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Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–Mass spectrometry
Objective: Obstructive sleep apnea (OSA) is considered a major sleep-related breathing problem with an increasing prevalence rate. Retrospective studies have revealed the risk of various comorbidities associated with increased severity of OSA. This study aims to identify novel metabolic biomarkers a...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732946/ https://www.ncbi.nlm.nih.gov/pubmed/36504723 http://dx.doi.org/10.3389/fmolb.2022.1026848 |
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author | Mohit, Tomar, Manendra Singh Araniti, Fabrizio Pateriya, Ankit Singh Kushwaha, Ram Awadh Singh, Bhanu Pratap Jurel, Sunit Kumar Singh, Raghuwar Dayal Shrivastava, Ashutosh Chand, Pooran |
author_facet | Mohit, Tomar, Manendra Singh Araniti, Fabrizio Pateriya, Ankit Singh Kushwaha, Ram Awadh Singh, Bhanu Pratap Jurel, Sunit Kumar Singh, Raghuwar Dayal Shrivastava, Ashutosh Chand, Pooran |
author_sort | Mohit, |
collection | PubMed |
description | Objective: Obstructive sleep apnea (OSA) is considered a major sleep-related breathing problem with an increasing prevalence rate. Retrospective studies have revealed the risk of various comorbidities associated with increased severity of OSA. This study aims to identify novel metabolic biomarkers associated with severe OSA. Methods: In total, 50 cases of OSA patients (49.74 ± 11.87 years) and 30 controls (39.20 ± 3.29 years) were included in the study. According to the polysomnography reports and questionnaire-based assessment, only patients with an apnea–hypopnea index (AHI >30 events/hour) exceeding the threshold representing severe OSA patients were considered for metabolite analysis. Plasma metabolites were analyzed using gas chromatography–mass spectrometry (GC-MS). Results: A total of 92 metabolites were identified in the OSA group compared with the control group after metabolic profiling. Metabolites and their correlated metabolic pathways were significantly altered in OSA patients with respect to controls. The fold-change analysis revealed markers of chronic kidney disease, cardiovascular risk, and oxidative stress-like indoxyl sulfate, 5-hydroxytryptamine, and 5-aminolevulenic acid, respectively, which were significantly upregulated in OSA patients. Conclusion: Identifying these metabolic signatures paves the way to monitor comorbid disease progression due to OSA. Results of this study suggest that blood plasma-based biomarkers may have the potential for disease management. |
format | Online Article Text |
id | pubmed-9732946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97329462022-12-10 Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–Mass spectrometry Mohit, Tomar, Manendra Singh Araniti, Fabrizio Pateriya, Ankit Singh Kushwaha, Ram Awadh Singh, Bhanu Pratap Jurel, Sunit Kumar Singh, Raghuwar Dayal Shrivastava, Ashutosh Chand, Pooran Front Mol Biosci Molecular Biosciences Objective: Obstructive sleep apnea (OSA) is considered a major sleep-related breathing problem with an increasing prevalence rate. Retrospective studies have revealed the risk of various comorbidities associated with increased severity of OSA. This study aims to identify novel metabolic biomarkers associated with severe OSA. Methods: In total, 50 cases of OSA patients (49.74 ± 11.87 years) and 30 controls (39.20 ± 3.29 years) were included in the study. According to the polysomnography reports and questionnaire-based assessment, only patients with an apnea–hypopnea index (AHI >30 events/hour) exceeding the threshold representing severe OSA patients were considered for metabolite analysis. Plasma metabolites were analyzed using gas chromatography–mass spectrometry (GC-MS). Results: A total of 92 metabolites were identified in the OSA group compared with the control group after metabolic profiling. Metabolites and their correlated metabolic pathways were significantly altered in OSA patients with respect to controls. The fold-change analysis revealed markers of chronic kidney disease, cardiovascular risk, and oxidative stress-like indoxyl sulfate, 5-hydroxytryptamine, and 5-aminolevulenic acid, respectively, which were significantly upregulated in OSA patients. Conclusion: Identifying these metabolic signatures paves the way to monitor comorbid disease progression due to OSA. Results of this study suggest that blood plasma-based biomarkers may have the potential for disease management. Frontiers Media S.A. 2022-11-21 /pmc/articles/PMC9732946/ /pubmed/36504723 http://dx.doi.org/10.3389/fmolb.2022.1026848 Text en Copyright © 2022 Mohit, Tomar, Araniti, Pateriya, Singh Kushwaha, Singh, Jurel, Singh, Shrivastava and Chand. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Mohit, Tomar, Manendra Singh Araniti, Fabrizio Pateriya, Ankit Singh Kushwaha, Ram Awadh Singh, Bhanu Pratap Jurel, Sunit Kumar Singh, Raghuwar Dayal Shrivastava, Ashutosh Chand, Pooran Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–Mass spectrometry |
title | Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–Mass spectrometry |
title_full | Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–Mass spectrometry |
title_fullStr | Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–Mass spectrometry |
title_full_unstemmed | Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–Mass spectrometry |
title_short | Identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–Mass spectrometry |
title_sort | identification of metabolic fingerprints in severe obstructive sleep apnea using gas chromatography–mass spectrometry |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732946/ https://www.ncbi.nlm.nih.gov/pubmed/36504723 http://dx.doi.org/10.3389/fmolb.2022.1026848 |
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