Bowler, R P

Abstract Rationale Impaired lung health may serve as a risk factor for, and a post-acute sequela of, SARS-CoV-2 infection. Yet most prior studies of lung health sequelae of COVID-19 lacked information on pre-infection lung health, a major antecedent confounder. To identify associations of COVID-19 with changes in lung health, we leveraged paired pre- and post-pandemic data on respiratory symptoms, lung function, and lung structure in a general population-based cohort. Methods We studied participants in the Multi-Ethnic Study of Atherosclerosis Lung Study with paired pre-pandemic (2016-2018) and post-pandemic (2022-2024) assessments of respiratory symptoms, spirometry, and lung CT. SARS-CoV-2 infection (2020-2023) was ascertained using a composite of questionnaires, serology, and medical record adjudication, and classified as severe (requiring hospitalization) or non-severe (not requiring hospitalization). Lung health was assessed by questionnaire (mMRC grade ≥2 dyspnea, chronic cough, chronic phlegm, chronic bronchitis), pre-bronchodilator spirometry (FEV1, FVC, FEV1:FVC), and lung density on non-contrast lung CT (% low attenuation areas, % high attenuation areas). Each post-pandemic lung health measure was regressed on infection history and the corresponding pre-pandemic lung health measure, adjusting for age at post-pandemic exam, sex, race/ethnicity, smoking status, and vaccination status at infection. Sensitivity analyses were included- (a) restriction to participants without pre-pandemic respiratory symptoms and (b) with additional adjustment for time since infection and variant wave. Results Among 2,015 participants (mean age 77.2±7.5 years; 55% female; 40% non-Hispanic White; 25% Black; 11% Asian; 23% Hispanic/Latino), 59 (3%) experienced severe and 506 (25%) non-severe SARS-CoV-2 infection. The prevalence of respiratory symptoms was similar or slightly higher at the post- vs. pre-pandemic exam (grade ≥2 dyspnea: 14% vs. 10%; chronic cough: 10% vs. 8%; chronic phlegm: 9% vs. 8%; chronic bronchitis: 4% vs. 4%; p-values <0.0001). Compared with participants without infection, those with severe infection had higher odds of chronic cough (OR:2.9, 95%CI:1.3-6.3) and chronic bronchitis (OR:3.3, 95%CI:1.2-9.0) at the post-pandemic exam (Table). Associations were stronger among individuals infected >90 days prior to the post-pandemic assessment and during pre-Omicron waves. Results were similar when restricted to participants without pre-pandemic symptoms. No significant associations were observed for non-severe infection (vs. no infection) or for any other lung health outcomes. Conclusions Controlling for pre-pandemic lung health, severe COVID-19 was associated with increased odds of chronic cough and chronic bronchitis but not with changes in lung function or structure in a general population-based sample. Non-severe COVID-19 was not associated with lung health outcomes. This abstract is funded by: OT2HL156812, R21HL165405, R01HL077612, 75N92025D00022, 75N92025D00026, 75N92020D00002, 75N92025D00024, 75N92025D00027, 75N92025D00025, 75N92025D00028

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 Mismatch of airway to lung size has been implicated in chronic obstructive pulmonary disease (COPD) pathophysiology, whether arising from disproportioned growth or from acquired pathologies over the life course. Lower airway-to-lung ratio (ALR), assessed reproducibly via computed tomography (CT) as the mean airway lumen diameters (trachea-to-subsegments) divided by the cubed root of lung volume, is associated with COPD risk and mortality (all-cause, respiratory, cardiovascular, lung cancer). We sought to characterize the airway biology associated with lower ALR by profiling bronchial gene expression across current and former smokers. Methods We leveraged clinical, radiologic, and bronchial epithelial RNA sequencing data from the SPIROMICS (SubPopulations and Intermediate Outcomes Measures in COPD Study) Phase 1 Bronchoscopy Sub-study (n = 161). To analyze transcriptional differential expression (DE) between participants based on CT-assessed ALR, we utilized DESeq2 and then identified impacted pathways by Ingenuity Pathway Analysis (IPA). Generating cell-type transcriptional signatures from lung and blood single cell sequencing atlases, we also assessed association of ALR with differences in airway cellular composition. Results Across all ever-smokers, after adjustment for age, sex, height, COPD status, smoking status, and pack-years, n = 1,324 genes from airway epithelial samples exhibited DE at a false discovery rate (FDR) <0.2. Per IPA, enriched pathways associated with lower ALR included neutrophilic degranulation and macrophage activity. In cell type analysis, lower ALR was associated with higher signatures of macrophages, neutrophils, dendritic cells, and B cells, and lower ciliated and club cell signatures. When we repeated DE analysis using the same covariate adjustments plus an interaction term for ALR and smoking, n = 713 DE genes were identified at FDR <0.2; the association of DE with lower ALR was overwhelmingly in current rather than former smokers (3699 vs. 2 genes). By pathway and cell type analyses, the association between lower ALR and heightened inflammation (greater macrophage and neutrophil signaling) and epithelial dysregulation (downregulated ciliated and club cell function) was primarily restricted to current smokers. Conclusions In adult ever-smokers, lower ALR is associated with transcriptomic evidence of heightened neutrophil- and macrophage-driven airway inflammation and reduced ciliated and club cell expression, associations that were most evident among current smokers. Whether these cross-sectional patterns, which suggest impaired mucociliary defense and a diminished capacity to clear inhaled particulates, are the result of pre-existing reduced ALR, its cause, or both, will require further investigation. This abstract is funded by: 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 Rationale Thymic stromal lymphopoietin (TSLP) is an epithelial alarmin chiefly implicated in T2 airway inflammation, though it may also participate in broader (non-T2) inflammatory processes. The utility of airway epithelial gene expression of TSLP and its specific receptor subunit, cytokine receptor-like factor 2 (CRLF2), as biomarkers of downstream inflammation and clinical outcomes remains unclear. Methods We analyzed RNA sequencing data from bronchial airway brushing obtained in phase 1 of the SPIROMICS bronchoscopy sub-study (N = 161: 23 never-smokers, 67 ever-smokers with COPD, 71 ever-smokers without COPD). We related airway TSLP and CRLF2 expression to clinical, hematologic/sputum measures, and gene-expression signatures of T2/eosinophilic and neutrophilic inflammation, including to our published T2 signature and to eosinophil and neutrophil signatures derived from single-cell atlas data. Associations used Spearman correlations and linear models adjusted for age, sex, race/ethnicity, COPD status, smoking status, and pack-years. Results CRLF2 expression correlated with gene-expression evidence of both T2/eosinophilic and neutrophilic inflammation: T2 signature (ρ = 0.26, p < 0.001), eosinophil signature (ρ = 0.44, p < 0.001), and neutrophil signature (ρ = 0.39, p < 0.001). CRLF2 was positively associated with log sputum neutrophil count (ρ = 0.24, p = 0.024) and blood neutrophil count (ρ = 0.036, p < 0.001), but not with sputum or blood eosinophil counts. Higher CRLF2 related to worse baseline lung function, particularly among current smokers, (lower post-bronchodilator FEV1: ρ = −0.21, p = 0.008), as well as greater 5-year FEV1 decline across current and former smokers (ρ = −0.22, p = 0.007). Significant findings persisted after covariate adjustment. In contrast, TSLP expression, although strongly correlated with expression of the epithelial alarmin IL33 (ρ = 0.53, p < 0.001), was negatively correlated with CRLF2 (ρ = −0.24, p = 0.002) and with eosinophil and neutrophil gene-expression signatures (ρ = −0.26 and −0.33, both p < 0.001). TSLP expression showed no association with sputum or blood cell counts. Conclusions In bronchial epithelium from SPIROMICS, gene expression of CRLF2, but not of TSLP, tracked with downstream alarmin-relevant inflammatory programs, consistent with a regulatory role in TSLP signaling through TSLPR, as well as with clinically meaningful outcomes. CRLF2 expression was associated with evidence of both T2/eosinophilic and neutrophilic inflammation, and with lower baseline FEV1 and accelerated FEV1 decline, suggesting engagement of a broader, clinically relevant inflammatory milieu. By contrast, epithelial TSLP transcript levels mirrored IL33 gene expression but were not associated with cellular or transcriptomic inflammation. These findings position CRLF2, rather than TSLP expression itself, as a potential airway biomarker of TSLP-axis activity and disease impact with potential implications for patient stratification as well as strategic approaches for TSLP-pathway-targeted therapies. This abstract is funded by: 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 Rationale Deficiency of alpha-1 antitrypsin (AAT), the protein encoded by SERPINA1, remains the strongest genetic risk factor for COPD. Individuals heterozygote for the type Z variant, termed the PI*MZ genotype (estimated global prevalence n = 35,000,000), experience varying respiratory morbidity beyond expected from tobacco smoking alone. AAT is a pleiotropic protein which inhibits not only neutrophil elastase but also neutrophil chemotaxis mediated by leukotriene B4 (LTB4) [by antagonizing the LTB4 receptor (LTB4R)] and interleukin-8 (IL-8). Although LTB-4 and IL-8 gene variants and protein levels have independently been implicated in COPD, their potential role in PI*MZ is unknown. Methods COPDGene and SPIROMICS are observational tobacco-smoke and COPD-enriched cohort studies. Participants were phenotyped by spirometry and CT imaging, with whole genome sequencing data available via TOPMed. We analyzed participants with PI*MM (COPDGene n = 9,732; SPIROMICS n = 2,105) and PI*MZ (COPDGene n = 300; SPIROMICS n = 83). LTB-4 (ALOX5AP, LTA4H, LTB4R) and IL-8 (CXCL8, CXCR1, CXCR2) associated gene variants (exonic and intronic) with a minor allele frequency of ≥ 0.05 were extracted. We assessed statistical interactions between MZ status and chest CT outcomes or FEV1% predicted using the SPAGxECCT package with adjustment for relatedness (genetic related matrix) and ancestry (top ten PCs). Baseline protein LTB4R (SPIROMICS only) and IL-8 (both cohorts) protein levels were measured via SomaScan, and statistical interactions between MZ status and AAT concentration were explored. Models were adjusted for age, sex, BMI, smoking status, and pack-years. Given the candidate gene discovery approach, significance was defined as nominal p-value<0.05 Results We identified the following number of variants in genes of the LTB-4 or IL-8 pathways: 87 shared between cohorts; 133 unique to COPDGene; 1 unique to SPIROMICS. LTB-4-related polymorphisms were associated (p < 0.05) with any radiographic outcomes in 22 variants (COPDGene) vs. 23 variants (SPIROMICS), with a single overlap (rs17074984 in ALOX5AP). Two LTB-4-pathway variants were associated with FEV1% predicted in SPIROMICS (both in ALOX5AP), but none in COPDGene. AAT plasma concentrations displayed a statistically significant interaction with FEV1% predicted (p = 0.046) and PRMemphysema (p = 0.013) for LTBR4, but not for IL-8. There was not an interaction by MM/MZ status and protein levels. Conclusions Predominantly LTB-4, and not IL-8, pathway-related genetic variants and protein levels may contribute to disease heterogeneity among PI*MZ individuals. The association of circulating LTB4R protein with reduced lung function, particularly among those with low AAT levels, further suggest interactions between AAT and the leukotriene pathway in PI*MZ individuals. Thus, PI*MZ individuals may uniquely derive benefit from leukotriene-targeting pharmacotherapy. This abstract is funded by: NIH NHLBI K23HL173570

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 Rationale Alpha-1 antitrypsin deficiency (AATD) continues to be underdiagnosed. The estimated prevalence of the most common severe deficient genotype, PI*ZZ, is ∼1:3500 or 100,000 Americans but only ∼10,000 such individuals have been recognized. Diagnostic delays persist and these delays are associated with worse disease. High-resolution CT (HRCT) imaging may contain latent phenotypic AATD signatures that are not visibly apparent to clinicians. We hypothesized a deep learning model could identify imaging patterns predictive of AATD from inspiratory HRCT scans. Methods Confirmed genotypes and HRCT scans were pooled from COPDGene, SPIROMICS, and GRADS, yielding 350 MM, 102 MZ, and 69 ZZ participants. Only scans reconstructed with smooth kernels (GE STANDARD, Philips B, Siemens B31f/B35f) and slice thickness < =1.25mm were used. The cohort was selected to have similar distributions of quantitative emphysema, sex, and age by matching five MZ and two MM subjects to each ZZ subject (<10-year age difference, same sex, and <3% difference in %LAA). Tobacco smoke-exposure was more prevalent among the MM/MZ groups given these were predominantly from the tobacco smoke-enriched COPDGene and SPIROMICS cohorts. A DenseNet121 convolutional neural network (CNN) was trained using the MONAI platform to predict genotype by outputting probabilities for MM, MZ, and ZZ. Input to the CNN was a three-channel LAA-910, LAA-950, and LAA-970 map downsampled to 2.0mm3 resolution to remove bias due to differences in scanner and image reconstruction parameters. Five-fold cross-validation was performed. Area under the curve (AUC) was calculated for classification of ZZ vs other genotypes (MM/MZ) and MZ vs MM, with statistical significance assessed at p < 0.05. Results The three groups were similar across age (mean±SD years: MM 57.9±8.8, MZ: 57.5±8.9, ZZ: 55.0±10.7), sex (% female: MM: 48.2%, MZ: 46.7%, ZZ: 47.6), and emphysema (LAA-950%: MM: 13.0±12.2, MZ: 12.4±11.8, ZZ: 13.1±11.9). There was a higher prevalence of ever smokers among MM (100%) and MZ (91.7%), compared to ZZ (47.6%). The model achieved an AUC of 0.806 (p < 0.001) for classification of ZZ vs other genotypes, demonstrating robust discriminatory ability for homozygous AATD. Classification of MZ vs other genotypes did not achieve an AUC significantly greater than 0.5. Conclusions This proof-of-concept study demonstrates that deep learning applied to routine inspiratory HRCT can accurately identify homozygous AATD. These findings support the potential role of imaging-based machine learning as a non-invasive screening tool for undiagnosed ZZ individuals. Future work will focus on cohort enrichment, integration with clinical parameters, and explainability of model-derived imaging features. This abstract is funded by: NIH NHLBI K23HL173570

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