title: Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data.
abstract: BACKGROUND
  Previous studies have identified asthma phenotypes based on small numbers of clinical, physiologic, or inflammatory characteristics. However, no studies have used a wide range of variables using machine learning approaches.
  
  
  OBJECTIVE
  We sought to identify subphenotypes of asthma by using blood, bronchoscopic, exhaled nitric oxide, and clinical data from the Severe Asthma Research Program with unsupervised clustering and then characterize them by using supervised learning approaches.
  
  
  METHODS
  Unsupervised clustering approaches were applied to 112 clinical, physiologic, and inflammatory variables from 378 subjects. Variable selection and supervised learning techniques were used to select relevant and nonredundant variables and address their predictive values, as well as the predictive value of the full variable set.
  
  
  RESULTS
  Ten variable clusters and 6 subject clusters were identified, which differed and overlapped with previous clusters. Patients with traditionally defined severe asthma were distributed through subject clusters 3 to 6. Cluster 4 identified patients with early-onset allergic asthma with low lung function and eosinophilic inflammation. Patients with later-onset, mostly severe asthma with nasal polyps and eosinophilia characterized cluster 5. Cluster 6 asthmatic patients manifested persistent inflammation in blood and bronchoalveolar lavage fluid and exacerbations despite high systemic corticosteroid use and side effects. Age of asthma onset, quality of life, symptoms, medications, and health care use were some of the 51 nonredundant variables distinguishing subject clusters. These 51 variables classified test cases with 88% accuracy compared with 93% accuracy with all 112 variables.
  
  
  CONCLUSIONS
  The unsupervised machine learning approaches used here provide unique insights into disease, confirming other approaches while revealing novel additional phenotypes.
authors.affiliation: Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pa. Electronic address: weiwu2@cs.cmu.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.affiliation: Center for Human Genomics, School of Medicine, Wake Forest University, Winston-Salem, NC.
authors.forename: Eugene
authors.initials: E
authors.lastname: Bleecker
authors.affiliation: Center for Human Genomics, School of Medicine, Wake Forest University, Winston-Salem, NC.
authors.forename: Wendy
authors.initials: W
authors.lastname: Moore
authors.affiliation: Division of Allergy and Immunology, University of Wisconsin, Madison, Wis.
authors.forename: William W
authors.initials: WW
authors.lastname: Busse
authors.affiliation: Division of Pulmonary & Critical Care Medicine, Washington University School of Medicine, St Louis, Mo.
authors.forename: Mario
authors.initials: M
authors.lastname: Castro
authors.affiliation: National Heart & Lung Institute, Imperial College, London, United Kingdom.
authors.forename: Kian Fan
authors.initials: KF
authors.lastname: Chung
authors.affiliation: Department of Internal Medicine, University of Texas Medical Branch, Galveston, Tex.
authors.forename: William J
authors.initials: WJ
authors.lastname: Calhoun
authors.affiliation: Department of Pulmonary, Allergy and Critical Care Medicine, Cleveland Clinic, Cleveland, Ohio.
authors.forename: Serpil
authors.initials: S
authors.lastname: Erzurum
authors.affiliation: Division of Pediatric Pulmonology, and Allergy/Immunology, Department of Pediatrics, School of Medicine, Case Western Reserve University, Cleveland, Ohio.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Gaston
authors.affiliation: Pulmonary Division, Brigham and Women's Hospital, Boston, Mass.
authors.forename: Elliot
authors.initials: E
authors.lastname: Israel
authors.affiliation: National Jewish Medical and Research Center, University of Colorado Health Sciences Center, Denver, Colo.
authors.forename: Douglas
authors.initials: D
authors.lastname: Curran-Everett
authors.affiliation: National Jewish Medical and Research Center, University of Colorado Health Sciences Center, Denver, Colo; Asthma Institute, Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pa. Electronic address: wenzelse@upmc.edu.
authors.forename: Sally E
authors.initials: SE
authors.lastname: Wenzel
mesh_terms: D000305:Adrenal Cortex Hormones / Q000008:administration & dosage; D000328:Adult; D017668:Age of Onset; D001249:Asthma / Q000150:complications / Q000188:drug therapy / Q000378:metabolism / Q000473:pathology / Q000503:physiopathology; D018893:Bronchoalveolar Lavage; D004802:Eosinophilia / Q000150:complications / Q000188:drug therapy / Q000378:metabolism / Q000473:pathology / Q000503:physiopathology; D005260:Female; D006801:Humans; D008168:Lung / Q000378:metabolism / Q000469:parasitology / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009298:Nasal Polyps / Q000150:complications / Q000188:drug therapy / Q000378:metabolism / Q000473:pathology / Q000503:physiopathology; D010641:Phenotype; D012720:Severity of Illness Index
chemical_list: D000305:Adrenal Cortex Hormones
keywords: Asthma phenotyping; supervised machine learning approaches; unsupervised approaches; variable analysis