Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19
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 assessin...
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2021-07-01
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doaj-8ff8b043cd734a649ee74f44c69b07b62021-07-25T11:22:56ZengNature Publishing GroupScientific Reports2045-23222021-07-0111111310.1038/s41598-021-94171-yTargeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19Yamilé López-Hernández0Joel Monárrez-Espino1Ana-Sofía Herrera-van Oostdam2Julio Enrique Castañeda Delgado3Lun Zhang4Jiamin Zheng5Juan José Oropeza Valdez6Rupasri Mandal7Fátima de Lourdes Ochoa González8Juan Carlos Borrego Moreno9Flor M. Trejo-Medinilla10Jesús Adrián López11José Antonio Enciso Moreno12David S. Wishart13Cátedras-CONACyT, Consejo Nacional de Ciencia y TecnologiaChristus Muguerza Hospital Chihuahua-University of MonterreyFaculty of Medicine, Autonomous University of San Luis PotosíCátedras-CONACyT, Consejo Nacional de Ciencia y TecnologiaThe Metabolomics Innovation Center, University of AlbertaThe Metabolomics Innovation Center, University of AlbertaUnidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro SocialThe Metabolomics Innovation Center, University of AlbertaUnidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro SocialDepartmento de Epidemiología, Hospital General de Zona #1 “Emilio Varela Luján”, Instituto Mexicano del Seguro SocialAutonomous University of ZacatecasMicroRNAs Laboratory, Academic Unit for Biological Sciences, Autonomous University of ZacatecasUnidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro SocialThe Metabolomics Innovation Center, University of AlbertaAbstract 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.https://doi.org/10.1038/s41598-021-94171-y |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
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 |
spellingShingle |
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 Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19 Scientific Reports |
author_facet |
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 |
author_sort |
Yamilé López-Hernández |
title |
Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19 |
title_short |
Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19 |
title_full |
Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19 |
title_fullStr |
Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19 |
title_full_unstemmed |
Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19 |
title_sort |
targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for covid-19 |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-07-01 |
description |
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. |
url |
https://doi.org/10.1038/s41598-021-94171-y |
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