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三种统计模型下 NHANES 中血清尿酸与多种金属联合暴露的关系。

Combined exposure to multiple metals on serum uric acid in NHANES under three statistical models.

机构信息

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No. 115, Dong-hu Road, Wuhan 430071, China.

Department of Public Health, Tongji Medical College, Huazhong University of Science and Technology, China.

出版信息

Chemosphere. 2022 Aug;301:134416. doi: 10.1016/j.chemosphere.2022.134416. Epub 2022 Apr 28.

Abstract

BACKGROUND

There are rare researches on the correlations between metals exposure and serum uric acid (SUA), and existing research has only investigated the single metal effect. This study aimed to investigate the combined effects of metal mixtures on SUA and hyperuricemia using three statistical models.

METHODS

In this study, the data were extracted from three cycle years of the National Health and Nutrition Examination Survey (NHANES). Subsequently, generalized linear regression, weighted quantile regression (WQS) and Bayesian kernel machine regression (BKMR) models were fitted to evaluate the correlations between metal mixtures and both SUA and hyperuricemia.

RESULTS

Of 3926 participants included, 19.13% participants had hyperuricemia. It was found using multi-metals generalized linear regression models that there were positive correlations of arsenic and cadmium with both outcomes. The negative correlations were identified in cobalt, iodine, and manganese with SUA concentration, whereas only cobalt was negatively correlated with hyperuricemia. Based on the WQS regression model fitted in positive direction, it was suggested that the WQS indices were significantly correlated with SUA (β = 6.64, 95% CI: 3.14-10.13) and hyperuricemia (OR = 1.25, 95% CI: 1.08-1.44); however, the result achieved by using the model fitted in negative direction indicated that the WQS indices were only significantly correlated with SUA (β = -5.29, 95%CI: 8.02 ∼ -2.56). With the use of the BKMR model, a significant increasing trend between metal mixtures and hyperuricemia was found, while no significant overall effect of metal mixtures on SUA was identified. The predominant roles of arsenic, cadmium, and cobalt in the change of SUA and hyperuricemia risk were found using all three models.

CONCLUSION

The finding of this study revealed that metal mixtures might have a positive combined effect on hyperuricemia. The mutual verification of two outcomes using the three different models provided strong public health implications for protecting people from heavy metal pollution and preventing hyperuricemia.

摘要

背景

目前关于金属暴露与血清尿酸(SUA)之间相关性的研究较少,且现有的研究仅调查了单一金属的影响。本研究旨在使用三种统计模型探讨金属混合物对 SUA 和高尿酸血症的联合作用。

方法

本研究从美国国家健康和营养检查调查(NHANES)的三个周期年中提取数据。随后,使用广义线性回归、加权分位数回归(WQS)和贝叶斯核机器回归(BKMR)模型来评估金属混合物与 SUA 和高尿酸血症之间的相关性。

结果

在纳入的 3926 名参与者中,有 19.13%的参与者患有高尿酸血症。多金属广义线性回归模型发现,砷和镉与这两个结果均呈正相关。钴、碘和锰与 SUA 浓度呈负相关,而仅钴与高尿酸血症呈负相关。基于正向拟合的 WQS 回归模型,表明 WQS 指数与 SUA(β=6.64,95%CI:3.14-10.13)和高尿酸血症(OR=1.25,95%CI:1.08-1.44)显著相关;然而,负向拟合模型的结果表明,WQS 指数仅与 SUA 显著相关(β=-5.29,95%CI:8.02~-2.56)。使用 BKMR 模型发现,金属混合物与高尿酸血症之间存在显著的递增趋势,而金属混合物对 SUA 无显著的总体影响。在所有三种模型中,砷、镉和钴在 SUA 和高尿酸血症风险变化中都起着主要作用。

结论

本研究结果表明,金属混合物可能对高尿酸血症有正向的联合作用。使用三种不同模型对两种结果进行相互验证,为保护人们免受重金属污染和预防高尿酸血症提供了有力的公共卫生意义。

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