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Cited by 265

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Identifying adult asthma phenotypes using a clustering approach

Open access
Siroux, V., Basagaña, X., Boudier, A., Pin, I., Garcia-Aymerich, J., Vesin, A., Slama, R., Jarvis, D., Anto, J.M., Kauffmann, F., Sunyer, J.·2011·European Respiratory Journal

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.

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Metadata

Authors
Siroux, V., Basagaña, X., Boudier, A., Pin, I., Garcia-Aymerich, J., Vesin, A., Slama, R., Jarvis, D., Anto, J. M., Kauffmann, F., & Sunyer, J.
Venue
European Respiratory Journal
Published
2011-01-13
Volume
38
Issue
2
Pages
310-317
ISSN
  • 0903-1936 (ISSN-L)
  • 1399-3003
OpenAlex
W2149686080
Cited by
265
References
30
FWCI
14.83
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Identifying adult asthma phenotypes using a clustering approach. https://app.cochat.ai/papers/10.1183/09031936.00120810
The paper
Siroux, V., Basagaña, X., Boudier, A., Pin, I., Garcia-Aymerich, J., Vesin, A., Slama, R., Jarvis, D., Anto, J. M., Kauffmann, F., & Sunyer, J. (2011). Identifying adult asthma phenotypes using a clustering approach. European Respiratory Journal. https://doi.org/10.1183/09031936.00120810

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.