Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current...

Heavy metals, including lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg), are pervasive environmental toxicants increasingly recognized as nontraditional contributors to cardiovascular and cardiometabolic disease. Large-scale epidemiology and mechanistic studies link chronic low-to-moderate exposure to hypertension, atherosclerosis, coronary heart disease (CHD), stroke, heart failure (HF), and premature cardiovascular mortality. Concurrently, exposure science has shifted from single-pollutant models to high-dimensional "exposome" frameworks that must address correlated mixtures, non-linear dose-response, and subgroup heterogeneity. Machine learning (ML) methods can; learn these complex patterns, but clinical and policy translation requires interpretability: understanding which metals drive risk, how risk changes across exposure ranges, and where interactions amplify harm. This narrative review synthesizes contemporary interpretable ML approaches as SHapley Additive exPlanations (SHAP), partial dependence tools, Bayesian kernel machine regression (BKMR), and mixture-caausal methods, with toxicological mechanisms and clinically relevant outcomes. We integrate evidence from recent NHANES analyses, electronic medical record (EMR) mining, and the All of Us Research Program, highlighting a consistent ML-derived hierarchy wherein cadmium and lead frequently reported as dominant risk drivers for cardiovascular disease (CVD) outcomes and mortality prediction improvements when metal biomarkers augment standard risk models. We discuss current limitations, cross-sectional designs, residual confounding, measurement error, and variability in model development, and propose a future research agenda highlighting longitudinal cohorts, standardized reporting, causal validation, and integration of omics and real-time exposure monitoring to enable personalized environmental prevention.