ArticleEnvironmental research2025
Gestational exposures to mixtures of multiple chemical classes and autism spectrum disorder in the MARBLES study.
Article in Environmental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sex-dependent vulnerability to early-life phthalates exposure and autism spectrum disorders: a systematic review.Frontiers in child and adolescent psychiatry · 2026Pooled it
- Review
- Pollutant Burden in Autism Spectrum Disorder: Mechanistic Convergence, Genetic Susceptibility, and Clinical Translation.Current issues in molecular biology · 2026Review
- Associations of prenatal metal exposures with autism-related outcomes in the ECHO cohort.Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- Early life phthalate and replacement plasticizer exposures and changes in early childhood brain functional connectivity and structural morphology.Environment international · 2026Article
- Prenatal Exposure to Per- and Polyfluoroalkyl Substances and Maternal Thyroid Function during Pregnancy in a Cohort with High Familial Likelihood of Autism Spectrum Disorder.Environmental science & technology · 2025Article
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Abstract
backgroundPrevious epidemiologic studies on gestational chemical exposures and autism spectrum disorder (ASD) often lack analysis of chemical mixtures or are limited to investigating certain chemical classes.
objectiveWe examined the impact of multi-class chemical mixtures on ASD risk, using data from the MARBLES (Markers of Autism Risks in Babies-Learning Early Signs) cohort.
methodsChildren were clinically assessed at age 3 and classified as ASD, typical development (TD), or non-TD with other neurodevelopmental concerns. In blood or urine from 105 pregnant mothers, we quantified 42 biomarkers across 5 chemical classes: per- and polyfluoroalkyl substances (PFAS), parabens, phenols, phthalates, and organophosphate esters (OPEs). We only analyzed 30 biomarkers detected in >50 % of the sample. After identifying clusters with similar chemical profiles via hierarchical clustering, we applied linear discriminant analysis (LDA) to compute LDA exposure summary scores. In covariate-adjusted models, we used LDA scores to assess co-adjusted, multipollutant associations (relative risk [RR]) with ASD or non-TD, via quasi-Poisson regression. We further examined overall mixture effect and chemical interactions with Bayesian kernel machine regression.
resultsWe identified four distinct clusters: PFAS (Cluster 1), OPEs (Cluster 2), parabens and triclosan (Cluster 3), and phthalates and bisphenol A (Cluster 4). Relative to TD, LDA scores for each cluster were associated with increased risk of ASD (RR [95 % CI]: 1.14 [1.03, 1.25], 1.12 [1.01, 1.24], 1.17 [1.07, 1.29], 1.17 [1.07, 1.28] for Cluster 1-4, respectively), whereas clusters 2 and 4 were associated with non-TD (1.07 [1.01, 1.14] and 1.12 [1.05, 1.19], respectively). Cumulative exposure across the four clusters was linked to increased risk of both ASD and non-TD. Potential interactions within and between clusters were observed.
conclusionThis study shows that considering multiple chemical classes resulted in stronger associations with ASD and non-TD risk, compared to when investigated separately in our previous studies.
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