Article

Beyond atopy: multiple patterns of sensitization in relation to asthma in a birth cohort study.

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.
American Journal of Respiratory and Critical Care Medicine (Impact Factor: 11.99). 02/2010; 181(11):1200-6. DOI: 10.1164/rccm.200907-1101OC
Source: PubMed

ABSTRACT 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.
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.
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).
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).
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.

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    • "This is referred to as the high-dimensional learning regime, which differs from classical statistics where a large number of samples of fixed dimensionality are available . As a concrete example, in order to analyze the effect of environmental and genetic factors on childhood asthma, clinician scientists in Manchester, UK have been conducting a longitudinal birth-cohort study since 1997 [3], [4]. The number of variables collected is of the order of d ≈ 10 6 (dominated by the genetic data) but the number of children in the study is small (n ≈ 10 3 ). "
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    • "This is referred to as the high-dimensional learning regime, which differs from classical statistics where a large number of samples of fixed dimensionality are available. As a concrete example, in order to analyze the effect of environmental and genetic factors on childhood asthma, clinician scientists in Manchester, UK have been conducting a longitudinal birth-cohort study since 1997 (Custovic et al., 2002; Simpson et al., 2010). The number of variables collected is of the order of d ≈ 10 6 (dominated by the genetic data) but the number of children in the study is small (n ≈ 10 3 ). "
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    ABSTRACT: The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive thresholding is proposed. It is shown that this algorithm is both structurally consistent and risk consistent and the error probability of structure learning decays faster than any polynomial in the number of samples under fixed model size. For the high-dimensional scenario where the size of the model d and the number of edges k scale with the number of samples n, sufficient conditions on (n,d,k) are given for the algorithm to satisfy structural and risk consistencies. In addition, the extremal structures for learning are identified; we prove that the independent (resp. tree) model is the hardest (resp. easiest) to learn using the proposed algorithm in terms of error rates for structure learning.