Exposure to Cadmium, Lead, Mercury and Arsenic and Uric Acid Levels: Results from NHANES 2007-2016

Background: Mechanisms underlying abnormal uric acid (UA) levels from exposure to heavy metals have not been not fully elucidated, especially in the context of mixtures.Objectives: To identify major heavy metals affected UA levels with a mixture exposure concept in the association model.Methods: 4794 adults from 2007-2016 National Health and Nutrition Examination Survey (NHANES) were involved. Serum UA (SUA) and SUA/SCr were used to estimate the UA levels, and cadmium (Cd), lead (Pb), mercury (Hg) and arsenic (As) in blood and/or urinary were evaluated in the study. We assessed the associations between heavy metals and UA levels using linear regression and Bayesian kernel machine regression (BKMR). Results: The median [P25, P75] SUA/SCr and SUA level were 6.22[5.27, 7.32] and 0.83[0.72, 0.98], respectively. There was no difference for SUA/SCr by gender, (men: 6.25[5.39, 7.29]; women: 6.17[5.17, 7.36], P=0.162), but men had higher SUA than women (men: 0.95[0.85, 1.05]; women: 0.72[0.64, 0.82], P<0.001). Blood Pb (βmen = 0.651 and βwomen =1.014) and urinary Cd (βmen = 0.252 and βwomen = 0.613) were positively associated with SUA/SCr, and urinary Pb (βmen = -0.462 and βwomen = -0.838) was inversely associated with SUA/SCr in multivariate linear regression analysis, but urinary As (βmen= 0.351) was positively associated with SUA/SCr only in men. BKMR showed that higher concentrations of exposure to a mixture of heavy metals was positively associated with higher UA levels, where Cd, Pb and urinary As contributed most to the overall mixture effect in men, while Pb and urinary Cd in women.Conclusions: Our study provided the first evidence that mixtures of metals are associated with the UA levels. Increased concentrations of metals, particularly blood Pb, urinary Cd and As (only in men) may increase the levels of UA.

Influencing Trend and Extent of Combined Exposure Levels of Total Arsenic and Inorganic Arsenic on Arsenic Methylation Capacity Among University Students: Findings from Bayesian Analysis Using Kernel Machine Regression

The arsenic (As) methylation capacity is an important determinant of the susceptibility to arsenic-related diseases. Total As (TAs) or inorganic As (iAs) was reported to associate with As methylation capacity individually, however, influencing trend and extent of their combined exposure levels on methylation capacity remains poorly understood. We measured urinary concentrations of iAs, monomethylarsonic (MMA), and dimethylarsinic (DMA) acids using HPLC-HG-AFS, and calculated the primary (PMI: (MMA+DMA)/TAs) and secondary (SMI: DMA/(MMA+DMA)) methylation capacity indexes in 209 university students in Hefei, China, a non-As endemic area. Subjects were given with a standardized questionnaire to inquire their sociodemographic characteristics. Bayesian kernal machine regression (BKMR) analysis was used to estimate the association of lnTAs and lniAs levels with methylation indices (ln%MMA, ln%DMA, lnPMI, lnSMI). The median concentration of iAs, MMA and DMA was 1.22, 0.92 and 12.17 μg/L, respectively; the proportions of iAs, MMA and DMA were 8.76%, 6.13% and 84.84%, respectively. Females had higher %DMA and lower %MMA, while males had lower %DMA and higher %MMA. The combined levels of lnTAs and lniAs showed monotonic decrease in change of ln%DMA and lnSMI other than ln%MMA, additionally, change in ln%PMI was decreased only when levels of lnTAs and lniAs were larger than their 60th percentiles compared to they were at 50th percentile. With regard to single exposure level, the lnTAs showed positive correlation with ln%DMA, lnPMI, lnSMI when lniAs was set at a specific level; while lniAs showed negative correlation with ln%DMA, lnPMI, lnSMI when lnTAs was set at a specific level; and all the dose-response relationships were nonlinear. Our results suggested that combined levels of TAs and iAs played an important role in reducing As methylation capacity, expecially iAs; and the reduction only occured when TAs and iAs were up to a certain combined level.

A More Accurate Estimation of Semiparametric Logistic Regression

Growing interest in genomics research has called for new semiparametric models based on kernel machine regression for modeling health outcomes. Models containing redundant predictors often show unsatisfactory prediction performance. Thus, our task is to construct a method which can guarantee the estimation accuracy by removing redundant variables. Specifically, in this paper, based on the regularization method and an innovative class of garrotized kernel functions, we propose a novel penalized kernel machine method for a semiparametric logistic model. Our method can promise us high prediction accuracies, due to its capability of flexibly describing the complicated relationship between responses and predictors and its compatibility of the interactions among the predictors. In addition, our method can also remove the redundant variables. Our numerical experiments demonstrate that our method yields higher prediction accuracies compared to competing approaches.