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Article Dans Une Revue European Respiratory Journal Année : 2023

Identification of rhinitis phenotypes using an unsupervised approach in the Constances cohort

Résumé

Unsupervised approach has been scarcely used for rhinitis in adults in population-based studies. We aimed to identify rhinitis phenotypes using this approach in the French population-based cohort Constances. Among participants answering the 2014 questionnaire, we included those with current rhinitis i.e. reporting sneezing, runny or blocked nose out a cold or the flu, in the last 12 months. Based on 25 variables referring to rhinitis characteristics (persistence, severity, triggers, seasonality, treatments) and co-morbidities, a dimension reduction with Factor Analysis of Mixed Data followed by the K-means algorithm were used to identify clusters. Among 5516 participants with current rhinitis (50 years old, 57% women, 20% ever-asthmatics), three clusters were identified: cluster 1 (C1) n=2586, 47%, C2 n=2379, 43% and C3 n=551, 10%. C1 was characterized by rhinitis with few identified allergic triggers (dust or dust mites, animals and pollens reported by less than 10% of participants and 53% of participants did not know what triggered their symptoms). In C2 and C3, more than 95% of participants reported ever nasal allergies and 100% of participants from C3 had severe rhinitis. Gradual increase was observed from C1 to C3 for asthma co-morbidity (C1=7%, C2=31%, C3=38%), combined medication (oral antihistamines and intranasal corticosteroids) (C1=7%, C2=37%, C3=56%) and eosinophils count (109/L) (C1=181, C2=208, C3=231), all p-trend <0.05. In conclusion, we identified three clusters related to non-allergic, mild/moderate allergic and severe allergic rhinitis highlighting the importance of its severity. It would be of interest to study the determinants of these three phenotypes.
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Dates et versions

inserm-04353753 , version 1 (19-12-2023)

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Marine Savouré, Jean Bousquet, Laurent Orsi, Marcel Goldberg, Bénédicte Leynaert, et al.. Identification of rhinitis phenotypes using an unsupervised approach in the Constances cohort. European Respiratory Journal, 2023, 62 (suppl 67), pp.PA1965. ⟨10.1183/13993003.congress-2023.PA1965⟩. ⟨inserm-04353753⟩
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