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基于铅、镉、汞和砷的暴露模式对 NHANES 2009-2014 人群进行分层,及其与心血管、肾脏和呼吸系统结局的关系。

Stratification of population in NHANES 2009-2014 based on exposure pattern of lead, cadmium, mercury, and arsenic and their association with cardiovascular, renal and respiratory outcomes.

机构信息

School of Public Health Administration, Anhui Medical University, Hefei 230032, Anhui, China.

Genmab US, Inc., Princeton, NJ 08540, USA.

出版信息

Environ Int. 2021 Apr;149:106410. doi: 10.1016/j.envint.2021.106410. Epub 2021 Feb 3.

Abstract

BACKGROUND

Environmental exposure to toxic metals is an important risk factor to human health. Traditional methods have examined associations between a health endpoint and exposure to heavy metals by either univariate or multiple regression. In the setting of ubiquitous heterogeneous environmental exposures, statistical methods that incorporate mixed exposures are increasingly relevant and may provide new insight into the association between metal exposure and important cardiovascular, renal and respiratory outcomes.

OBJECTIVE

The objective of this study was to classify the population of National Health and Nutrition Examination Survey (NHANES) into different exposure subgroups using modern unsupervised clustering methods based on lead, cadmium, mercury, and arsenic measured in urine or whole blood, and to assess the association between the identified exposure groups and twelve important health endpoints.

METHODS

We analyzed a sub-cohort of 9662 subjects participating in the 6 cycles (2003-2004 to 2013-2014) of NHANES study. The urine levels of 3 heavy metals (total arsenic, lead, cadmium) and blood levels of 3 heavy metals (lead, cadmium and mercury) were analyzed using a two-step approach. In the first step, we stratified the population into subgroups using unsupervised clustering (k-medoids) based on levels of metals either in urine or in blood. Then, we examine the association between 12 health endpoints and identified exposure subgroups while controlling for age, sex, race/ethnicity, education, smoking status, BMI, and urinary creatinine.

RESULTS

The k-medoids algorithm clustered NHANES population into 2 groups based on either blood or urinary levels of heavy metals. The concentrations of all the three heavy metals were significantly different between the identified groups in blood (p < 2.2e-16) or in urine (p = 0). The group with higher concentrations was defined as the "high-exposure" group, while the group with lower concentrations was defined as "low-exposure" group. Association analysis with health outcomes suggested that the high-exposure group according to either blood or urinary metal levels had significantly higher total mortality (1.63-1.64 times higher, p < 0.0001), mortality caused by malignant neoplasms (2.05-2.62 times higher, p < 0.0002), Gamma-glutamyl transferase (GGT) (1.03-1.05 times higher, p < 0.0001). In addition, the high-exposure group based on blood levels was also significantly associated with SBP, death related to hypertension, heart disease and chronic lower respiratory disease, while the high-exposure group based on urinary concentrations had higher mortality related to nephritis.

CONCLUSIONS

We proposed an unsupervised clustering method to stratify the population into high- and low-exposure groups based on the co-exposure of heavy metals. The high-exposure groups, characterized by higher metal concentrations, had significant higher GGT, SBP, DBP, and mortality rates suggesting the detrimental effects of exposure to these heavy metals. The stratification of the NHANES population based on exposure patterns provides an informative method to study the impact of metal exposures on health outcomes.

摘要

背景

环境暴露于有毒金属是影响人类健康的一个重要风险因素。传统方法通过单变量或多变量回归检验健康终点与重金属暴露之间的关联。在普遍存在异质环境暴露的情况下,纳入混合暴露的统计方法越来越重要,并且可能为金属暴露与重要心血管、肾脏和呼吸系统结局之间的关联提供新的见解。

目的

本研究的目的是使用基于尿液或全血中测量的铅、镉、汞和砷的现代无监督聚类方法将 NHANES 研究的亚队列人群分为不同的暴露亚组,并评估确定的暴露组与 12 个重要健康终点之间的关联。

方法

我们分析了参加 NHANES 研究 6 个周期(2003-2004 年至 2013-2014 年)的 9662 名受试者的亚队列。使用两步法分析了 3 种重金属(总砷、铅、镉)的尿液水平和 3 种重金属(铅、镉和汞)的血液水平。在第一步中,我们基于金属在尿液或血液中的水平使用无监督聚类(k-均值)将人群分层为亚组。然后,我们在控制年龄、性别、种族/民族、教育程度、吸烟状况、BMI 和尿肌酐的情况下,检查 12 个健康终点与确定的暴露亚组之间的关联。

结果

k-均值算法根据血液或尿液中重金属的水平将 NHANES 人群分为 2 组。在血液(p<2.2e-16)或尿液(p=0)中,两组的所有 3 种重金属的浓度均有显著差异。浓度较高的组定义为“高暴露”组,而浓度较低的组定义为“低暴露”组。与健康结果的关联分析表明,根据血液或尿液中金属水平确定的高暴露组的总死亡率(高 1.63-1.64 倍,p<0.0001)、恶性肿瘤死亡率(高 2.05-2.62 倍,p<0.0002)、γ-谷氨酰转移酶(GGT)(高 1.03-1.05 倍,p<0.0001)均显著升高。此外,基于血液水平的高暴露组还与 SBP、与高血压相关的死亡率、心脏病和慢性下呼吸道疾病显著相关,而基于尿液浓度的高暴露组的死亡率与肾炎相关。

结论

我们提出了一种无监督聚类方法,根据重金属的共同暴露将人群分为高暴露组和低暴露组。以较高金属浓度为特征的高暴露组的 GGT、SBP、DBP 和死亡率均显著升高,提示这些重金属暴露具有有害影响。基于暴露模式对 NHANES 人群进行分层为研究金属暴露对健康结果的影响提供了一种有益的方法。

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