美国西奈山伊坎医学院Eimear E. Kenny课题组提出建立精细的人口健康监测系统的框架。相关论文于2021年4月16日 发表于国际顶尖学术期刊《细胞》。

他们提出了一个框架，用于与基因组数据一起重新利用电子健康记录（EHR）中的数据，以探索可能影响疾病负担的人口统计学联系。使用来自纽约市不同生物库的数据，他们确定了17个具有最新遗传血统的社区。他们观察到1,177个与特定人群在统计学上相关的健康预后，并证明在造成孟德尔疾病的遗传变异分离方面存在显著差异。

他们还证明了良好的人口结构可以影响群体内复杂疾病风险的预测。这项工作加强了将基因组数据链接到EHR的效用，并提供了对人口健康进行精细监测的框架。

据了解，了解人口健康差异是公平精确卫生工作的重要组成部分。流行病学研究通常依赖于种族和民族的定义，但是这些人口标签可能无法充分反映影响特定亚人群的疾病负担和环境因素。

附：英文原文

Title: Toward a fine-scale population health monitoring system

Author: Gillian M. Belbin, Sinead Cullina, Stephane Wenric, Emily R. Soper, Benjamin S. Glicksberg, Denis Torre, Arden Moscati, Genevieve L. Wojcik, Ruhollah Shemirani, Noam D. Beckmann, Ariella Cohain, Elena P. Sorokin, Danny S. Park, Jose-Luis Ambite, Steve Ellis, Adam Auton, Erwin P. Bottinger, Judy H. Cho, Ruth J.F. Loos, Noura S. Abul-Husn, Noah A. Zaitlen, Christopher R. Gignoux, Eimear E. Kenny

Issue&Volume: 2021/04/15

Abstract: Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

DOI: 10.1016/j.cell.2021.03.034

Source: https://www.cell.com/cell/fulltext/S0092-8674(21)00365-2