Novel, computational IgE-clustering in a population-based cross-sectional study: Mapping the allergy burden

Abstract

Background: Even though the prevalence of allergies is increasing, population-based data are still scarce. As a read-out for chronic inflammatory information, new methods are needed to integrate individual biological measurements and lifestyle parameters to mitigate the consequences and costs of allergic burden for society. Methods: More than 480.000 data points were collected from 1462 Luxembourg adults during the representative, cross-sectional European Health Examination Survey, spanning health and lifestyle reports. Deep IgE-profiles based on unsupervised clustering were correlated with data of the health survey. Findings: 42.6% of the participants reported a physician-diagnosed allergy and 44% were found to be IgE-positive to at least one allergen or extract. The main sensitization sources were tree pollens followed by grass pollens and mites (52.4%, 51.8% and 40.3% of sensitized participants respectively), suggesting seasonal as well as perennial burden. The youngest group of participants (25–34 years old) showed the highest burden of sensitization, with 18.2% of them having IgE to 10 or more allergen groups. Unsupervised clustering revealed that the biggest cluster of 24.4% of participants was also the one with the highest medical need, marked by their multi-sensitization to respiratory sources. Interpretation: Our novel approach to analyzing large biosample datasets together with health information allows the measurement of the chronic inflammatory disease burden in the general population and led to the identification of the most vulnerable groups in need of better medical care.