TY - JOUR AU - Kang, Ying AU - Xu, Su AU - Deng, Yongfeng AU - Yu, Yongquan TI - Nonpersistent pesticide exposure and circulating sex hormones in postmenopausal women: evidence from NHANES 2013-2016 JO - Journal of Environmental Exposure Assessment PY - 2026 VL - 5 IS - 3 SP - EP - 33 SN - ISSN 2771-5949 (Online) AB -

Non-persistent pesticides have been associated with altered sex-hormone profiles, but evidence among postmenopausal women remains limited. This cross-sectional study examined associations of individual urinary pesticide biomarkers and their mixture with circulating sex hormones among 593 postmenopausal women from the 2013-2016 National Health and Nutrition Examination Survey (NHANES). Survey-weighted linear regression evaluated individual biomarkers in a directed acyclic graph (DAG)-informed primary model and two progressively expanded sensitivity models incorporating lifestyle, clinical, and dietary covariates. Bayesian kernel machine regression (BKMR) evaluated overall mixture associations, biomarker-specific relative importance, and exploratory bivariate exposure-response patterns. In the primary weighted models, 3,5,6-trichloro-2-pyridinol (TCPY) and para-nitrophenol (PNP) were inversely associated with total testosterone (TT) and the free androgen index (FAI), and the directions were generally consistent across expanded models. No clear associations with estradiol (E2) or sex hormone-binding globulin (SHBG) were observed in the primary models. In BKMR, higher joint biomarker percentiles were associated with lower TT and FAI. PNP had the highest posterior inclusion probability (PIP) for TT (0.921), whereas PNP (0.607) and TCPY (0.523) had the highest PIPs for the FAI. These cross-sectional findings suggest inverse associations of the pesticide-biomarker mixture with androgen-related hormones, with PNP and TCPY showing the highest outcome-specific PIPs within the fitted BKMR models; however, causality cannot be established.

KW - Non-persistent pesticides KW - sex hormone KW - postmenopausal women KW - Bayesian kernel machine regression DO - 10.20517/jeea.2026.16 UR - https://dx.doi.org/10.20517/jeea.2026.16