Language
English
Publication Date
7-18-2025
Journal
iScience
DOI
10.1016/j.isci.2025.112966
PMID
40687815
PMCID
PMC12274847
PubMedCentral® Posted Date
6-19-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Chronic obstructive pulmonary disease (COPD) is a severe, progressive, and heterogeneous disease with a poor outcome. Inflammation plays a central role in disease pathogenesis; however, the interplay between immune changes and disease heterogeneity has been difficult to unravel. We performed a multilevel immunoinflammatory characterization of patients with COPD using flow cytometry, cytokine profiling, single-cell, or spatial transcriptomics in combination with machine learning algorithms. Our cross-cohort analysis demonstrated shared skewing of immune profiles in COPD lungs toward adaptive immune cells. We furthermore identified a subgroup of patients with COPD with a distinct immune profile, characterized by increased antigen-presenting cells, mast cells, and CD8+ cells, and circulating IL-1β, IFN-β, and GM-CSF, that were associated with increased emphysema severity and decreased gas exchange parameters independent of their GOLD-stage. Our findings suggest that unbiased immune profiling can refine disease classification and reveal inflammation-driven disease subtypes with potential relevance for prognosis and treatment strategies.
Keywords
respiratory medicine, machine learning
Published Open-Access
yes
Recommended Citation
Bordag, Natalie; Jandl, Katharina; Syarif, Ayu Hutami; et al., "Machine Learning Assisted Immune Profiling of COPD Identifies a Unique Emphysema Subtype Independent of Gold Stage" (2025). Faculty and Staff Publications. 4801.
https://digitalcommons.library.tmc.edu/baylor_docs/4801
Graphical Abstract
Included in
Critical Care Commons, Health Services Research Commons, Medical Sciences Commons, Pulmonology Commons, Sleep Medicine Commons