Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning

• Published in: PloS one Vol. 19; no. 5; p. e0302977
• Main Authors: Elgedawy, Gamalat A, Samir, Mohamed, Elabd, Naglaa S, Elsaid, Hala H, Enar, Mohamed, Salem, Radwa H, Montaser, Belal A, AboShabaan, Hind S, Seddik, Randa M, El-Askaeri, Shimaa M, Omar, Marwa M, Helal, Marwa L
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30-05-2024; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Amino acids; Analysis; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - blood; Biometry; Blood; Chromatography; Coronaviruses; COVID-19; COVID-19 - blood; COVID-19 - diagnosis; COVID-19 - metabolism; Disease transmission; Ethylenediaminetetraacetic acid; Female; Genomes; Global health; Health aspects; Health care; Humans; Infections; Learning algorithms; Machine Learning; Male; Mass spectrometry; Medicine and Health Sciences; Metabolism; Metabolites; Metabolome; Metabolomics; Metabolomics - methods; Middle Aged; NMR; Nuclear magnetic resonance; Parameters; Patients; Physiological aspects; Prediction models; Public health; Research and Analysis Methods; Respiratory distress syndrome; SARS-CoV-2 - isolation & purification; Scientific imaging; Severe acute respiratory syndrome coronavirus 2; Severity of Illness Index; Signs and symptoms; Tauroursodeoxycholic acid; Ventilation; Ventilators; Viral diseases; World health; China; Taiwan; United States > US; Egypt
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0302977

After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity.
Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models.
The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness.
Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.