Late Breaking Abstract - Cluster analysis of treatable traits in the U-BIOPRED adult severe asthma cohort

Abstract

Background: Identification of treatable traits (TTs) permits a precision medicine strategy for chronic airways diseases that is label free and based on the identification of genetic, phenotypic or psychosocial characteristics for which there are evidence based therapeutic choices. We have previously shown that TTs are common in severe asthma. It remains unknown whether groups of patients with similar patterns of TTs exist. Methods: We conducted a prospective observational cohort study of adults with severe asthma as part of the U-BIOPRED project. 23 TTs were identified across 3 categories (pulmonary, extra-pulmonary and behavioural) and were included in the cluster analysis as dichotomous variables. Cluster identification was conducted using two-step cluster analysis. We explored the differences in clinical characteristics between the clusters using ANOVA. Results: We identified 6 clusters: i) “classic asthma” (high airways reversibility, high eosinophils); ii) pulmonary TT high (high prevalence of multiple pulmonary TTs); iii) steroid insensitive (high eosinophils, despite good medication adherence); iv) reflux and cough (high reflux, high cough); v) TT high (high prevalence of TTs across all categories), and vi) TT Low (low prevalence TT across categories). There were statistically significant and clinically relevant differences in exacerbation rates, asthma control and quality of life between clusters. Conclusions: We have applied a new approach for the characterisation of asthma, based on the identification of TTs, which we believe should be considered in treatment strategies. The association of trait profiles with clinically important endpoints merits further investigation.