Barr, R. Graham

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 Eosinophilic airway inflammation in COPD is associated with more frequent exacerbations,lower lung function,and corticosteroid responsiveness.DNA methylation captures information about genetic factors and environmental exposures, but is under-studied in eosinophilic COPD.We hypothesized that sputum cell methylation patterns reflect genomic regulation of inflammatory endotypes in COPD by DNA sequence variation. Methods We analyzed DNA previously extracted from induced sputum cells for a prior study(PMID38261629) for which sufficient quantity DNA remained for methylation arrays.For this analysis,we focused on n = 132 SPIROMICS participants with ≥20 pack-years smoking history.Samples were stratified into tertiles based on sputum eosinophil proportions,previously determined from sputum cell differential analysis by the centralized sputum analysis core for SPIROMICS(Univ of North Carolina).We assessed proportion of DNA methylation (β values) spanning 936,866 CpG sites using the Illumina Infinium Methylation EPICv2.0 BeadChip Array,which we transformed into M values(meffil R package). To identify differentially methylated regions (DMRs)(FDR<0.05 in minimum of 2 CpG sites per gene region),we contrasted CpG β values between the high-eosinophil and low-eosinophil tertiles(N = 44 each),using the DMRcate package.We tested for enrichment of genes overlapping with significant DMRs using Enrichr (GWAS Catalog 2025 gene set library).To identify genetic variants associated with DNA methylation levels,we performed Methylation Quantitative Trait Loci (meQTL) analysis using TensorQTL.For each CpG site,we tested associations with SNPs within a ± 500 kb window (cis-meQTL).The linear regression model included age,sex,BMI,smoking status,smoking pack-years,study site,inhaled corticosteroid use, the first two genetic principal components, cell-type proportions (macrophages/monocytes, neutrophils, squamous epithelial cells, lymphocytes), and eosinophil tertile . Significant meQTLs were identified at an FDR<0.05 using Benjamini-Hochberg correction. Results A total of 532 eosinophil-associated DMRs (FDR < 0.05) were identified between the high- and low-eosinophil groups,encompassing 446 unique genes.The top enriched pathways related to counts of eosinophils (adjusted p = 0. 00002464), lymphocytes (adjusted p = 0.0001860), and the sum of eosinophils-plus-basophils (adjusted p = 0.0001860).We identified 18,432 significant SNP-CpG/meQTL pairs (FDR < 0.05),suggesting extensive genetic influence on airway DNA methylation patterns.Among the n = 521 eosinophil-associated CpGs that overlapped with meQTLs,rs55687040 (TENT2,a gene involved in mRNA and miRNA processing) is a GWAS locus for FEV1/FVC ratio (PMID: 26634245), suggesting possible genetic control of airway eosinophilic methylation signatures. Conclusions Our findings disclose distinct DNA methylation signatures associated with sputum eosinophil proportion in COPD.A subset of these CpGs are regulated by genetic variants,suggesting an integrated genetic-epigenetic mechanism underlying airway eosinophilic inflammation with the potential to influence COPD risk and severity. This abstract is funded by: SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C), grants from the NIH/NHLBI (U01 HL137880, U24 HL141762, R01 HL182622, and R01 HL144718), 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.; 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; and Theravance Biopharma 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 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 RATIONALE: Susceptibility to severe COVID-19 and long COVID may be influenced by pre-existing lung structure. ECG-gated, non-contrast cardiac CT scans, originally acquired for assessing coronary artery calcium, are available in multiple longitudinal cohort studies and have been previously validated against lung CT scans for densitometry; however, protocol variation across cohorts may hinder direct comparison. To address this, we developed a quality control (QC) rubric to grade cardiac CT scans for standardized quantitative lung measures across cohorts. METHODS: C4R assessed SARS-CoV-2 infection and outcomes in 14 NHLBI cohorts, of which 10 (six community-based and four longitudinal case-control studies) included CT scans (12,459 participants with lung and 13,752 with cardiac CTs). Logic rules were applied to categorize scans (Table): Grade “A-B” scans had 2-3.5mm slice thickness, at least 20 axial slices, no lung cropping in axial view and lung and airway volumes greater than the cohort-specific 1st percentile. “C-D” scans had one lung obscured by cropping around the heart, so quantitative measures were restricted to the lung in complete view. “F-H” scans were deemed unusable, suffering from low slice count (<20), severe image cropping, low lung or airway volumes (< 1st%ile). Visual inspection was performed on a sample from each category to ensure accuracy. Percent of lung voxels in low attenuation areas < -950 HU (LAA950), high attenuation areas (-600 to -250 HU, HAA) and 15th percentile HU value (perc15) were measured. Within-participant variability was quantified using the intraclass correlation coefficient (ICC). RESULTS: We performed QC on cardiac CT scans from an initial sample of 6,275 participants from three C4R cohorts (ARIC, CARDIA, MASALA), imaged with GE or Siemens scanners. Of these, 5,125 (81.7%) were graded “A-B”, 496 (7.9%) were “C-D” and 654 (10.4%) were “F-H”. The percentage of “F-H” scans varied widely across cohorts [<1% to 26%] due to image cropping. Among the 5,621 cardiac CT scans graded A-D, the mean±SD for LAA950, HAA and perc15 respectively were 1.1±2.1%, 7.2±4.7% and -887±33 HU. Repeated imaging was available for 107 participants (time interval 5.1±0.7y). For paired scans in grades A-B (n=101/107), the within-participant correlation for LAA950, HAA and perc15 were 0.91, 0.38 and 0.74 respectively. CONCLUSION: Initial application of QC to cardiac CT scans identified scans with high reproducibility, with the aim to provide valid quantitative lung metrics on over 20,000 highly characterized participants in C4R.

Abstract Introduction Resilient smokers, who maintain respiratory health despite smoking, remain poorly characterized. Airway epithelial cell gene expression in resilient smokers can provide insight into mechanisms and pathways that may protect smokers from developing smoking-related lung disease. The objective of this study is to identify genes and potential pathways associated with lung-related resilience. Methods Resilient smokers were defined based on a previously published multi-dimensional definition which includes the absence of: 1) spirometric airflow limitation, 2) symptoms (cough/sputum, dyspnea), 3) radiographic (HRCT) abnormalities, 4) exacerbations, and 5) an accelerated FEV1 decline. “Non-resilient” smokers had preserved spirometry but showed abnormalities in at least one domain of resilience. We analyzed airway epithelial brush samples from 21 resilient and 34 non-resilient smokers from SPIROMICS. Differential gene expression was performed using DESeq2 package in R, which estimates differential expression using generalized linear models. We adjusted for sequencing batch, age (continuous), race, gender, and current smoking status. While controlling for these covariates we evaluated differential gene expression between resilient and non-resilient smokers, the interaction between resilience and smoking status, and in each sub-category of resilient (absence of symptoms, radiographic abnormalities, exacerbations, and accelerated decline in FEV1). Results We identified 98 genes that were differentially expressed (FDR ≤0.1) when comparing resilient to non-resilient smokers, 20 of those genes were up regulated in resilient smokers (Figure 1). Notable genes included cadherin-12 (CDH12), aspartate beta-hydroxylase (ASPH), and protein kinase AMP-activated alpha 2 (PRKAA2). CDH12 is involved in cell adhesion and epithelial integrity. ASPH influences cell adhesion and signaling. PRKAA2 plays role in cellular homeostasis and stress response, which may be altered in non-resilient smokers, affecting cellular metabolism and inflammation. BPI folding containing family A member (BPIFA1) was significantly associated with smoking status in the interaction analysis, suggesting its role in modulating the impact of smoking on resilience. BPIFA1 has anti-microbial properties, regulates immune responses, and contributes mucosal surface protection. Sub-category analysis identified 17 genes associated with resilience to symptoms and 2 genes related to physiologic resilience (FEV1 decline), though no significant genes were associated with radiographic or exacerbation resilience. Conclusion Differential gene expression of airway epithelial cells identified distinct expression patterns between resilient and non-resilient smokers. Specifically, genes associated with epithelial cell adhesion, signaling, and cellular homeostasis were up regulated in resilient smokers. These findings suggest that resilient smokers may have transcriptomic profiles and potential mechanisms that mitigate the effects of cigarette smoke.

Abstract Rationale: Adverse social determinants of health (SDOH) are common in COPD and associated with increased mortality. However, cause-specific mortality is understudied. We evaluated associations of neighborhood-level SDOH with all-cause and cause-specific mortality among individuals with or at-risk for COPD. Methods: SPIROMICS is an ongoing, multi-center, longitudinal observational study of individuals across the US with current or former personal smoking histories (≥20 pack-years), with or without spirometrically-defined COPD. We established geocoded neighborhood-level SDOH at baseline using 2010 census tract data, including specifically, area deprivation index (ADI) (range 0-100, 100 = most disadvantaged), poverty level (percentage of households below the poverty line), poor food access (>1/3 of residents with no access to fresh food stores within 1 mile in urban areas and 10 miles in rural areas), and food desert (low income plus poor food access). We ascertained all-cause and cause-specific mortality through 12/31/2021 using the National Death Index. Using Cox proportional hazards models, we evaluated the risk of all-cause mortality for each SDOH. We utilized competing-risk regression models to evaluate the risk of cardiovascular, respiratory, COPD (a subset of respiratory), and neoplastic causes of death individually with each SDOH. Models were adjusted for demographics, smoking status, pack-years, BMI categories, coronary heart disease, FEV1% predicted and study site. Effect modification by lung function was assessed using multiplicative interactions between SDOH and FEV1% predicted. Results: Among 2977 participants, mean ADI was 40.1±27.8. Within a participant's neighborhood, on average 10.4±10.4% of households were below the poverty line, 933 (31.3%) had poor food access, and 220 (7.4%) resided in a food desert. During a median follow-up of 7.8 years (IQR 5.6-9.2), 744 (25.0%) participants died, most commonly from respiratory causes including COPD. Neither ADI nor neighborhood poverty level were associated with all-cause mortality or any specific cause of death (Table). Despite lack of associations between food access and mortality (Table), individuals residing in a food desert had elevated risks of all-cause (HR: 1.36 [95%CI: 1.03-1.79]) and cardiovascular (HR: 2.62 [95%CI: 1.5-4.35]) deaths. There was no significant effect modification by lung function. Conclusions: In a large cohort of individuals with personal smoking histories, with or without COPD, living in a food desert was associated with increased all-cause and cardiovascular mortality. However, neighborhood poverty level and food access, the two components of food desert, were not independently associated with mortality, suggesting the association of multimodal SDOH variables with mortality in this population.

Abstract Rationale: MUC5B and MUC5AC are neighboring genes on chromosome 11p15 that encode the two predominant mucins secreted in the respiratory airways. Increased airway mucin concentrations and an imbalance between these two mucins drive COPD symptoms and the development of airflow obstruction or COPD, but the epigenomic regulation of this loci has not been characterized in the airways. Methods: We isolated DNA from bronchial airway brushings of 72 participants with >20 pack-years smoking history from the Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS). 36 participants had GOLD-based moderate-to-severe COPD (FEV1/FVC<0.70, FEV1<80%) and 36 were without or with mild COPD. We used the Illumina Infinium Methylation EPIC BeadChip Array to assess proportion of DNA methylation (β) spanning 689,937 CpG sites. We evaluated proportional DNA methylation (β) at 96 CpG loci within a 103kB region using matrixQTL for cis-expression quantitative trait methylation (eQTM: 10kB flanks relative to MUC5AC/MUC5B) integrating bulk RNA-seq data and mQTL models integrating gene sequencing data to identify DNA variation associated with proportion eQTM methylation. Models included age, sex, BMI, clinical site, current smoking vs past smoking, total pack-years, three ancestral components of ancestry, chipset, and position. Results: We identified a top eQTM, cg00524486, marginally associated with MUC5B expression (coefficient estimate=8.75, punadjusted=0.0014, pFDR=0.065, Figure 1B). This eQTM was associated with CT scan parametric response mapping-based functional small airways disease (PRM-fSAD, beta coefficient[β]=3.64, p=0.011). We tested for DNA variation associated with methylation of this top eQTM which identified two methylation quantitative trait loci (mQTL: rs885454 [β=0.044, minor allele frequency[MAF]=0.09, pFDR=0.01], rs868902 [β=0.020, MAF=0.43, pFDR=0.08, Figure 1A) associated with cg00524486 methylation, of which the more frequent mQTL, rs868902, was marginally associated with PRM-fSAD (p=0.07) in the larger SPIROMICS cohort with MUC5B DNA sequencing data. Conclusions: This preliminary multi-omic study of the lower airway epithelium demonstrates that the expression of MUC5B is epigenetically regulated by neighboring CpG probe methylation (eQTM) represented by DNA variation (mQTL). These studies require further confirmation in a larger dataset of airway samples and support a role for epigenetic variation as a biologic contributor and biomarker of early COPD pathogenesis.

Abstract Chronic obstructive pulmonary disease (COPD) and pulmonary emphysema were the third-leading cause of death globally in 2019. Besides alpha-1 antitrypsin deficiency, emphysema lacks disease-modifying medications due to its heterogeneity and there remains a need to further investigate emphysema subtypes to advance its diagnosis and treatment. Recent unsupervised machine learning of the texture and anatomical location of emphysema on computed tomography (CT) images of the lung defined six quantitative CT emphysema subtypes uniquely associated with specific gene variants, environmental exposures, symptoms, physiology and clinical events. We aim to identify associations of plasma proteins with each CT emphysema subtype cross-sectionally in a multi-ethnic general population sample. MESA is a prospective cohort study that recruited 6,814 participants from six communities who self-identified as White, Black, Hispanic, or Asian race/ethnicity, ages 45-84 years old and free of clinical cardiovascular disease in 2000-02. The MESA Lung Study performed CT scans among 3,137 participants in 2010-12, which were labeled with previously described (PMID: 37268414) CT emphysema subtypes. Proteomics was assessed using SomaScan version 4.1 assay (7,596 plasma proteins). Linear regression models were adjusted for age, height, weight, sex, race, educational attainment, smoking status, pack years, urine cotinine level, body-mass index (BMI), estimated glomerular filtration rate (eGFR), CT scanner manufacturer and other CT emphysema subtypes. Results were adjusted to reflect a False Discovery Rate (FDR) p-value of less than 5% using the Benjamini-Hochberg method. Among 2,343 participants with both CT and proteomic data (mean age 69.5 years, 52.3% women, 23.7% with COPD), we identified statistically significant associations of proteins with the vanishing lung subtype (42 proteins; top hit Natural cytotoxicity triggering receptor 3 ligand 1, q = 2.9[asterisk]10-9), restrictive combined pulmonary fibrosis/emphysema (CPFE) subtype (11 proteins; top hit Integrin alpha-2/b1, q=1.0[asterisk]10-5), the combined bronchitis-apical emphysema subtype (9 proteins; top hit Desmoglein-2, q=8.8[asterisk]10-13), and the diffuse emphysema subtype (4 proteins; top hit Pulmonary surfactant-associated protein D, q=5.7[asterisk]10-3). No significant proteins were associated with the obstructive CPFE or senile subtypes after FDR correction. Most proteins were uniquely associated with one CT emphysema subtype, while a few proteins were associated with more than one subtype. This study identified specific proteins for different CT emphysema subtypes, many of which have been related less specifically to emphysema, including pulmonary surfactant production, anti-inflammatory pathways, and mitochondrial deregulation, amongst others. Validation of these proteomic associations can provide insight into further pathway assessment, clinical biomarker discovery, and targeted prevention of emphysema in the general population.

Abstract RATIONALE: Airway-to-lung ratio (AirLR), calculated as the mean of airway lumen diameters at standard anatomic locations divided by the cube-root of total lung volume, is associated with COPD risk regardless of smoking status, and we have previously shown an association between total-pulmonary-vascular-volume-to-lung-size relationship with AirLR. [PMCID: PMC11284327] Using dual energy computed tomography (DECT), we recently developed a deep learning-based algorithm for pulmonary arterial segmentation to compute pulmonary-arterial-to-lung ratio (ArtLR) which correlated significantly with AirLR and markers COPD. Here we automate image processing in a larger cohort to estimate ArtLR, introduce an alternative ArtLR standardization and correlate it to COPD-related measures of lung structure and function. METHODS: Using a SPIROMICS sub-cohort (110 participants), imaged via contrast enhanced DECT at functional residual capacity (FRC), an automatic deep learning pipeline segmented the arteries from the pulmonary vascular tree. Another pipeline automatically extracted the centerlines and calculated average arterial segment diameters. A paired non-contrast total lung capacity (TLC) scan was registered to the DECT FRC scan to transfer airway segment labels from the TLC to FRC domain. AirLR was calculated as the mean of airway lumen diameters at standard anatomic locations (trachea-to-subsegments) divided by cube root of lung volume [TLC or FRC]. A simple Long Short-Term Memory (LSTM) network was used to find corresponding anatomic arterial segments. Associations between ArtLR and %Emphysema, AirLR, pre- and post-bronchodilator FEV1/FVC, and a texture-based (AMFM) measure of %broncho-vascular bundles was assessed using linear regression to adjust for age, sex and BMI. RESULTS: In adjusted analyses, a 1-SD decrement in ArtLR-TLC was associated with 4.198 units increase in %Emphysema-910 (95%CI: 1.360-7.037; p = 0.0041), and 0.8198 units decrease in %Bronchovascular (95%CI: 0.5037-1.136; p < 0.0001). A 1-SD increment in ArtLR-TLC was associated with 0.2515 SD increase in AirLR-Outer (95%CI: 0.04947SD-0.4536SD; p = 0.0152), and 0.02083 units increase in PreBronch-FEV1FVC (95%CI: 0.001814-0.03984; p = 0.0321). Sex and BMI show significant associations with ArtLR-TLC, where female is associated with 1.068 SD decrease in ArtLR, and a unit increase in BMI is associated with 0.05788 SD increase in ArtLR (both p-values < 0.0001), and age was not association with ArtLR. CONCLUSIONS: An automated pipeline for pulmonary arterial tree segmentation and standardization from contrast enhanced DECT FRC scans demonstrates a significant relationship between ArtLR and AirLR as well as between ArtLR and %emphysema, and FEV1/FVC. A standardized method of arterial tree size assessment provides for expanded exploration of vascular vs airway disease etiologies.