D'Alessandro Angelo, Akpan Imo, Thomas Tiffany, Reisz Julie, Cendali Francesca, Gamboni Fabia, Nemkov Travis, Thangaraju Kiruphagaran, Katneni Upendra, Tanaka Kenichi, Kahn Stacie, Wei Alexander, Valk Jacob, Hudson Krystalyn, Roh David, Moriconi Chiara, Zimring James, Hod Eldad, Spitalnik Steven, Buehler Paul, Francis Richard
University of Colorado Denver.
Columbia University Irving Medical Center.
Res Sq. 2021 May 10:rs.3.rs-480167. doi: 10.21203/rs.3.rs-480167/v1.
The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. Exploratory studies evaluating the impact of COVID-19 infection on the plasma metabolome have been performed, often with small numbers of patients, and with or without relevant control data; however, determining the impact of biological and clinical variables remains critical to understanding potential markers of disease severity and progression. The present large study, including relevant controls, sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831), testing positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on 831 plasma samples from acutely ill patients while in the emergency department, at admission, and during hospitalization. We then performed additional lipidomics analyses of the 60 subjects with the lowest and highest body mass index, either COVID-19 positive or negative. Omics data were correlated to detailed data on patient characteristics and clinical laboratory assays measuring coagulation, hematology and chemistry analytes. Significant changes in arginine/proline/citrulline, tryptophan/indole/kynurenine, fatty acid and acyl-carnitine metabolism emerged as highly relevant markers of disease severity, progression and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half yielding ~ 78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for follow-up mechanistic studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses.
2019年冠状病毒病(COVID-19)大流行是一项持续存在的全球性挑战。已经开展了探索性研究来评估COVID-19感染对血浆代谢组的影响,这些研究的患者数量通常较少,且有或没有相关对照数据;然而,确定生物学和临床变量的影响对于理解疾病严重程度和进展的潜在标志物仍然至关重要。本项大型研究纳入了相关对照,旨在了解急性病患者(n = 831)样本的独立和重叠代谢特征,这些患者COVID-19检测呈阳性(n = 543)或阴性(n = 288)。在急诊科、入院时和住院期间,对831例急性病患者的血浆样本进行高通量代谢组学分析,并辅以抗原和酶活性测定。然后,我们对60名体重指数最低和最高的受试者进行了额外的脂质组学分析,这些受试者COVID-19检测呈阳性或阴性。组学数据与患者特征的详细数据以及测量凝血、血液学和化学分析物的临床实验室检测相关。精氨酸/脯氨酸/瓜氨酸、色氨酸/吲哚/犬尿氨酸、脂肪酸和酰基肉碱代谢的显著变化作为这些患者生物学和临床变量的函数,成为疾病严重程度、进展和预后的高度相关标志物。此外,通过输入COVID-19患者队列一半的所有代谢组学和临床数据来训练机器学习模型,然后在另一半上进行测试,预测准确率约为78%。最后,这项大型前瞻性观察研究积累的大量信息为后续的机制研究和数据共享机会奠定了基础,这将推动我们对COVID-19和其他急性危重病血浆代谢特征的理解。