Abstract
BACKGROUND: The heterogeneity of chronic obstructive pulmonary disease (COPD) is increasingly recognized. To characterize the heterogeneity of COPD, we aimed to identify subtypes related to quantitative CT by using principal component analysis (PCA) and cluster analysis. METHODS: The data of 1879 participants in the SPIROMICS study were obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center. A combination of PCA and k-means clustering was used to analyze the data from these participants in the SPIROMICS study. We randomly split the samples into training and validation sets. Clusters were evaluated for their relationship with acute exacerbation risk throughout the entire follow-up period. The results of the training set were confirmed in the validation set. To avoid sampling errors, we conducted 10 random sampling cycles. Normalized mutual information (NMI) was applied in every cycle to evaluate the stability of clustering. RESULTS: We identified five clusters related to quantitative CT characterized as follows: (1) male-dominated low disease impact cluster, (2) obesity with relatively high symptom burden cluster, (3) airway wall lesion cluster, (4) lung upper region zone-predominant emphysema cluster, (5) severe emphysema cluster. There are significant differences in acute exacerbation risk among these five clusters. CONCLUSIONS: Cluster analysis identified 5 clusters related to quantitative CT of all participants in the SPIROMICS cohort with significant differences in baseline characteristics and acute exacerbation risk. The stability of clustering results was validated through NMI in 10 sampling cycles. In addition, dimensionality reduction results showed high reproducibility in different studies.