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

• Published in: Cell reports. Medicine Vol. 2; no. 8; p. 100369
• Main Authors: Sindelar, Miriam, Stancliffe, Ethan, Schwaiger-Haber, Michaela, Anbukumar, Dhanalakshmi S., Adkins-Travis, Kayla, Goss, Charles W., O’Halloran, Jane A., Mudd, Philip A., Liu, Wen-Chun, Albrecht, Randy A., García-Sastre, Adolfo, Shriver, Leah P., Patti, Gary J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 17-08-2021; Elsevier
• Subjects: Adult; biomarker; Biomarkers - blood; COVID-19; COVID-19 - metabolism; Female; Humans; lipidomics; longitudinal metabolite profiling; Longitudinal Studies; Machine Learning; Male; Metabolome; metabolomics; Metabolomics - methods; Middle Aged; Patient Acuity; Plasma - chemistry; Plasma - metabolism; Prognosis; SARS-CoV-2; SARS-CoV-2 - metabolism; Severity of Illness Index; severity prediction; untargeted metabolomics; COVID-19; SARS-CoV-2; untargeted metabolomics; severity prediction; metabolomics; longitudinal metabolite profiling; biomarker; machine learning; lipidomics
• ISSN: 2666-3791; 2666-3791
• DOI: 10.1016/j.xcrm.2021.100369

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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Longitudinal profiling of plasma during COVID-19 reveals dynamic metabolic changesDecreases in LPC and PC lipids early in the disease course predict severe COVID-19Plasma LPCs and PCs are decreased in hamsters infected with SARS-CoV-2
Sindelar et al. combine untargeted metabolomics analysis of human plasma and machine learning to construct a model that predicts COVID-19 disease severity from the levels of 22 prognostic metabolites. The authors show that these metabolites change early in the disease course and are ultimately restored to control levels upon recovery.