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

https://doi.org/10.1016/j.xcrm.2021.100369Get rights and content
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open access

Highlights

  • Longitudinal profiling of plasma during COVID-19 reveals dynamic metabolic changes

  • Decreases in LPC and PC lipids early in the disease course predict severe COVID-19

  • Plasma LPCs and PCs are decreased in hamsters infected with SARS-CoV-2

Summary

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.

Keywords

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

Data and code availability

The raw LC/MS data as well as the processed metabolic profiles and corresponding metadata for the human (deidentified) and animal samples is publicly available on the Metabolomics Workbench repository (NMRD:ST001849, ST001853; https://doi.org/10.21228/M80981). Custom code used to perform the ML analyses is available on GitHub (https://github.com/e-stan/covid_19_analysis)

Cited by (0)

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Present address: Biomedical Translation Research Center, Academia Sinica, Taipei 11571, Taiwan

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These authors contributed equally

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These authors contributed equally

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