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

Roberts, Ivayla ORCID: 0000-0003-3525-9787, Muelas, Marina Wright, Taylor, Joseph, Davison, Andrew ORCID: 0000-0001-5501-4475, Xu, Yun, Grixti, Justine ORCID: 0000-0003-1117-2580, Gotts, Nigel, Sorokin, Anatolii, Goodacre, Royston and Kell, Douglas ORCID: 0000-0001-5838-7963 (2020) Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome. 2020.12.09.20246389-.

Text Roberts et al 2022_COVID metabolomics.pdf - Published version Download (2MB) | Preview

Abstract

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. 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). High resolution untargeted LC-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. The predictors were selected for their relevant biological function and include cytosine 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). 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:	4.1 Discovery and preclinical testing of markers and technologies, 2 Aetiology, 4.2 Evaluation of markers and technologies, 4 Detection, screening and diagnosis, 2.1 Biological and endogenous factors, 3 Good Health and Well Being
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User:	Symplectic Admin
Date Deposited:	09 Feb 2022 10:42
Last Modified:	14 Mar 2024 22:30
DOI:	10.1101/2020.12.09.20246389
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3148587