These authors contributed equally to this work.
Multiple atopy phenotypes and their associations with asthma: similar findings from two birth cohorts
Article first published online: 29 APR 2013
© 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Volume 68, Issue 6, pages 764–770, June 2013
How to Cite
Multiple atopy phenotypes and their associations with asthma: similar findings from two birth cohorts. Allergy 2013; 68: 764–770., , , , , , , , .
Edited by: Stephan Weidinger
- Issue published online: 22 MAY 2013
- Article first published online: 29 APR 2013
- Manuscript Accepted: 18 JAN 2013
- Asthma UK. Grant Number: 04/014
- MRC. Grant Number: G0601361
- National Institute of Health. Grant Number: R01 HL082925
- British Medical Association and National Asthma Campaign. Grant Number: 364
- atopic sensitization;
- birth cohort;
- cluster analysis;
- machine learning
Although atopic sensitization is one of the strongest risk factors for asthma, its relationship with asthma is poorly understood. We hypothesize that ‘atopy’ encompasses multiple sub-phenotypes that relate to asthma in different ways.
In two population-based birth cohorts (Manchester and Isle of Wight – IoW), we used a machine learning approach to independently cluster children into different classes of atopic sensitization in an unsupervised manner, based on skin prick and sIgE tests taken throughout childhood and adolescence. We examined the qualitative cluster properties and their relationship to asthma and lung function.
A five-class solution best described the data in both cohorts, with striking similarity between the classes across the two populations. Compared with nonsensitized class, children in the class with sensitivity to a wide variety of allergens (~1/3 of children atopic by conventional definition) were much more likely to have asthma (aOR [95% CI0; 20.1 [10.9–40.2] in Manchester and 11.9 [7.3–19.4] in IoW). The relationship between asthma and conventional atopy was much weaker (5.5 [3.4–8.8] in Manchester and 5.8 [4.1–8.3] in IoW). In both cohorts, children in this class had significantly poorer lung function (FEV1/FVC lower by 4.4% in Manchester and 2.6% in IoW; P < 0.001), most reactive airways, highest eNO and most hospital admissions for asthma (P < 0.001).
By adopting a machine learning approach to longitudinal data on allergic sensitization from two independent unselected birth cohorts, we identified latent classes with strikingly similar patterns of atopic response and association with clinical outcomes, suggesting the existence of multiple atopy phenotypes.