Epidemiologic studies focus on combined effects of multiple metals on bone mineral density (BMD) are scarce. Therefore, this study was conducted to examine associations of multiple metals exposure with BMD. Data of adults aged ≥20 years (n = 2545) from the US National Health and Nutrition Examination Survey (NHANES, 2011-2016) were collected and analyzed. Concentrations of metals were measured in blood (cadmium [Cd], lead [Pb], mercury [Hg], and manganese [Mn]) and serum (copper [Cu], selenium [Se], and zinc [Zn]) using inductively coupled plasma mass spectrometry and inductively coupled plasma dynamic reaction cell mass spectrometry, respectively. The weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) models were performed to determine the joint effects of multiple metals exposure on lumbar and total BMD. The linear regression analyses showed Pb was negatively associated with BMDs. The WQS regression analyses revealed that the WQS index was inversely related to lumbar (β = -0.022, 95% CI: -0.036, -0.008) and total BMD (β = -0.015, 95% CI: -0.024, -0.006), and Se, Mn, and Pb were the main contributors for the combined effects. Additionally, nonlinear dose-response relationships between Pb, Mn, and Se and BMD, as well as a synergistic interaction of Pb and Mn, were found in the BKMR analyses. Our findings suggested co-exposure to Cd, Pb, Hg, Mn, Cu, Se, and Zn (above their 50th percentiles) was associated with reduced BMD, and Pb, Mn, and Se were the main contributors driving the overall effects.
Keywords: Bayesian kernel machine regression; Bone mineral density; Joint effect; Multiple metals; Weighted quantile sum regression.
Copyright © 2021 Elsevier Ltd. All rights reserved.