INTRODUCTION
Allergic rhinitis (AR) is one of the most common chronic diseases of childhood(1) and its symptoms can have a major effect on quality of life (QOL), emotional well-being, sleep, daily activities, and productivity of children and adolescents, especially when they are poorly controlled(2).
For many years classification of AR was based on the temporal pattern of symptoms as seasonal and perennial. Several other attempts were also made to classify patients according to sensitization patterns and co-morbidities(3-5). Following the introduction of ARIA guidelines, the classification was mainly based on symptom severity and persistence(6). ARIA guidelines have additionally emphasized the link between allergic rhinitis and asthma, and introduced the concept of “one airway, one disease”(6).
In addition to these classical, mainly consensus-driven classifications, novel epidemiologic approaches resorting to data-driven phenotyping strategies have recently emerged to identify phenotypes of allergic diseases(7). These methods allow analysis of large datasets without prior hypotheses, and identify latent structures within such datasets which cannot be detected by traditional approaches. This approach has been successfully used to identify subtypes of childhood wheezing(8), asthma(9), allergic sensitization(10, 11), atopic dermatitis(12), and allergic rhinitis in adults(13, 14).
We aimed to identify distinct subgroups amongst children with AR, and to ascertain their association with clinical patterns of symptoms, allergic sensitization and concomitant physician-diagnosed asthma.