Expanding the environmental scope: an environment-wide association study for mental well-being

Assessing the impact of per- and polyfluoroalkyl substances on pregnancy loss: An environmental-wide association study in Northwest China

BACKGROUND

Per- and polyfluoroalkyl substances (PFAS) are emerging environmental contaminants with potential metabolic and endocrine-disrupting properties that may affect women's reproductive health. However, their specific role in pregnancy loss remains relatively understudied.

OBJECTIVES

This study aimed to assess the association between exposure to multiple PFAS and the association with pregnancy loss (PL) in a population from Northwest China using an environment-wide association study (EWAS) approach.

METHODS

A case-control study was conducted at a university-affiliated reproductive center, enrolling 348 women with a history of PL and 320 controls with at least one live birth and no history of pregnancy loss. Serum concentrations of ten PFAS were quantified using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) following a standardized extraction and purification protocol. Demographic and clinical data were collected via a structured questionnaire. An elastic net regression model (ENRM) was employed for variable selection, identifying PFASs to be included in the subsequent EWAS analysis. Associations between individual PFAS and PL were then evaluated using logistic regression, and EWAS was utilized to explore the overall associations between PFAS mixture exposure and PL, accounting for potential interactions and multicollinearity.

RESULTS

The EWAS identified five PFASs-PFPeS, PFHxS, PFOA, PFOS, and PFNA-with significantly higher exposure odds in pregnancy loss cases versus live birth controls (adjusted ORs: 1.82 [1.31-2.62], 1.34 [1.19-1.51], 1.73 [1.46-2.07], 1.33 [1.14-1.56], and 1.73 [1.40-2.15], respectively). Subgroup analyses confirmed robust associations, with PFOA consistently linked to elevated exposure odds across demographic and clinical strata.

CONCLUSION

This study demonstrates a significant association between exposure to multiple PFAS, notably PFPeS, PFHxS, PFOA, PFOS, and PFNA, and an increased risk of pregnancy loss in a Northwest Chinese population. The particularly robust association observed with PFOA across diverse subgroups underscores its potential as a significant environmental risk factor for PL. These findings highlight the need for further research to elucidate the underlying mechanisms and to develop strategies for reducing PFAS exposure among women of reproductive age.

Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort

Effective personalized well-being interventions require the ability to predict who will thrive or not, and the understanding of underlying mechanisms. Here, using longitudinal data of a large population cohort (the Netherlands Twin Register, collected 1991-2022), we aim to build machine learning prediction models for adult well-being from the exposome and genome, and identify the most predictive factors (N between 702 and 5874). The specific exposome was captured by parent and self-reports of psychosocial factors from childhood to adulthood, the genome was described by polygenic scores, and the general exposome was captured by linkage of participants' postal codes to objective, registry-based exposures. Not the genome (R 2 = -0.007 [-0.026-0.010]), but the general exposome (R 2 = 0.047 [0.015-0.076]) and especially the specific exposome (R 2 = 0.702 [0.637-0.753]) were predictive of well-being in an independent test set. Adding the genome (P = 0.334) and general exposome (P = 0.695) independently or jointly (P = 0.029) beyond the specific exposome did not improve prediction. Risk/protective factors such as optimism, personality, social support and neighborhood housing characteristics were most predictive. Our findings highlight the importance of longitudinal monitoring and promises of different data modalities for well-being prediction.

Association between trajectories of the neighborhood social exposome and mental health in late adolescence: A FinnTwin12 cohort study

BACKGROUND

Adolescent mental health problems impose a significant burden. Exploring evolving social environments could enhance comprehension of their impact on mental health. We aimed to depict the trajectories of the neighborhood social exposome from middle to late adolescence and assess the intricate relationship between them and late adolescent mental health.

METHODS

Participants (n = 3965) from the FinnTwin12 cohort with completed questionnaires at age 17 were used. Nine mental health measures were assessed. The social exposome comprised 28 neighborhood social indicators. Trajectories of these indicators from ages 12 to 17 were summarized via latent growth curve modeling into growth factors, including baseline intercept. Mixture effects of all growth factors were assessed through quantile-based g-computation. Repeated generalized linear regressions identified significant growth factors. Sex stratification was performed.

RESULTS

The linear-quadratic model was the most optimal trajectory model. No mixture effect was detected. Regression models showed some growth factors saliently linked to the p-factor, internalizing problems, anxiety, hyperactivity, and aggression. The majority of them were baseline intercepts. Quadratic growth factors about mother tongues correlated with anxiety among sex-combined participants and males. The linear growth factor in the proportion of households of couples without children was associated with internalizing problems in females.

LIMITATIONS

We were limited to including only neighborhood-level social exposures, and the multilevel contextual exposome situation interfered with our assessment.

CONCLUSIONS

Trajectories of the social neighborhood exposome modestly influenced late adolescent mental health. Tackling root causes of social inequalities through targeted programs for living conditions could improve adolescent mental health.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.