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Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome.
Roberts, Ivayla; Wright Muelas, Marina; Taylor, Joseph M; Davison, Andrew S; Xu, Yun; Grixti, Justine M; Gotts, Nigel; Sorokin, Anatolii; Goodacre, Royston; Kell, Douglas B.
  • Roberts I; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
  • Wright Muelas M; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK. m.wright-muelas@liverpool.ac.uk.
  • Taylor JM; Department of Clinical Biochemistry and Metabolic Medicine, Liverpool Clinical Laboratories, Royal Liverpool University Hospitals Trust, Liverpool, UK.
  • Davison AS; Department of Clinical Biochemistry and Metabolic Medicine, Liverpool Clinical Laboratories, Royal Liverpool University Hospitals Trust, Liverpool, UK.
  • Xu Y; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
  • Grixti JM; Centre for Metabolomics Research (CMR), Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
  • Gotts N; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
  • Sorokin A; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
  • Goodacre R; Centre for Metabolomics Research (CMR), Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
  • Kell DB; Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
Metabolomics ; 18(1): 6, 2021 12 20.
Article in English | MEDLINE | ID: covidwho-2310631
ABSTRACT

INTRODUCTION:

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.

OBJECTIVES:

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).

METHODS:

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.

RESULTS:

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).

CONCLUSION:

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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Chromatography, Liquid / Tandem Mass Spectrometry / Metabolomics / COVID-19 Type of study: Cohort study / Observational study / Prognostic study / Qualitative research / Randomized controlled trials Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: Metabolomics Year: 2021 Document Type: Article Affiliation country: S11306-021-01859-3

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Chromatography, Liquid / Tandem Mass Spectrometry / Metabolomics / COVID-19 Type of study: Cohort study / Observational study / Prognostic study / Qualitative research / Randomized controlled trials Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: Metabolomics Year: 2021 Document Type: Article Affiliation country: S11306-021-01859-3