Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model

Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been strongly associated with chronic kidney disease (CKD). An interpretable machine learning (ML) model was developed to predict CKD using data from the National Health and Nutrition Examination Survey (NHANES) database spanning from 2007 to 2016. Four ML algorithms-random forest classifier (RF), XGBoost (XGB), k-nearest neighbors (KNN), and support vector machine (SVM)-were used alongside traditional logistic regression to predict CKD. The study included 6910 U.S. adults, with XGB showing the highest predictive accuracy, achieving an area under the curve (AUC) of 0.817 (95 % CI: 0.789, 0.844). The selected model was interpreted using Shapley additive explanations (SHAP) and partial dependence plot (PDP). The SHAP method identified key predictive features for CKD in urinary metabolites of XEs-methyl paraben (MeP), mono-(carboxynonyl) phthalate (MCNP), and triclosan (TCS)-and suggested personalized CKD care should focus on XE control. PDP results confirmed that, within certain ranges, MeP levels positively impacted the model, MCNP levels negatively impacted it, and TCS had a mixed effect. The synergistic effects suggested that managing urinary MeP levels could be essential for the effective control of CKD. In summary, our research highlights the significant predictive potential of XEs for CKD, especially MeP, MCNP, and TCS.