Jue Tao Lim1, Yue Qian Tan1, Linda Valeri2, Jingyi Lee3, Per Poh Geok3, Sin Eng Chia4, Choon Nam Ong5, Wei Jie Seow6. 1. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore. 2. Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA. 3. NUS Environmental Research Institute, National University of Singapore, Singapore. 4. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore. 5. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore; NUS Environmental Research Institute, National University of Singapore, Singapore. 6. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore. Electronic address: ephswj@nus.edu.sg.
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.
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.
Authors: Julian Markovich Rozenberg; Margarita Kamynina; Maksim Sorokin; Marianna Zolotovskaia; Elena Koroleva; Kristina Kremenchutckaya; Alexander Gudkov; Anton Buzdin; Nicolas Borisov Journal: Biomedicines Date: 2022-05-05
Authors: Herbert Ryan Marini; Antonio Micali; Giovanni Squadrito; Domenico Puzzolo; José Freni; Pietro Antonuccio; Letteria Minutoli Journal: Nutrients Date: 2022-02-04 Impact factor: 5.717