• Alexander J Bell, Sundaresh Ram, Wassim W Labaki, Susan Murray, Ella A Kazerooni, Stefanie Galban, Fernando J Martinez, Charles R Hatt, Jennifer M Wang, Vladimir Ivanov, Paul McGettigan, Edward Khokhlovich, Enrico Maiorino, Rahul Suryadevara, Adel Boueiz, Peter Castaldi, Evgeny M Mirkes, Andrei Zinovyev, Alexander N Gorban, Craig J Galban, and MeiLan K Han.
• University of Michigan, Department of Radiology, Ann Arbor, Michigan, United States; alejbell@med.umich.edu.
• Am. J. Respir. Crit. Care Med. 2024 Sep 13.

BackgroundChronic 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.MethodsCross-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.ResultsOur 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.ConclusionThis 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.

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