Background: While current epidemiological studies have documented associations between environmental metals and renal dysfunction, the majority have concentrated on plasma metal levels. The relationship between urinary metal exposure and chronic kidney disease (CKD) remains contentious, particularly within specific demographic groups.
Methods: This cross-sectional study included 2919 rural Chinese adults recruited between 2018 and 2019. Urine metals were measured by ICP-MS. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify metals significantly associated with CKD. Then, we used binary logistic regression, along with restricted cubic spline (RCS) models, to assess the individual exposure effects of specific metals on CKD. Quantile g-computation, weighted quantile sum regression, and Bayesian kernel machine regression (BKMR) models were applied to evaluate combined effects of metal exposures on CKD. Gender-stratified analyses were also conducted to explore these associations.
Results: LASSO identified seven metals (V, Cu, Rb, Sr, Ba, W, Pb) with significant impacts on CKD. In single-metal models, Cu and W exhibited a positive correlation with CKD, whereas V, Rb, Sr, Ba, and Pb showed significant negative correlations (all p < 0.05). RCS analysis revealed nonlinear associations between V, Cu, Ba, Pb, and CKD (all p-nonlinear < 0.05). In the multi-metal model, quantile-based g-computation demonstrated a collective negative association with CKD risk for the seven mixed urinary metal exposures (OR (95% CI) = -0.430 (-0.656, -0.204); p < 0.001), with V, Rb, Sr, Ba, and Pb contributing to this effect. The WQS model analysis further confirmed this joint negative association (OR (95% CI): -0.885 (-1.083, -0.899); p < 0.001), with V as the main contributor. BKMR model analysis indicated an overall negative impact of the metal mixture on CKD risk. Interactions may exist between V and Cu, as well as Cu and Sr and Pb. The female subgroup in the BKMR model demonstrated consistency with the overall association.
Conclusions: Our study findings demonstrate a negative association between the urinary metal mixture and CKD risk, particularly notable in females. Joint exposure to multiple urinary metals may involve synergistic or antagonistic interactions influencing renal function. Further research is needed to validate these observations and elucidate underlying mechanisms.
Keywords: bayesian kernel machine regression; chronic kidney disease; quantile g-computation; urinary metal; weighted quantile sum.