biostudies-literatureWu WNCATS NIH HHSNCRR NIH HHSNIDA NIH HHSNHLBI NIH HHSNIGMS NIH HHS1280-8https://www.ebi.ac.uk/biostudies/studies/S-EPMC4038417biostudies-literatureUnknown133(5)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. OBJECTIVES: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. CONCLUSION:The unsupervised machine learning approaches used here provide unique insights into disease, confirming other approaches while revealing novel additional phenotypes.The Journal of allergy and clinical immunologyUnsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data.PMC4038417HL69149M01 RR03186HL69167R01GM087694M01RR07122R01-HL69174U10 HL109257HL69349R01 HL069349R01 HL069149U10 HL109250U10 HL109172M01 RR007122R01 HL069167M01 RR003186R01 HL069155HL69130R01 HL069130HL69174R01 HL069174M01 RR018390R01 HL069170HL69170HL69116UL1 TR000005P30 DA035778HL69155HL087665UL1 TR000427R01 GM087694U10 HL109164R01 HL069116R01 HL087665Erzurum SCurran-Everett DIsrael EWenzel SEBusse WWBleecker ECastro MChung KFWu WMoore WCalhoun WJGaston BfalseUnsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data.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. OBJECTIVES: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. CONCLUSION:The unsupervised machine learning approaches used here provide unique insights into disease, confirming other approaches while revealing novel additional phenotypes.2014-01-01T00:00:00Z2014 May2020-10-31T09:14:36Z2019-03-27T01:29:03ZS-EPMC40384172458934410.1016/j.jaci.2013.11.042