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Publications

Journal Article Research Support, Non-U.S. Gov't Epublish 61 citations

PubMed: 34282210 DOI: 10.1038/s41598-021-94171-y

Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19.

Sci Rep 11(1):14732

Authors: Yamilé López-Hernández, Joel Monárrez-Espino, Ana-Sofía Herrera-van Oostdam, Julio Enrique Castañeda Delgado, Lun Zhang, Jiamin Zheng, Juan José Oropeza Valdez, Rupasri Mandal, Fátima de Lourdes Ochoa González, Juan Carlos Borrego Moreno, Flor M Trejo-Medinilla, Jesús Adrián López, José Antonio Enciso Moreno, David S Wishart

Affiliations

  • Cátedras-CONACyT, Consejo Nacional de Ciencia y Tecnologia, 03940, México, México.
  • Autonomous University of Zacatecas, 98000, Zacatecas, Mexico.
  • Christus Muguerza Hospital Chihuahua-University of Monterrey, 31000, Chihuahua, Mexico.
  • Faculty of Medicine, Autonomous University of San Luis Potosí, 78210, San Luis Potosi, Mexico.
  • Cátedras-CONACyT, Consejo Nacional de Ciencia y Tecnologia, 03940, México, México. Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, 98000, Zacatecas, México.
  • The Metabolomics Innovation Center, University of Alberta, Edmonton, AB, T6G1C9, Canada.
  • Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, 98000, Zacatecas, México.
  • Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, 98000, Zacatecas, México. Doctorado en Ciencias Básicas, Universidad Autónoma de Zacatecas, Zacatecas, México.
  • Departmento de Epidemiología, Hospital General de Zona #1 "Emilio Varela Luján", Instituto Mexicano del Seguro Social, 98000, Zacatecas, México.
  • Autonomous University of Zacatecas, 98000, Zacatecas, Mexico. Doctorado en Ciencias Básicas, Universidad Autónoma de Zacatecas, Zacatecas, México.
  • MicroRNAs Laboratory, Academic Unit for Biological Sciences, Autonomous University of Zacatecas, 98000, Zacatecas, Mexico.

Abstract

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.

MeSH

Natural Science Disciplines, Diagnosis, Research Design, Plasma, Biomarkers, Pyruvic Acid, Polymerase Chain Reaction, Metabolomics, Covid-19 Testing

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