PDF(Pubmed)
Abstract：
The persistence of coronavirus disease 2019 (COVID-19)-related hospitalization severely threatens medical systems worldwide and has increased the need for reliable detection of acute status and prediction of mortality. We applied a systems biology approach to discover acute-stage biomarkers that could predict mortality. A total 247 plasma samples were collected from 103 COVID-19 (52 surviving COVID-19 patients and 51 COVID-19 patients with mortality), 51 patients with other infectious diseases (IDCs) and 41 healthy controls (HCs). Paired plasma samples were obtained from survival COVID-19 patients within 1 day after hospital admission and 1-3 days before discharge. There were clear differences between COVID-19 patients and controls, as well as substantial differences between the acute and recovery phases of COVID-19. Samples from patients in the acute phase showed suppressed immunity and decreased steroid hormone biosynthesis, as well as elevated inflammation and proteasome activation. These findings were validated by enzyme-linked immunosorbent assays and metabolomic analyses in a larger cohort. Moreover, excessive proteasome activity was a prominent signature in the acute phase among patients with mortality, indicating that it may be a key cause of poor prognosis. Based on these features, we constructed a machine learning panel, including four proteins [C-reactive protein (CRP), proteasome subunit alpha type (PSMA)1, PSMA7, and proteasome subunit beta type (PSMB)1)] and one metabolite (urocortisone), to predict mortality among COVID-19 patients (area under the receiver operating characteristic curve: 0.976) on the first day of hospitalization. Our systematic analysis provides a novel method for the early prediction of mortality in hospitalized COVID-19 patients.
摘要：
2019年冠状病毒病（COVID-19）相关住院的持续存在严重威胁着全球医疗系统，并增加了对可靠检测急性期和预测死亡率的需求。我们应用系统生物学方法来发现可以预测死亡率的急性期生物标志物。共收集了103例COVID-19（52例存活的COVID-19患者和51例死亡的COVID-19患者）的247份血浆样本，51例其他传染病（IDCs）患者和41例健康对照（HCs）。在入院后1天内和出院前1-3天内，从存活的COVID-19患者中获取配对血浆样本。COVID-19患者和对照组之间存在明显差异，以及COVID-19急性期和恢复期之间的实质性差异。急性期患者的样本显示免疫力受到抑制，类固醇激素生物合成减少，以及升高的炎症和蛋白酶体激活。这些发现通过酶联免疫吸附测定和代谢组学分析在更大的队列中得到了验证。此外，过度的蛋白酶体活性是死亡患者急性期的一个显著特征，这可能是预后不良的关键原因。基于这些特征,我们构建了一个机器学习小组，包括四种蛋白质[C反应蛋白（CRP），蛋白酶体亚基α型（PSMA）1，PSMA7和蛋白酶体亚基β型（PSMB）1）]和一种代谢物（尿可的松），预测COVID-19患者住院第一天的死亡率（受试者工作特征曲线下面积：0.976）。我们的系统分析为COVID-19住院患者死亡率的早期预测提供了一种新方法。
