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Beyond Atopy

Multiple Patterns of Sensitization in Relation to Asthma in a Birth Cohort Study

¹ The University of Manchester, Manchester Academic Health Science Centre, NIHR Translational Research Facility in Respiratory Medicine, University Hospital of South Manchester NHS Foundation Trust, Manchester, United Kingdom; ² Stochastic Systems Group, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts; ³ Microsoft Research Cambridge, Cambridge, United Kingdom; ⁴ eScience Research Group, Microsoft Research, Redmond, Washington; ⁵ The University of Manchester, Northwest Institute for Bio-Health Informatics, Manchester, United Kingdom

Correspondence and requests for reprints should be addressed to Angela Simpson, M.D., University of Manchester, ERC Building, Second Floor, Wythenshawe Hospital, Manchester M23 9LT, UK. E-mail: angela.simpson{at}manchester.ac.uk

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.

Clinical trial registered with www.controlled-trials.com (ISRCTN72673620).

Key Words: asthma • atopy • unsupervised clustering • Bayesian inference • machine learning in epidemiology

AT A GLANCE COMMENTARY Scientific Knowledge on the Subject In epidemiologic studies and clinical practice, children are classified as atopic if they have a positive IgE or skin prick test. What This Study Adds to the Field By adopting a machine learning approach, we have identified that IgE antibody responses do not reflect a single phenotype of atopy, but rather multiple different atopic vulnerabilities. We have demonstrated that only one of these atopic classes (multiple early atopic vulnerability) predicts asthma.