Objectives

An environment-wide association study (EWAS) may be useful to comprehensively test and validate associations between environmental factors and cardiovascular disease (CVD) in an unbiased manner.

Approach and results

Data from National Health and Nutrition Examination Survey (1999–2014) were randomly 50:50 spilt into training set and testing set. CVD was ascertained by a self-reported diagnosis of myocardial infarction, coronary heart disease or stroke. We performed multiple linear regression analyses associating 203 environmental factors and 132 clinical phenotypes with CVD in training set (false discovery rate < 5%) and significant factors were validated in the testing set (P < 0.05). Random forest (RF) model was used for multicollinearity elimination and variable importance ranking. Discriminative power of factors for CVD was calculated by area under the receiver operating characteristic (AUROC). Overall, 43,568 participants with 4084 (9.4%) CVD were included. After adjusting for age, sex, race, body mass index, blood pressure and socio-economic level, we identified 5 environmental variables and 19 clinical phenotypes associated with CVD in training and testing dataset. Top five factors in RF importance ranking were: waist, glucose, uric acid, and red cell distribution width and glycated hemoglobin. AUROC of the RF model was 0.816 (top 5 factors) and 0.819 (full model). Sensitivity analyses reveal no specific moderators of the associations.

Conclusion

Our systematic evaluation provides new knowledge on the complex array of environmental correlates of CVD. These identified correlates may serve as a complementary approach to CVD risk assessment. Our findings need to be probed in further observational and interventional studies.

中文翻译：

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从全景角度看环境因素与心血管疾病之间的关联：《 1999-2014年美国国家健康与营养调查》中的一项全环境关联研究

目标

整个环境的关联研究（EWAS）对于以无偏见的方式全面测试和验证环境因素与心血管疾病（CVD）之间的关联可能很有用。

方法和结果

来自国家健康和营养检查调查（1999-2014）的数据被随机以50:50的比例散布到训练集和测试集中。通过自我报告的心肌梗塞，冠心病或中风的诊断来确定CVD。我们进行了多元线性回归分析，将203种环境因素和132种临床表型与CVD中的训练集相关联（错误发现率<5％），并且在测试集中验证了重要因素（P <0.05）。随机森林（RF）模型用于多重共线性消除和变量重要性排序。通过接收器工作特性（AUROC）下的面积来计算CVD因子的判别力。总体而言，纳入了43568名具有4084（9.4％）CVD的参与者。在调整了年龄，性别，种族，体重指数，血压和社会经济水平之后，我们在训练和测试数据集中确定了5种与CVD相关的环境变量和19种临床表型。射频重要性排名的前五位因素是：腰围，葡萄糖，尿酸，红细胞分布宽度和糖化血红蛋白。RF模型的AUROC为0.816（前5个因素）和0.819（完整模型）。敏感性分析表明，没有特定的协会主持人。

结论

我们的系统评估为CVD与环境相关的复杂因素提供了新的知识。这些确定的相关因素可以作为CVD风险评估的补充方法。我们的发现需要在进一步的观察性和介入性研究中进行探索。