Systemic inflammation mediates the relationship between urinary cadmium and chronic cough risk: findings based on multiple statistical models.

Abstract

Epidemiological research examining the relationship between urinary cadmium and the risk of chronic cough remains scarce. This study included 2965 participants for a cross-sectional study from the NHANES. The weighted quantile sum (WQS) regression, bayesian kernel machine regression (BKMR), machine learning models (support vector machines, random forests, decision trees, and XGBoost), restricted cubic spline (RCS), and logistic regression were applied to comprehensively evaluate the performance of urinary metals in predicting chronic cough risk. Finally, the mediation effect model was employed to evaluate the role of systematic inflammation in the relationship between urinary cadmium and the risk of chronic cough. Urinary cadmium correlated with an increasing risk of chronic cough in the multivariate logistic regression model (OR: 2.83, 95% CI: 1.60-4.99). Both the WQS regression and BKMR consistently suggested a positive relationship between urinary mixed metal and chronic cough risk. Among the four machine learning models used to evaluate urinary metals and the risk of chronic cough, the random forests model showed better predictive performance (AUC = 0.69). The random forests suggested that the top five important indicators for predicting chronic cough risk were urinary cadmium, thallium, molybdenum, cesium, and uranium. Finally, the mediation effect model suggested that the systematic inflammation (lymphocytes: 4.24%, systemic immune inflammation index: 5.11%) partially mediated the relationship between urinary cadmium and chronic cough risk. This study discovered that urinary cadmium was elevated in correlation with the increasing risk of chronic cough. Systematic inflammations may partially mediate this association. Improving exposure to urinary cadmium may reduce the risk of chronic cough.