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Limited evidence was found in the associations of volatile organic compound (VOC) exposure with bone health indicators. This study aimed to explore the associations of individual and combined metabolites of VOCs (mVOCs) in urine, a representative of the internal exposure level of VOCs, with bone mineral density (BMD), osteoporosis (OP) and fracture, and potential mediators. Data of the National Health Examination and Nutrition Survey 2005-2006 and 2013-2014 was used. Multiple linear and logistic regression modeling were performed to analyze the associations of individual mVOC with bone health indicators. The least absolute shrinkage and selection operator (LASSO) regression was adopted to select mVOCs that were more relevant to bone health indicators for further weight quantile sum (WQS) analysis used for analyzing the associations between multiple VOC co-exposure and bone health indicators. Mediation analysis was used to identify potential mediators. Seventeen mVOC members with detection rate of50% in urine of all 3478 participants aged ≥20 years (1829 females) were involved. Levels of most mVOCs were higher in women than men. Eight mVOCs were negatively associated with BMDs, and two and four mVOCs were positively associated with OP and fracture risks, respectively. WQS regression revealed decreased femoral neck BMD (β = -0.010 g/cm

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.