Data-driven adult asthma phenotypes based on clinical characteristics are associated with asthma outcomes twenty years later.


Journal

Allergy
ISSN: 1398-9995
Titre abrégé: Allergy
Pays: Denmark
ID NLM: 7804028

Informations de publication

Date de publication:
05 2019
Historique:
received: 10 07 2018
revised: 23 10 2018
accepted: 21 11 2018
pubmed: 15 12 2018
medline: 19 5 2020
entrez: 15 12 2018
Statut: ppublish

Résumé

Research based on cluster analyses led to the identification of particular phenotypes confirming phenotypic heterogeneity of asthma. The long-term clinical course of asthma phenotypes defined by clustering analysis remains unknown, although it is a key aspect to underpin their clinical relevance. We aimed to estimate risk of poor asthma events between asthma clusters identified 20 years earlier. The study relied on two cohorts of adults with asthma with 20-year follow-up, ECRHS (European Community Respiratory Health Survey) and EGEA (Epidemiological study on Genetics and Environment of Asthma). Regression models were used to compare asthma characteristics (current asthma, asthma exacerbations, asthma control, quality of life, and FEV The analysis included 1325 adults with ever asthma. For each asthma characteristic assessed at follow-up, the risk for adverse outcomes differed significantly between the seven asthma clusters identified at baseline. As compared with the mildest asthma phenotype, ORs (95% CI) for asthma exacerbations varied from 0.9 (0.4 to 2.0) to 4.0 (2.0 to 7.8) and the regression estimates (95% CI) for FEV Our findings show that the long-term risk for poor asthma outcomes differed between comprehensive adult asthma phenotypes identified 20 years earlier, and suggest a strong tracking of asthma activity and impaired lung function over time.

Sections du résumé

BACKGROUND
Research based on cluster analyses led to the identification of particular phenotypes confirming phenotypic heterogeneity of asthma. The long-term clinical course of asthma phenotypes defined by clustering analysis remains unknown, although it is a key aspect to underpin their clinical relevance. We aimed to estimate risk of poor asthma events between asthma clusters identified 20 years earlier.
METHODS
The study relied on two cohorts of adults with asthma with 20-year follow-up, ECRHS (European Community Respiratory Health Survey) and EGEA (Epidemiological study on Genetics and Environment of Asthma). Regression models were used to compare asthma characteristics (current asthma, asthma exacerbations, asthma control, quality of life, and FEV
RESULTS
The analysis included 1325 adults with ever asthma. For each asthma characteristic assessed at follow-up, the risk for adverse outcomes differed significantly between the seven asthma clusters identified at baseline. As compared with the mildest asthma phenotype, ORs (95% CI) for asthma exacerbations varied from 0.9 (0.4 to 2.0) to 4.0 (2.0 to 7.8) and the regression estimates (95% CI) for FEV
CONCLUSION
Our findings show that the long-term risk for poor asthma outcomes differed between comprehensive adult asthma phenotypes identified 20 years earlier, and suggest a strong tracking of asthma activity and impaired lung function over time.

Identifiants

pubmed: 30548629
doi: 10.1111/all.13697
doi:

Types de publication

Journal Article Multicenter Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

953-963

Subventions

Organisme : Medical Research Council
ID : G0901214
Pays : United Kingdom
Organisme : National PHRC 2012
Pays : International
Organisme : Medical Research Council
ID : 92091
Pays : United Kingdom
Organisme : Scientific committee "AGIR pour les maladies Chroniques"
Pays : International

Informations de copyright

© 2018 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

Auteurs

Anne Boudier (A)

IAB, Team of Environmental Epidemiology Applied To Reproduction and Respiratory Health, INSERM, Université Grenoble Alpes, CNRS, Grenoble, France.

Sébastien Chanoine (S)

IAB, Team of Environmental Epidemiology Applied To Reproduction and Respiratory Health, INSERM, Université Grenoble Alpes, CNRS, Grenoble, France.
Faculté de Pharmacie, Université Grenoble Alpes, Grenoble, France.
Pôle Pharmacie, CHU Grenoble Alpes, Grenoble, France.

Simone Accordini (S)

Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy.

Josep M Anto (JM)

ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.

Xavier Basagaña (X)

ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.

Jean Bousquet (J)

Epidemiological and Public Health Approaches, INSERM, U1168: Aging and Chronic Diseases, Villejuif, France.

Pascal Demoly (P)

Pneumology Department, CHU Montpellier, Montpellier, France.

Judith Garcia-Aymerich (J)

ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.

Frédéric Gormand (F)

Pneumology Department, CHU de Lyon, Lyon, France.

Joachim Heinrich (J)

Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital of Ludwig Maximilians University, Comprehensive Pneumology Centre Munich, German Centre for Lung Research, Muenchen, Germany.

Christer Janson (C)

Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden.

Nino Künzli (N)

Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland.

Régis Matran (R)

CHU, Université de Lille, Lille, France.

Christophe Pison (C)

Clinique Universitaire de Pneumologie, Pôle Thorax et Vaisseaux, CHU de Grenoble, INSERM U1055, Université Grenoble Alpes, Grenoble, France.

Chantal Raherison (C)

INSERM Bordeaux Population Health Research Center, Team EPICENE, UMR 1219, Université Bordeaux, Bordeaux, France.

Jordi Sunyer (J)

ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.

Raphaëlle Varraso (R)

Epidemiological and Public Health Approaches, INSERM, U1168: Aging and Chronic Diseases, Villejuif, France.

Deborah Jarvis (D)

National Heart and Lung Institute, Imperial College, London, UK.

Bénédicte Leynaert (B)

Unit 1152, Team of Epidemiology, INSERM, University Paris-Diderot, Paris, France.

Isabelle Pin (I)

IAB, Team of Environmental Epidemiology Applied To Reproduction and Respiratory Health, INSERM, Université Grenoble Alpes, CNRS, Grenoble, France.
Pediatric Department, CHU Grenoble, Grenoble, France.

Valérie Siroux (V)

IAB, Team of Environmental Epidemiology Applied To Reproduction and Respiratory Health, INSERM, Université Grenoble Alpes, CNRS, Grenoble, France.

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