Asymptomatic infection of COVID-19 is a global threat for public health. Unfortunately, the study about metabolic dysregulation of asymptomatic infection is barely investigated. Here, we performed carboxylic submetabolome profiling of serum from 62 asymptomatic and 122 control individuals, by a highly sensitive chemical isotope labelling method. Twenty-one discriminative carboxylic features, including 12-hydroxyeicosatetraenoic acid, cholic acid, glycoursodeoxycholic acid and 15,16-dihydroxyoctadeca-9,12-dienoic acid were discovered to be dysregulated in asymptomatic patients. This panel containing 21 carboxylic features could accurately identify asymptomatic patients based on a random forest model, providing an accuracy of 85.7% with only 3.6% false positive rate and 7.1% false negative rate. The dysregulated metabolites found in asymptomatic patients covered several important pathways, such as arachidonic acid metabolism, synthesis of bile acid, β-oxidation of fatty acids, activation of macrophage and platelet aggregation. This work provided valuable knowledge about serum biomarkers and molecular clues associated with asymptomatic COVID-19 patients.
Keywords: COVID-19 asymptomatic infection; Carboxylic metabolic dysregulation; Chemical isotope labelling; Machine learning.
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