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Cluster analysis and clinical asthma phenotypes.

ABSTRACT: 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.To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups.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.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).Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms.

SUBMITTER: Haldar P

PROVIDER: S-EPMC3992366 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Cluster analysis and clinical asthma phenotypes.

Haldar Pranab P Pavord Ian D ID Shaw Dominic E DE Berry Michael A MA Thomas Michael M Brightling Christopher E CE Wardlaw Andrew J AJ Green Ruth H RH

American journal of respiratory and critical care medicine 20080514 3

<h4>Rationale</h4>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.<h4>Objectives</h4>To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups.<h4>Methods</h4>We performed k-means cluster analysis in three independent asthma populations. Clusters of a p ...[more]

PMID: 18480428

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Project description:BackgroundAsthma is a heterogeneous disease with variability among patients in characteristics such as lung function, symptoms and control, body weight, markers of inflammation, and responsiveness to glucocorticoids (GC). Cluster analysis of well-characterized cohorts can advance understanding of disease subgroups in asthma and point to unsuspected disease mechanisms. We utilized an hypothesis-free cluster analytical approach to define the contribution of obesity and related variables to asthma phenotype.Methodology and principal findingsIn a cohort of clinical trial participants (n = 250), minimum-variance hierarchical clustering was used to identify clinical and inflammatory biomarkers important in determining disease cluster membership in mild and moderate persistent asthmatics. In a subset of participants, GC sensitivity was assessed via expression of GC receptor alpha (GCRα) and induction of MAP kinase phosphatase-1 (MKP-1) expression by dexamethasone. Four asthma clusters were identified, with body mass index (BMI, kg/m(2)) and severity of asthma symptoms (AEQ score) the most significant determinants of cluster membership (F = 57.1, p<0.0001 and F = 44.8, p<0.0001, respectively). Two clusters were composed of predominantly obese individuals; these two obese asthma clusters differed from one another with regard to age of asthma onset, measures of asthma symptoms (AEQ) and control (ACQ), exhaled nitric oxide concentration (F(E)NO) and airway hyperresponsiveness (methacholine PC(20)) but were similar with regard to measures of lung function (FEV(1) (%) and FEV(1)/FVC), airway eosinophilia, IgE, leptin, adiponectin and C-reactive protein (hsCRP). Members of obese clusters demonstrated evidence of reduced expression of GCRα, a finding which was correlated with a reduced induction of MKP-1 expression by dexamethasoneConclusions and significanceObesity is an important determinant of asthma phenotype in adults. There is heterogeneity in expression of clinical and inflammatory biomarkers of asthma across obese individuals. Reduced expression of the dominant functional isoform of the GCR may mediate GC insensitivity in obese asthmatics.

Project description:BACKGROUND: Evaluation of the airway transcriptome may reveal patterns of gene expression that are associated with clinical phenotypes of asthma. To define transcriptomic endotypes of asthma (TEA) we analyzed gene expression in induced sputum that correlate with phenotypes of disease. METHODS: Gene expression was measured in sputum of subjects with asthma using Affymetrix HuGene ST 1.0 microarrays. Unsupervised clustering analysis of genes in pathways selected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) identified TEA clusters. Clinical characteristics were compared and logistic regression analysis of matched blood samples defined an expression profile to determine the TEA cluster assignment in a cohort of children with asthma for validation. RESULTS: Three TEA clusters were identified. TEA cluster 1 had the most subjects with a history of intubation (P = 0.05), a lower pre-bronchodilator FEV1 (P = 0.006), a higher bronchodilator response (P = 0.03), and higher exhaled nitric oxide levels (P = 0.04), compared to the other TEA clusters. TEA cluster 2, the smallest cluster had the most subjects that were hospitalized for asthma (P = 0.04). Subjects in TEA cluster 3, the largest cluster, had normal lung function, low exhaled nitric oxide levels, and lower inhaled steroid requirements. Evaluation of TEA clusters in children confirmed that TEA clusters 1 and 2 are associated with a history of intubation (P = 5.58 x 10-06) and hospitalization (P = 0.01), respectively. CONCLUSIONS: Patterns of gene expression in the sputum and blood reveal TEA clusters that are associated with severe asthma phenotypes in children and adults. Gene expression was measured in sputum of subjects with asthma using Affymetrix HuGene ST 1.0 microarrays. Unsupervised clustering analysis of genes in pathways selected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) identified TEA clusters. Clinical characteristics were compared and logistic regression analysis of matched blood samples defined an expression profile to determine the TEA cluster assignment in a cohort of children with asthma for validation.

Project description:BackgroundAsthma is a disease of varying severity and differing disease mechanisms. To date, studies aimed at stratifying asthma into clinically useful phenotypes have produced a number of phenotypes that have yet to be assessed for stability and to be validated in independent cohorts. The aim of this study was to define and validate, for the first time ever, clinically driven asthma phenotypes using two independent, severe asthma cohorts: ADEPT and U-BIOPRED.MethodsFuzzy partition-around-medoid clustering was performed on pre-specified data from the ADEPT participants (n = 156) and independently on data from a subset of U-BIOPRED asthma participants (n = 82) for whom the same variables were available. Models for cluster classification probabilities were derived and applied to the 12-month longitudinal ADEPT data and to a larger subset of the U-BIOPRED asthma dataset (n = 397). High and low type-2 inflammation phenotypes were defined as high or low Th2 activity, indicated by endobronchial biopsies gene expression changes downstream of IL-4 or IL-13.ResultsFour phenotypes were identified in the ADEPT (training) cohort, with distinct clinical and biomarker profiles. Phenotype 1 was "mild, good lung function, early onset", with a low-inflammatory, predominantly Type-2, phenotype. Phenotype 2 had a "moderate, hyper-responsive, eosinophilic" phenotype, with moderate asthma control, mild airflow obstruction and predominant Type-2 inflammation. Phenotype 3 had a "mixed severity, predominantly fixed obstructive, non-eosinophilic and neutrophilic" phenotype, with moderate asthma control and low Type-2 inflammation. Phenotype 4 had a "severe uncontrolled, severe reversible obstruction, mixed granulocytic" phenotype, with moderate Type-2 inflammation. These phenotypes had good longitudinal stability in the ADEPT cohort. They were reproduced and demonstrated high classification probability in two subsets of the U-BIOPRED asthma cohort.ConclusionsFocusing on the biology of the four clinical independently-validated easy-to-assess ADEPT asthma phenotypes will help understanding the unmet need and will aid in developing tailored therapies.Trial registrationNCT01274507 (ADEPT), registered October 28, 2010 and NCT01982162 (U-BIOPRED), registered October 30, 2013.

Project description:Classification of chronic heart failure (HF) is on the basis of criteria that may not adequately capture disease heterogeneity. Improved phenotyping may help inform research and therapeutic strategies.This study used cluster analysis to explore clinical phenotypes in chronic HF patients.A cluster analysis was performed on 45 baseline clinical variables from 1,619 participants in the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) study, which evaluated exercise training versus usual care in chronic systolic HF. An association between identified clusters and clinical outcomes was assessed using Cox proportional hazards modeling. Differential associations between clinical outcomes and exercise testing were examined using interaction testing.Four clusters were identified (ranging from 248 to 773 patients in each), in which patients varied considerably among measures of age, sex, race, symptoms, comorbidities, HF etiology, socioeconomic status, quality of life, cardiopulmonary exercise testing parameters, and biomarker levels. Differential associations were observed for hospitalization and mortality risks between and within clusters. Compared with cluster 1, risk of all-cause mortality and/or all-cause hospitalization ranged from 0.65 (95% confidence interval [95% CI]: 0.54 to 0.78) for cluster 4 to 1.02 (95% CI: 0.87 to 1.19) for cluster 3. However, for all-cause mortality, cluster 3 had a disproportionately lower risk of 0.61 (95% CI: 0.44 to 0.86). Evidence suggested differential effects of exercise treatment on changes in peak oxygen consumption and clinical outcomes between clusters (p for interaction <0.04).Cluster analysis of clinical variables identified 4 distinct phenotypes of chronic HF. Our findings underscore the high degree of disease heterogeneity that exists within chronic HF patients and the need for improved phenotyping of the syndrome. (Exercise Training Program to Improve Clinical Outcomes in Individuals With Congestive Heart Failure; NCT00047437).

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Cluster analysis and clinical asthma phenotypes.

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Cluster analysis and clinical asthma phenotypes.

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