Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography–mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal–Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management.
【저자키워드】 COVID-19, SARS-CoV-2, Infectious disease, Lipidomics, lipids, metabolic phenotyping, liquid chromatography-mass spectrometry (LC-MS), 【초록키워드】 Treatment, respiratory infections, Stratification, SARS-COV-2 infection, Diagnosis, Symptom, sensitivity, specificity, Cohort, Patient, Logistic regression, Clinical management, Lipid, Analysis, AUC, COVID-19 symptom, Precision, profile, healthy control, individual, cohorts, healthy controls, validation cohort, effect size, ROC AUC, predictive power, Kruskal–Wallis test, feature, effective, produced, develop, evaluate, reported, required, applied, was performed, facilitate, Improved, returned, healthy controls;, metabolic effect, SARS-CoV-2-positive individual, 【제목키워드】 application, Potential,