Wells, J M

Abstract Rationale Chronic lower respiratory diseases (CLRD), including chronic obstructive pulmonary disease (COPD), were the third-leading causes of death globally in 2019. Quantitative computed tomography (CT) measures of airway wall and lumen areas are associated with greater respiratory symptoms, reduced FEV1, accelerated lung function decline, and higher risk of incident COPD. Even in individuals without COPD, greater airway wall thickness—corresponding to smaller absolute wall and lumen areas—predicts incident COPD and CLRD-related hospitalizations and mortality. Aims To identify protein signatures and biological pathways underlying airway lumen and wall areas using cross-sectional plasma protein associations with CT measures. Methods SPIROMICS is a multicenter longitudinal case-control study that recruited 2,983 adults with COPD and a 20+ packyear smoking history and controls aged 40-80 years between 2010-2015. Standardized chest CT scans were labeled with airway measures. A total of 1,255 plasma proteins measured by SomaScan were included. Linear regression models adjusted for demographic, clinical, and imaging covariates. Significance was defined by a False Discovery Rate (FDR) p-value (q-value) <0.05 using the Benjamini-Hochberg method. Replication was performed in two external cohorts with inverse variance-weighted meta-analysis. Proteins meeting nominal replication underwent elastic net regression and pathway enrichment (Gene Ontology [GO] and Reactome). Results Among 2,065 participants with CT and proteomic data (mean age 63.2 years, 46.3% women, 61.6% with COPD), proteins meeting replication were significantly associated with lumen area (2 proteins; top hit a disintegrin and metalloproteinase with thrombospondin motifs 5, q = 0.016), wall area (3 proteins; top hit leptin, q = 0.011), and percent wall area (16 proteins; top hit roundabout homolog 2, q = 0.002). Elastic net selected 2 proteins for lumen area, 3 for wall area, and 12 for percent wall area. Pathway enrichment revealed FDR-significant pathways only for percent wall area via GO (50 pathways; top hit axogenesis) and Reactome (1 pathway; diseases of glycosylation). Conclusions This study identified distinct proteins, signatures, and biological pathways associated with CT airway measures of lumen area, wall area, and percent wall area. Proteins linked to airway lumen and wall area are involved in metabolic and vasoactive processes, while those for percent wall area are involved in vascular remodeling, cell adhesion, and extracellular matrix organization—highlighting structural pathophysiologic features of COPD. These findings may inform biomarker discovery and prediction of disease onset and progression. This abstract is funded by: Funding: Daniel E. Guzman, MD, MSE was funded by T32-HL144442. SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C, 75N92024D00012), grants from the NIH/NHLBI (U01HL137880, U24HL141762, R01HL182622, R01HL144718, and R01HL093081), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from Amgen; AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; Boehringer-Ingelheim Pharmaceuticals, Inc.; Bristol Myers Squibb; Chiesi Farmaceutici S.p.A.; Forest Research Institute, Inc.; Genentech; GlaxoSmithKline; Grifols Therapeutics, Inc.; Ikaria, Inc.; MGC Diagnostics; Novartis Pharmaceuticals Corporation; Nycomed GmbH; Polarean; ProterixBio; Regeneron Pharmaceuticals, Inc.; Sanofi; Sunovion; Takeda Pharmaceutical Company; Theravance Biopharma; Verona; and Mylan/Viatris.

Abstract Background Muscle loss in COPD results from both disuse atrophy and systemic inflammation. We sought to identify inflammatory biomarkers associated with loss of pectoralis muscle area in COPD using serial CT scans and peripheral blood proteomics. Design We analyzed five-year longitudinal data from COPDGene, an ongoing observational cohort of current and former tobacco smokers with or without airflow obstruction. We quantified pectoralis major muscle (PMM) area on chest CT scans using a 3D convolutional neural network (CNN) trained on CT volumes and annotations provided by the TotalSegmentator project. We compared participants with CT PMM data and plasma proteins by SOMAScan (1K platform) from Phase 2 and 3 (paired n = 1,191). The correlation coefficient (r) between longitudinal changes in protein and in PMM area was plotted against the false discovery rate (FDR)-adjusted p-values. Proteins significantly associated with muscle loss (blue) or gain (red) were visualized using volcano plots. We then analyzed a subset (paired n = 500) of individuals with BMI>25 in Phase 2 whose BMI declined in Phase 3. Proteomic models were adjusted for age and clinical center. Pathway analysis utilized G:Profiler. Results Of the participants with paired proteomic and PMM data, mean PMM area was 21.7 ± 17.3 cm² at Phase 2 and 21.3 ± 17.9 cm² at Phase 3, with an annual change in PMM area of -0.31 + 0.99 cm². Multiple proteins were associated with muscle loss from Phase 2 to 3 (including leptin, FABP, and growth hormone receptor) and muscle gain (including F172A, GMEB2, and RNF41) (Fig 1a). Significant pathways were related to cancer, growth factor receptor dysregulation, and PI3K/AKT signaling. When restricting to those with BMI>25 who experienced BMI decline at Phase 3, muscle loss was associated with increased leptin and PACAP, versus association of gain with increased GDF118, EF1β, and FJF6 (Fig 1b). In the entire group, leptin declined between Phase 2 to Phase 3 in n = 571 participants and increased or was unchanged in n = 620. Compared with leptin decline, unchanged or increased leptin was associated with significantly greater interval PMM loss (−2.25 ± 9.75 cm² vs + 0.98 ± 9.43 cm²; p < 0.001) (Fig 1c). Conclusion Loss of PMM over five years was associated with several plasma proteins, including FABP and growth hormone receptor. The top biomarker, leptin, is a pleiotropic adipokine that regulates energy balance and inflammation. Integrating imaging and proteomics may identify future therapeutic targets to mitigate skeletal muscle loss in COPD. This abstract is funded by: This work was supported by NHLBI grants U01 HL089897 and U01 HL089856 and by NIH contract 75N92023D00011