Continuous evaluation of participants in the COPDGene (chronic obstructive pulmonary disease) study will provide useful insights that will facilitate better identification of disease subtypes, according to findings published in CHEST.
COPD is a heterogeneous syndrome with many COPD subtypes, and there has not been a consensus on how many subtypes exist or how to define them. The COPDGene study enrolled a total of 10,192 current and former smokers across the full spectrum of lung function at 21 different centers across the United States. In the current review, researchers placed the COPDGene clustering studies in context with other highly cited COPD clustering studies and summarized the main COPD subtype findings.
The researchers found that most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. In addition, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut points. Furthermore, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory COPD subtypes. Finally, trajectory-based COPD subtyping captured differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity.
The authors concluded that, “Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.”
Disclosure: This clinical trial was supported by AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, and Sunovion. Please see the original reference for a full list of authors’ disclosures.
Castaldi PJ, Boueiz A, Yun J, et al; for the COPD Gene Investigators. Machine learning characterization of COPD subtypes: Insights from the COPD gene study [published online December 27, 2019]. CHEST. doi:10.1016/j.chest.2019.11.039