Hu Hui, Laden Francine, Hart Jaime, James Peter, Fishe Jennifer, Hogan William, Shenkman Elizabeth, Bian Jiang
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Exposome. 2023 Apr 11;3(1):osad005. doi: 10.1093/exposome/osad005. eCollection 2023 May.
Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals' spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18-24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.
环境暴露与新冠病毒疾病(COVID-19)的严重程度有关。以往的研究仅考察了极少数环境因素,而且往往是单独考察,没有考虑整体环境或暴露组。此外,现有的重症COVID-19风险预测模型主要依赖人口统计学和临床因素。为了填补这些空白,我们开展了一项空间和背景暴露组全关联研究(ExWAS),并利用个体空间和背景暴露组的丰富信息,开发了COVID-19住院的多暴露组评分(PES)。从OneFlorida+临床研究网络获取了2020年3月至2021年10月期间50368名18岁及以上患者的个体层面电子健康记录,这些患者的严重急性呼吸综合征冠状病毒2(SARS-CoV-2)聚合酶链反应/抗原实验室检测呈阳性或被诊断为COVID-19。基于地理编码的居住史,将来自10个数据源的总共194个空间和背景暴露组因素在时空上与每位患者相联系。我们在ExWAS中采用了标准的两阶段程序,并使用梯度提升决策树模型开发和验证了PES。确定了四项与COVID-19住院显著相关的暴露组指标,包括2-氯苯乙酮、食物获取不便、社区贫困以及健身中心使用机会减少。在不考虑暴露组因素的所有患者中,初始预测模型的曲线下检验面积(AUC)为0.778。纳入暴露组数据后,检验AUC增加到0.787。在针对无合并症人群和18 - 24岁人群的亚组分析中也观察到了类似结果。这项关于COVID-19住院的空间和背景暴露组研究证实了先前报道的风险因素,但也产生了值得更深入评估的新预测指标。