Articles producció científica> Medicina i Cirurgia

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19

  • Identification data

    Identifier: imarina:9291513
  • Authors:

    Baiges-Gaya, G
    Iftimie, S
    Castañé, H
    Rodríguez-Tomàs, E
    Jiménez-Franco, A
    López-Azcona, AF
    Castro, A
    Camps, J
    Joven, J
  • Others:

    Author, as appears in the article.: Baiges-Gaya, G; Iftimie, S; Castañé, H; Rodríguez-Tomàs, E; Jiménez-Franco, A; López-Azcona, AF; Castro, A; Camps, J; Joven, J
    Department: Medicina i Cirurgia
    URV's Author/s: Alkhoury, Nadine / Baiges Gaya, Gerard / Camps Andreu, Jorge / Castañé Vilafranca, Helena / Castro Salomó, Antoni / Iftimie Iftimie, Simona Mihaela / Joven Maried, Jorge / Rodriguez Tomas, Elisabet
    Keywords: Sars-cov-2 Metabolomics Machine learning Enveloped viruses Covid-19 Biomarkers xylitol survival sars-cov-2 multi-omics metabolomics machine learning inactivation disease diagnosis covid-19
    Abstract: Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.
    Thematic Areas: Química Molecular biology Materiais General medicine Farmacia Ensino Biochemistry & molecular biology Biochemistry
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: simonamihaela.iftime@urv.cat jorge.camps@urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat nadine.alkhoury@estudiants.urv.cat helena.castane@estudiants.urv.cat gerard.baiges@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat elisabet.rodriguezt@estudiants.urv.cat jorge.joven@urv.cat antoni.castro@urv.cat
    Author identifier: 0000-0003-0714-8414 0000-0002-3165-3640 0000-0003-2749-4541 0000-0001-5441-6333
    Record's date: 2024-07-20
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.mdpi.com/2218-273X/13/1/163
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Biomolecules. 13 (1): 163-
    APA: Baiges-Gaya, G; Iftimie, S; Castañé, H; Rodríguez-Tomàs, E; Jiménez-Franco, A; López-Azcona, AF; Castro, A; Camps, J; Joven, J (2023). Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19. Biomolecules, 13(1), 163-. DOI: 10.3390/biom13010163
    Article's DOI: 10.3390/biom13010163
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2023
    Publication Type: Journal Publications
  • Keywords:

    Biochemistry,Biochemistry & Molecular Biology,Molecular Biology
    Sars-cov-2
    Metabolomics
    Machine learning
    Enveloped viruses
    Covid-19
    Biomarkers
    xylitol
    survival
    sars-cov-2
    multi-omics
    metabolomics
    machine learning
    inactivation
    disease
    diagnosis
    covid-19
    Química
    Molecular biology
    Materiais
    General medicine
    Farmacia
    Ensino
    Biochemistry & molecular biology
    Biochemistry
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