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Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis
Marcos Matabuena1, Francisco Javier Salgado2, Juan José Nieto-Fontarigo3
1Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), University of Santiago of Compostela, Santiago de Compostela, Spain.
Archivos De Bronconeumologia
|February 2, 2023
Summary
This study identified four distinct asthma phenotypes using cluster analysis. These phenotypes, based on clinical factors, offer a clearer classification for asthma patients.
Area of Science:
- Pulmonology
- Clinical Research
- Data Science
Background:
- Asthma phenotype classification lacks established definitions and consistent cluster study results.
- Unsupervised cluster analysis is needed to identify homogeneous groups representing distinct asthma phenotypes.
- Clinically relevant asthma phenotypes may share common asthma-related outcomes.
Purpose of the Study:
- To develop a classification algorithm for asthma phenotypes.
- To identify distinct clusters representing clinically relevant asthma phenotypes.
- To explore asthma-related outcomes associated with identified phenotypes.
Main Methods:
- A multicenter prospective cohort study (MEGA study) included 512 adult asthma patients.
- Standardized clinical histories were collected for all participants.
- Kernel k-groups algorithm was employed for unsupervised cluster analysis.
Main Results:
- Four distinct asthma clusters were identified.
- Cluster 1: Adult-onset, atopic, good lung function, controlled asthma.
- Cluster 2: Adolescent-onset, atopic, normal lung function, smokers, low adherence.
- Cluster 3: Adult-onset, non-atopic, overweight, severe asthma, combination therapy.
- Cluster 4: Elderly-onset, female, atopic, high BMI, smokers, comorbidities.
Conclusions:
- Four clinically relevant asthma phenotypes were defined using unsupervised cluster analysis.
- These phenotypes are differentiated by FEV1, age of onset, BMI, atopy, severity, control, smoking, and comorbidities.
- This classification advances the understanding of asthma heterogeneity and potential outcome associations.
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