Suppr超能文献

五种统计模型下 NHANES 中多种金属联合暴露与心血管疾病的关系。

Combined exposure to multiple metals on cardiovascular disease in NHANES under five statistical models.

机构信息

Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, PR China.

AMITA Health Saint Joseph Hospital Chicago, 2900 N. Lake Shore Drive, Chicago, IL, 60657, USA.

出版信息

Environ Res. 2022 Dec;215(Pt 3):114435. doi: 10.1016/j.envres.2022.114435. Epub 2022 Sep 26.

Abstract

BACKGROUND

It is well-documented that heavy metals are associated with cardiovascular disease (CVD). However, there is few studies exploring effect of metal mixture on CVD. Therefore, the primary objective of present study was to investigate the joint effect of heavy metals on CVD and to identify the most influential metals in the mixture.

METHODS

Original data for study subjects were obtained from the National Health and Nutrition Examination Survey. In this study, adults with complete data on 12 kinds of urinary metals (antimony, arsenic, barium, cadmium, cobalt, cesium, molybdenum, mercury, lead, thallium, tungsten, and uranium), cardiovascular disease, and core covariates were enrolled. We applied five different statistical strategies to examine the CVD risk with metal exposure, including multivariate logistic regression, adaptive elastic net combined with Environmental Risk Score, Quantile g-computation, Weighted Quantile Sum regression, and Bayesian kernel machine regression.

RESULTS

Higher levels of cadmium, tungsten, cobalt, and antimony were significantly associated with Increased risk of CVD when covariates were adjusted for multivariate logistic regression. The results from multi-pollutant strategies all indicated that metal mixture was positively associated with the risk of CVD. Based on the results of multiple statistical strategies, it was determined that cadmium, tungsten, cobalt, and antimony exhibited the strongest positive correlations, whereas barium, lead, molybdenum, and thallium were most associated with negative correlations.

CONCLUSION

Overall, our study demonstrates that exposure to heavy metal mixture is linked to a higher risk of CVD. Meanwhile, this association may be driven primarily by cadmium, tungsten, cobalt, and antimony. Further prospective studies are warranted to validate or refute our primary findings as well as to identify other important heavy metals linked with CVD.

摘要

背景

重金属与心血管疾病(CVD)有关,这已得到充分证明。然而,很少有研究探讨金属混合物对 CVD 的影响。因此,本研究的主要目的是探讨重金属对 CVD 的联合作用,并确定混合物中最具影响力的金属。

方法

本研究的原始数据来自全国健康和营养检查调查。在这项研究中,纳入了 12 种尿液金属(锑、砷、钡、镉、钴、铯、钼、汞、铅、铊、钨和铀)、心血管疾病和核心协变量完整数据的成年人。我们应用了五种不同的统计策略来检查金属暴露与 CVD 风险的关系,包括多变量逻辑回归、自适应弹性网络与环境风险评分相结合、分位数 g 计算、加权分位数总和回归和贝叶斯核机器回归。

结果

在调整多变量逻辑回归中的协变量后,较高水平的镉、钨、钴和锑与 CVD 风险增加显著相关。多污染物策略的结果均表明金属混合物与 CVD 风险呈正相关。基于多种统计策略的结果,确定镉、钨、钴和锑表现出最强的正相关,而钡、铅、钼和铊与负相关最相关。

结论

总的来说,我们的研究表明,重金属混合物的暴露与 CVD 风险的增加有关。同时,这种关联可能主要由镉、钨、钴和锑驱动。需要进一步的前瞻性研究来验证或反驳我们的主要发现,并确定与 CVD 相关的其他重要重金属。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验