Resumen de Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis
Marcos Matabuena, Francisco Javier Salgado, José Antonio Nieto, María José Álvarez Puebla, Ebymar Arismendi Núñez, Pilar Barranco Sanz, I. Bobolea, Laura Caballero Ballesteros, Jose Antonio Cañas, Blanca Cárdaba, María Jesús Cruz, Elena Curto, F. Javier Domínguez Ortega, Juan Alberto Luna Porta, Carlos Martínez Rivera, Joaquim Mullol i Miret, Javier Muñoz Gutiérrez, Francisco Javier Rodríguez García, José María Olaguíbel Rivera, César Picado Vallés, Vicente Plaza Moral, Santiago Quirce Gancedo, Manuel Rial, Christian Romero Mesones, Beatriz Sastre, L. Soto Retes, Antonio Luis Valero Santiago, Marcela Valverde Monge, Victoria del Pozo Abejón, Joaquín Sastre Merlín, Francisco Javier González Barcala
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.
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