibiotics orally. Antibiotic exposure during pregnancy was associated with an increased risk of PDS only at 6 months after delivery (OR = 1.13, 95% CI [1.00, 1.26]), adjusted for potential confounding factors. An increase in Edinburgh Postnatal Depression Scale scores in relation to antibiotic exposure during pregnancy was primarily observed via psychological distress during pregnancy. Although a causal link was not established, antibiotic exposure during pregnancy may be a contributing risk factor for PDS. Therefore, when antibiotic administration is required, clinical practitioners and perinatal care providers should consider the potential risk for PDS.

3,4-Methylenedioxymethamphetamine (MDMA; Ecstasy) is a widely abused recreational drug that has also gained interest for potential clinical applications in mental health. With the growing recognition of gut microbiota's role in mental health, this study examined whether repeated oral MDMA administration could affect gut microbiota in the small intestine, cecum, and colon of male rats. Repeated oral MDMA administration (10 mg/kg/day for 14 days) caused significant changes in the gut microbiota across these regions, with distinct effects observed in each. PICRUSt2 analysis revealed significant alterations in several metabolic pathways in these regions, indicating potential shifts in microbial functional capabilities associated with MDMA treatment. Untargeted metabolomics analysis revealed that MDMA significantly altered levels of two metabolites-ferulic acid and methylmalonic acid-in the colon, without changes in the blood, small intestine, or cecum. Notably, methylmalonic acid levels in the colon positively correlated with Lawsonibacter and Oscillibacter. These findings suggest that repeated oral MDMA treatment can alter gut microbiota composition across intestinal regions, potentially contributing to its pharmacological effects.

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.

The increasing exposure to environmental chemicals calls for comprehensive non-targeted analysis to detect unrecognized substances in human samples. We examined human serum samples to classify compounds as endogenous or exogenous using public databases and to explore the relationships between exposure markers and metabolic patterns. Serum samples from 84 pregnant women at 32 weeks gestation were analyzed using LC-QToFMS. Using the PubChemLite for Exposomics database, we annotated and classified 106 compounds (51 endogenous, 55 exogenous). The compound patterns were analyzed using three dimensional reduction methods: Principal Component Analysis (PCA), regularized Generalized Canonical Correlation Analysis (rGCCA), and Uniform Manifold Approximation and Projection (UMAP). OPTICS clustering applied to these methods revealed two distinct clusters, with 89 % of significant compounds overlapping between clusters. The detected exogenous compounds included dietary substances, phthalates, nitrogenous compounds, and parabens. Pathway enrichment analysis showed that chemical exposure was linked to changes in amino acid metabolism, protein and mineral transport, and energy metabolism. While we found associations between exposure and metabolite changes, we could not establish causality. Our approach of analyzing both exogenous and endogenous chemicals from the same dataset using PubChemLite database presents a new method for exposome research, despite limitations in sample size and peak annotation accuracy. These findings contribute to understanding multiple chemical exposures and their metabolic effects in human biomonitoring.