Pratte, K.A.

Abstract Rationale Interstitial lung abnormalities (ILA) are abnormal findings on chest CT scans that may represent early-stages of interstitial lung disease. The presence of fibrosis (traction bronchiectasis and/or honeycombing) identifies those with ILA at an increased risk for mortality, however, determining who will progress remains a challenge, and the role of blood-based biomarkers in ILA risk-stratification is unknown. Methods Data from four prospective cohort studies were used to identify associations between plasma proteins measured with DNA aptamers (SomaScan) and ILA, fibrosis, and mortality. The Genetic Epidemiology of COPD Study (COPDGene) was used to identify protein associations, and findings were replicated by meta-analysis of the Age, Gene/Environment Susceptibility-Reykjavik study (AGES-Reykjavik), SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS), and Framingham Heart Study (FHS). Protein associations with ILA and with fibrotic ILA compared to non-fibrotic ILA were performed using multivariable logistic regression; among those with ILA, Cox proportional hazards models were used to identify protein associations with mortality. Models were adjusted for age, gender, race, BMI, smoking status, pack-year smoking history, and for mortality, COPD. Benjamini-Hochberg false discovery rate p-values <0.05 were considered significant. Results 524 (10.7%) of 4,899 COPDGene participants had ILA, of whom 57 (10.9%) had fibrosis; of 8,146 AGES-Reykjavik, SPIROMICS and FHS participants included in the meta-analysis, 666 (8.2%) had ILA, of whom 198 (29.7%) had fibrosis. In COPDGene, 230 proteins were associated with ILA; the most significant of which were SFTPD (adjusted odds ratio per standard deviation [SD] of protein [OR]=2.21, p = 3.7E-41) and SFTPB (OR = 1.82, p = 7.9E-37). In the meta-analysis, 159 of the ILA-associated proteins were replicated. Of the 230 ILA-associated proteins in COPDGene, 9 were associated with fibrosis, and 5 of these 9 proteins were associated with mortality among those with ILA. The 5 proteins associated with all three outcomes were WFDC2 (ORILA=1.90, p = 2.6E-34; ORFibrosis=2.00, p = 0.0001; adjusted hazards of mortality per SD of protein [HR]=1.59, p = 3.4E-05), GDF15 (ORILA=1.67, p = 3.6E-23; ORFibrosis=1.78, p = 0.0006; HR = 1.63, p = 1.4E-06), SCGB3A1 (ORILA=1.52, p = 4.5e-14; ORFibrosis=1.80, p = 0.0002; HR = 1.25, p = 0.02), TREM1 (ORILA=1.44, p = 1.4e-14; ORFibrosis=1.62, p = 0.001; HR = 1.25, p = 0.02) ICAM5 (ORILA=1.42, p = 1.2e-11; ORFibrosis=1.79, p = 0.0001; HR = 1.31, p = 0.002). In the meta-analysis, 6 of the 9 fibrosis-associated proteins and all 5 ILA mortality-associated proteins replicated. Conclusion This represents the first plasma proteomic screen demonstrating reproducible biomarker associations not only with ILA presence, but also with measures of severity and adverse outcomes. These results provide compelling targets for further study, including their prospective evaluation as clinical biomarkers for early disease identification and prognostication. This abstract is funded by: Multiple Sources

Abstract Rationale To construct multi-trait polygenic scores (PRS) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validate their performance in diverse cohorts, and identify PRS-related proteins for potential therapeutic targeting. Methods PRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic white participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), MassGeneral Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012). Results PRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects (RE) OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE beta 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRS that were also validated with measured protein levels in COPDGene and UK biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include AGER (RAGE), IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations. Conclusions Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management. Trial registration. COPDGene: NCT00608764; ECLIPSE: NCT00292552. This abstract is funded by: NIH and the COPD Foundation

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