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. Supplementary Information The online version contains supplementary material available at 10.1007/s11306-021-01859-3.
【저자키워드】 COVID-19, serum, Untargeted metabolomics, UHPLC-MS/MS, LC–MS/MS Orbitrap, 【초록키워드】 Treatment, metabolomics, Bayesian, severity, Infection, host response, outcome, metabolism, nucleic acid, Cohort, Viral, Viral load, energy metabolism, Patient, Kynurenine, Mild, Hospital admission, cross, metabolites, predictor, predict, metabolite, marker, Analysis, Deceased, COVID-19 patient, ROC Curve, capture, Predictive, high resolution, Planning, among others, deoxycytidine, supplementary material, profile, infected individual, infected individuals, statistical significance, logistic regression model, viral nucleic acid, property, Bayesian logistic regression model, validation study, ROC AUC, host inflammatory response, positive, qualitative detection, blind, UHPLC, patient treatment, Host, approach, objective, Result, selected, predicted, include, was performed, subset, discharged, uninfected control, diagnosis of COVID-19, 【제목키워드】 metabolomics, severity, outcome, Prognostic marker, COVID-19 patient serum, reveal,