TY - JOUR
T1 - Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity
AU - Sindelar, Miriam
AU - Stancliffe, Ethan
AU - Schwaiger-Haber, Michaela
AU - Anbukumar, Dhanalakshmi S.
AU - Adkins-Travis, Kayla
AU - Goss, Charles W.
AU - O'Halloran, Jane A.
AU - Mudd, Philip A.
AU - Liu, Wen Chun
AU - Albrecht, Randy A.
AU - García-Sastre, Adolfo
AU - Shriver, Leah P.
AU - Patti, Gary J.
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/8/17
Y1 - 2021/8/17
N2 - 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.
AB - 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.
KW - COVID-19
KW - SARS-CoV-2
KW - biomarker
KW - lipidomics
KW - longitudinal metabolite profiling
KW - machine learning
KW - metabolomics
KW - severity prediction
KW - untargeted metabolomics
UR - http://www.scopus.com/inward/record.url?scp=85112056592&partnerID=8YFLogxK
U2 - 10.1016/j.xcrm.2021.100369
DO - 10.1016/j.xcrm.2021.100369
M3 - Article
C2 - 34308390
AN - SCOPUS:85112056592
SN - 2666-3791
VL - 2
JO - Cell Reports Medicine
JF - Cell Reports Medicine
IS - 8
M1 - 100369
ER -