Research exploring the development and outcome of COVID-19 infections has led to the need to find better diagnostic and prognostic biomarkers. This cross-sectional study used targeted metabolomics to identify potential COVID-19 biomarkers that predicted the course of the illness by assessing 110 endogenous plasma metabolites from individuals admitted to a local hospital for diagnosis/treatment. Patients were classified into four groups (≈ 40 each) according to standard polymerase chain reaction (PCR) COVID-19 testing and disease course: PCR−/controls (i.e., non-COVID controls), PCR+/not-hospitalized, PCR+/hospitalized, and PCR+/intubated. Blood samples were collected within 2 days of admission/PCR testing. Metabolite concentration data, demographic data and clinical data were used to propose biomarkers and develop optimal regression models for the diagnosis and prognosis of COVID-19. The area under the receiver operating characteristic curve (AUC; 95% CI) was used to assess each models’ predictive value. A panel that included the kynurenine: tryptophan ratio, lysoPC a C26:0, and pyruvic acid discriminated non-COVID controls from PCR+/not-hospitalized (AUC = 0.947; 95% CI 0.931–0.962). A second panel consisting of C10:2, butyric acid, and pyruvic acid distinguished PCR+/not-hospitalized from PCR+/hospitalized and PCR+/intubated (AUC = 0.975; 95% CI 0.968–0.983). Only lysoPC a C28:0 differentiated PCR+/hospitalized from PCR+/intubated patients (AUC = 0.770; 95% CI 0.736–0.803). If additional studies with targeted metabolomics confirm the diagnostic value of these plasma biomarkers, such panels could eventually be of clinical use in medical practice.
【저자키워드】 Biomarkers, Biochemistry, Microbiology, Computational biology and bioinformatics, Chemical biology, 【초록키워드】 COVID-19, Prognostic biomarkers, metabolomics, Biomarker, Biomarkers, Prognosis, cross-sectional, hospital, diagnostic, Diagnosis, Local, outcome, Predictive value, polymerase chain reaction, PCR testing, COVID-19 testing, cross-sectional study, PCR, COVID-19 infection, Regression model, Patient, Control, Tryptophan, Kynurenine, plasma, prognostic, characteristic, disease, metabolite, Concentration, Predictive, receiver operating characteristic, Chain Reaction, Clinical use, Intubated, pyruvic acid, butyric acid, 95% CI, Blood samples, Clinical data, COVID-19 infections, individual, diagnostic value, demographic data, blood sample, disease course, four groups, receiver, controls, polymerase chain, Course, predicted, identify, was used, collected, develop, were used, four group, 【제목키워드】 COVID-19, metabolomics, diagnostic, prognostic biomarker, identify,