Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

Roberts, Ivayla, Muelas, Marina Wright, Taylor, Joseph M, Davison, Andrew S ORCID: 0000-0001-5501-4475, Xu, Yun ORCID: 0000-0003-3228-5111, Grixti, Justine M, Gotts, Nigel, Sorokin, Anatolii, Goodacre, Royston ORCID: 0000-0003-2230-645X and Kell, Douglas B ORCID: 0000-0001-5838-7963
(2022) Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome. METABOLOMICS, 18 (1). 6-.

Access the full-text of this item by clicking on the Open Access link.


<h4>Introduction</h4>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, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.<h4>Objectives</h4>Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased).<h4>Methods</h4>High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. 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 was created.<h4>Results</h4>The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), 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. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86).<h4>Conclusion</h4>Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Item Type: Article
Uncontrolled Keywords: COVID-19, Serum, Untargeted metabolomics, UHPLC-MS, MS, LC-MS, MS Orbitrap
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 10 Jan 2022 14:28
Last Modified: 18 Jan 2023 21:18
DOI: 10.1007/s11306-021-01859-3
Open Access URL: https://link.springer.com/article/10.1007/s11306-0...
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3146125