Characterization of Rhinitis According to the Asthma Status in Adults Using an Unsupervised Approach in the EGEA Study

Emilie Burte, Jean Bousquet, Raphaëlle Varraso, Frédéric Gormand, Jocelyne Just, Régis Matran, Isabelle Pin, Valérie Siroux, Bénédicte Jacquemin and Rachel Nadif

PLOS ONE, 2015, vol. 10, issue 8, 1-18

Abstract: Background: The classification of rhinitis in adults is missing in epidemiological studies. Objective: To identify phenotypes of adult rhinitis using an unsupervised approach (data-driven) compared with a classical hypothesis-driven approach. Methods: 983 adults of the French Epidemiological Study on the Genetics and Environment of Asthma (EGEA) were studied. Self-reported symptoms related to rhinitis such as nasal symptoms, hay fever, sinusitis, conjunctivitis, and sensitivities to different triggers (dust, animals, hay/flowers, cold air…) were used. Allergic sensitization was defined by at least one positive skin prick test to 12 aeroallergens. Mixture model was used to cluster participants, independently in those without (Asthma-, n = 582) and with asthma (Asthma+, n = 401). Results: Three clusters were identified in both groups: 1) Cluster A (55% in Asthma-, and 22% in Asthma+) mainly characterized by the absence of nasal symptoms, 2) Cluster B (23% in Asthma-, 36% in Asthma+) mainly characterized by nasal symptoms all over the year, sinusitis and a low prevalence of positive skin prick tests, and 3) Cluster C (22% in Asthma-, 42% in Asthma+) mainly characterized by a peak of nasal symptoms during spring, a high prevalence of positive skin prick tests and a high report of hay fever, allergic rhinitis and conjunctivitis. The highest rate of polysensitization (80%) was found in participants with comorbid asthma and allergic rhinitis. Conclusion: This cluster analysis highlighted three clusters of rhinitis with similar characteristics than those known by clinicians but differing according to allergic sensitization, and this whatever the asthma status. These clusters could be easily rebuilt using a small number of variables.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0136191

DOI: 10.1371/journal.pone.0136191

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