Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis
(2023) In Archivos de Bronconeumologia 59(4). p.223-231- Abstract
Introduction: The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. Methods: We performed a multicentre prospective cohort study, including adult patients with asthma (N = 512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. Results: Four... (More)
Introduction: The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. Methods: We performed a multicentre prospective cohort study, including adult patients with asthma (N = 512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. Results: Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities. Conclusion: We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps.
(Less)
- author
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Asthma, Asthma endotypes, Asthma phenotypes, Clustering analysis
- in
- Archivos de Bronconeumologia
- volume
- 59
- issue
- 4
- pages
- 223 - 231
- publisher
- Elsevier
- external identifiers
-
- scopus:85150051248
- pmid:36732158
- ISSN
- 0300-2896
- DOI
- 10.1016/j.arbres.2023.01.007
- language
- English
- LU publication?
- yes
- id
- 93bf32fc-cd25-4f09-90a8-6eaf4fc62a83
- date added to LUP
- 2023-04-04 15:05:22
- date last changed
- 2024-04-18 10:33:39
@article{93bf32fc-cd25-4f09-90a8-6eaf4fc62a83,
abstract = {{<p>Introduction: The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. Methods: We performed a multicentre prospective cohort study, including adult patients with asthma (N = 512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. Results: Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities. Conclusion: We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps.</p>}},
author = {{Matabuena, Marcos and Salgado, Francisco Javier and Nieto-Fontarigo, Juan José and Álvarez-Puebla, María J. and Arismendi, Ebymar and Barranco, Pilar and Bobolea, Irina and Caballero, María L. and Cañas, José Antonio and Cárdaba, Blanca and Cruz, María Jesus and Curto, Elena and Domínguez-Ortega, Javier and Luna, Juan Alberto and Martínez-Rivera, Carlos and Mullol, Joaquim and Muñoz, Xavier and Rodriguez-Garcia, Javier and Olaguibel, José María and Picado, César and Plaza, Vicente and Quirce, Santiago and Rial, Manuel J. and Romero-Mesones, Christian and Sastre, Beatriz and Soto-Retes, Lorena and Valero, Antonio and Valverde-Monge, Marcela and Del Pozo, Victoria and Sastre, Joaquín and González-Barcala, Francisco Javier}},
issn = {{0300-2896}},
keywords = {{Asthma; Asthma endotypes; Asthma phenotypes; Clustering analysis}},
language = {{eng}},
number = {{4}},
pages = {{223--231}},
publisher = {{Elsevier}},
series = {{Archivos de Bronconeumologia}},
title = {{Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis}},
url = {{http://dx.doi.org/10.1016/j.arbres.2023.01.007}},
doi = {{10.1016/j.arbres.2023.01.007}},
volume = {{59}},
year = {{2023}},
}