Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis

Marcos Matabuena, Francisco Javier Salgado, Juan José Nieto-Fontarigo*, María J. Álvarez-Puebla, Ebymar Arismendi, Pilar Barranco, Irina Bobolea, María L. Caballero, José Antonio Cañas, Blanca Cárdaba, María Jesus Cruz, Elena Curto, Javier Domínguez-Ortega, Juan Alberto Luna, Carlos Martínez-Rivera, Joaquim Mullol, Xavier Muñoz, Javier Rodriguez-Garcia, José María Olaguibel, César PicadoVicente Plaza, Santiago Quirce, Manuel J. Rial, Christian Romero-Mesones, Beatriz Sastre, Lorena Soto-Retes, Antonio Valero, Marcela Valverde-Monge, Victoria Del Pozo, Joaquín Sastre, Francisco Javier González-Barcala

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)223-231
Number of pages9
JournalArchivos de bronconeumologia
Issue number4
Publication statusPublished - Apr 2023


  • Asthma
  • Asthma endotypes
  • Asthma phenotypes
  • Clustering analysis
  • Prospective Studies
  • Humans
  • Male
  • Asthma/drug therapy
  • Hypersensitivity, Immediate
  • Phenotype
  • Female
  • Cluster Analysis
  • Cohort Studies


Dive into the research topics of 'Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis'. Together they form a unique fingerprint.

Cite this