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.