title: Cluster analysis and clinical asthma phenotypes.
abstract: BACKGROUND
  Heterogeneity in asthma expression is multidimensional, including variability in clinical, physiologic, and pathologic parameters. Classification requires consideration of these disparate domains in a unified model.
  
  
  OBJECTIVE
  To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups.
  
  
  METHODS
  We performed k-means cluster analysis in three independent asthma populations. Clusters of a population managed in primary care (n = 184) with predominantly mild to moderate disease, were compared with a refractory asthma population managed in secondary care (n = 187). We then compared differences in asthma outcomes (exacerbation frequency and change in corticosteroid dose at 12 mo) between clusters in a third population of 68 subjects with predominantly refractory asthma, clustered at entry into a randomized trial comparing a strategy of minimizing eosinophilic inflammation (inflammation-guided strategy) with standard care.
  
  
  RESULTS
  Two clusters (early-onset atopic and obese, noneosinophilic) were common to both asthma populations. Two clusters characterized by marked discordance between symptom expression and eosinophilic airway inflammation (early-onset symptom predominant and late-onset inflammation predominant) were specific to refractory asthma. Inflammation-guided management was superior for both discordant subgroups leading to a reduction in exacerbation frequency in the inflammation-predominant cluster (3.53 [SD, 1.18] vs. 0.38 [SD, 0.13] exacerbation/patient/yr, P = 0.002) and a dose reduction of inhaled corticosteroid in the symptom-predominant cluster (mean difference, 1,829 mug beclomethasone equivalent/d [95% confidence interval, 307-3,349 mug]; P = 0.02).
  
  
  CONCLUSIONS
  Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms.
authors.affiliation: Institute for Lung Health, Glenfield Hospital, Leicester, United Kingdom.
authors.forename: Pranab
authors.initials: P
authors.lastname: Haldar
authors.affiliation: Institute for Lung Health, Glenfield Hospital, Leicester, United Kingdom.
authors.forename: Ian D
authors.initials: ID
authors.lastname: Pavord
authors.affiliation: Institute for Lung Health, Glenfield Hospital, Leicester, United Kingdom.
authors.forename: Dominic E
authors.initials: DE
authors.lastname: Shaw
authors.affiliation: Institute for Lung Health, Glenfield Hospital, Leicester, United Kingdom.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Berry
authors.affiliation: Department of General Practice, University of Aberdeen, Aberdeen, United Kingdom.
authors.forename: Michael
authors.initials: M
authors.lastname: Thomas
authors.affiliation: Institute for Lung Health, Glenfield Hospital, Leicester, United Kingdom.
authors.forename: Christopher E
authors.initials: CE
authors.lastname: Brightling
authors.affiliation: Institute for Lung Health, Glenfield Hospital, Leicester, United Kingdom.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Wardlaw
authors.affiliation: Institute for Lung Health, Glenfield Hospital, Leicester, United Kingdom.
authors.forename: Ruth H
authors.initials: RH
authors.lastname: Green
mesh_terms: D000305:Adrenal Cortex Hormones / Q000627:therapeutic use; D000328:Adult; D000368:Aged; D001249:Asthma / Q000145:classification* / Q000188:drug therapy / Q000503:physiopathology; D002965:Classification; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007249:Inflammation / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D011446:Prospective Studies
chemical_list: D000305:Adrenal Cortex Hormones