Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts
Do COPD subtypes really exist? COPD heterogeneity and clustering in 10 independent cohorts
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DOI：
10.1136/thoraxjnl-2016-209846
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发表时间：
2017-11-01
期刊：
影响因子：
10
通讯作者：
Garcia-Aymerich, Judith
中科院分区：
文献类型：
--
作者：
Castaldi, Peter J.;Benet, Marta;Garcia-Aymerich, Judith
COPD是一种异质性疾病，但对COPD亚型的具体定义几乎没有共识。无监督聚类提供了对COPD异质性进行“无偏”数据驱动评估的希望。多个研究小组已经使用聚类分析确定了COPD亚型，目的我们在北美和欧洲的10个队列中进行聚类分析，以评估（1）COPD相关临床特征的相关模式和（2）方法我们使用相同的方法和跨群组的共同COPD相关特征（FEV 1、FEV 1/FVC、FVC、体重指数、改良医学研究理事会评分、哮喘和心血管共病）研究了17146名COPD个体。通过主成分分析（PCA）评估这些临床特征之间的相关模式。聚类分析使用k-中心点和层次聚类，聚类解决方案的一致性进行量化与归一化互信息（NMI），一个度量，范围从0到1，较高的值表示更大的concordance.Results跨研究的COPD聚类亚型的再现性是适度的（中位数NMI范围0.17-0.43）。对于排除不明显属于任何聚类的个体的方法，一致性更好，但仍不理想（中位数NMI范围为0.32-0.60）。来自PCA的COPD临床特征的连续表示更加一致跨study.Conclusions相同的聚类分析在多个COPD队列表现出适度的可重复性。COPD异质性更好地表征为同一个体内不同程度共存的连续疾病特征，而不是相互排斥的COPD亚型。
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.