4.6 Article

Phenotyping asthma, rhinitis and eczema in MeDALL population-based birth cohorts: an allergic comorbidity cluster

Journal

ALLERGY
Volume 70, Issue 8, Pages 973-984

Publisher

WILEY
DOI: 10.1111/all.12640

Keywords

allergy; asthma; cluster analysis; eczema; rhinitis

Funding

  1. Mechanisms of the Development of ALLergy (MeDALL) within European Union [261357]

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BackgroundAsthma, rhinitis and eczema often co-occur in children, but their interrelationships at the population level have been poorly addressed. We assessed co-occurrence of childhood asthma, rhinitis and eczema using unsupervised statistical techniques. MethodsWe included 17209 children at 4years and 14585 at 8years from seven European population-based birth cohorts (MeDALL project). At each age period, children were grouped, using partitioning cluster analysis, according to the distribution of 23 variables covering symptoms ever' and in the last 12months', doctor diagnosis, age of onset and treatments of asthma, rhinitis and eczema; immunoglobulin E sensitization; weight; and height. We tested the sensitivity of our estimates to subject and variable selections, and to different statistical approaches, including latent class analysis and self-organizing maps. ResultsTwo groups were identified as the optimal way to cluster the data at both age periods and in all sensitivity analyses. The first (reference) group at 4 and 8years (including 70% and 79% of children, respectively) was characterized by a low prevalence of symptoms and sensitization, whereas the second (symptomatic) group exhibited more frequent symptoms and sensitization. Ninety-nine percentage of children with comorbidities (co-occurrence of asthma, rhinitis and/or eczema) were included in the symptomatic group at both ages. The children's characteristics in both groups were consistent in all sensitivity analyses. ConclusionAt 4 and 8years, at the population level, asthma, rhinitis and eczema can be classified together as an allergic comorbidity cluster. Future research including time-repeated assessments and biological data will help understanding the interrelationships between these diseases.

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