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 - Funding Information:
This work was supported by funding from the National Institutes of Health grants R24OD024624 (G.J.P.) and R35ES2028365 (G.J.P.). This study used samples obtained from the Washington University School of Medicine’s COVID-19 biorepository, which is supported by the Barnes-Jewish Hospital Foundation ; the Siteman Cancer Center grant P30 CA091842 from the National Cancer Institute of the National Institutes of Health; and the Washington University Institute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the view of the NIH. This work was also partly supported by CRIP ( Center for Research for Influenza Pathogenesis ), a NIAID supported Center of Excellence for Influenza Research and Surveillance (CEIRS, contract HHSN272201400008C ; W.-C.L., R.A.A., and A.G.-S.); by CRIPT (Center for Research for Influenza Pathogenesis and Transmission), a NIAID supported Center of Excellence for Influenza Research and Response (CEIRR, contract 75N93019R00028; R.A.A and A.G.-S.); by NIAID grant U19AI135972 ; by NCI grant U54CA260560 ; by supplements to NIAID grants U19AI135972 , U19AI142733 , and DOD grant W81XWH-20-1-0270 ; by the Defense Advanced Research Projects Agency ( HR0011-19-2-0020 ); by the generous support of the JPB Foundation and the Open Philanthropy Project (research grant 2020-215611 (5384)) ; and by anonymous donors (A.G.-S.). The graphical abstract, Figure 1 C, and Figure 6 A were created with BioRender.com .
Funding Information:
The A.G.-S. laboratory has received research support from Pfizer, Pharmamar, Blade Therapeutics, Avimex, Dynavax, Kenall Manufacturing, ImmunityBio, Nanocomposix, Senhwa Biosciences, and 7Hills Pharma. A.G.-S. has consulting agreements for the following companies involving cash and/or stock: Vivaldi Biosciences, Contrafect, 7 Hills Pharma, Avimex, Vaxalto, Accurius, and Esperovax. G.J.P. is a scientific advisor for Cambridge Isotope Laboratories. The Patti laboratory has a collaboration agreement with Agilent Technologies and Thermo Fisher Scientific. All other authors declare no competing interests.
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 -