Associations between organophosphorus pesticides exposure and age-related macular degeneration risk in U.S. adults: analysis from interpretable machine learning approaches.

Aim: To investigate the associations between urinary dialkyl phosphate (DAP) metabolites of organophosphorus pesticides (OPPs) exposure and age-related macular degeneration (AMD) risk.

Methods: Participants were drawn from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2008. Urinary DAP metabolites were used to construct a machine learning (ML) model for AMD prediction. Several interpretability pipelines, including permutation feature importance (PFI), partial dependence plot (PDP), and SHapley Additive exPlanations (SHAP) analyses were employed to analyze the influence from exposure features to prediction outcomes.

Results: A total of 1845 participants were included and 137 were diagnosed with AMD. Receiver operating characteristic curve (ROC) analysis evaluated Random Forests (RF) as the best ML model with its optimal predictive performance among eleven models. PFI and SHAP analyses illustrated that DAP metabolites were of significant contribution weights in AMD risk prediction, higher than most of the socio-demographic covariates. Shapley values and waterfall plots of randomly selected AMD individuals emphasized the predictive capacity of ML with high accuracy and sensitivity in each case. The relationships and interactions visualized by graphical plots and supported by statistical measures demonstrated the indispensable impacts from six DAP metabolites to the prediction of AMD risk.

Conclusion: Urinary DAP metabolites of OPPs exposure are associated with AMD risk and ML algorithms show the excellent generalizability and differentiability in the course of AMD risk prediction.