School of Public Health, Ningxia Medical University, No.1160, Shengli Street, Xingqing District, Yinchuan, 750004, People's Republic of China.
Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan Ningxia, 750004, People's Republic of China.
Environ Sci Pollut Res Int. 2023 Oct;30(48):105756-105769. doi: 10.1007/s11356-023-29695-z. Epub 2023 Sep 16.
Previous studies on the association between metals and dyslipidemia are not completely consistent. There are few studies investigating the relationship between mixed metal exposure and dyslipidemia as well as the effects of metals on dyslipidemia in community-dwelling elderly. To evaluate the correlations and interaction effect between the urinary concentrations of metals and the risk of dyslipidemia in community-dwelling elderly. We designed a case-control study to assess the correlation between urine metals and dyslipidemia in elderly people in the Yinchuan. The urinary levels of 13 metals, including calcium, vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, and thallium, were measured by inductively coupled plasma-mass spectrometry (ICP-MS), and the blood biochemical analyzer was used to measure the blood lipid levels of 3384 senior individuals from four different areas of Yinchuan city. Logistic regression and restricted cubic splines (RCS) were used to explore the correlation and dose-response relationship between urinary metals and the risk of dyslipidemia. Least absolute shrinkage and selection operator (LASSO) regression was used to select metals, and then weighted quantile sum (WQS) regression was used to explore the weight of each metal in mixed metals. Bayesian kernel machine regression (BKMR) was used to explore the interactions between metals on dyslipidemia risk. (1) After selection by LASSO regression, in the multi-metal model, compared with the lowest quartile, the adjusted ORs (95%CI) of the highest quartiles were 0.47 (0.37-0.60) for Fe, 1.43 (1.13-1.83) for Zn, 1.46 (1.11-1.92) for As, 0.59 (0.44-0.80) for Se, 1.53 (1.18-2.00) for Mo, and 1.36 (1.07-1.73) for Te. (2) In the WQS regression model, Fe and Mo accounted for the largest weight in the negative and positive effects of dyslipidemia, respectively. (3) In the BKMR model, there may be a positive interaction between Te and Se on dyslipidemia. Among the mixed metals, Fe, As, Se, Mo, and Te were associated with the prevalence of dyslipidemia, with Fe and Mo contributing the most. There may be certain interactions between Te and Se.
先前关于金属与血脂异常之间关联的研究结果并不完全一致。针对混合金属暴露与血脂异常之间的关系以及金属对社区居住老年人血脂异常的影响,相关研究也较少。为了评估社区居住老年人尿液中金属浓度与血脂异常风险之间的相关性和相互作用效应,我们设计了一项病例对照研究,以评估银川老年人尿液中金属与血脂异常之间的相关性。采用电感耦合等离子体质谱法(ICP-MS)测定了 13 种金属(包括钙、钒、铁、钴、锌、铜、砷、硒、钼、镉、碲和铊)的尿液浓度,使用血液生化分析仪测量了来自银川市四个不同地区的 3384 名老年人的血脂水平。采用 logistic 回归和限制性立方样条(RCS)探讨了尿液金属与血脂异常风险之间的相关性和剂量-反应关系。采用最小绝对收缩和选择算子(LASSO)回归选择金属,然后采用加权求和(WQS)回归探讨混合金属中各金属的权重。采用贝叶斯核机器回归(BKMR)探讨了金属对血脂异常风险的相互作用。(1)经过 LASSO 回归选择后,在多金属模型中,与最低四分位相比,最高四分位的调整后比值比(95%CI)分别为铁的 0.47(0.37-0.60)、锌的 1.43(1.13-1.83)、砷的 1.46(1.11-1.92)、硒的 0.59(0.44-0.80)、钼的 1.53(1.18-2.00)和碲的 1.36(1.07-1.73)。(2)在 WQS 回归模型中,铁和钼在血脂异常的负向和正向作用中分别占最大权重。(3)在 BKMR 模型中,碲和硒之间可能存在血脂异常的正相互作用。在混合金属中,铁、砷、硒、钼和碲与血脂异常的患病率相关,其中铁和钼的贡献最大。碲和硒之间可能存在一定的相互作用。