Cluster analysis in phenotyping a Portuguese population
C.C.. Loureiroa,,b, , P.. Sa-Coutoc, A.. Todo-Bomb,d, J.. Bousquete,f
a Pneumology Unit, Hospitais da Universidade de Coimbra, Centro Hospitalar e Universit??rio de Coimbra, Coimbra, Portugal
b Centre of Pneumology, Faculty of Medicine, University of Coimbra, Portugal
c Center for Research and Development in Mathematics and Applications, Department of Mathematics, University of Aveiro, Aveiro, Portugal
d Immunoallergology Unit, Hospitais da Universidade de Coimbra, Centro Hospitalar e Universit??rio de Coimbra, Coimbra, Portugal
e University Hospital Arnaud de Villeneuve, Montpellier, France
f Inserm, CESP Centre for Research in Epidemiology and Population Health, U1018, Respiratory and Environmental Epidemiology Team, Villejuif, France
Related article:

Unbiased cluster analysis using clinical parameters has identified asthma phenotypes. Adding inflammatory biomarkers to this analysis provided a better insight into the disease mechanisms. This approach has not yet been applied to asthmatic Portuguese patients.


To identify phenotypes of asthma using cluster analysis in a Portuguese asthmatic population treated in secondary medical care.


Consecutive patients with asthma were recruited from the outpatient clinic. Patients were optimally treated according to GINA guidelines and enrolled in the study. Procedures were performed according to a standard evaluation of asthma. Phenotypes were identified by cluster analysis using Ward's clustering method.


Of the 72 patients enrolled, 57 had full data and were included for cluster analysis. Distribution was set in 5 clusters described as follows: cluster (C) 1, early onset mild allergic asthma; C2, moderate allergic asthma, with long evolution, female prevalence and mixed inflammation; C3, allergic brittle asthma in young females with early disease onset and no evidence of inflammation; C4, severe asthma in obese females with late disease onset, highly symptomatic despite low Th2 inflammation; C5, severe asthma with chronic airflow obstruction, late disease onset and eosinophilic inflammation.


In our study population, the identified clusters were mainly coincident with other larger-scale cluster analysis. Variables such as age at disease onset, obesity, lung function, FeNO (Th2 biomarker) and disease severity were important for cluster distinction.

Asthma, Phenotypes, Cluster analysis
Journal: Cluster analysis in phenotyping a Portuguese population
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Additional Material

This article belongs to the Journal: Pulmonology


  • Impact Factor: 1.560(2016)
  • 5-years Impact Factor: 1,100
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