Abstract

Previous studies have indicated that exposure to a single toxic metal can cause renal tubular damage, while evidence about the effects of multimetal exposure on renal tubular damage is relatively limited. We aimed to evaluate the relationships of multimetal coexposure with renal tubular damage in adults in heavy metal-polluted rural regions of China. A cross-sectional study of 1918 adults in China's heavy metal-contaminated rural regions was conducted. Inductively coupled plasma-mass spectrometry (ICP-MS) was used to measure the plasma levels of 18 metals in participants, and immune turbidimetry was used to measure sensitive biological indicators, reflecting renal tubular damage (including retinol-binding protein and β2-microglobulin). Least absolute shrinkage and selection operator (LASSO) penalized regression analysis, logistic and linear regression analysis, restricted cubic spline (RCS) regression analysis and the Bayesian kernel machine regression (BKMR) method were used to explore associations of multimetal coexposure with renal tubular damage risk or renal tubular damage indicators. Plasma selenium, cadmium, arsenic, and iron were identified as the main plasma metals associated with renal tubular damage risk after dimensionality reduction. Multimetal regression models showed that selenium was positively associated, and iron was negatively associated with renal tubular damage risk or its biological indicators. Multimetal RCS analyses additionally revealed a non-linear relationship of selenium with renal tubular damage risk. The BKMR models showed that the metal mixtures were positively associated with biological indicators of renal tubular damage when the metal mixtures were above the 50th percentile of concentration. Our findings indicated that natural exposure to high levels of multimetal mixtures increases the risk of renal tubular damage. Under the conditions of multimetal exposure, selenium was positively associated, and iron was negatively associated with renal tubular damage risk or its biological indicators.

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