覃玲巧, 赵敏, 许琪, 陈伊静, 刘忠典, 何土凤, 钟秋安. 血清尿酸在重金属暴露与代谢综合征关系中的中介效应[J]. 环境与职业医学, 2024, 41(8): 884-891. DOI: 10.11836/JEOM24044
引用本文: 覃玲巧, 赵敏, 许琪, 陈伊静, 刘忠典, 何土凤, 钟秋安. 血清尿酸在重金属暴露与代谢综合征关系中的中介效应[J]. 环境与职业医学, 2024, 41(8): 884-891. DOI: 10.11836/JEOM24044
QIN Lingqiao, ZHAO Min, XU Qi, CHEN Yijing, LIU Zhongdian, HE Tufeng, ZHONG Qiu’an. Mediating effect of serum uric acid on the relationship between heavy metal exposure and metabolic syndrome[J]. Journal of Environmental and Occupational Medicine, 2024, 41(8): 884-891. DOI: 10.11836/JEOM24044
Citation: QIN Lingqiao, ZHAO Min, XU Qi, CHEN Yijing, LIU Zhongdian, HE Tufeng, ZHONG Qiu’an. Mediating effect of serum uric acid on the relationship between heavy metal exposure and metabolic syndrome[J]. Journal of Environmental and Occupational Medicine, 2024, 41(8): 884-891. DOI: 10.11836/JEOM24044

血清尿酸在重金属暴露与代谢综合征关系中的中介效应

Mediating effect of serum uric acid on the relationship between heavy metal exposure and metabolic syndrome

  • 摘要:
    背景 重金属暴露可能与代谢综合征(MetS)风险和血清尿酸相关,血清尿酸在重金属暴露和MetS之间的作用目前尚不清楚。
    目的 探讨重金属暴露与MetS和血清尿酸之间的关系,分析血清尿酸在二者之间的作用。
    方法 2021年在广西柳州采用方便抽样的方法选取571名当地成年人作为研究对象。通过问卷调查、体格检查收集研究对象的一般人口学特征、生活行为习惯及生理生化指标等信息。采集研究对象空腹血液、中段晨尿以检测生化指标,使用电感耦合等离子体质谱仪检测尿液16种重金属浓度。采用最小绝对收缩和选择算子(LASSO)回归筛选与MetS相关的重金属,并使用多因素logistic回归和线性回归模型评估筛选的重金属浓度与MetS风险及血清尿酸的关联,贝叶斯核机器回归(BKMR)模型评估多金属联合暴露对MetS风险的影响,识别主要效应金属。采用广义结构方程模型评估血清尿酸在重金属暴露与MetS风险之间的中介效应。
    结果 LASSO回归共筛选出9种重金属与MetS风险相关。多因素logistic回归显示,尿液中锌和铜浓度与MetS风险呈正相关(P趋势<0.05),钒浓度与MetS风险呈负相关(P趋势<0.05)。与低浓度组相比,锌(OR=2.37,95%CI:1.33~4.20)和铜(OR=2.29,95%CI:1.26~4.18)的高浓度组MetS风险增加,而钒的高浓度组MetS风险降低(OR=0.47,95%CI:0.27~0.84)。BKMR模型识别的主要效应金属与多因素logistic回归结果一致。多因素线性回归显示,尿液中锌和钒浓度与血清尿酸水平相关(P趋势<0.05)。与低浓度组相比,锌的高浓度组血清尿酸水平增加(β=0.07,95%CI:0.03~0.11),钒的高浓度组血清尿酸水平降低(β=−0.06,95%CI:−0.09~−0.02)。中介效应分析显示,血清尿酸在锌和钒浓度与MetS风险的关系中发挥了中介效应,中介效应占比分别为8.33%和16.67%。
    结论 重金属锌、铜和钒暴露与MetS风险密切相关,锌和钒暴露与血清尿酸水平相关,且血清尿酸在锌和钒暴露与MetS风险的关系中发挥了部分中介作用。

     

    Abstract:
    Background Heavy metal exposure may be associated with the risk of metabolic syndrome (MetS) and serum uric acid. The role of serum uric acid in the relationship between heavy metal exposure and MetS is currently unclear.
    Objective To evaluate the relationships of heavy metal exposure with MetS and serum uric acid, and to quantify the role of serum uric acid in the relationship.
    Methods In 2021, convenience sampling was used to select 571 local adults in Liuzhou, Guangxi. Demographic characteristics, lifestyle habits, and physiological and biochemical indicators were collected through questionnaire surveys and physical examinations. Fasting blood and mid-stream morning urine were also collected. The concentrations of 16 heavy metals in urine were measured using inductively coupled plasma mass spectrometry. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify heavy metals associated with MetS. Logistic regression and linear regression models were employed to evaluate the association between the selected heavy metals and MetS as well as serum uric acid. Bayesian kernel machine regression (BKMR) model was utilized to assess the impact of combined exposures to multiple metals on the risk of MetS and identify the main effect metals. Generalized structural equation model was used to evaluate potential mediating effect of serum uric acid on the relationship between heavy metal exposure and MetS.
    Results The LASSO regression identified a total of 9 heavy metals that were associated with MetS. The logistic regression revealed a positive correlation between zinc and copper in urine and MetS (P trend<0.05), while vanadium showed a negative correlation with MetS (P trend<0.05). Compared to the low concentration groups, the high concentration groups of zinc (OR=2.37, 95%CI: 1.33, 4.20) and copper (OR=2.29, 95%CI: 1.26, 4.18) had an increased risk of MetS, while the high concentration group of vanadium showed a decreased risk of MetS (OR=0.47, 95%CI: 0.27, 0.84). The main effect metals identified by the BKMR model were consistent with the results of logistic regression. The linear regression analysis demonstrated an association between urinary zinc and vanadium concentrations and serum uric acid levels (P trend<0.05). Compared to the low concentration group, the high concentration group of zinc showed an increase in serum uric acid level (β=0.07, 95%CI: 0.03, 0.11), while the high concentration group of vanadium showed a decrease in serum uric acid level (β=-0.06, 95%CI: -0.09, -0.02). The mediation analysis revealed that serum uric acid played a mediating role in the relationship between urinary zinc and vanadium concentrations and MetS, with mediation proportions of 8.33% and 16.67%, respectively.
    Conclusion Exposure to heavy metals zinc, copper, and vanadium are closely associated with MetS. Zinc and vanadium exposures are correlated with serum uric acid levels, and serum uric acid plays a partial mediating role in the relationship between zinc and vanadium exposures and MetS.

     

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