A novel approach to the relation of multi-pollutant effect and kidney dysfunction: data analysis from the Korean National Environmental Health Survey Cycle 3 (2015-2017).

Abstract

Background: Traditional statistical models for estimating the impact of multiple environmental chemicals on kidney outcomes have limitations. This study aimed to evaluate the risk prediction of kidney disease in the general population using innovative methodologies.

Methods: Serum persistent organic pollutant (POP), urinary chemical, serum creatinine, and urinary albumin levels were measured in a subpopulation of adults (n = 1,266) drawn from the Korean National Environmental Health Survey Cycle 3 (n = 3,787). Various machine learning (ML) models, including bagging, ridge, lasso, and random forest, were used to predict chronic kidney disease (CKD) risk, and their results were compared with those of conventional logistic regression methods. Furthermore, the weighted quantile sum (WQS) approach, which assigns weights to mixture components, was employed to evaluate multi-pollutant effects. Presplit was attempted to incorporate existing domain knowledge.

Results: A total of 42 variables, including baseline characteristics and laboratory findings, were analyzed during the ML modeling process. The decision tree algorithm generally outperformed logistic regression in risk prediction. Based on the decision tree models, lipid-corrected polychlorinated biphenyl 153 (PCB153) emerged as the strongest predictor of CKD. PCB153 remained a significant predictor of CKD in middle-aged adults (<50 years; p = 0.01) following age stratification. Particularly among middle-aged adults with hemoglobin levels >13.25 g/dL, CKD risk was predicted to be 71.4% in the high serum PCB153 group.

Conclusion: Current observations showed that utilizing both WQS regression and ML-based predictions offers valuable insights. In the models, POPs, particularly PCB153, were identified as important risk factors for CKD in Korean adults.