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Keywords:

  • asthma;
  • atopic sensitization;
  • birth cohort;
  • cluster analysis;
  • machine learning

Abstract

Background

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.

Methods

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.

Results

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; < 0.001), most reactive airways, highest eNO and most hospital admissions for asthma (< 0.001).

Conclusions

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.