Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus.

Background: Diabetes Mellitus (DM) is a global health concern with rising prevalence, and its link to PFAS exposure remains unclear. No machine learning (ML) models have yet been developed to predict DM based on PFAS exposure.

Methods: We analyzed data from 10471 participants in National Health and Nutrition Examination Survey (NHANES, 2003-2018). Twelve ML models were compared, with LightGBM showing the best performance (AUC = 0.84, sensitivity = 0.83, accuracy = 73 %). Variable importance, Partial Dependence Analysis (PDA), SHapley Additive exPlanations (SHAP), and LOWESS smoothing were applied to assess predictor contributions and nonlinear effects. We developed a web-based calculator using Gradio to translate our findings into a clinical risk assessment tool.

Results: PFOA was identified as the strongest predictor and was negatively associated with DM risk. PFOS, PFNA, and MPAH showed positive associations, while PFDE had a slightly negative association. A PFOA threshold of 2.48 ng/ML was identified, below which DM risk was markedly reduced. At low PFOA levels, PFOS and PFNA exhibited mild synergistic effects, but these diminished at higher concentrations. SHAP analyses confirmed PFAS dominant protective contribution, and nonlinear patterns were observed for multiple PFAS. The deployed calculator provides clinicians with an accessible tool to assess individual DM risk based on patient profiles including PFAS exposure.

Conclusion: This study provides novel ML-based insights into the associations between PFAS and DM. These findings warrant prospective validation and may inform environmental health strategies for diabetes prevention.