Body mass index mediates the association between heavy metal exposure and overactive bladder risk: insights from a...

While environmental heavy metal exposure has been linked to various metabolic disorders, its association with overactive bladder (OAB) remains poorly characterized. Emerging evidence suggests body mass index (BMI) may mediate heavy metal-induced metabolic dysregulation, though underlying pathways remain unclear. This study investigates the interplay between heavy metal exposure, BMI, and OAB risk via explainable machine learning (ML) and mediation analysis.

Drawing on data from the National Health and Nutrition Examination Survey (NHANES) [2005-2010], we identified OAB-associated heavy metals via least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm, then developed ten ML models. The optimal model, Extreme Gradient Boosting (XGBoost), was selected based on performance metrics and interpreted via Permutation Feature Importance (PFI), Shapley Additive Explanations (SHAP), and Partial Dependence Plots (PDP). Dose-response relationships, mixture effects, and BMI-mediated pathways were validated through logistic regression (LR), restricted cubic splines (RCS), Bayesian kernel machine regression (BKMR), and mediation analysis.

Among 3,201 eligible participants, blood lead, blood iron, urinary barium, urinary cadmium, urinary thallium, and urinary mercury were identified as OAB-associated metals. The XGBoost model achieved superior predictive performance [area under the curve (AUC): 0.736]. PFI highlighted hypertension, urinary cadmium, and age as key OAB determinants, while SHAP emphasized urinary cadmium and blood iron as primary predictors. PDP revealed a positive cadmium-OAB association and an inverse iron-OAB relationship. LR confirmed blood iron [odds ratio (OR) =0.72, 95% confidence interval (CI): 0.57-0.90] and urinary cadmium (OR =1.23, 95% CI: 1.06-1.42) as independent risk factors. RCS demonstrated linear trends for cadmium/iron and nonlinear trends for lead. BKMR analysis confirmed a positive overall mixture effect (conditional posterior inclusion probabilities =0.9860), with urinary cadmium showing the strongest exposure-response relationship. Mediation analysis indicated BMI mediated 14.80% of iron's protective effect and partially counteracted cadmium/lead risks (mediation proportions: -17.33%).

Urinary cadmium, blood lead, and iron emerge as critical OAB risk modulators, with BMI serving as a partial mediator. Integrating explainable ML with conventional epidemiology elucidates environmental-metabolic interactions in OAB pathogenesis, underscoring the need for heavy metal screening and BMI management in high-risk populations.