Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

Miriam Sindelar, Ethan Stancliffe, Michaela Schwaiger-Haber, Dhanalakshmi S. Anbukumar, Kayla Adkins-Travis, Charles W. Goss, Jane A. O'Halloran, Philip A. Mudd, Wen Chun Liu, Randy A. Albrecht, Adolfo García-Sastre, Leah P. Shriver, Gary J. Patti

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish
Article number100369
JournalCell Reports Medicine
Volume2
Issue number8
DOIs
StatePublished - Aug 17 2021

Keywords

  • COVID-19
  • SARS-CoV-2
  • biomarker
  • lipidomics
  • longitudinal metabolite profiling
  • machine learning
  • metabolomics
  • severity prediction
  • untargeted metabolomics

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