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The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, existing metabolomics studies are either underpowered, measure only a restricted subset of metabolites (\'targeted metabolomics\'), compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. We here provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict patient\'s infection severity (i.e. mild or severe) and potential outcome (i.e. discharged or deceased). High resolution untargeted LC-MS/MS analysis was performed on patient serum using both positive and negative ionization. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model. The predictors were selected for their relevant biological function and include cytosine (reflecting viral load), kynurenine (reflecting host inflammatory response), nicotinuric acid, and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.
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?:doi
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10.1101/2020.12.09.20246389
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document_parses/pdf_json/e13a18010cedbea0055914c90a742d862d1e42b6.json
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Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.
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