title: Predicting intermediate phenotypes in asthma using bronchoalveolar lavage-derived cytokines.
abstract: An important problem in realizing personalized medicine is the development of methods for identifying disease subtypes using quantitative proteomics. Recently we found that bronchoalveolar lavage (BAL) cytokine patterns contain information about dynamic lung responsiveness. In this study, we examined physiological data from 1,048 subjects enrolled in the US Severe Asthma Research Program (SARP) to identify four largely separable, quantitative intermediate phenotypes. Upper extremes in the study population were identified for eosinophil- or neutrophil-predominant inflammation, bronchodilation in response to albuterol treatment, or methacholine sensitivity. We evaluated four different statistical ("machine") learning methods to predict each intermediate phenotype using BAL A-cytokine measurements on a 76 subject subset. Comparison of these models using area under the ROC curve and overall classification accuracy indicated that logistic regression and multivariate adaptive regression splines produced the most accurate methods to predict intermediate asthma phenotypes. These robust classification methods will aid future translational studies in asthma targeted at specific intermediate phenotypes.
authors: Sealy Center for Molecular Medicine, University of Texas Medical Branch, Galveston, USA. arbrasie@utmb.edu
authors: Allan R
authors: AR
authors: Brasier
authors: Sundar
authors: S
authors: Victor
authors: Hyunsu
authors: H
authors: Ju
authors: William W
authors: WW
authors: Busse
authors: Douglas
authors: D
authors: Curran-Everett
authors: Eugene
authors: E
authors: Bleecker
authors: Mario
authors: M
authors: Castro
authors: Kian Fan
authors: KF
authors: Chung
authors: Benjamin
authors: B
authors: Gaston
authors: Elliot
authors: E
authors: Israel
authors: Sally E
authors: SE
authors: Wenzel
authors: Serpil C
authors: SC
authors: Erzurum
authors: Nizar N
authors: NN
authors: Jarjour
authors: William J
authors: WJ
authors: Calhoun
mesh_terms: D000293:Adolescent; D017668:Age of Onset; D001249:Asthma / Q000175:diagnosis* / Q000276:immunology*; D001992:Bronchoalveolar Lavage Fluid / Q000276:immunology*; D002648:Child; D002675:Child, Preschool; D016207:Cytokines / Q000276:immunology / Q000378:metabolism*; D004804:Eosinophils / Q000276:immunology; D005260:Female; D006801:Humans; D016130:Immunophenotyping; D007223:Infant; D016015:Logistic Models; D008297:Male; D009504:Neutrophils / Q000276:immunology; D057285:Precision Medicine; D011237:Predictive Value of Tests; D040901:Proteomics; D012720:Severity of Illness Index; D055815:Young Adult
chemical_list: D016207:Cytokines