title: Phenotyping of difficult asthma using longitudinal physiological and biomarker measurements reveals significant differences in stability between clusters.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Mathematics, The College at Brockport, State University of New York, Brockport, NY, USA.|Division of Clinical Epidemiology, McGill University Health Centre, Montreal, QC, Canada.|School of Physical & Occupational Therapy, McGill University, Montreal, QC, Canada.
authors.forename: T
authors.initials: T
authors.lastname: Zaihra
authors.affiliation: Keenan and Li Ka Shing Knowledge Institute of Saint Michael's Hospital, Toronto, ON, Canada.|Institute of Medical Sciences and Department of Medicine, University of Toronto, Toronto, ON, Canada.
authors.forename: C J
authors.initials: CJ
authors.lastname: Walsh
authors.affiliation: Division of Clinical Epidemiology, McGill University Health Centre, Montreal, QC, Canada.|School of Physical & Occupational Therapy, McGill University, Montreal, QC, Canada.|Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Ave. W., Montreal, QC, H3A 1A2, Canada.
authors.forename: S
authors.initials: S
authors.lastname: Ahmed
authors.affiliation: Montreal Chest Institute, McGill University Health Centre, Montreal, QC, Canada.|Meakins Christie Laboratories, and McGill University, Montreal, QC, Canada.
authors.forename: C
authors.initials: C
authors.lastname: Fugère
authors.affiliation: Montreal Chest Institute, McGill University Health Centre, Montreal, QC, Canada.|Meakins Christie Laboratories, and McGill University, Montreal, QC, Canada.
authors.forename: Q A
authors.initials: QA
authors.lastname: Hamid
authors.affiliation: Montreal Chest Institute, McGill University Health Centre, Montreal, QC, Canada.|Meakins Christie Laboratories, and McGill University, Montreal, QC, Canada.
authors.forename: R
authors.initials: R
authors.lastname: Olivenstein
authors.affiliation: Montreal Chest Institute, McGill University Health Centre, Montreal, QC, Canada.|Meakins Christie Laboratories, and McGill University, Montreal, QC, Canada.
authors.forename: J G
authors.initials: JG
authors.lastname: Martin
authors.affiliation: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Ave. W., Montreal, QC, H3A 1A2, Canada. andrea.benedetti@mcgill.ca.|Department of Medicine, McGill University, Montreal, QC, Canada. andrea.benedetti@mcgill.ca.|Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, QC, Canada. andrea.benedetti@mcgill.ca.
authors.forename: A
authors.initials: A
authors.lastname: Benedetti
mesh_terms: D000305:Adrenal Cortex Hormones / Q000627:therapeutic use; D000328:Adult; D001249:Asthma / Q000145:classification* / Q000175:diagnosis*; D015415:Biomarkers; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D018450:Disease Progression; D004804:Eosinophils / Q000166:cytology; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D011792:Quebec; D012129:Respiratory Function Tests; D012720:Severity of Illness Index; D013183:Sputum / Q000166:cytology
chemical_list: D000305:Adrenal Cortex Hormones; D015415:Biomarkers
keywords: Asthma; Exacerbation and Clusters; Longitudinal; Phenotype; Predictors