The impact of exposure to metals on chronic kidney disease (CKD) has only been investigated in two-way or single metal interactions in previous studies. We investigated the associations between five single metals in blood and their mixed exposure and CKD by using the machine learning approach. Relevant data were extracted from the National Health and Nutrition Examination Survey (NHANES 2011-2020), and the level of five metals in blood detected by inductively coupled plasma mass spectrometry was considered as exposures, namely, cadmium (Cd), lead (Pb), total mercury (Hg), manganese (Mn), and selenium (Se). The correlations between individual metal and metal mixtures and CKD were then evaluated by survey-multivariable logistic regression (SMLR), generalized weighted quantile sum (WQS), and Bayesian kernel machine regression (BKMR). Altogether, our study included 12,412 participants representing 572.6 million non-institutionalized US adults. Several single metals with the high quartile of exposure showed a positive association with the CKD ratio including Cd [(AOR = 1.873, 95% CI: 1.537, 2.284), Q4], Pb [(AOR = 1.559, 95% CI: 1.295, 1.880), Q4], and total Hg [(AOR = 1.169, 95% CI: 1.018, 1.343), Q2], while Mn [(AOR = 0.796, 95% CI: 0.684, 0.927), Q2] and Se [(AOR = 0.805, 95% CI: 0.664, 0.976), Q4] were negatively associated with the CKD ratio. In light of the positive fit of the WQS regression model, a significantly positive correlation was found between mixed metals and CKD (AOR = 1.373, 95% CI: 1.224, 1.539) after full covariate adjustment, and a similar finding was also detected in the BKMR model. Our study revealed that each single metal including Cd, Pb, and total Hg might have a positive association with CKD while this association was negative for both Mn and Se. The five metals might have a positive joint effect on CKD.
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http://dx.doi.org/10.1007/s11356-023-30177-5 | DOI Listing |
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