Cross-Sectional Estimation of Endogenous Biomarker Associations with Prenatal Phenols, Phthalates, Metals, and Polycyclic Aromatic Hydrocarbons in Single-Pollutant and Mixtures Analysis Approaches

Background: Humans are exposed to mixtures of toxicants that can impact several biological pathways. We investigated the associations between multiple classes of toxicants and an extensive panel of biomarkers indicative of lipid metabolism, inflammation, oxidative stress, and angiogenesis. Methods: We conducted a cross-sectional study of 173 participants (median 26 wk gestation) from the LIFECODES birth cohort. We measured exposure analytes of multiple toxicant classes [metals, phthalates, phenols, and polycyclic aromatic hydrocarbons (PAHs)] in urine samples. We also measured endogenous biomarkers (eicosanoids, cytokines, angiogenic markers, and oxidative stress markers) in either plasma or urine. We estimated pair-wise associations between exposure analytes and endogenous biomarkers using multiple linear regression after adjusting for covariates. We used adaptive elastic net regression, hierarchical Bayesian kernel machine regression, and sparse-group LASSO regression to evaluate toxicant mixtures associated with individual endogenous biomarkers. Results: After false-discovery adjustment (q Discussion: This study characterizes cross-sectional endogenous biomarker signatures associated with individual and mixtures of prenatal toxicant exposures. These results can help inform the prioritization of specific pairs or clusters of endogenous biomarkers and exposure analytes for investigating health outcomes. https://doi.org/10.1289/EHP7396
Introduction Observational studies are leveraging increasingly high-dimensional data sets, both in terms of data collected on exogenous environmental toxicants and endogenous biomarkers. Historically, studies have characterized single-pollutant associations between individual [...]