Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi, Jiangsu 214023, China.
Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
Ecotoxicol Environ Saf. 2024 Jul 1;279:116470. doi: 10.1016/j.ecoenv.2024.116470. Epub 2024 May 20.
Several studies have suggested an association between exposure to various metals and the onset of type 2 diabetes (T2D). However, the results vary across different studies. We aimed to investigate the associations between serum metal concentrations and the risk of developing T2D among 8734 participants using a prospective cohort study design. We utilized inductively coupled plasmamass spectrometry (ICP-MS) to assess the serum concentrations of 27 metals. Cox regression was applied to calculate the hazard ratios (HRs) for the associations between serum metal concentrations on the risk of developing T2D. Additionally, 196 incident T2D cases and 208 healthy control participants were randomly selected for serum metabolite measurement using an untargeted metabolomics approach to evaluate the mediating role of serum metabolite in the relationship between serum metal concentrations and the risk of developing T2D with a nested casecontrol study design. In the cohort study, after Bonferroni correction, the serum concentrations of zinc (Zn), mercury (Hg), and thallium (Tl) were positively associated with the risk of developing T2D, whereas the serum concentrations of manganese (Mn), molybdenum (Mo), barium (Ba), lutetium (Lu), and lead (Pb) were negatively associated with the risk of developing T2D. After adding these eight metals, the predictive ability increased significantly compared with that of the traditional clinical model (AUC: 0.791 vs. 0.772, P=8.85×10). In the nested casecontrol study, a machine learning analysis revealed that the serum concentrations of 14 out of 1579 detected metabolites were associated with the risk of developing T2D. According to generalized linear regression models, 7 of these metabolites were significantly associated with the serum concentrations of the identified metals. The mediation analysis showed that two metabolites (2-methyl-1,2-dihydrophthalazin-1-one and mestranol) mediated 46.81% and 58.70%, respectively, of the association between the serum Pb concentration and the risk of developing T2D. Our study suggested that serum Mn, Zn, Mo, Ba, Lu, Hg, Tl, and Pb were associated with T2D risk. Two metabolites mediated the associations between the serum Pb concentration and the risk of developing T2D.
几项研究表明，接触各种金属与 2 型糖尿病（T2D）的发病有关。然而，不同研究的结果存在差异。我们旨在通过前瞻性队列研究设计，调查 8734 名参与者血清金属浓度与 T2D 发病风险之间的关联。我们使用电感耦合等离子体质谱法（ICP-MS）评估血清中 27 种金属的浓度。应用 Cox 回归计算血清金属浓度与 T2D 发病风险之间的风险比（HR）。此外，我们随机选择了 196 例 T2D 病例和 208 例健康对照者进行血清代谢物测量，使用无靶向代谢组学方法评估血清代谢物在血清金属浓度与 T2D 发病风险之间的关系中的中介作用，采用巢式病例对照研究设计。在队列研究中，经 Bonferroni 校正后，血清锌（Zn）、汞（Hg）和铊（Tl）浓度与 T2D 发病风险呈正相关，而血清锰（Mn）、钼（Mo）、钡（Ba）、镥（Lu）和铅（Pb）浓度与 T2D 发病风险呈负相关。加入这 8 种金属后，与传统临床模型相比，预测能力显著提高（AUC：0.791 与 0.772，P=8.85×10）。在巢式病例对照研究中，机器学习分析显示，在 1579 种检测到的代谢物中，有 14 种与 T2D 发病风险相关。根据广义线性回归模型，这 14 种代谢物中有 7 种与所鉴定金属的血清浓度显著相关。中介分析表明，两种代谢物（2-甲基-1,2-二氢邻苯二甲嗪-1-酮和孕二烯酮）分别介导了血清 Pb 浓度与 T2D 发病风险之间 46.81%和 58.70%的关联。本研究表明，血清 Mn、Zn、Mo、Ba、Lu、Hg、Tl 和 Pb 与 T2D 风险相关。两种代谢物介导了血清 Pb 浓度与 T2D 发病风险之间的关联。
