BACKGROUND Chronic obstructive pulmonary disease (COPD) exhibits considerable progression heterogeneity. We hypothesized that elastic principal graph analysis (EPGA) would identify distinct clinical phenotypes and their longitudinal relationships. METHODS Cross-sectional data from 8,972 tobacco-exposed COPDGene participants, with and without COPD, were used to train a model with EPGA, using thirty clinical, physiologic and CT features. Principal component analysis (PCA) was used to reduce data dimensionality to six principal components. An elastic principal tree was fitted to the reduced space. 4,585 participants from COPDGene Phase 2 were used to test longitudinal trajectories. 2,652 participants from SPIROMICS tested external reproducibility. RESULTS Our analysis used cross-sectional data to create an elastic principal tree, where the concept of time is represented by distance on the tree. Six clinically distinct tree segments were identified that differed by lung function, symptoms, and CT features: 1) Subclinical (SC); 2) Parenchymal Abnormality (PA); 3) Chronic Bronchitis (CB); 4) Emphysema Male (EM); 5) Emphysema Female (EF); and 6) Severe Airways (SA) disease. Cross-sectional SPIROMICS data confirmed similar groupings. 5-year data from COPDGene mapped longitudinal changes onto the tree. 29% of patients changed segment during follow-up; longitudinal trajectories confirmed a net flow of patients along the tree, from SC towards Emphysema, although alternative trajectories were noted, through airway disease predominant phenotypes, CB and SA. CONCLUSION This novel analytic methodology provides an approach to defining longitudinal phenotypic trajectories using cross sectional data. These insights are clinically relevant and could facilitate precision therapy and future trials to modify disease progression.

中文翻译：

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COPD 表型的时间探索：来自 COPDGene 和 SPIROMICS 队列的见解。


背景 慢性阻塞性肺疾病 （COPD） 表现出相当大的进展异质性。我们假设弹性主图分析 （EPGA） 将识别不同的临床表型及其纵向关系。方法 来自 8,972 名烟草暴露 COPDGene 参与者（有和没有 COPD）的横断面数据用于使用 EPGA 训练模型，使用 30 个临床、生理和 CT 特征。主成分分析 （PCA） 用于将数据维度减少到 6 个主成分。在缩小的空间上安装了一棵弹性主树。来自 COPDGene 第 2 阶段的 4,585 名参与者被用于测试纵向轨迹。来自 SPIROMICS 的 2,652 名参与者测试了外部重现性。结果我们的分析使用横截面数据创建了一个弹性主树，其中时间的概念由树上的距离表示。确定了六个临床上不同的树段，它们因肺功能、症状和 CT 特征而异：1） 亚临床 （SC）;2） 实质异常 （PA）;3） 慢性支气管炎 （CB）;4） 男性肺气肿 （EM）;5） 女性肺气肿 （EF）;6） 严重气道 （SA） 疾病。横截面 SPIROMICS 数据证实了类似的分组。来自 COPDGene 的 5 年数据将纵向变化映射到树上。29% 的患者在随访期间改变了节段;纵向轨迹证实了沿树从 SC 到肺气肿的患者净流，尽管通过气道疾病的主要表型 CB 和 SA 注意到了替代轨迹。结论 这种新颖的分析方法提供了一种使用横截面数据定义纵向表型轨迹的方法。 这些见解具有临床相关性，可以促进精准治疗和未来的试验，以改变疾病进展。