Association between serum heavy metals and prostate cancer risk – A multiple metal analysis. (November 2019) - Lim, Jue Tao; Tan, Yue Qian; Valeri, Linda; Lee, Jingyi; Geok, Per Poh; Chia, Sin Eng; Ong, Choon Nam; Seow, Wei Jie
Abstract: Background: Prostate cancer is one of the most prevalent cancers in men. Exposure to heavy metals and their association with prostate cancer risk has been studied extensively, but combined effects remain largely inconclusive. Objectives: To elucidate the association between serum concentrations of heavy metals and prostate cancer risk. Methods: Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the concentrations of a panel of 10 heavy metals (Mn, Cu, Zn, As, Se, Sb, Co, Cu, Cd and Pb) in serum samples of 141 cases and 114 controls in the Singapore Prostate Cancer Study. Linear probit regression models were used to estimate risk differences (RDs) and 95% confidence intervals (CIs) for the associations between log-centered serum metal concentrations and prostate cancer risk with adjustment for potential confounders. Bayesian kernel machine regression (BKMR) models were used to account for nonlinear, interactive, and joint metal effects. Results: Using probit regression, four heavy metals (As, Zn, Mn, Sb) were significantly and positively associated with prostate cancer risk in the unadjusted models. Using BKMR analysis, both As and Zn had positive risk differences on prostate cancer risk when all other metals were held fixed at the 25th and 50th percentiles (RD, 25th percentile: As: 0.15, Zn: 0.19, RD, 50th percentile: As: 0.45, Zn: 0.37). In addition, the overall mixture risk difference was positive and the 95% credible intervals did not include 0 when all metals in the mixture were jointly above their 55th percentile, as compared to when all metals were below their median values. Conclusions: In summary, we found positive associations between the serum levels of As and Zn and prostate cancer risk on the risk difference scale using BKMR models. The overall mixture effect was also associated with increased prostate cancer risk. Future studies are warranted to validate these findings in prospective studies. Highlights: Interactive and non-linear associations for the serum metal levels with prostate cancer risk were observed Bayesian kernel machine regression mixture analysis show positive association between serum metals and prostate cancer risk Mixture effects increase prostate cancer risk beyond the risk associated with individual metal exposures Potential synergistic interactions between multiple metals on prostate cancer risk