Profiling metabolites and lipoproteins in COMETA, an Italian cohort of COVID-19 patients

• Published in: PLoS pathogens Vol. 18; no. 4; p. e1010443
• Main Authors: Ghini, Veronica, Meoni, Gaia, Pelagatti, Lorenzo, Celli, Tommaso, Veneziani, Francesca, Petrucci, Fabrizia, Vannucchi, Vieri, Bertini, Laura, Luchinat, Claudio, Landini, Giancarlo, Turano, Paola
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.04.2022; Public Library of Science (PLoS)
• Subjects: Accuracy; Asymptomatic; Biological markers; Biology and Life Sciences; Biomarkers; Blood lipoproteins; Coronaviruses; COVID-19; Discrimination; Disease transmission; Edetic Acid; Ethylenediaminetetraacetic acids; Healing; Health aspects; Hospitalization; Humans; Identification and classification; Infections; Lipid metabolism; Lipids; Lipoproteins; Medicine and Health Sciences; Metabolites; Metabolomics; Metabolomics - methods; NMR; Nuclear magnetic resonance; Pandemics; Plasma; Plasma spectra; Profiling; Proteolipids; Research and analysis methods; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Ventilators; Viral diseases; Italy
• ISSN: 1553-7374; 1553-7366; 1553-7374
• DOI: 10.1371/journal.ppat.1010443

Metabolomics and lipidomics have been used in several studies to define the biochemical alterations induced by COVID-19 in comparison with healthy controls. Those studies highlighted the presence of a strong signature, attributable to both metabolites and lipoproteins/lipids. Here, 1 H NMR spectra were acquired on EDTA-plasma from three groups of subjects: i) hospitalized COVID-19 positive patients (≤21 days from the first positive nasopharyngeal swab); ii) hospitalized COVID-19 positive patients (>21 days from the first positive nasopharyngeal swab); iii) subjects after 2–6 months from SARS-CoV-2 eradication. A Random Forest model built using the EDTA-plasma spectra of COVID-19 patients ≤21 days and Post COVID-19 subjects, provided a high discrimination accuracy (93.6%), indicating both the presence of a strong fingerprint of the acute infection and the substantial metabolic healing of Post COVID-19 subjects. The differences originate from significant alterations in the concentrations of 16 metabolites and 74 lipoprotein components. The model was then used to predict the spectra of COVID-19>21 days subjects. In this group, the metabolite levels are closer to those of the Post COVID-19 subjects than to those of the COVID-19≤21 days; the opposite occurs for the lipoproteins. Within the acute phase patients, characteristic trends in metabolite levels are observed as a function of the disease severity. The metabolites found altered in COVID-19≤21 days patients with respect to Post COVID-19 individuals overlap with acute infection biomarkers identified previously in comparison with healthy subjects. Along the trajectory towards healing, the metabolome reverts back to the “healthy” state faster than the lipoproteome.