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Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts.

ABSTRACT: BACKGROUND:COPD is a heterogeneous disease, but there is little consensus on specific definitions for COPD subtypes. Unsupervised clustering offers the promise of 'unbiased' data-driven assessment of COPD heterogeneity. Multiple groups have identified COPD subtypes using cluster analysis, but there has been no systematic assessment of the reproducibility of these subtypes. OBJECTIVE:We performed clustering analyses across 10 cohorts in North America and Europe in order to assess the reproducibility of (1) correlation patterns of key COPD-related clinical characteristics and (2) clustering results. METHODS:We studied 17?146 individuals with COPD using identical methods and common COPD-related characteristics across cohorts (FEV1, FEV1/FVC, FVC, body mass index, Modified Medical Research Council score, asthma and cardiovascular comorbid disease). Correlation patterns between these clinical characteristics were assessed by principal components analysis (PCA). Cluster analysis was performed using k-medoids and hierarchical clustering, and concordance of clustering solutions was quantified with normalised mutual information (NMI), a metric that ranges from 0 to 1 with higher values indicating greater concordance. RESULTS:The reproducibility of COPD clustering subtypes across studies was modest (median NMI range 0.17-0.43). For methods that excluded individuals that did not clearly belong to any cluster, agreement was better but still suboptimal (median NMI range 0.32-0.60). Continuous representations of COPD clinical characteristics derived from PCA were much more consistent across studies. CONCLUSIONS:Identical clustering analyses across multiple COPD cohorts showed modest reproducibility. COPD heterogeneity is better characterised by continuous disease traits coexisting in varying degrees within the same individual, rather than by mutually exclusive COPD subtypes.

SUBMITTER: Castaldi PJ

PROVIDER: S-EPMC6013053 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts.

Castaldi Peter J PJ Benet Marta M Petersen Hans H Rafaels Nicholas N Finigan James J Paoletti Matteo M Marike Boezen H H Vonk Judith M JM Bowler Russell R Pistolesi Massimo M Puhan Milo A MA Anto Josep J Wauters Els E Lambrechts Diether D Janssens Wim W Bigazzi Francesca F Camiciottoli Gianna G Cho Michael H MH Hersh Craig P CP Barnes Kathleen K Rennard Stephen S Boorgula Meher Preethi MP Dy Jennifer J Hansel Nadia N NN Crapo James D JD Tesfaigzi Yohannes Y Agusti Alvar A Silverman Edwin K EK Garcia-Aymerich Judith J

Thorax 20170621 11

<h4>Background</h4>COPD is a heterogeneous disease, but there is little consensus on specific definitions for COPD subtypes. Unsupervised clustering offers the promise of 'unbiased' data-driven assessment of COPD heterogeneity. Multiple groups have identified COPD subtypes using cluster analysis, but there has been no systematic assessment of the reproducibility of these subtypes.<h4>Objective</h4>We performed clustering analyses across 10 cohorts in North America and Europe in order to assess t ...[more]

PMID: 28637835

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Project description:IntroductionChronic obstructive pulmonary disease (COPD) can progress across several domains, complicating the identification of the determinants of disease progression. In our previous work, we applied k-means clustering to spirometric and chest radiological measures to identify four COPD-related subtypes: 'relatively resistant smokers (RRS)', 'mild upper lobe-predominant emphysema (ULE)', 'airway-predominant disease (AD)' and 'severe emphysema (SE)'. In the current study, we examined the associations of these subtypes to longitudinal COPD-related health measures as well as blood transcriptomic and plasma proteomic biomarkers.MethodsWe included 8266 non-Hispanic white and African-American smokers from the COPDGene study. We used linear regression to investigate cluster associations to 5-year prospective changes in spirometric and radiological measures and to gene expression and protein levels. We used Cox-proportional hazard test to test for cluster associations to prospective exacerbations, comorbidities and mortality.ResultsThe RRS, ULE, AD and SE clusters represented 39%, 15%, 26% and 20% of the studied cohort at baseline, respectively. The SE cluster had the greatest 5-year FEV1 (forced expiratory volume in 1 s) and emphysema progression, and the highest risks of exacerbations, cardiovascular disease and mortality. The AD cluster had the highest diabetes risk. After adjustments, only the SE cluster had an elevated respiratory mortality risk, while the ULE, AD and SE clusters had elevated all-cause mortality risks. These clusters also demonstrated differential protein and gene expression biomarker associations, mostly related to inflammatory and immune processes.ConclusionCOPD k-means subtypes demonstrate varying rates of disease progression, prospective comorbidities, mortality and associations to transcriptomic and proteomic biomarkers. These findings emphasise the clinical and biological relevance of these subtypes, which call for more study for translation into clinical practice.Trail registration numberNCT00608764.

Project description:One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naÃ¯ve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. Briefly, gene expression was derived from whole blood samples in ECLIPSE subjects and peripheral blood mononuclear cells (PBMCs) for the COPDGene subjects. Gene expression profiling was performed using the Affymetrix Human U133 Plus2 array. Gene expression data were log-transformed, and background correction and normalization were performed for the merged ECLIPSE and COPDGene samples using robust multi-array averaging and quantile normalization as implemented in the affy Bioconductor package[27]. Of the 136 COPDGene subjects reported in a previous publication[13], one self-reported African-American subject was removed from analysis, which was conducted on the remaining 135 non-Hispanic white subjects. To identify a set of genes associated with COPD, we performed differential expression analysis for 38,519 probesets in ECLIPSE that passed quality control measures. Normalized probeset intensities were related to measures indicative of two primary dimensions of pulmonary impairment in COPD airway obstruction as indicated by two measures of spirometric lung function (FEV1 (% of predicted) and FEV1/FVC) and lung parenchymal destruction, i.e., emphysema (as quantified by the percentage of low attenuation area less than -950 Hounsfield units on lung computed tomography, %LAA-950). The analysis was conducted using the limma Bioconductor package, and the false discovery rate was controlled at 5%. The following covariates were included in the differential expression analysis age, pack-years of cigarette smoke exposure, and gender. After standardizing gene expression data from 229 ECLIPSE subjects by the variance of each probe set, we applied NMF[29] and NBS[6] to identify meta-patients (i.e. subtypes or subject clusters) and meta-genes (i.e. representative subtype expression profiles). Cross-sectional study of smokers. 229 subjects from the ECLIPSE study were analyzed in the model discovery phase. 135 subjects from the COPDGene Study (GSE42057) were used for replication. Please note that the entire data set for total 364 samples including the re-analyzed samples is provided in the *364samples.txt files.

Project description:One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naïve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. Briefly, gene expression was derived from whole blood samples in ECLIPSE subjects and peripheral blood mononuclear cells (PBMCs) for the COPDGene subjects. Gene expression profiling was performed using the Affymetrix Human U133 Plus2 array. Gene expression data were log-transformed, and background correction and normalization were performed for the merged ECLIPSE and COPDGene samples using robust multi-array averaging and quantile normalization as implemented in the affy Bioconductor package[27]. Of the 136 COPDGene subjects reported in a previous publication[13], one self-reported African-American subject was removed from analysis, which was conducted on the remaining 135 non-Hispanic white subjects. To identify a set of genes associated with COPD, we performed differential expression analysis for 38,519 probesets in ECLIPSE that passed quality control measures. Normalized probeset intensities were related to measures indicative of two primary dimensions of pulmonary impairment in COPD airway obstruction as indicated by two measures of spirometric lung function (FEV1 (% of predicted) and FEV1/FVC) and lung parenchymal destruction, i.e., emphysema (as quantified by the percentage of low attenuation area less than -950 Hounsfield units on lung computed tomography, %LAA-950). The analysis was conducted using the limma Bioconductor package, and the false discovery rate was controlled at 5%. The following covariates were included in the differential expression analysis age, pack-years of cigarette smoke exposure, and gender.

Project description:BackgroundAsthma and chronic obstructive pulmonary disease (COPD) are airway diseases with similar clinical manifestations, despite differences in pathophysiology. Asthma-COPD overlap (ACO) is a condition characterized by overlapping clinical features of both diseases. There have been few reports regarding the prevalence of ACO in COPD and severe asthma cohorts. ACO is heterogeneous; patients can be classified on the basis of phenotype differences. This study was performed to analyze the prevalence of ACO in COPD and severe asthma cohorts. In addition, this study compared baseline characteristics among ACO patients according to phenotype.MethodsPatients with COPD were prospectively enrolled into the Korean COPD subgroup study (KOCOSS) cohort. Patients with severe asthma were prospectively enrolled into the Korean Severe Asthma Registry (KoSAR). ACO was defined in accordance with the updated Spanish criteria. In the COPD cohort, ACO was defined as bronchodilator response (BDR) ≥ 15% and ≥ 400 mL from baseline or blood eosinophil count (BEC) ≥ 300 cells/μL. In the severe asthma cohort, ACO was defined as age ≥ 35 years, smoking ≥ 10 pack-years, and post-bronchodilator forced expiratory volume in 1 s/forced vital capacity < 0.7. Patients with ACO were divided into four groups according to smoking history (threshold: 20 pack-years) and BEC (threshold: 300 cells/μL).ResultsThe prevalence of ACO significantly differed between the COPD and severe asthma cohorts (19.8% [365/1,839] vs. 12.5% [104/832], respectively; P < 0.001). The percentage of patients in each group was as follows: group A (light smoker with high BEC) - 9.1%; group B (light smoker with low BEC) - 3.7%; group C (moderate to heavy smoker with high BEC) - 73.8%; and group D (moderate to heavy smoker with low BEC) - 13.4%. Moderate to heavy smoker with high BEC group was oldest, and showed weak BDR response. Age, sex, BDR, comorbidities, and medications significantly differed among the four groups.ConclusionThe prevalence of ACO differed between COPD and severe asthma cohorts. ACO patients can be classified into four phenotype groups, such that each phenotype exhibits distinct characteristics.

Dataset Information

Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts.

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Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts.

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