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Multiple biomarkers predict disease severity, progression and mortality in COPD

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by multiple subtypes and variable disease progression. Blood biomarkers have been variably associated with subtype, severity, and disease progression. Just as combined clinical variables are more highly...

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Autores principales: Zemans, Rachel L., Jacobson, Sean, Keene, Jason, Kechris, Katerina, Miller, Bruce E., Tal-Singer, Ruth, Bowler, Russell P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5470282/
https://www.ncbi.nlm.nih.gov/pubmed/28610627
http://dx.doi.org/10.1186/s12931-017-0597-7
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author Zemans, Rachel L.
Jacobson, Sean
Keene, Jason
Kechris, Katerina
Miller, Bruce E.
Tal-Singer, Ruth
Bowler, Russell P.
author_facet Zemans, Rachel L.
Jacobson, Sean
Keene, Jason
Kechris, Katerina
Miller, Bruce E.
Tal-Singer, Ruth
Bowler, Russell P.
author_sort Zemans, Rachel L.
collection PubMed
description BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by multiple subtypes and variable disease progression. Blood biomarkers have been variably associated with subtype, severity, and disease progression. Just as combined clinical variables are more highly predictive of outcomes than individual clinical variables, we hypothesized that multiple biomarkers may be more informative than individual biomarkers to predict subtypes, disease severity, disease progression, and mortality. METHODS: Fibrinogen, C-Reactive Protein (CRP), surfactant protein D (SP-D), soluble Receptor for Advanced Glycation Endproducts (sRAGE), and Club Cell Secretory Protein (CC16) were measured in the plasma of 1465 subjects from the COPDGene cohort and 2746 subjects from the ECLIPSE cohort. Regression analysis was performed to determine whether these biomarkers, individually or in combination, were predictive of subtypes, disease severity, disease progression, or mortality, after adjustment for clinical covariates. RESULTS: In COPDGene, the best combinations of biomarkers were: CC16, sRAGE, fibrinogen, CRP, and SP-D for airflow limitation (p < 10(−4)), SP-D, CRP, sRAGE and fibrinogen for emphysema (p < 10(−3)), CC16, fibrinogen, and sRAGE for decline in FEV(1) (p < 0.05) and progression of emphysema (p < 10(−3)), and all five biomarkers together for mortality (p < 0.05). All associations except mortality were validated in ECLIPSE. The combination of SP-D, CRP, and fibrinogen was the best model for mortality in ECLIPSE (p < 0.05), and this combination was also significant in COPDGene. CONCLUSION: This comprehensive analysis of two large cohorts revealed that combinations of biomarkers improve predictive value compared with clinical variables and individual biomarkers for relevant cross-sectional and longitudinal COPD outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12931-017-0597-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-54702822017-06-19 Multiple biomarkers predict disease severity, progression and mortality in COPD Zemans, Rachel L. Jacobson, Sean Keene, Jason Kechris, Katerina Miller, Bruce E. Tal-Singer, Ruth Bowler, Russell P. Respir Res Research BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by multiple subtypes and variable disease progression. Blood biomarkers have been variably associated with subtype, severity, and disease progression. Just as combined clinical variables are more highly predictive of outcomes than individual clinical variables, we hypothesized that multiple biomarkers may be more informative than individual biomarkers to predict subtypes, disease severity, disease progression, and mortality. METHODS: Fibrinogen, C-Reactive Protein (CRP), surfactant protein D (SP-D), soluble Receptor for Advanced Glycation Endproducts (sRAGE), and Club Cell Secretory Protein (CC16) were measured in the plasma of 1465 subjects from the COPDGene cohort and 2746 subjects from the ECLIPSE cohort. Regression analysis was performed to determine whether these biomarkers, individually or in combination, were predictive of subtypes, disease severity, disease progression, or mortality, after adjustment for clinical covariates. RESULTS: In COPDGene, the best combinations of biomarkers were: CC16, sRAGE, fibrinogen, CRP, and SP-D for airflow limitation (p < 10(−4)), SP-D, CRP, sRAGE and fibrinogen for emphysema (p < 10(−3)), CC16, fibrinogen, and sRAGE for decline in FEV(1) (p < 0.05) and progression of emphysema (p < 10(−3)), and all five biomarkers together for mortality (p < 0.05). All associations except mortality were validated in ECLIPSE. The combination of SP-D, CRP, and fibrinogen was the best model for mortality in ECLIPSE (p < 0.05), and this combination was also significant in COPDGene. CONCLUSION: This comprehensive analysis of two large cohorts revealed that combinations of biomarkers improve predictive value compared with clinical variables and individual biomarkers for relevant cross-sectional and longitudinal COPD outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12931-017-0597-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-13 2017 /pmc/articles/PMC5470282/ /pubmed/28610627 http://dx.doi.org/10.1186/s12931-017-0597-7 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Zemans, Rachel L.
Jacobson, Sean
Keene, Jason
Kechris, Katerina
Miller, Bruce E.
Tal-Singer, Ruth
Bowler, Russell P.
Multiple biomarkers predict disease severity, progression and mortality in COPD
title Multiple biomarkers predict disease severity, progression and mortality in COPD
title_full Multiple biomarkers predict disease severity, progression and mortality in COPD
title_fullStr Multiple biomarkers predict disease severity, progression and mortality in COPD
title_full_unstemmed Multiple biomarkers predict disease severity, progression and mortality in COPD
title_short Multiple biomarkers predict disease severity, progression and mortality in COPD
title_sort multiple biomarkers predict disease severity, progression and mortality in copd
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5470282/
https://www.ncbi.nlm.nih.gov/pubmed/28610627
http://dx.doi.org/10.1186/s12931-017-0597-7
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