Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach

Metabolic syndrome (MetS) is a major global public health concern due to its rising prevalence and association with increased risks of cardiovascular disease and type 2 diabetes. Emerging evidence suggests that environmental chemical exposures may play a significant role in the development of MetS by disrupting metabolic pathways. This study used data from 2,960 participants in the Korean National Environmental Health Survey (KoNEHS) cycle 4 (2018–2020) to examine associations between environmental exposures and MetS risk through machine learning (ML) approaches. Eight ML algorithms were applied, with the multilayer perceptron (MLP) and random forest (RF) models identified as optimal predictors. The MLP achieved an AUC of 0.79, and the RF achieved the highest F₁ score of 0.82. Both models highlighted PFOA and PFOS, alongside age and BMI, as key predictors. SHapley Additive exPlanations (SHAP) and partial dependence plots (PDP) revealed both linear and nonlinear exposure–response patterns, suggesting threshold effects for key chemicals. These findings underscore the importance of incorporating environmental exposures into MetS risk assessments. The ML models provided robust predictive performance and novel insights into chemical and metabolic interactions, advocating for regulatory measures to reduce harmful exposures and integrate environmental factors into MetS prevention strategies.