Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters.

BACKGROUND: Although the heterogeneous nature of asthma has prompted asthma phenotyping with physiological or biomarker data, these studies have been mostly cross-sectional. Longitudinal studies that assess the stability of phenotypes based on a combination of physiological, clinical and biomarker data are currently lacking. Our objective was to assess the longitudinal stability of clusters derived from repeated measures of airway and physiological data over a 1-year period in moderate and severe asthmatics. METHODS: A total of 125 subjects, 48 with moderate asthma (MA) and 77 with severe asthma (SA) were evaluated every 3 months and monthly, respectively, over a 1-year period. At each 3-month time point, subjects were grouped into 4 asthma clusters (A, B, C, D) based on a combination of clinical (duration of asthma), physiological (FEV1 and BMI) and biomarker (sputum eosinophil count) variables, using k-means clustering. RESULTS: Majority of subjects in clusters A and C had severe asthma (93 % of subjects in cluster A and 79.5 % of subjects in cluster C at baseline). Overall, a total of 59 subjects (47 %) had stable cluster membership, remaining in clusters with the same subjects at each evaluation time. Cluster A was the least stable (21 % stability) and cluster B was the most stable cluster (71 % stability). Cluster stability was not influenced by changes in the dosage of inhaled corticosteroids. CONCLUSION: Asthma phenotyping based on clinical, physiologic and biomarker data identified clusters with significant differences in longitudinal stability over a 1-year period. This finding indicates that the majority of patients within stable clusters can be phenotyped with reasonable accuracy after a single measurement of lung function and sputum eosinophilia, while patients in unstable clusters will require more frequent evaluation of these variables to be properly characterized.

Exacerbation risk in severe asthma is stratified by inflammatory phenotype using longitudinal measures of sputum eosinophils.

BACKGROUND: Airway inflammatory phenotyping is increasingly applied to subjects with asthma. However, its relationship to clinical outcomes in difficult asthma is incompletely elucidated. OBJECTIVE: The goal of our study was to determine the relationship between exacerbation rates and phenotypes of difficult asthma based on the longitudinal measures of sputum eosinophils and neutrophils. METHODS: Subjects in the longitudinal observational study from two tertiary care centres that completed 1 year of observation and provided at least three sputum samples were classified by inflammatory phenotypes using previously established thresholds. Kaplan-Meier curves and univariable and multivariable Cox proportional hazard models were used to determine the association between inflammatory phenotypes and exacerbation rate. RESULTS: During the study, 115 exacerbations occurred in 73 severe asthmatic subjects. Subjects with the persistently eosinophilic phenotype had a significantly shorter time to first exacerbation and greater risk of exacerbation over a 1-year period than those with the non-eosinophilic phenotype based on the univariable and multivariable Cox proportional hazard model (hazard ratio [HR], 3.24; 95% confidence interval [CI], 1.35-7.72; adjusted HR, 3.90; 95% CI, 1.34-11.36). No significant differences in time to first exacerbation or exacerbation risk over a 1-year period were observed among the neutrophilic phenotypes. CONCLUSIONS: The persistent eosinophilic phenotype is associated with increased exacerbation risk compared with the non-eosinophilic phenotype in severe asthma. No differences in time to first exacerbation or exacerbation risk over a 1-year period were detected among neutrophilic phenotypes.