Summary of Study ST001849

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001166. The data can be accessed directly via it's Project DOI: 10.21228/M80981 This work is supported by NIH grant, U2C- DK119886.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST001849
Study TitleLongitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity (part I)
Study SummaryThere 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 determine disease severity. Through analysis of longitudinal samples, we confirm that the majority of these markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.
Washington University in St. Louis
Last NamePatti
First NameGary
AddressMcMillen Chemistry Laboratory Washington University 1 Brookings Dr @ Throop Drive Rm 102 St. Louis, MO 63130-4899
Submit Date2021-01-29
Num Groups3
Total Subjects339
Num Males184
Num Females155
Raw Data AvailableYes
Raw Data File Type(s)mzML
Analysis Type DetailLC-MS
Release Date2021-06-30
Release Version1
Gary Patti Gary Patti application/zip

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Combined analysis:

Analysis ID AN002993 AN002994 AN002995 AN002996
Analysis type MS MS MS MS
Chromatography type HILIC HILIC Reversed phase Reversed phase
Chromatography system Agilent 1290 Infinity II Agilent 1290 Infinity II Agilent 1290 Infinity II Agilent 1290 Infinity II
Column SeQuant ZIC-pHILIC (100 x 2.1mm,5um) SeQuant ZIC-pHILIC (100 x 2.1mm,5um) Waters ACQUITY UPLC HSS T3 (150 x 2.1mm,1.8um) Waters ACQUITY UPLC HSS T3 (150 x 2.1mm,1.8um)
MS instrument type QTOF QTOF QTOF QTOF
MS instrument name Agilent 6540 QTOF Agilent 6540 QTOF Agilent 6545 QTOF Agilent 6545 QTOF
Units Relative Intensity Relative Intensity Relative Intensity Relative Intensity