ABSTRACT
Background The global healthcare burden of COVID19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and for understanding the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes.Methods In this study, using a targeted metabolomics approach, we studied the metabolic signatures of COVID-19 patients and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, and disease severity and patient outcomes.Results A comparison of survival rates between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH, and indicators of carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods.Conclusions In conclusion, these metabolic predictors of COVID19 can be further validated as potential biomarkers to identify patients at risk of poor outcomes. Finally, integrating machine learning models in metabolome analysis of COVID-19 patients can improve our understanding of disease severity and mortality by providing insights into the relationship between metabolites and the survival probability, which can help lead the development of clinical risk models and potential therapeutic strategies.