Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods

Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as potential risk factors, but their complex relationships with CKD may be underestimated by traditional linear methods. This study explores the association between urinary VOC metabolites and CKD risk using a combination of epidemiological and interpretable machine learning approaches. Data from the National Health and Nutrition Examination Survey (2011–March 2020 pre-pandemic) were analyzed to examine 15 urinary VOC metabolites. Analytical methods included multivariable logistic regression, LASSO regression, and five machine learning models: Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Significant associations were observed for metabolites including CEMA (N-Acetyl-S-(2-carboxyethyl)-L-cysteine) (OR = 1.66, 95% CI: 1.17–2.37), DHBMA (N-Acetyl-S-(3,4-dihydroxybutyl)-L-cysteine) (OR = 1.95, 95% CI: 1.38–2.76), HMPMA (N-Acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine) (OR = 2.18, 95% CI: 1.53–3.10), and PGA (Phenylglyoxylic acid) (OR = 1.66, 95% CI: 1.22–2.27). The XGBoost model demonstrated strong predictive performance, with SHAP analysis highlighting DHBMA as a key predictor. Inverse associations were observed for AAMA (N-Acetyl-S-(2-carbamoylethyl)-L-cysteine) and CYMA (N-Acetyl-S-(2-cyanoethyl)-L-cysteine) in their highest quartiles. This integrated approach identified significant associations between specific urinary VOC metabolites and CKD risk, particularly DHBMA. These findings underscore the role of environmental VOC exposure in CKD pathogenesis and may inform targeted prevention strategies.