article · Pollutants
Background: Human exposure to environmental endocrine-disrupting chemicals (EDCs) rarely occurs in isolation, yet most epidemiological research has assessed chemicals individually. PFASs, toxic metals, phthalates, and VOCs are ubiquitous contaminants with well-documented reproductive toxicity. Objective: The aim of this study was to investigate the joint and individual effects of 28 EDCs spanning four chemical classes on six reproductive hormone biomarkers in a nationally representative U.S. population—using an innovative approach that simultaneously characterizes nonlinear mixture effects and chemical interactions across multiple exposure domains. Methods: This cross-sectional study used NHANES 2017–2018 data (n = 9254). Multivariable linear regression and Bayesian Kernel Machine Regression (BKMR) characterized individual and mixture associations, respectively. Missing data were handled using multiple imputations by chained equations. Survey design weights were applied in linear regression models. Results: Linear regression revealed heterogeneous associations across chemical classes and hormones. PFOA was positively associated with SHBG (β = 12.35; 95% CI: 8.33, 16.38) and LH (β = 6.91; 95% CI: 1.44, 12.38), while mercury was inversely associated with estradiol (β = −3.38; 95% CI: −5.12, −1.65). BKMR analyses identified pronounced non-monotonic dose–response relationships and emergent mixture effects not predictable from single-chemical analyses for all six hormones. Posterior inclusion probabilities identified cadmium, PFOA, MEHP, and MBzP as the most influential predictors across hormone endpoints. Conclusions: Concurrent real-world exposure to PFASs, toxic metals, phthalates, and VOCs is associated with measurable, nonlinear alterations in reproductive hormone profiles. Chemical mixture effects cannot be reliably predicted from single-pollutant analyses, underscoring the necessity of mixture-based methodologies in environmental reproductive epidemiology. Prospective studies are needed to establish causal temporality and identify critical windows of susceptibility.
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DOI: 10.3390/pollutants6020031
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