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Allergy. 2019 May;74(5):953-963. doi: 10.1111/all.13697. Epub 2019 Jan 15.

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

Author information

1
IAB, Team of Environmental Epidemiology Applied To Reproduction and Respiratory Health, INSERM, Université Grenoble Alpes, CNRS, Grenoble, France.
2
Faculté de Pharmacie, Université Grenoble Alpes, Grenoble, France.
3
Pôle Pharmacie, CHU Grenoble Alpes, Grenoble, France.
4
Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy.
5
ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.
6
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
7
CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
8
Epidemiological and Public Health Approaches, INSERM, U1168: Aging and Chronic Diseases, Villejuif, France.
9
Pneumology Department, CHU Montpellier, Montpellier, France.
10
Pneumology Department, CHU de Lyon, Lyon, France.
11
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.
12
Department of Medical Sciences: Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden.
13
Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland.
14
CHU, Université de Lille, Lille, France.
15
Clinique Universitaire de Pneumologie, Pôle Thorax et Vaisseaux, CHU de Grenoble, INSERM U1055, Université Grenoble Alpes, Grenoble, France.
16
INSERM Bordeaux Population Health Research Center, Team EPICENE, UMR 1219, Université Bordeaux, Bordeaux, France.
17
National Heart and Lung Institute, Imperial College, London, UK.
18
Unit 1152, Team of Epidemiology, INSERM, University Paris-Diderot, Paris, France.
19
Pediatric Department, CHU Grenoble, Grenoble, France.

Abstract

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 FEV1 ) at follow-up and the course of FEV1  between seven cluster-based asthma phenotypes identified 20 years earlier.

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 FEV1 % predicted varied from 0.6 (-3.5 to 4.6) to -9.9 (-14.2 to -5.5) between clusters. Change in FEV1 over time did not differ significantly across clusters.

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.

KEYWORDS:

asthma; clustering; follow-up; lung function; phenotypes

PMID:
30548629
DOI:
10.1111/all.13697

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