Component-specific clusters for diagnosis and prediction of allergic airway diseases

Abstract

Background: Previous studies which applied machine learning on multiplex component-resolved diagnostics arrays identified clusters of allergen components which are biologically plausible and reflect the sources of allergenic proteins and their structural homogeneity. Sensitization to different clusters is associated with different clinical outcomes.

Objective: To investigate whether within different allergen component sensitization clusters, the internal within-cluster sensitization structure, including the number of c-sIgE responses and their distinct patterns, alters the risk of clinical expression of symptoms.

Methods: In a previous analysis in a population-based birth cohort, by clustering component-specific (c-s)IgEs, we derived allergen component clusters from infancy to adolescence. In the current analysis, we defined each subject's within-cluster sensitization structure which captured the total number of c-sIgE responses in each cluster and intra-cluster sensitization patterns. Associations between within-cluster sensitization patterns and clinical outcomes (asthma and rhinitis) in early-school age and adolescence were examined using logistic regression and binomial generalized additive models.

Results: Intra-cluster sensitization patterns revealed specific associations with asthma and rhinitis (both contemporaneously and longitudinally) that were previously unseen using binary sensitization to clusters. A more detailed description of the subjects' within-cluster c-sIgE responses in terms of the number of positive c-sIgEs and unique sensitization patterns added new information relevant to allergic diseases, both for diagnostic and prognostic purposes. For example, the increase in the number of within-cluster positive c-sIgEs at age 5 years was correlated with the increase in prevalence of asthma at ages 5 and 16 years, with the correlations being stronger in the prediction context (e.g. for the largest 'Broad' component cluster, contemporaneous: r = .28, p = .012; r = .22, p = .043; longitudinal: r = .36, p = .004; r = .27, p = .04).

Conclusion: Among sensitized individuals, a more detailed description of within-cluster c-sIgE responses in terms of the number of positive c-sIgE responses and distinct sensitization patterns, adds potentially important information relevant to allergic diseases.

Keywords: asthma; component‐resolved diagnostics; diagnosis; machine learning; prognosis; rhinitis.