Metabolomics has been increasingly utilized in studying host response to
infections and under-standing the progression of multi-system disorders
such as COVID-19. The analysis of metabo-lites in response to SARS-CoV-2
infection provides a snapshot of the endogenous host metabo-lism and its
role in shaping the interaction with SARS-CoV-2. The current study
investigated the metabolic signatures of mortality and severity in
COVID-19 patients using a targeted metabo-lomics approach. Blood plasma
concentrations were quantified through LC-MS using MxP Quant 500 kit. 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 and those with low levels showed statistically significant
differences in survival outcomes. We further used four metabolic markers
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.