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Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review
INTRODUCTION: Metabolomics is an emerging area of research and has the potential to identify clinical biomarkers for predicting or diagnosing cystic fibrosis (CF) pulmonary exacerbations (PEx). OBJECTIVE: To identify clinically promising metabolites across different sample sources that can be used t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192952/ https://www.ncbi.nlm.nih.gov/pubmed/35712620 http://dx.doi.org/10.3389/fped.2022.896439 |
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author | Nguyen, Anna-Lisa V. Haas, Dominic Bouchard, Mégane Quon, Bradley S. |
author_facet | Nguyen, Anna-Lisa V. Haas, Dominic Bouchard, Mégane Quon, Bradley S. |
author_sort | Nguyen, Anna-Lisa V. |
collection | PubMed |
description | INTRODUCTION: Metabolomics is an emerging area of research and has the potential to identify clinical biomarkers for predicting or diagnosing cystic fibrosis (CF) pulmonary exacerbations (PEx). OBJECTIVE: To identify clinically promising metabolites across different sample sources that can be used to predict or diagnose PEx in CF. EVIDENCE REVIEW: Searches for original literature were completed through EMBASE, MEDLINE, and all databases on the Web of Science with no restrictions on language or publication date. Gray literature was collected through Google Scholar. Additional studies were obtained by contacting authors and searching reference lists of candidate papers. The patient population included individuals with CF. Studies involving patients who underwent lung transplantation were excluded. The outcome was the prediction or diagnosis of pulmonary exacerbations from metabolites directly measured from biological samples. Search results were downloaded and imported into Covidence and duplicates were removed automatically. Any remaining duplicates were manually tagged and excluded. Two independent reviewers screened each abstract for eligibility and repeated this process for full texts. Risk of bias was conducted using QUADAS-2 by two independent reviewers. A third author resolved any remaining conflicts. RESULTS: A combined 3974 relevant abstracts were identified and 115 full texts were assessed for eligibility. The final 25 studies underwent data extraction for study design, patient demographics, studied metabolites, concentration values, and diagnostic accuracy values. Included studies differed considerably in methodologies, sample specimen types (exhaled breath condensate [EBC], sputum, saliva, plasma, urine), and disease states. We identified 19 unique metabolites that were measured by two or more studies of which 2 have the potential to predict PEx (EBC 4-hydroxycyclohexylcarboxylic acid [4-HCHC] and lactic acid) and 6 to diagnose PEx (EBC 4-HCHC and lactic acid, sputum lactic acid and nitrate, and plasma arginine and methionine). CONCLUSION AND RELEVANCE: This systematic review has identified promising metabolites for further study in CF. Certain metabolites may provide clinical potential in predicting or diagnosing PEx, but further validation studies are required. With better tools to aid in the earlier identification of PEx, clinicians can implement preventative measures to mitigate airway damage. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/ |
format | Online Article Text |
id | pubmed-9192952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91929522022-06-15 Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review Nguyen, Anna-Lisa V. Haas, Dominic Bouchard, Mégane Quon, Bradley S. Front Pediatr Pediatrics INTRODUCTION: Metabolomics is an emerging area of research and has the potential to identify clinical biomarkers for predicting or diagnosing cystic fibrosis (CF) pulmonary exacerbations (PEx). OBJECTIVE: To identify clinically promising metabolites across different sample sources that can be used to predict or diagnose PEx in CF. EVIDENCE REVIEW: Searches for original literature were completed through EMBASE, MEDLINE, and all databases on the Web of Science with no restrictions on language or publication date. Gray literature was collected through Google Scholar. Additional studies were obtained by contacting authors and searching reference lists of candidate papers. The patient population included individuals with CF. Studies involving patients who underwent lung transplantation were excluded. The outcome was the prediction or diagnosis of pulmonary exacerbations from metabolites directly measured from biological samples. Search results were downloaded and imported into Covidence and duplicates were removed automatically. Any remaining duplicates were manually tagged and excluded. Two independent reviewers screened each abstract for eligibility and repeated this process for full texts. Risk of bias was conducted using QUADAS-2 by two independent reviewers. A third author resolved any remaining conflicts. RESULTS: A combined 3974 relevant abstracts were identified and 115 full texts were assessed for eligibility. The final 25 studies underwent data extraction for study design, patient demographics, studied metabolites, concentration values, and diagnostic accuracy values. Included studies differed considerably in methodologies, sample specimen types (exhaled breath condensate [EBC], sputum, saliva, plasma, urine), and disease states. We identified 19 unique metabolites that were measured by two or more studies of which 2 have the potential to predict PEx (EBC 4-hydroxycyclohexylcarboxylic acid [4-HCHC] and lactic acid) and 6 to diagnose PEx (EBC 4-HCHC and lactic acid, sputum lactic acid and nitrate, and plasma arginine and methionine). CONCLUSION AND RELEVANCE: This systematic review has identified promising metabolites for further study in CF. Certain metabolites may provide clinical potential in predicting or diagnosing PEx, but further validation studies are required. With better tools to aid in the earlier identification of PEx, clinicians can implement preventative measures to mitigate airway damage. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/ Frontiers Media S.A. 2022-05-31 /pmc/articles/PMC9192952/ /pubmed/35712620 http://dx.doi.org/10.3389/fped.2022.896439 Text en Copyright © 2022 Nguyen, Haas, Bouchard and Quon. 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 | Pediatrics Nguyen, Anna-Lisa V. Haas, Dominic Bouchard, Mégane Quon, Bradley S. Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review |
title | Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review |
title_full | Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review |
title_fullStr | Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review |
title_full_unstemmed | Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review |
title_short | Metabolomic Biomarkers to Predict and Diagnose Cystic Fibrosis Pulmonary Exacerbations: A Systematic Review |
title_sort | metabolomic biomarkers to predict and diagnose cystic fibrosis pulmonary exacerbations: a systematic review |
topic | Pediatrics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9192952/ https://www.ncbi.nlm.nih.gov/pubmed/35712620 http://dx.doi.org/10.3389/fped.2022.896439 |
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