高血压的精确环境关联研究
A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models
摘要：
我们考虑的问题，在精确的健康分组的人到亚群的基础上，他们的脆弱性程度的风险因素。这些亚群不能用传统的聚类技术发现，因为它们的质量是用一个有监督的度量来评估的：在它们内部的观察结果上对响应变量建模的容易程度。相反，我们应用监督干部模型（SCM），它确实使用这个指标。我们扩展了SCM的形式主义，使其可以应用于多元回归和二进制分类问题。我们还开发了一种方法来使用条件熵，以评估的过程中，一个主题是分配他们的干部的信心。使用SCM，我们推广了全环境关联研究（EWAS）工作流程，以便能够对人群风险的异质性进行建模。在我们的EWAS中，我们考虑了200多个环境暴露因素，并发现它们与舒张压，收缩压和高血压的相关性。这就要求调整SCM，使其适用于复杂调查设计产生的数据。在纠正假阳性后，我们发现25个暴露变量与我们的至少一个响应变量有显著关联。其中8个是显着的一个发现的亚群，但不是总人口。其中一些关联已经被以前的研究人员确定，而另一些似乎是新的。我们详细研究了几个发现的亚群，我们发现它们是可解释的，它们提出了进一步的研究问题。
英文摘要：
We consider the problem in precision health of grouping people into subpopulations based on their degree of vulnerability to a risk factor. These subpopulations cannot be discovered with traditional clustering techniques because their quality is evaluated with a supervised metric: the ease of modeling a response variable over observations within them. Instead, we apply the supervised cadre model (SCM), which does use this metric. We extend the SCM formalism so that it may be applied to multivariate regression and binary classification problems. We also develop a way to use conditional entropy to assess the confidence in the process by which a subject is assigned their cadre. Using the SCM, we generalize the environment-wide association study (EWAS) workflow to be able to model heterogeneity in population risk. In our EWAS, we consider more than two hundred environmental exposure factors and find their association with diastolic blood pressure, systolic blood pressure, and hypertension. This requires adapting the SCM to be applicable to data generated by a complex survey design. After correcting for false positives, we found 25 exposure variables that had a significant association with at least one of our response variables. Eight of these were significant for a discovered subpopulation but not for the overall population. Some of these associations have been identified by previous researchers, while others appear to be novel. We examine several discovered subpopulations in detail, and we find that they are interpretable and that they suggest further research questions.
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