Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort

Abstract

BackgroundThe relationship between allergic sensitisation and asthma is complex; the data about the strength of this association are conflicting. We propose that the discrepancies arise in part because allergic sensitisation may not be a single entity (as considered conventionally) but a collection of several different classes of sensitisation. We hypothesise that pairings between immunoglobulin E (IgE) antibodies to individual allergenic molecules (components), rather than IgE responses to ‘informative’ molecules, are associated with increased risk of asthma.Methods and findingsIn a cross-sectional analysis among 461 children aged 11 years participating in a population-based birth cohort, we measured serum-specific IgE responses to 112 allergen components using a multiplex array (ImmunoCAP Immuno‑Solid phase Allergy Chip [ISAC]). We characterised sensitivity to 44 active components (specific immunoglobulin E [sIgE] > 0.30 units in at least 5% of children) among the 213 (46.2%) participants sensitised to at least one of these 44 components. We adopted several machine learning methodologies that offer a powerful framework to investigate the highly complex sIgE–asthma relationship. Firstly, we applied network analysis and hierarchical clustering (HC) to explore the connectivity structure of component-specific IgEs and identify clusters of component-specific sensitisation (‘component clusters’). Of the 44 components included in the model, 33 grouped in seven clusters (C.sIgE-1–7), and the remaining 11 formed singleton clusters. Cluster membership mapped closely to the structural homology of proteins and/or their biological source. Components in the pathogenesis-related (PR)-10 proteins cluster (C.sIgE-5) were central to the network and mediated connections between components from grass (C.sIgE-4), trees (C.sIgE-6), and profilin clusters (C.sIgE-7) with those in mite (C.sIgE-1), lipocalins (C.sIgE-3), and peanut clusters (C.sIgE-2). We then used HC to identify four common ‘sensitisation clusters’ among study participants: (1) multiple sensitisation (sIgE to multiple components across all seven component clusters and singleton components), (2) predominantly dust mite sensitisation (IgE responses mainly to components from C.sIgE-1), (3) predominantly grass and tree sensitisation (sIgE to multiple components across C.sIgE-4–7), and (4) lower-grade sensitisation. We used a bipartite network to explore the relationship between component clusters, sensitisation clusters, and asthma, and the joint density-based nonparametric differential interaction network analysis and classification (JDINAC) to test whether pairwise interactions of component-specific IgEs are associated with asthma. JDINAC with pairwise interactions provided a good balance between sensitivity (0.84) and specificity (0.87), and outperformed penalised logistic regression with individual sIgE components in predicting asthma, with an area under the curve (AUC) of 0.94, compared with 0.73. We then inferred the differential network of pairwise component-specifi...

Dust-mite inducing asthma: what advice can be given to patients?

“…In most studies of allergen control (and in clinical practice), individuals are assigned as either sensitized or not based on arbitrary cut-off points on skin tests or measurement of specific serum IgE. However, a sizeable proportion of such defined "atopic" individuals have no evidence of symptoms upon exposure, and it has been suggested that sensitization may include several different subtypes (benign and pathologic (67)), which differ in their association with asthma(68,69).…”

Прогнозирование Тяжелого Течения Бронхиальной Астмы У Детей

“…(2018), які вивчали специфічні IgE у сироватці крові на 112 алергенних компонентах найбільш сучасним методом мультиплексної матриці ImmunoCAP у когорті дітей віком 11 років (n = 461). Методами кластеризації, логістичної регресії виявлено 18 алергенних компонентів, що з доведеними високими чутливістю (0,84) та специфічністю (0,87) є значущими при формуванні імунопатологічної відповіді [11].…”

Immune maturation and modulation in childhood allergies : Aspects of epigenetic, mucosal and systemic immune mediators in allergy development and prevention

“…as well as to previously machine learning-derived allergy clusters [290][291][292][293] , to assess involvement in allergy development. Subsequently, possible predictive and longitudinal effects on allergy outcomes of the circulating chemokine levels were investigated using logistic regression and generalised estimating equation (GEE) models.…”