Quantitative LC–MS study of compounds found predictive of COVID-19 severity and outcome

Abstract

Abstract Introduction Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients. Objectives In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome. Methods A targeted LC–MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3′,4′-didehydro-3′-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients’ levels. Results & conclusion Finally, we demonstrate the added value of the kynurenic acid/tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.