TY  - JOUR
AU  - Liu, Jiangtao
AU  - Li, Shulan
AU  - Li, Yuanyuan
AU  - Xia, Wei
AU  - Liu, Hongxiu
AU  - Xu, Shunqing
TI  - Sex-specific effects of prenatal metal exposure on child anogenital distance: an advanced computational analysis
JO  - Journal of Environmental Exposure Assessment
PY  - 2026
VL  - 5
IS  - 3
SP  - 
EP  - 31
SN  - ISSN 2771-5949 (Online)
AB  - <p>Whether prenatal metal mixtures exert sex-dependent effects on anogenital distance (AGD) - a biomarker of prenatal androgen action - remains unknown. To address this gap, we conducted a prospective cohort study of 248 mother-infant pairs, measuring 10 metals in first-trimester maternal plasma and AGD at age 2 years. To evaluate the associations and identify the most important metal contributors, we combined single-metal linear regression with three complementary advanced computational approaches (extreme gradient boosting, Bayesian additive regression trees, Bayesian kernel machine regression). We further applied propensity score stratification and overlap weighting to strengthen causal inference. The analyses revealed sex-specific patterns: in males, cadmium (Cd), thallium (Tl), and arsenic (As) were inversely associated with anopenile distance (AGD-AP), with Cd showing the strongest effect (-6.96%, <i>P<sub>FDR</sub></i> = 0.013), followed by As (-2.83%, <i>P<sub>FDR</sub></i> = 0.033) and Tl (-2.16%, <i>P<sub>FDR</sub></i> = 0.040). In females, nickel (Ni) was suggestively positively associated with anoclitoral distance (AGD-AC) (6.94%, <i>P</i> = 0.014), though not significant after false discovery rate correction. Machine learning identified Tl and Cd as the top predictors in males, and As in females. These associations were supported by propensity score-based sensitivity analyses, particularly for male Tl and Cd. No significant associations were found for AGD-AS/AGD-AF in either sex. These findings demonstrate that prenatal metal exposure exerts sex-specific opposing effects on AGD, with Cd and Tl in males showing the most robust inverse associations. Our results highlight the value of integrating machine learning with traditional epidemiological approaches and underscore the importance of sex-stratified analyses in mixture exposure analysis.</p>
KW  - Heavy metals
KW  - anogenital distance
KW  - prenatal exposure
KW  - machine learning
KW  - sex-specific effects
DO  - 10.20517/jeea.2026.33
UR  - https://dx.doi.org/10.20517/jeea.2026.33