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Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters
BACKGROUND: Although the heterogeneous nature of asthma has prompted asthma phenotyping with physiological or biomarker data, these studies have been mostly cross-sectional. Longitudinal studies that assess the stability of phenotypes based on a combination of physiological, clinical and biomarker d...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4862112/ https://www.ncbi.nlm.nih.gov/pubmed/27165150 http://dx.doi.org/10.1186/s12890-016-0232-2 |
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author | Zaihra, T. Walsh, C. J. Ahmed, S. Fugère, C. Hamid, Q. A. Olivenstein, R. Martin, J. G. Benedetti, A. |
author_facet | Zaihra, T. Walsh, C. J. Ahmed, S. Fugère, C. Hamid, Q. A. Olivenstein, R. Martin, J. G. Benedetti, A. |
author_sort | Zaihra, T. |
collection | PubMed |
description | BACKGROUND: Although the heterogeneous nature of asthma has prompted asthma phenotyping with physiological or biomarker data, these studies have been mostly cross-sectional. Longitudinal studies that assess the stability of phenotypes based on a combination of physiological, clinical and biomarker data are currently lacking. Our objective was to assess the longitudinal stability of clusters derived from repeated measures of airway and physiological data over a 1-year period in moderate and severe asthmatics. METHODS: A total of 125 subjects, 48 with moderate asthma (MA) and 77 with severe asthma (SA) were evaluated every 3 months and monthly, respectively, over a 1-year period. At each 3-month time point, subjects were grouped into 4 asthma clusters (A, B, C, D) based on a combination of clinical (duration of asthma), physiological (FEV(1) and BMI) and biomarker (sputum eosinophil count) variables, using k-means clustering. RESULTS: Majority of subjects in clusters A and C had severe asthma (93 % of subjects in cluster A and 79.5 % of subjects in cluster C at baseline). Overall, a total of 59 subjects (47 %) had stable cluster membership, remaining in clusters with the same subjects at each evaluation time. Cluster A was the least stable (21 % stability) and cluster B was the most stable cluster (71 % stability). Cluster stability was not influenced by changes in the dosage of inhaled corticosteroids. CONCLUSION: Asthma phenotyping based on clinical, physiologic and biomarker data identified clusters with significant differences in longitudinal stability over a 1-year period. This finding indicates that the majority of patients within stable clusters can be phenotyped with reasonable accuracy after a single measurement of lung function and sputum eosinophilia, while patients in unstable clusters will require more frequent evaluation of these variables to be properly characterized. |
format | Online Article Text |
id | pubmed-4862112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48621122016-05-11 Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters Zaihra, T. Walsh, C. J. Ahmed, S. Fugère, C. Hamid, Q. A. Olivenstein, R. Martin, J. G. Benedetti, A. BMC Pulm Med Research Article BACKGROUND: Although the heterogeneous nature of asthma has prompted asthma phenotyping with physiological or biomarker data, these studies have been mostly cross-sectional. Longitudinal studies that assess the stability of phenotypes based on a combination of physiological, clinical and biomarker data are currently lacking. Our objective was to assess the longitudinal stability of clusters derived from repeated measures of airway and physiological data over a 1-year period in moderate and severe asthmatics. METHODS: A total of 125 subjects, 48 with moderate asthma (MA) and 77 with severe asthma (SA) were evaluated every 3 months and monthly, respectively, over a 1-year period. At each 3-month time point, subjects were grouped into 4 asthma clusters (A, B, C, D) based on a combination of clinical (duration of asthma), physiological (FEV(1) and BMI) and biomarker (sputum eosinophil count) variables, using k-means clustering. RESULTS: Majority of subjects in clusters A and C had severe asthma (93 % of subjects in cluster A and 79.5 % of subjects in cluster C at baseline). Overall, a total of 59 subjects (47 %) had stable cluster membership, remaining in clusters with the same subjects at each evaluation time. Cluster A was the least stable (21 % stability) and cluster B was the most stable cluster (71 % stability). Cluster stability was not influenced by changes in the dosage of inhaled corticosteroids. CONCLUSION: Asthma phenotyping based on clinical, physiologic and biomarker data identified clusters with significant differences in longitudinal stability over a 1-year period. This finding indicates that the majority of patients within stable clusters can be phenotyped with reasonable accuracy after a single measurement of lung function and sputum eosinophilia, while patients in unstable clusters will require more frequent evaluation of these variables to be properly characterized. BioMed Central 2016-05-10 /pmc/articles/PMC4862112/ /pubmed/27165150 http://dx.doi.org/10.1186/s12890-016-0232-2 Text en © Zaihra et al. 2016 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 Article Zaihra, T. Walsh, C. J. Ahmed, S. Fugère, C. Hamid, Q. A. Olivenstein, R. Martin, J. G. Benedetti, A. Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters |
title | Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters |
title_full | Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters |
title_fullStr | Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters |
title_full_unstemmed | Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters |
title_short | Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters |
title_sort | phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4862112/ https://www.ncbi.nlm.nih.gov/pubmed/27165150 http://dx.doi.org/10.1186/s12890-016-0232-2 |
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