Abstract
BACKGROUND: The metabolomic profiles of individuals with different clinical manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have not been clearly characterized. METHODS: We performed metabolomics analysis of 166 individuals, including 62 healthy controls, 16 individuals with asymptomatic SARS-CoV-2 infection, and 88 patients with moderate (n = 42) and severe (n = 46) symptomatic 2019 coronavirus disease (COVID-19; 17 with short-term and 34 with long-term nucleic-acid test positivity). By examining differential expression, we identified candidate metabolites associated with different SARS-CoV-2 infection presentations. Functional and machine learning analyses were performed to explore the metabolites' functions and verify their candidacy as biomarkers. RESULTS: A total of 417 metabolites were detected. We discovered 70 differentially expressed metabolites that may help differentiate asymptomatic infections from healthy controls and COVID-19 patients with different disease severity. Cyclamic acid and N-Acetylneuraminic Acid were identified to distinguish symptomatic infected patients and asymptomatic infected patients. Shikimic Acid, Glycyrrhetinic acid and 3-Hydroxybutyrate can supply significant insights for distinguishing short-term and long-term nucleic-acid test positivity. CONCLUSION: Metabolomic profiling may highlight novel biomarkers for the identification of individuals with asymptomatic SARS-CoV-2 infection and further our understanding of the molecular pathogenesis of COVID-19.