BACKGROUNDWe constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting.METHODSPRSmix+, 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), Mass General 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).RESULTSPRSmix+ 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 β 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 PRSs 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 RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.CONCLUSIONSMulti-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 REGISTRATIONCOPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Genetic variants near Hedgehog interacting protein (HHIP) have been consistently associated with increased risk for chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide. However, HHIP's role in COPD pathogenesis remains elusive. Canonically, HHIP is a negative regulator of the Hedgehog pathway and downstream GLI1 and GLI2 activation. The Hedgehog pathway plays an important role in wound healing, specifically in activating transcription factors that drive the epithelial-mesenchymal transition (EMT), which in its intermediate state (partial EMT) is necessary for the collective movement of cells closing a wound. Herein, we use a systems biology approach to propose a mechanism to explain HHIP's role in faulty epithelial wound healing, which could contribute to the development of emphysema, a key feature of COPD. Using two different Boolean models, we show dysfunctional HHIP results in a lack of negative feedback on GLI, triggering a full EMT, where cells become mesenchymal and do not properly close the wound. We validate these Boolean models with experimental evidence gathered from published scientific literature. Finally, we show evidence supporting our hypothesis in single-cell and single-nucleus RNA-Seq data from different COPD cohorts and Hhip heterozygous knockout mice. Overall, our analyses suggest that aberrant wound healing due to dysfunctional HHIP, combined with chronic epithelial damage through cigarette smoke exposure, may be a primary cause of COPD-associated emphysema.

Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, leading to classifications that only partially reflect disease heterogeneity. Here, we introduce a variational autoencoder-based subtyping pipeline that jointly embeds clinical and gene expression data into a single subject-level representation. We evaluate the framework in the COPDGene study, a large study of current and former smoking individuals with and without COPD. Prediction experiments show that the embeddings have predictive accuracy comparable to or better than other unsupervised embedding approaches. Using trajectory learning approaches, we identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and longitudinal outcomes. Finally, we show that our findings generalize to an external validation cohort. Overall, our approach enables a transition from isolated phenotypic or molecular subtyping toward an integrated and clinically meaningful understanding of COPD heterogeneity.

Whole genome sequence (WGS) data in multi-ancestry samples supports discovery of low-frequency or population-specific genetic variants associated with chronic obstructive pulmonary disease (COPD) and lung function. We performed single variant, structural variant, and gene-based analysis of pulmonary function (FEV1, FVC and FEV1/FVC) and COPD case–control status in 44,287 multi-ancestry participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We validated findings using the UK Biobank and assessed implicated genes using lung single-cell RNA-seq (scRNA-seq) data sets. Applying a genome-wide significance threshold (P < 5 × 10–9), we replicated known loci and identified novel associations near LY86, MAGI1, GRK7, and LINC02668. Colocalization with gene expression quantitative trait loci (eQTL) from the Lung Tissue Research Consortium highlighted known candidate genes including ADAM19, THSD4, C4B, and PSMA4, which were not identified through other eQTL sources. Multi-ancestry analysis improved fine-mapping resolution (e.g., HTR4 and RIN3). Gene-based analysis identified and replicated HMCN1. In human lung scRNA-seq data sets, lung epithelial cells and immune cell types showed enriched expression, while fibroblasts showed higher expression for HMCN1. CRISPR targeting HMCN1 in IMR90 demonstrated reduced expression of collagen genes. Large-scale multi-ancestry WGS analysis improves variant discovery and fine-mapping resolution for lung function and COPD and highlights biologically relevant genes and pathways.

Background: Coronary artery calcification (CAC) is a strong independent predictor of CAD, yet the underlying molecular mechanisms remain incompletely understood. We aimed to identify novel molecular mediators of CAC using a multi-omics approach, with a focus on those proteins likely to have a direct effect on the vasculature. Methods: We conducted a proteome-wide association study in a pooled cohort of CARDIA, MESA, and FHS participants with Olink plasma proteomic profiling and CT-based CAC scoring (n = 6468). We identified proteins independently associated with CAC after adjusting for established CAC risk factors (age, sex, BMI, hypertension, diabetes mellitus, chronic kidney disease, statin use, ethnicity, and smoking status). These proteins were further evaluated for their ability to predict myocardial infarction in an expanded cohort of MESA, FHS, and UK Biobank participants (n=57,198). To prioritize proteins involved in vascular smooth muscle cell (VSMC)-mediated calcification, we identified proteins with differential gene expression in human VSMCs cultured in osteogenic media versus normal media. Causal effects of these proteins were evaluated using Two-sample Mendelian randomization (using inverse variance weighting (IVW), Egger and maximum likelihood) with plasma proteomics GWAS summary statistics as exposures and the largest existing CAC GWAS meta-analysis summary statistics as the outcome. Results: Of 2,805 proteins measured, 365 were independently associated with CAC scores (FDR p<0.05), and enriched in pathways related to inflammatory response, immune cell signaling, ECM organization, and key vascular cell signaling pathways (Fig. 1). Among these, 216 proteins also independently predicted myocardial infarction. Transcriptomic analysis in human VSMCs identified 43 genes upregulated under osteogenic conditions, of which 8 demonstrated evidence of causal effects on CAC via Mendelian Randomization. These proteins are implicated in pathways related to immune cell signaling, bone signaling, and metabolic regulation. Conclusion: Using a multi-omics strategy integrating proteomic, transcriptomic, and genomic data, we identified several novel candidate molecular drivers of CAC with supporting evidence for their roles in myocardial infarction and VSMC osteogenic phenotypic modulation. These findings provide promising avenues for future mechanistic studies in vascular calcification.

Background Chronic Obstructive Pulmonary Disease (COPD) has a broad spectrum of clinical characteristics. The aetiology of these differences is not well understood. The objective of this study is to assess whether respiratory genetic variants cluster by phenotype and associate with COPD heterogeneity. Methods We clustered genome-wide association studies of COPD, lung function, and asthma and phenotypes from the UK Biobank using non-negative matrix factorization. We constructed cluster-specific genetic risk scores and tested these scores for association with phenotypes in non-Hispanic white subjects in the COPDGene study. Findings We identified three clusters from 482 variants and 44 traits from genetic associations in 379,337 UK Biobank participants. Variants from asthma, COPD, and lung function were found in all three clusters. Clusters displayed varying effects on white blood cell counts, height, and body mass index (BMI)-related phenotypes in the UK Biobank. In the COPDGene cohort, cluster-specific genetic risk scores were associated with differences in steroid use, BMI, lymphocyte counts, and chronic bronchitis, as well as variations in gene and protein expression. Interpretation Our results suggest that multi-phenotype analysis of obstructive lung disease-related risk variants may identify genetically driven phenotypic patterns in COPD.

Background: Small airway disease (SAD) is associated with FEV1 decline. This study aimed to assess the association between SAD and the progression to COPD in high-risk individuals. Methods: We analyzed data from COPDGene participants with ≥10 pack-years of cigarette smoking who had normal spirometry or preserved ratio impaired spirometry (PRISm). Normal spirometry was defined as both post-bronchodilator FEV1 and FEV1/FVC≥ lower limit of normal (LLN); PRISm was defined as FEV1 < LLN with FEV1/FVC ≥ LLN. Participants underwent inspiratory and expiratory chest CT. Using Parametric Response Mapping (PRM), we quantified SAD as the percent of voxels (%PRMfSAD) with non-emphysematous gas trapping (≥ -950 Hounsfield units (HU) at inspiration and < -856 HU at expiration). %Emphysema was defined as the percentage of voxels with ≥ -950 Hounsfield units (HU) at inspiration and < -856 HU at expiration. We used logistic regression models to examine the association of %PRMfSAD and %emphysema at enrollment, as well as changes between enrollment and the 5-year follow-up visit with progression to moderate COPD (outcome), defined as both FEV1 and FEV1/FVC

BACKGROUND:Chronic Obstructive Pulmonary Disease (COPD) is a complex and heterogeneous disease. Emphysema-predominant and non-emphysema predominant COPD are two major disease subtypes capturing important aspects of COPD heterogeneity. Molecular differences between these COPD subtypes are unknown. METHODS:We assessed plasma proteomic associations (using SomaScan) with emphysema-predominant vs. non-emphysema predominant COPD subtypes in COPDGene; replication of significant associations was performed in SPIROMICS. We performed pathway analyses on COPD subtype plasma proteomic associations and used weighted gene correlation network analysis to find COPD subtype-associated protein correlation networks. We tested previously reported COPD genetic variants for association with COPD subtypes and COPD subtype-associated proteomic biomarkers. FINDINGS:One hundred and twenty-four proteins were significantly associated with COPD subtypes in COPDGene, with 64 proteins (65 SOMAmers) validated in SPIROMICS. Higher correlations were observed between proteomic biomarkers with greater expression levels in non-emphysema predominant participants with COPD. Cell adhesion, collagen-containing extracellular matrix, and epithelial mesenchymal transition were biological pathways enriched for COPD subtype proteomic associations. One COPD subtype-associated correlation network module was identified, including highly connected proteomic biomarkers like PXDN and EFNA2. We observed significant genetic effects on COPD subtypes for rs2579762 in LRMDA and on COPD subtype-associated proteomic biomarkers including sRAGE and Ganglioside GM2 Activator. INTERPRETATION:We identified and replicated multiple plasma proteomic biomarkers associated with emphysema-predominant vs. non-emphysema predominant COPD. Pathway analyses, correlation-based network analyses, and genetic association analyses of these proteins may provide insight into the molecular heterogeneity of COPD. FUNDING:National Heart, Lung, and Blood Institute (NIH).

ImportanceChronic obstructive pulmonary disease (COPD) is often undiagnosed. Although genetic risk plays a significant role in COPD susceptibility, its utility in guiding spirometry testing and identifying undiagnosed cases is unclear.ObjectiveTo determine whether a COPD polygenic risk score (PRS) enhances the identification of undiagnosed COPD beyond a case-finding questionnaire (eg, the Lung Function Questionnaire) using conventional risk factors and respiratory symptoms.Design, Setting, and ParticipantsThis cross-sectional analysis of participants 35 years or older who reported no history of physician-diagnosed COPD was conducted using data from 2 observational studies: the community-based Framingham Heart Study (FHS) and the COPD-enriched Genetic Epidemiology of COPD (COPDGene) study.ExposuresModified Lung Function Questionnaire (mLFQ) scores and COPD PRS.Main Outcomes and MeasuresThe primary outcome was spirometry-defined moderate to severe COPD (forced expiratory volume in the first second of expiration/forced vital capacity [FEV1/FVC] <0.7 and FEV1 [percent predicted] <80%). The performance of logistic models was assessed using the PRS, mLFQ score, and PRS plus mLFQ score for predicting spirometry-defined COPD.ResultsAmong 3385 FHS participants (median age, 52.0 years; 45.9% male) and 4095 COPDGene participants (median age, 56.8 years; 55.5% male) who reported no history of COPD, 160 (4.7%) FHS and 775 (18.9%) COPDGene participants had spirometry-defined COPD. Adding the PRS to the mLFQ score significantly improved the area under the curve from 0.78 to 0.84 (P < .001) in FHS, 0.69 to 0.72 (P = .04) in COPDGene non-Hispanic African American, and 0.75 to 0.78 (P < .001) in COPDGene non-Hispanic White participants. At a risk threshold for spirometry referral of 10%, the addition of the PRS to the mLFQ score correctly reclassified 13.8% (95% CI, 6.6%-21.0%) of COPD cases in FHS, but not in COPDGene.Conclusions and RelevanceA COPD PRS enhances the identification of undiagnosed COPD beyond a conventional case-finding approach in the general population. Further research is needed to assess its impact on COPD diagnosis and outcomes.

Introduction: The genetic determinants of pulmonary vascular diseases in COPD are poorly understood. In this study, we examined the association between high-confidence loss-of-function and predicted missense variants and CT-derived pulmonary vascular metrics in a large cohort of smokers enriched for COPD. Methods: We analyzed COPDGene study participants with complete TOPMed whole genome sequencing (WGS), clinical, and vascular imaging data. Using WGS Annotator, we integrated outputs from VEP, SIFT, LRT, MutationTaster and Polyphen annotation tools to identify predicted deleterious missense variants and high-confidence loss-of-function variants. We conducted gene-based rare variant testing with REGENIE, applying multiple minor allele frequency cutoffs. For association testing, we focused on CT-quantified pulmonary artery-to-aorta (PA/A) ratio, pulmonary blood vessel volume for vessels with a cross-sectional area ≤5 mm² (bv5), total pulmonary blood vessel volume (tbv), and the bv5/tbv ratio. We included age, sex, race, smoking pack-years, current smoking status, and principal components of genetic ancestry as covariates. We adjusted for multiple testing with Bonferroni correction, accounting for the total number of genes tested. Results: Our preliminary analysis of 3,597 phenotyped individuals revealed no significant associations with high-confidence loss-of-function variants. However, we identified a significant association between predicted deleterious missense variants in the TSLP gene and PA/A ratio (P-value=2x10-7). Other top PA/A hits included variants in RNASEH1, WBP2, FBXO3, CSNK1G1, and LDHAL6B, though they did not reach exome-wide significance. Missense variants in CNTF, TMEM50B, HSPA2, and HCLS1 showed the strongest association signals with pulmonary vascular pruning (bv5/tbv) but also fell short of exome-wide significance. Conclusion: In a preliminary analysis in COPDGene, we identified a potential association with TSLP and pulmonary vascular remodeling. Ongoing research with larger cohorts aims to validate these findings and elucidate the biological impact of the rare variants, ultimately informing targeted therapies for pulmonary vascular disease in COPD.

Emphysema's significant morbidity and mortality underscore the need for reliable outcome metrics in clinical trials. However, commonly accepted chronic obstructive pulmonary disease outcome measures do not adequately capture emphysema severity or progression. Computed tomography (CT) metrics have been validated as accurate indicators of pathological emphysema and predictors of chronic obstructive pulmonary disease progression, exacerbations, and mortality. This position paper reviews the evidence supporting CT densitometry as a biomarker for emphysema, establishes implementation standards, and highlights areas for future research. A systematic literature review addressed three key questions: whether CT densitometry can be used as a diagnostic biomarker of emphysema, whether CT densitometry can be used as a prognostic biomarker, and whether longitudinal change in densitometry can be used as a disease progression monitoring biomarker. Emphysema metrics, such as the percentage of low attenuation areas below -950 Hounsfield units, are validated, highly reproducible diagnostic and prognostic biomarkers. Volume-adjusted lung density is recommended for disease monitoring. Both metrics demonstrate a scan-rescan intraclass correlation coefficient of 0.99 with proper technique. The paper also discusses relevant CT physics, techniques, and sources of variation, including technical factors, physiological changes, and software analysis. Key recommendations for clinical trials include using standardized CT techniques, proper subject selection, and longitudinal evaluation with volume-adjusted lung density.

Genes associated with the same disease frequently engage in mutual biological interactions, e.g., perturbation within a specific neighborhood in the molecular interactome, often referred to as the disease module. This has propelled the advancement of network-based approaches toward elucidating the molecular bases of human diseases. Although many computational methods have been developed to integrate the molecular interactome and omics profiles to extract such context-dependent disease modules, approaches that leverage multi-omics for disease-module detection are still lacking. Here, we developed a statistical physics approach based on the random-field O(n) model (RFOnM) to fill this gap. We applied the RFOnM approach to integrate gene-expression data and genome-wide association studies or mRNA data and DNA methylation for several complex diseases with the human interactome. We found that the RFOnM approach outperforms existing single omics methods in most of the complex diseases considered in this study.

OBJECTIVE:Examine clinical and demographic variables associated with new onset depression and anxiety symptoms and assess moderation by sex in COPDGene, a cohort study of current and former smokers at risk for or with chronic obstructive pulmonary disease (COPD). METHODS:In the COPDGene study, 2653 adults had the hospital anxiety (HADS-A) and depression (HADS-D) scales available at phase 2 and 3, as well as clinical and demographic variables available at 2 and non-elevated HADS at phase 2. We defined new onset depression symptoms as HADS-D elevated at phase 3 (HADS-D ≥ 8) versus no new onset as HADS-D not elevated at either phase (HADS-D < 8). New onset anxiety symptoms were defined identically using HADS-A. We used logistic regression models among all participants and stratified by sex and assessed sex interactions for variables associated with the outcome for only one sex. RESULTS:Among males, COPD Assessment test (CAT) score was positively associated with new onset depression (β = 0.08, p = 1.9 × 10-5) and anxiety (β = 0.06, p = 1.4 × 10-3) symptoms. Among females, the modified Medical Research Council (mMRC) dyspnea was positively associated with new onset anxiety symptoms (β = 0.33, p = 1.4 × 10-3). We found sex by CAT score (β = -0.06, p = 0.02) and sex by mMRC dyspnea (β = 0.42, p = 5.1 × 10-3) interactions on new onset anxiety symptoms, and sex by CAT score interaction (β = -0.05, p = 0.04) on new onset depression symptoms. CONCLUSIONS:These findings highlight the importance of understanding factors that increase risk for depression and anxiety among smokers at risk for or with COPD and are moderated by sex.

RATIONALE Occupational exposures including Vapors, Gas, Dust or Fumes (VGDF) and additional Social Determinants of Health (SDOH) may contribute to adverse respiratory outcomes, although molecular causes are understudied. DNA methylation is influenced by multiple environmental exposures and may reveal novel insights into complex diseases. Studying the interaction between VGDF, SDOH and DNA methylation (DNAm) on lung function may reveal molecular mechanisms associated with COPD. METHODS COPDGene is a large-scale multicenter longitudinal cohort. The IlluminaEPIC array was used to assay leukocyte DNA methylation (DNAm) for 5,433 COPDGene blood samples from the five-year visit. VGDF were categorized by self-report of occupational exposures. Poverty was defined as self-reported annual income less than $15,000. Robust linear regression was performed across all samples to address the association between site-specific CpG methylation and lung function (FEV1), including the evaluation of interactions between VGDF and poverty and DNAm. All models were adjusted for age, age2, sex, height, height2, pack-years of smoking, cell proportions, genetic ancestry and the smoking-associated CpG site near AHRR (cg05575921). P-values were set at 10-3 for interactions. Gene set enrichment analysis was performed using GO and KEGG. Epigenetic age acceleration was calculated using the difference between the Horvath pan-tissue clock and chronologic age in years. RESULTS The prevalence of dusty jobs (yes, N=1405) and fumes (yes, N=1402) is 26%, with 786 subjects reporting exposures to both dust and fumes; 243 with VGDF were classified with income in the poverty range. We identified 4,101 CpGs demonstrating a significant interaction between methylation and fumes exposure, and 23,635 significant marks for the interaction term between dust exposure and DNAm in association with FEV1. There were 1,207 overlapping associations between CpGs for dust and fumes for FEV1. In a three-way comparison that included 2,201 associations for the interaction between poverty and DNAm, we identified 29 differentially methylated genes, including F2RL3, MYLK and ITPK1. Pathway enrichment included inflammatory/immune processes associated with VGDF and poverty. We observed epigenetic age acceleration of 1.8 years associated with occupational dust and 2.3 years associated with occupational fumes; epigenetic age acceleration increased to 2.7 years (95% CI: 2.0, 3.4) when VGDF exposures were associated with low income. CONCLUSIONS Lung function may be impacted by VGDF and poverty through epigenetic mechanisms. Analyses of VGDF and lung function highlight molecular associations related to both inflammation and accelerated aging, with further perturbation in the context of social disparities, thus revealing compounding effects on lung health.

Most genetic variants associated with complex traits and diseases occur in non-coding genomic regions and are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterize 14,324 ancestrally diverse RNA-sequencing samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and integrate whole genome sequencing data to perform cis and trans expression and splicing quantitative trait locus (cis-/trans-e/sQTL) analyses in six tissues and cell types, most notably whole blood (N=6,454) and lung (N=1,291). We show this dataset enables greater detection of secondary cis-e/sQTL signals than was achieved in previous studies, and that secondary cis-eQTL and primary trans-eQTL signal discovery is not saturated even though eGene discovery is. Most TOPMed trans-eQTL signals colocalize with cis-e/sQTL signals, suggesting many trans signals are mediated by cis signals. We fine-map European UK BioBank GWAS signals from 164 traits and colocalize the resulting 34,107 fine-mapped GWAS signals with TOPMed e/sQTL signals, finding that of 10,611 GWAS signals with a colocalization, 7,096 GWAS signals colocalize with at least one secondary e/sQTL signal. These results demonstrate that larger e/sQTL analyses will continue to uncover secondary e/sQTL signals, and that these new signals will benefit GWAS interpretation.

Background Frailty is a syndrome of increased vulnerability to stressors and dysregulation of biological processes. The cellular mechanisms by which frailty develops remain unclear. Methods The COPDGene cohort enrolled individuals aged 45-80 with a minimum 10 pack-year smoking history. We defined frailty at Phase 2 (5 year follow-up) of the COPDGene study using a modified Fried Frailty Phenotype (which is based on five components: weakness, shrinking, low activity, fatigue, and slowness). DNA methylation (cross-sectional) was measured using Illumina 850k EPIC array. We identified differentially methylated CpGs between frail and robust participants at Bonferroni significance (adjusted for age, sex, race, smoking exposure, forced expiratory volume in one second (FEV1) % predicted, and cell type composition). Plasma protein expression (cross-sectional) was measured using the SomaScan 5k platform. We assessed differential protein expression (at FDR <0.05) between frail and robust participants (adjusted for age, sex, race, clinical center, white blood cell count, platelet count, smoking exposure, FEV1% predicted of the Global Lung Function Initiative (GLI) reference values, and self-reported diabetes, hypertension, hyperlipidemia, heart disease, and kidney disease). Lastly, we assessed for frailty-associated epigenetic age acceleration. Results Among 2483 participants at Phase 2 with a cigarette smoking history (mean age = 66, 1293[52%] male, 410[17%] frail), we found 346 genes with differentially methylated CpGs in frail individuals. These included genes related to cytokine signaling (IL22RA2, TNFAIP3, TRAF5), T- and B-cell signaling (RHOH, PRKCB), and other innate and adaptive immune processes (CASP6, WDFY1, PIK3AP1, LAMP3K). Genes involved in DNA repair and chromatin remodeling were also identified. There were 743 genes encoding frailty-associated plasma proteins, of which 15 genes overlapped with methylation findings. Overlap genes were involved in inflammatory pathways (CD55, IL1RN, CAMP, CD300A), wound healing (FAP), and proteostasis (HSPB6). Gene set enrichment analyses revealed enrichment in gene sets related to inflammatory response, EGFR signaling, and extracellular matrix organization (Fig 1). Evaluation of the interaction term of lung function (FEV1% predicted GLI) with frailty demonstrated differential levels of immune-related proteins as well as the retinoic acid receptor CRABP2. Lastly, in adjusted analyses, frail individuals had increased epigenetic age acceleration (3.5 yr, 95% CI 2.1-5.0) compared to robust individuals. Conclusion The epigenetic and proteomic changes of frailty were linked to genes involved in inflammation. Although analyses adjusted for chronologic age, we identified multiple hallmarks of aging in frailty-associated genes, including in chronic inflammation, proteostasis, and chromatin remodeling. These multi-omic findings implicate inflammaging in frailty among individuals with a smoking history.

Importance:Individuals at risk for chronic obstructive pulmonary disease (COPD) but without spirometric airflow obstruction can have respiratory symptoms and structural lung disease on chest computed tomography. Current guidelines recommend COPD diagnostic schemas that do not incorporate imaging abnormalities. Objective:To determine whether a multidimensional COPD diagnostic schema that includes respiratory symptoms and computed tomographic imaging abnormalities identifies additional individuals with disease. Design, Setting, and Participants:This cohort study included 2 longitudinal cohorts: the Genetic Epidemiology of COPD (COPDGene), which enrolled 10 305 participants between November 9, 2007, and April 15, 2011, with longitudinal follow-up through August 31, 2022; and the Canadian Cohort Obstructive Lung Disease (CanCOLD), which enrolled 1561 participants between November 26, 2009, and July 15, 2015, with follow-up through December 31, 2023. Exposure:Exposure included the new multidimensional COPD diagnostic schema, defined by (1) major diagnostic category: presence of the major criterion (airflow obstruction based on postbronchodilator forced expiratory volume in the first second of expiration [FEV1]/forced vital capacity ratio <0.70) and at least 1 of 5 minor criteria (emphysema or bronchial wall thickening on computed tomography, dyspnea, poor respiratory quality of life, and chronic bronchitis); or (2) minor diagnostic category: presence of least 3 of 5 minor criteria (which must include emphysema and bronchial wall thickening for individuals with respiratory symptoms potentially due to other causes). Main Outcomes and Measures:All-cause mortality, respiratory cause-specific mortality, exacerbations, and annualized change in FEV1. Results:Among 9416 adults in COPDGene (mean [SD] age at enrollment, 59.6 [9.0] years; 5035 [53.5%] were men; 3071 [32.6%] were Black; 6345 (67.4%) were White; 4943 [52.5%] currently smoked), 811 of 5250 individuals (15.4%) without airflow obstruction were newly classified as having COPD by minor diagnostic category, and 282 of 4166 individuals (6.8%) with airflow obstruction were classified as not having COPD. Reclassified individuals with a new COPD diagnosis had greater all-cause mortality (adjusted hazard ratio, 1.98; 95% CI, 1.67-2.35; P < .001) and respiratory-specific mortality (adjusted hazard ratio, 3.58; 95% CI, 1.56-8.20; P = .003), more exacerbations (adjusted incidence rate ratio, 2.09; 95% CI, 1.79-2.44; P < .001), and more rapid FEV1 decline (adjusted β = -7.7 mL/y; 95% CI, -13.2 to -2.3; P = .006) compared with individuals classified as not having COPD. Among individuals with airflow obstruction on spirometry, those no longer classified as having COPD based on this new diagnostic schema had outcomes similar to those without airflow obstruction. Among 1341 adults in CanCOLD, individuals newly classified as having COPD experienced more exacerbations (adjusted incidence rate ratio, 2.09; 95% CI, 1.25-3.51; P < .001). Conclusions and Relevance:A new COPD diagnostic schema integrating respiratory symptoms, respiratory quality of life, spirometry, and structural lung abnormalities on computed tomographic imaging newly classified some individuals as having COPD. These individuals had an increased risk of all-cause and respiratory-related death, frequent exacerbations, and rapid lung function decline compared with individuals classified as not having COPD. Some individuals with airflow obstruction without respiratory symptoms or evidence of structural lung disease were no longer classified as having COPD.

Rationale: Interstitial lung abnormalities (ILAs) are visually identified changes on chest computed tomography (CT) scans that may represent early or mild pulmonary fibrosis. Quantitative interstitial abnormalities (QIAs) measure potential parenchymal lung injury on chest CT scans using an automated algorithm. It is not known if combining these visual and quantitative assessments improves prediction of imaging progression. Objectives: To assess the utility of quantitative imaging to predict imaging progression of ILAs and adverse clinical outcomes in a cohort of smokers. Methods: ILA presence, subtypes, and progression, as well as QIAs, were assessed on chest CT scans from participants ∼5 years apart in the COPDGene (Genetic Epidemiology of COPD) study. Multivariable logistic regression assessed associations with ILA progression, and Cox proportional hazards assessed the relationship between ILA progression and mortality. Measurements and Main Results: A total of 4,373 participants had serial CT scans, and 544 (12%) had ILAs on at least one; of those, 391 (72%) had imaging visual progression, and 153 (28%) did not. Specific imaging features were associated progression (e.g., traction bronchiectasis; odds ratio, 3.1; 95% confidence interval [CI], 1.3-7.3; P = 0.003). Among those with ILAs, baseline quantitative measures (QIAs and FVC) were not associated with progression; however, visual imaging progression was associated with increased longitudinal change of QIAs (mean difference, 6.5%; 95% CI, 4.9-8.1%; P < 0.0001). In ILAs, QIA increase was associated with an increased rate of mortality independent of FVC decline (hazard ratio, 1.05; 95% CI, 1.01-1.09; P = 0.009). Conclusions: Baseline quantitative measures (QIAs and FVC) were not associated with visual ILA progression; however, longitudinal change in QIAs was correlated with imaging progression and adverse clinical outcomes.

Background Interstitial lung abnormalities (ILA) share common risk factors with coronary heart disease (CHD), including increased age and cigarette smoking; however, the relationship between ILA and CHD has not been well described. Methods Participants from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease study (COPDGene) and Age Gene/Environment Susceptibility (AGES)-Reykjavik studies with ILA assessment, clinical CHD and coronary artery calcium (CAC) data were included. In both cohorts, CHD was defined by clinical history and additionally by CAC >100. Multivariable logistic regression assessed the relationship between ILA and CHD; Cox proportional hazards models were used to assess mortality associated with ILA and CHD. Results 9% of participants with CHD had ILA in both COPDGene and AGES-Reykjavik. Participants with ILA had increased odds of CHD defined by clinical history in COPDGene (OR 1.6, 95% CI 1.2-2.0; p<0.001) and AGES-Reykjavik (OR 1.6, 95% CI 1.2-2.0; p<0.001); similar results were seen with CAC >100. In both COPDGene and AGES-Reykjavik, participants with both CHD and ILA had a greater risk of death compared to those with CHD but without ILA (HR 2.0, 95% CI 1.4-2.7; p<0.001; and HR 1.3, 95% CI 1.1-1.4; p<0.001, respectively). In AGES-Reykjavik, ILA was associated with an over 9-fold increase in the odds of a respiratory death (OR 9.6, 95% CI 3.2-29.0; p<0.0001) among participants with CHD. Conclusion ILA are a common co-occurrence with CHD and associated with worse mortality, suggesting that ILA are a clinically important comorbidity in patients with CHD.