Title : Metabolic predictors of COVID-19 mortality and severity: A survival analysis study
Abstract:
Metabolomics has been increasingly utilized in studying the host response to viral infections and for understanding the progression of multi-system disorders such as COVID-19. The analysis of metabolites in response to SARS-CoV-2 infection provides a snapshot of the endogenous host metabolism and its role in shaping the interaction with SARS-CoV-2. In this study, using a targeted metabolomics approach, the metabolic signatures of mortality and severity were studied in COVID-19 patients. Blood plasma concentrations were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including different classes of lipids and small organic molecules. We utilized Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers and patient outcomes. 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 showed statistically significant differences in survival outcomes. We further used four 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. These metabolic predictors 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 mortality by providing insight into the relationship between metabolites and survival probability, which can lead to the development of potential therapeutics and clinical risk models.
