Rationale: The pattern of IgE response (over time or to specific allergens) may reflect different atopic vulnerabilities which are related to the presence of asthma in a fundamentally different way from current definition of atopy.
Objectives: To redefine the atopic phenotype by identifying latent structure within a complex dataset, taking into account the timing and type of sensitization to specific allergens, and relating these novel phenotypes to asthma.
Methods: In a population-based birth cohort in which multiple skin and IgE tests have been taken throughout childhood, we used a machine learning approach to cluster children into multiple atopic classes in an unsupervised way. We then investigated the relation between these classes and asthma (symptoms, hospitalizations, lung function and airway reactivity).
Measurements and main results: A five-class model indicated a complex latent structure, in which children with atopic vulnerability were clustered into four distinct classes (Multiple Early [112/1053, 10.6%]; Multiple Late [171/1053, 16.2%]; Dust Mite [47/1053, 4.5%]; and Non-dust Mite [100/1053, 9.5%]), with a fifth class describing children with No Latent Vulnerability (623/1053, 59.2%). The association with asthma was considerably stronger for Multiple Early compared with other classes and conventionally defined atopy (odds ratio [95% CI]: 29.3 [11.1-77.2] versus 12.4 [4.8-32.2] versus 11.6 [4.8-27.9] for Multiple Early class versus Ever Atopic versus Atopic age 8). Lung function and airway reactivity were significantly poorer among children in Multiple Early class. Cox regression demonstrated a highly significant increase in risk of hospital admissions for wheeze/asthma after age 3 yr only among children in the Multiple Early class (HR 9.2 [3.5-24.0], P < 0.001).
Conclusions: IgE antibody responses do not reflect a single phenotype of atopy, but several different atopic vulnerabilities which differ in their relation with asthma presence and severity.