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Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection
This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO c...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635335/ https://www.ncbi.nlm.nih.gov/pubmed/34851990 http://dx.doi.org/10.1371/journal.pone.0259909 |
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author | D’Amora, Paulo Silva, Ismael Dale C. G. Budib, Maria Auxiliadora Ayache, Ricardo Silva, Rafaela Moraes Siufi Silva, Fabricio Colacino Appel, Robson Mateus Júnior, Saturnino Sarat Pontes, Henrique Budib Dorsa Alvarenga, Ana Carolina Arima, Emilli Carvalho Martins, Wellington Galhano Silva, Nakal Laurenço F. Diaz, Ricardo Sobhie Salzgeber, Marcia B. Palma, Anton M. Evans, Steven S. Nagourney, Robert A. |
author_facet | D’Amora, Paulo Silva, Ismael Dale C. G. Budib, Maria Auxiliadora Ayache, Ricardo Silva, Rafaela Moraes Siufi Silva, Fabricio Colacino Appel, Robson Mateus Júnior, Saturnino Sarat Pontes, Henrique Budib Dorsa Alvarenga, Ana Carolina Arima, Emilli Carvalho Martins, Wellington Galhano Silva, Nakal Laurenço F. Diaz, Ricardo Sobhie Salzgeber, Marcia B. Palma, Anton M. Evans, Steven S. Nagourney, Robert A. |
author_sort | D’Amora, Paulo |
collection | PubMed |
description | This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management. |
format | Online Article Text |
id | pubmed-8635335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-86353352021-12-02 Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection D’Amora, Paulo Silva, Ismael Dale C. G. Budib, Maria Auxiliadora Ayache, Ricardo Silva, Rafaela Moraes Siufi Silva, Fabricio Colacino Appel, Robson Mateus Júnior, Saturnino Sarat Pontes, Henrique Budib Dorsa Alvarenga, Ana Carolina Arima, Emilli Carvalho Martins, Wellington Galhano Silva, Nakal Laurenço F. Diaz, Ricardo Sobhie Salzgeber, Marcia B. Palma, Anton M. Evans, Steven S. Nagourney, Robert A. PLoS One Research Article This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management. Public Library of Science 2021-12-01 /pmc/articles/PMC8635335/ /pubmed/34851990 http://dx.doi.org/10.1371/journal.pone.0259909 Text en © 2021 D’Amora et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article D’Amora, Paulo Silva, Ismael Dale C. G. Budib, Maria Auxiliadora Ayache, Ricardo Silva, Rafaela Moraes Siufi Silva, Fabricio Colacino Appel, Robson Mateus Júnior, Saturnino Sarat Pontes, Henrique Budib Dorsa Alvarenga, Ana Carolina Arima, Emilli Carvalho Martins, Wellington Galhano Silva, Nakal Laurenço F. Diaz, Ricardo Sobhie Salzgeber, Marcia B. Palma, Anton M. Evans, Steven S. Nagourney, Robert A. Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection |
title | Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection |
title_full | Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection |
title_fullStr | Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection |
title_full_unstemmed | Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection |
title_short | Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection |
title_sort | towards risk stratification and prediction of disease severity and mortality in covid-19: next generation metabolomics for the measurement of host response to covid-19 infection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635335/ https://www.ncbi.nlm.nih.gov/pubmed/34851990 http://dx.doi.org/10.1371/journal.pone.0259909 |
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