Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore.
Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA.
Environ Int. 2019 Nov;132:105109. doi: 10.1016/j.envint.2019.105109. Epub 2019 Sep 3.
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.
To elucidate the association between serum concentrations of heavy metals and prostate cancer risk.
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.
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.
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.
前列腺癌是男性最常见的癌症之一。大量研究已经探讨了重金属暴露及其与前列腺癌风险的关联，但联合效应仍存在很大的不确定性。
阐明血清重金属浓度与前列腺癌风险之间的关联。
采用电感耦合等离子体质谱法（ICP-MS）测定 141 例前列腺癌病例和 114 例对照血清样本中 10 种重金属（Mn、Cu、Zn、As、Se、Sb、Co、Cu、Cd 和 Pb）的浓度。线性概率回归模型用于估计血清金属浓度的对数中心化与前列腺癌风险之间的关联的风险差异（RD）和 95%置信区间（CI），并调整潜在混杂因素。贝叶斯核机器回归（BKMR）模型用于解释非线性、交互和联合金属效应。
使用概率回归，未校正模型中四种重金属（As、Zn、Mn、Sb）与前列腺癌风险呈显著正相关。使用 BKMR 分析，当所有其他金属固定在第 25 和 50 百分位数时，As 和 Zn 对前列腺癌风险均有正向风险差异（RD，25%百分位数：As：0.15，Zn：0.19，RD，50%百分位数：As：0.45，Zn：0.37）。此外，当混合物中所有金属的总和均高于第 55 百分位数时，与当所有金属均低于中位数时相比，总体混合物风险差异为正，95%可信区间不包括 0。
总之，我们发现使用 BKMR 模型，在风险差异尺度上，血清中 As 和 Zn 的水平与前列腺癌风险之间存在正相关。总体混合物效应也与前列腺癌风险增加相关。未来的研究需要在前瞻性研究中验证这些发现。
