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Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study
Several relatively recently published studies have shown changes in plasma metabolites in various viral diseases such as Zika, Dengue, RSV or SARS-CoV-1. The aim of this study was to analyze the metabolome profile of patients during acute COVID-19 approximately one month after the acute infection an...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9321209/ https://www.ncbi.nlm.nih.gov/pubmed/35888766 http://dx.doi.org/10.3390/metabo12070641 |
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author | Liptak, Peter Baranovicova, Eva Rosolanka, Robert Simekova, Katarina Bobcakova, Anna Vysehradsky, Robert Duricek, Martin Dankova, Zuzana Kapinova, Andrea Dvorska, Dana Halasova, Erika Banovcin, Peter |
author_facet | Liptak, Peter Baranovicova, Eva Rosolanka, Robert Simekova, Katarina Bobcakova, Anna Vysehradsky, Robert Duricek, Martin Dankova, Zuzana Kapinova, Andrea Dvorska, Dana Halasova, Erika Banovcin, Peter |
author_sort | Liptak, Peter |
collection | PubMed |
description | Several relatively recently published studies have shown changes in plasma metabolites in various viral diseases such as Zika, Dengue, RSV or SARS-CoV-1. The aim of this study was to analyze the metabolome profile of patients during acute COVID-19 approximately one month after the acute infection and to compare these results with healthy (SARS-CoV-2-negative) controls. The metabolome analysis was performed by NMR spectroscopy from the peripheral blood of patients and controls. The blood samples were collected on 3 different occasions (at admission, during hospitalization and on control visit after discharge from the hospital). When comparing sample groups (based on the date of acquisition) to controls, there is an indicative shift in metabolomics features based on the time passed after the first sample was taken towards controls. Based on the random forest algorithm, there is a strong discriminatory predictive value between controls and different sample groups (AUC equals 1 for controls versus samples taken at admission, Mathew correlation coefficient equals 1). Significant metabolomic changes persist in patients more than a month after acute SARS-CoV-2 infection. The random forest algorithm shows very strong discrimination (almost ideal) when comparing metabolite levels of patients in two various stages of disease and during the recovery period compared to SARS-CoV-2-negative controls. |
format | Online Article Text |
id | pubmed-9321209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93212092022-07-27 Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study Liptak, Peter Baranovicova, Eva Rosolanka, Robert Simekova, Katarina Bobcakova, Anna Vysehradsky, Robert Duricek, Martin Dankova, Zuzana Kapinova, Andrea Dvorska, Dana Halasova, Erika Banovcin, Peter Metabolites Article Several relatively recently published studies have shown changes in plasma metabolites in various viral diseases such as Zika, Dengue, RSV or SARS-CoV-1. The aim of this study was to analyze the metabolome profile of patients during acute COVID-19 approximately one month after the acute infection and to compare these results with healthy (SARS-CoV-2-negative) controls. The metabolome analysis was performed by NMR spectroscopy from the peripheral blood of patients and controls. The blood samples were collected on 3 different occasions (at admission, during hospitalization and on control visit after discharge from the hospital). When comparing sample groups (based on the date of acquisition) to controls, there is an indicative shift in metabolomics features based on the time passed after the first sample was taken towards controls. Based on the random forest algorithm, there is a strong discriminatory predictive value between controls and different sample groups (AUC equals 1 for controls versus samples taken at admission, Mathew correlation coefficient equals 1). Significant metabolomic changes persist in patients more than a month after acute SARS-CoV-2 infection. The random forest algorithm shows very strong discrimination (almost ideal) when comparing metabolite levels of patients in two various stages of disease and during the recovery period compared to SARS-CoV-2-negative controls. MDPI 2022-07-13 /pmc/articles/PMC9321209/ /pubmed/35888766 http://dx.doi.org/10.3390/metabo12070641 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liptak, Peter Baranovicova, Eva Rosolanka, Robert Simekova, Katarina Bobcakova, Anna Vysehradsky, Robert Duricek, Martin Dankova, Zuzana Kapinova, Andrea Dvorska, Dana Halasova, Erika Banovcin, Peter Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study |
title | Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study |
title_full | Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study |
title_fullStr | Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study |
title_full_unstemmed | Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study |
title_short | Persistence of Metabolomic Changes in Patients during Post-COVID Phase: A Prospective, Observational Study |
title_sort | persistence of metabolomic changes in patients during post-covid phase: a prospective, observational study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9321209/ https://www.ncbi.nlm.nih.gov/pubmed/35888766 http://dx.doi.org/10.3390/metabo12070641 |
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