Identifying adult asthma phenotypes using a clustering approach

TLDR: The aim of the present study was to identify distinct asthma phenotypes by applying latent class analysis (LCA), a model-based clustering method, to two large epidemiological studies, and revealed four distinct asthma Phenotypes in each sample.

Abstract: There is a need to improve asthma characterisation by integrating multiple aspects of the disease. The aim of the present study was to identify distinct asthma phenotypes by applying latent class analysis (LCA), a model-based clustering method, to two large epidemiological studies. Adults with asthma who participated in the follow-up of the Epidemiological Study on the Genetics and Environment of Asthma (EGEA2) (n = 641) and the European Community Respiratory Health Survey (ECRHSII) (n = 1,895) were included. 19 variables covering personal characteristics, asthma symptoms, exacerbations and treatment, age of asthma onset, allergic characteristics, lung function and airway hyperresponsiveness were considered in the LCA. Four asthma phenotypes were distinguished by the LCA in each sample. Two phenotypes were similar in EGEA2 and ECRHSII: active treated allergic childhood-onset asthma and active treated adult-onset asthma. The other two phenotypes were composed of subjects with inactive or mild untreated asthma, who differed by atopy status and age of asthma onset (childhood or adulthood). The phenotypes clearly discriminated populations in terms of quality of life, and blood eosinophil and neutrophil counts. The LCAs revealed four distinct asthma phenotypes in each sample. Considering these more homogeneous phenotypes in future studies may lead to a better identification of risk factors for asthma.