Prenatal environmental exposures associated with sex differences in childhood obesity and neurodevelopment

Probiotic supplementation mitigates sex-dependent nociceptive changes and gut dysbiosis induced by prenatal opioid exposure

The gut microbiome has emerged as a promising target for modulating adverse effects of opioid exposure due to its significant role in health and disease. Opioid use disorder (OUD) has become increasingly prevalent, specifically in women of reproductive age, contributing to an increased incidence of offspring exposed to opioids in utero. Recent studies have shown that prenatal opioid exposure (POE) is associated with notable changes to the maternal gut microbiome, with subsequent implications for the offspring's microbiome and other adverse outcomes. However, the role of the gut microbiome in mediating sex-based differences in pain sensitivity has not yet been investigated. In this study, both male and female C57BL/6 offspring were used to determine sex-based differences in nociception and gut microbial composition as a result of POE. Our data reveals significant sex-based differences in offspring prenatally exposed to opioids. The gut microbiome of opioid-exposed females showed an enrichment of commensal bacteria including Lactobacillus compared to opioid-exposed males. Additionally, POE females demonstrated decreased nociceptive sensitivity, while males demonstrated increased nociceptive sensitivity. RNA sequencing of the prefrontal cortex showed sex-based differences in several canonical pathways, including an increase in the opioid signaling pathway of opioid-exposed females, which was not observed in males. Microbiome modification via maternal probiotic supplementation attenuated sex-based differences throughout the early stages of life. Together, our study provides further insight on sex-based differences arising from POE and highlights the pivotal role of the gut microbiome as a modifiable target for mitigating its negative effects.

Sex-specific association between maternal childhood adversities and offspring's weight gain in a Brazilian cohort

Maternal adverse childhood experiences (ACEs) are linked to negative health and developmental outcomes in offspring. However, whether maternal ACEs influence infant weight gain in the first months of life, and if this effect differs by infant sex, remains unclear. This study included 352 full-term newborns from low-risk pregnancies and their mothers in low-income settings in Brazil. Anthropometric data (weight, length, head circumference) and other information (feeding type, offspring sex, family income) were collected at delivery (W0), discharge (W1), and up to 8 weeks postpartum (W2). ACEs were assessed using the CDC-Kaiser Questionnaire, and weight gain was calculated as the difference between W2 and W1, divided by the number of days between measurements. The association between maternal ACEs and offspring weight gain was positive only in male offspring (unstandardized coefficient (male) = 1.82, SE = 0.438, p < 0.001); for each 1-point increase in the ACEs score (e.g., from 0 to 1), weight gain increased by 1.8 g/day. These findings indicate that maternal ACEs are associated with increased weight gain in male infants during the first two months of life, potentially increasing the risk of future obesity. Further research is required to investigate the underlying biological mechanisms and their neurodevelopmental implications.

Considerations for using tree-based machine learning to assess causation between demographic and environmental risk factors and health outcomes

Evaluation of the heterogeneous treatment effect (HTE) allows for the assessment of the causal effect of a therapy or intervention while considering heterogeneity in individual factors within a population. Machine learning (ML) methods have previously been employed for HTE evaluation, addressing the limitations associated with modelling complex systems. In this work, three tree-based ML algorithms, causal random forest (CRF), causal Bayesian additive regression trees (CBART), and causal rule ensemble (CRE), are used to analyze the potential causation of benzene exposure to cause childhood acute myeloid leukemia (AML). Data for this analysis is generated by drawing samples from a previously developed model that estimates AML probability given as input demographic information and benzene exposure. Comparison is drawn between the three tree-based algorithms in terms of the predicted average treatment effect (ATE), the regression coefficient of determination, and the computational time of each algorithm. Minimal difference is reported between the three tree-based algorithms in terms of the ATE, as well as the regression coefficient of determination. However, CRF outperforms CBART in terms of algorithm computational time. Moreover, CRF allows for both continuous and binary treatment variables, as opposed to CBART and CRE, making it better suited to environmental health studies, where exposure levels of pollutants shall be considered continuous. Following the comparison of all three algorithms, the influence of adding Gaussian noise to the treatment and outcome variables, as well as outliers, is investigated using CRF. A set of considerations is drawn to guide researchers in using these algorithms. These considerations detail the simulation settings, applications, and results interpretation and aim to provide prompt information in decision-making surrounding the establishment of pollutant exposure thresholds in environmental risk assessments.

Independent and joint effects of neighborhood-level environmental and socioeconomic exposures on body mass index in early childhood: The environmental influences on child health outcomes (ECHO) cohort

Past studies support the hypothesis that the prenatal period influences childhood growth. However, few studies explore the joint effects of exposures that occur simultaneously during pregnancy. To explore the feasibility of using mixtures methods with neighborhood-level environmental exposures, we assessed the effects of multiple prenatal exposures on body mass index (BMI) from birth to age 24 months. We used data from two cohorts: Healthy Start (n = 977) and Maternal and Developmental Risks from Environmental and Social Stressors (MADRES; n = 303). BMI was measured at delivery and 6, 12, and 24 months and standardized as z-scores. We included variables for air pollutants, built and natural environments, food access, and neighborhood socioeconomic status (SES). We used two complementary statistical approaches: single-exposure linear regression and quantile-based g-computation. Models were fit separately for each cohort and time point and were adjusted for relevant covariates. Single-exposure models identified negative associations between NO2 and distance to parks and positive associations between low neighborhood SES and BMI z-scores for Healthy Start participants; for MADRES participants, we observed negative associations between O3 and distance to parks and BMI z-scores. G-computations models produced comparable results for each cohort: higher exposures were generally associated with lower BMI, although results were not significant. Results from the g-computation models, which do not require a priori knowledge of the direction of associations, indicated that the direction of associations between mixture components and BMI varied by cohort and time point. Our study highlights challenges in assessing mixtures effects at the neighborhood level and in harmonizing exposure data across cohorts. For example, geospatial data of neighborhood-level exposures may not fully capture the qualities that might influence health behavior. Studies aiming to harmonize geospatial data from different geographical regions should consider contextual factors when operationalizing exposure variables.

Current perspectives on perinatal mental health and neurobehavioral development: focus on regulation, coregulation and self-regulation

PURPOSE OF REVIEW

Perinatal mental health research provides an important perspective on neurobehavioral development. Here, we aim to review the association of maternal perinatal health with offspring neurodevelopment, providing an update on (self-)regulation problems, hypothesized mechanistic pathways, progress and challenges, and implications for mental health.

RECENT FINDINGS

(1) Meta-analyses confirm that maternal perinatal mental distress is associated with (self-)regulation problems which constitute cognitive, behavioral, and affective social-emotional problems, while exposure to positive parental mental health has a positive impact. However, effect sizes are small. (2) Hypothesized mechanistic pathways underlying this association are complex. Interactive and compensatory mechanisms across developmental time are neglected topics. (3) Progress has been made in multiexposure studies. However, challenges remain and these are shared by clinical, translational and public health sciences. (4) From a mental healthcare perspective, a multidisciplinary and system level approach employing developmentally-sensitive measures and timely treatment of (self-)regulation and coregulation problems in a dyadic caregiver-child and family level approach seems needed. The existing evidence-base is sparse.

SUMMARY

During the perinatal period, addressing vulnerable contexts and building resilient systems may promote neurobehavioral development. A pluralistic approach to research, taking a multidisciplinary approach to theoretical models and empirical investigation needs to be fostered.