Journal
CELL REPORTS MEDICINE
Volume 2, Issue 8, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.xcrm.2021.100369
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Funding
- National Institutes of Health [R24OD024624, R35ES2028365]
- Barnes-Jewish Hospital Foundation
- Siteman Cancer Center from the National Cancer Institute of the National Institutes of Health [P30 CA091842]
- Washington University Institute of Clinical and Translational Sciences from the National Center for Advancing Translational Sciences of the National Institutes of Health [UL1TR002345]
- CRIP (Center for Research for Influenza Pathogenesis), a NIAID [HHSN272201400008C]
- NIAID [U19AI135972, U19AI142733]
- CRIPT (Center for Research for Influenza Pathogenesis and Transmission), a NIAID [75N93019R00028]
- NCI [U54CA260560]
- DOD [W81XWH-20-1-0270]
- Defense Advanced Research Projects Agency [HR0011-19-2-0020]
- JPB Foundation
- Open Philanthropy Project [2020-215611 (5384)]
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Through untargeted metabolomics analysis of plasma from COVID-19 patients, a predictive model of disease severity was established based on metabolic profiles, including metabolites directly related to disease progression that return to baseline levels upon recovery. These metabolites were also validated to be altered in a hamster model of COVID-19.
There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.
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