Biological Age, Chronological Age and Survival in Pulmonary Fibrosis: A Causal Mediation Analysis.

RATIONALE: Accelerated biological aging has been implicated in the development of interstitial lung disease (ILD) and other diseases of aging but remains poorly understood. OBJECTIVES: To identify plasma proteins that mediate the relationship between chronological age and survival association in patients with ILD. METHODS: Causal mediation analysis was performed to identify plasma proteins that mediated the chronological age-survival relationship in an idiopathic pulmonary fibrosis (IPF) discovery cohort. Proteins mediating this relationship after adjustment for false discovery were advanced for testing in an independent ILD validation cohort and explored in a chronic obstructive pulmonary disease (COPD) cohort. A proteomic-based measure of biological age was constructed and survival analysis performed assessing the impact of biological age and peripheral blood telomere length on the chronological age-survival relationship. RESULTS: Twenty-two proteins mediated the chronological age-survival relationship after adjustment for false discovery in the IPF discovery cohort (n=874), with nineteen remaining significant mediators of this relationship in the ILD validation cohort (n=983) and one mediating this relationship in the COPD cohort. Latent transforming growth factor beta binding protein 2 and ectodysplasin A2 receptor showed the strongest mediation across cohorts. A proteomic measure of biological age completely attenuated the chronological age-survival association and better discriminated survival than chronological age. Results were robust to adjustment for peripheral blood telomere length, which did not mediate the chronological age-survival relationship. CONCLUSIONS: Molecular measures of aging completely mediate the relationship between chronological age and survival, suggesting that chronological age has no direct effect on ILD survival.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes.

Rationale: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. Objectives: Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics. Methods: We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups. Measurements and Main Results: We examined two high-risk omics-defined groups in non-overlapping test sets (n=1,133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "High activity" (low PRS/high TRS) and "severe risk" (high PRS/high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signaling processes compared to a low-risk (low PRS, low TRS) reference subgroup. "High activity" but not "severe risk" participants had greater prospective FEV 1 decline (COPDGene: -51 mL/year; ECLIPSE: - 40 mL/year) and their proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Conclusions: Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

Association of Ground Glass Opacities with Systemic Inflammation and Progression of Emphysema.

RATIONAL: Ground glass opacities (GGO) in the absence of interstitial lung disease are understudied. OBJECTIVE: To assess the association of GGO with white blood cells (WBCs) and progression of quantified chest CT emphysema. METHODS: We analyzed data of participants in the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS). Chest radiologists and pulmonologists labeled regions of the lung as GGO and adaptive multiple feature method (AMFM) trained the computer to assign those labels to image voxels and quantify the volume of the lung with GGO (%GGOAMFM). We used multivariable linear regression, zero-inflated negative binomial, and proportional hazards regression models to assess the association of %GGOAMFM with WBC, changes in %emphysema, and clinical outcomes. MEASUREMENTS AND MAIN RESULTS: Among 2,714 participants, 1,680 had COPD and 1,034 had normal spirometry. Among COPD participants, based on the multivariable analysis, current smoking and chronic productive cough was associated with higher %GGOAMFM. Higher %GGOAMFM was cross-sectionally associated with higher WBCs and neutrophils levels. Higher %GGOAMFM per interquartile range at visit 1 (baseline) was associated with an increase in emphysema at one-year follow visit by 11.7% (Relative increase; 95%CI 7.5-16.1%;P<0.001). We found no association between %GGOAMFM and one-year FEV1 decline but %GGOAMFM was associated with exacerbations and all-cause mortality during a median follow-up time of 1,544 days (Interquartile Interval=1,118-2,059). Among normal spirometry participants, we found similar results except that %GGOAMFM was associated with progression to COPD at one-year follow-up. CONCLUSIONS: Our findings suggest that GGOAMFM is associated with increased systemic inflammation and emphysema progression.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Preacinar Arterial Dilation Mediates Outcomes of Quantitative Interstitial Abnormalities in COPDGene.

RATIONALE: Quantitative interstitial abnormalities (QIA) are a computed tomography (CT) measure of early parenchymal lung disease associated with worse clinical outcomes including exercise capacity and symptoms. The presence of pulmonary vasculopathy in QIA and its role in the QIA-outcome relationship is unknown. OBJECTIVES: To quantify radiographic pulmonary vasculopathy in quantitative interstitial abnormalities (QIA) and determine if this vasculopathy mediates the QIA-outcome relationship. METHODS: Ever-smokers with QIA, outcome, and pulmonary vascular mediator data were identified from the COPDGene cohort. CT-based vascular mediators were: right ventricle-to-left ventricle ratio (RV/LV), pulmonary artery-to-aorta ratio (PA/Ao), and pre-acinar intraparenchymal arterial dilation (PA volume 5-20mm2 in cross-sectional area, normalized to total arterial volume). Outcomes were: six-minute walk distance (6MWD) and modified Medical Council Research Council (mMRC) Dyspnea score ≥2. Adjusted causal mediation analyses were used to determine if the pulmonary vasculature mediated the QIA effect on outcomes. Associations of pre-acinar arterial dilation with select plasma biomarkers of pulmonary vascular dysfunction were examined. MAIN RESULTS: Among 8,200 participants, QIA burden correlated positively with vascular damage measures including pre-acinar arterial dilation. Pre-acinar arterial dilation mediated 79.6% of the detrimental impact of QIA on 6MWD (56.2-100%, p<0.001). PA/Ao was a weak mediator and RV/LV was a suppressor. Similar results were observed in the QIA-mMRC relationship. Pre-acinar arterial dilation correlated with increased pulmonary vascular dysfunction biomarker levels including angiopoietin-2 and NT-proBNP. CONCLUSIONS: Parenchymal quantitative interstitial abnormalities (QIA) deleteriously impact outcomes primarily through pulmonary vasculopathy. Pre-acinar arterial dilation may be a novel marker of pulmonary vasculopathy in QIA.

The impact of cannabis use proximal to sleep and cannabinoid metabolites on sleep architecture.

STUDY OBJECTIVES: Cannabis is a common sleep aid, however the effects of its use prior to sleep are poorly understood. This study aims to determine the impact of non-medical whole plant cannabis use three hours prior to sleep and measured cannabis metabolites on polysomnogram measures. METHODS: This is a cross-sectional study of 177 healthy adults who provided detailed cannabis use history, underwent a one-night home sleep test (HST) and had measurement of eleven plasma and urinary cannabinoids, quantified using mass spectroscopy, the morning after the HST. Multivariable models were used to assess the relationship between cannabis use proximal to sleep, which was defined as use three hours before sleep, and individual HST measurements. Correlation between metabolite concentrations and polysomnogram measures were assessed. RESULTS: In adjusted models, cannabis use proximal to sleep was associated with increased wake after sleep onset (median 60.5 versus 45.8 minutes), rate ratio 1.59 (1.22, 2.05), and increased proportion of stage one sleep (median 15.2% versus 12.3%), effect estimate 0.16 (0.06, 0.25). Compared to non-users, frequent cannabis users (>20 days per month) also had increased wake after sleep onset and stage one sleep, in addition to increased REM latency and decreased percent sleep efficiency. Delta-9 tetrahydrocannabinol metabolites correlated with these HST measures. CONCLUSIONS: Cannabis use proximal to sleep was associated with minimal changes in sleep architecture. Its use wasn't associated with measures of improved sleep including increased sleep time or efficiency and may be associated with poor quality sleep through increase wake onset and stage 1 sleep.

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood.

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer's disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole genome sequencing of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European American (EA) ancestry group compared to those of Hispanic American (HA), African American (AA), and East Asian (EAS) ancestry. Further, we identified two genes ( CFHR1 and LRP6 ) that harbor multiple rare, putatively deleterious variants associated with mLOY susceptibility, show that subsets of human hematopoietic stem cells are enriched for activity of mLOY susceptibility variants, and that certain alleles on chromosome Y are more likely to be lost than others.

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.

Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease.

Background: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features. Methods: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS. Results: We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts. Conclusions: In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study.

INTRODUCTION/RATIONALE: Protein biomarkers may help enable the prediction of incident interstitial features on chest CT. METHODS: We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p<0.001), and which of those were associated with interstitial features longitudinally (multivariable mixed effects model FDR p<0.05). To predict incident interstitial features, we trained four random forest classifiers in a two-thirds random subset of COPDGene: (1) imaging and demographic information, (2) univariate screen biomarkers, (3) multivariable confirmation biomarkers and (4) multivariable confirmation biomarkers available in a separate testing cohort (Pittsburgh Lung Screening Study (PLuSS)). We evaluated classifier performance in the remaining one-third of COPDGene, and, for the final model, also in PLuSS. RESULTS: In COPDGene, 1305 biomarkers were available and 20 differed between those with and without interstitial features at baseline. Of these, 11 were associated with feature progression over a mean of 5.5 years of follow-up, and of these 4 were available in PLuSS, (angiopoietin-2, matrix metalloproteinase 7, macrophage inflammatory protein 1 alpha) over a mean of 8.8 years of follow-up. The area under the curve (AUC) of classifiers using demographics and imaging features in COPDGene and PLuSS were 0.69 and 0.59, respectively. In COPDGene, the AUC of the univariate screen classifier was 0.78 and of the multivariable confirmation classifier was 0.76. The AUC of the final classifier in COPDGene was 0.75 and in PLuSS was 0.76. The outcome for all of the models was the development of incident interstitial features. CONCLUSIONS: Multiple novel and previously identified proteomic biomarkers are associated with interstitial features on chest CT and may enable the prediction of incident interstitial diseases such as idiopathic pulmonary fibrosis.

The Effect of Chronic Altitude Exposure on COPD Outcomes in the SPIROMICS Cohort.

RATIONALE: Individuals with COPD have airflow obstruction and maldistribution of ventilation. For those living at high altitude, any gas exchange abnormality is compounded by reduced partial pressures of inspired oxygen. OBJECTIVES: Does residence at higher-altitude exposure affect COPD outcomes, including lung function, imaging characteristics, symptoms, health status, functional exercise capacity, exacerbations, or mortality? METHODS: From the SPIROMICS cohort, we identified individuals with COPD living below 1,000 ft (305 m) elevation (n= 1,367) versus above 4,000 ft (1,219 m) elevation (n= 288). Multivariable regression models were used to evaluate associations of exposure to high altitude with COPD-related outcomes. MEASUREMENTS AND MAIN RESULTS: Living at higher altitude was associated with reduced functional exercise capacity as defined by 6MWD (-32.3 m, (-55.7 to -28.6)). There were no differences in patient-reported outcomes as defined by symptoms (CAT, mMRC), or health status (SGRQ). Higher altitude was not associated with a different rate of FEV1 decline. Higher altitude was associated with lower odds of severe exacerbations (IRR 0.65, (0.46 to 0.90)). There were no differences in small airway disease, air trapping, or emphysema. In longitudinal analyses, higher altitude was associated with increased mortality (HR 1.25, (1.0 to 1.55)); however, this association was no longer significant when accounting for air pollution. CONCLUSIONS: Chronic altitude exposure is associated with reduced functional exercise capacity in individuals with COPD, but this did not translate into differences in symptoms or health status. Additionally, chronic high-altitude exposure did not affect progression of disease as defined by longitudinal changes in spirometry.

Demographic and Clinical Factors Associated With SARS-CoV-2 Spike 1 Antibody Response Among Vaccinated US Adults: the C4R Study.

This study investigates correlates of anti-S1 antibody response following COVID-19 vaccination in a U.S. population-based meta-cohort of adults participating in longstanding NIH-funded cohort studies. Anti-S1 antibodies were measured from dried blood spots collected between February 2021-August 2022 using Luminex-based microsphere immunoassays. Of 6245 participants, mean age was 73 years (range, 21-100), 58% were female, and 76% were non-Hispanic White. Nearly 52% of participants received the BNT162b2 vaccine and 48% received the mRNA-1273 vaccine. Lower anti-S1 antibody levels are associated with age of 65 years or older, male sex, higher body mass index, smoking, diabetes, COPD and receipt of BNT16b2 vaccine (vs mRNA-1273). Participants with a prior infection, particularly those with a history of hospitalized illness, have higher anti-S1 antibody levels. These results suggest that adults with certain socio-demographic and clinical characteristics may have less robust antibody responses to COVID-19 vaccination and could be prioritized for more frequent re-vaccination.

A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference.

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate.

Loss of Airway Phylogenetic Diversity Is Associated with Clinical and Pathobiological Markers of Disease Development in Chronic Obstructive Pulmonary Disease.

Rationale: The airway microbiome has the potential to shape chronic obstructive pulmonary disease (COPD) pathogenesis, but its relationship to outcomes in milder disease is unestablished. Objectives: To identify sputum microbiome characteristics associated with markers of COPD in participants of the Subpopulations and Intermediate Outcome Measures of COPD Study (SPIROMICS). Methods: Sputum DNA from 877 participants was analyzed using 16S ribosomal RNA gene sequencing. Relationships between baseline airway microbiota composition and clinical, radiographic, and mucoinflammatory markers, including longitudinal lung function trajectory, were examined. Measurements and Main Results: Participant data represented predominantly milder disease (Global Initiative for Chronic Obstructive Lung Disease stage 0-2 obstruction in 732 of 877 participants). Phylogenetic diversity (i.e., range of different species within a sample) correlated positively with baseline lung function, decreased with higher Global Initiative for Chronic Obstructive Lung Disease stage, and correlated negatively with symptom burden, radiographic markers of airway disease, and total mucin concentrations (P < 0.001). In covariate-adjusted regression models, organisms robustly associated with better lung function included Alloprevotella, Oribacterium, and Veillonella species. Conversely, lower lung function, greater symptoms, and radiographic measures of small airway disease were associated with enrichment in members of Streptococcus, Actinobacillus, Actinomyces, and other genera. Baseline sputum microbiota features were also associated with lung function trajectory during SPIROMICS follow-up (stable/improved, decline, or rapid decline groups). The stable/improved group (slope of FEV1 regression ⩾66th percentile) had greater bacterial diversity at baseline associated with enrichment in Prevotella, Leptotrichia, and Neisseria species. In contrast, the rapid decline group (FEV1 slope ⩽33rd percentile) had significantly lower baseline diversity associated with enrichment in Streptococcus species. Conclusions: In SPIROMICS, baseline airway microbiota features demonstrate divergent associations with better or worse COPD-related outcomes.

Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study.

Rationale: Quantitative interstitial abnormalities (QIAs) are early measures of lung injury automatically detected on chest computed tomography scans. QIAs are associated with impaired respiratory health and share features with advanced lung diseases, but their biological underpinnings are not well understood. Objectives: To identify novel protein biomarkers of QIAs using high-throughput plasma proteomic panels within two multicenter cohorts. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (COPDGene [Genetic Epidemiology of Chronic Obstructive Pulmonary Disease]) and 2,925 participants in a younger population cohort (CARDIA [Coronary Artery Disease Risk in Young Adults]) using the SomaLogic SomaScan assays. We measured QIAs using a local density histogram method. We assessed the associations between proteomic biomarker concentrations and QIAs using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg false discovery rate-corrected P ⩽ 0.05). Measurements and Main Results: In total, 852 proteins were significantly associated with QIAs in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including biological processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; cellular components in extracellular regions; and molecular functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIAs in an older, smoking population with a higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.

Lung Structure and Risk of Sleep Apnea in SPIROMICS.

Rationale: The SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) is a prospective cohort study that enrolled 2981 participants with the goal of identifying new chronic obstructive pulmonary disease (COPD) subgroups and intermediate markers of disease progression. Individuals with COPD and obstructive sleep apnea (OSA) experience impaired quality of life and more frequent exacerbations. COPD severity also associates with computed tomography scan-based emphysema and alterations in airway dimensions. Objectives: The objective was to determine whether the combination of lung function and structure influences the risk of OSA among current and former smokers. Methods: Using 2 OSA risk scores, the Berlin Sleep Questionnaire (BSQ), and the DOISNORE50 (Diseases, Observed apnea, Insomnia, Snoring, Neck circumference > 18 inches, Obesity with body mass index [BMI] > 32, R = are you male, Excessive daytime sleepiness, 50 = age ≥ 50) (DIS), 1767 current and former smokers were evaluated for an association of lung structure and function with OSA risk. Measurements and Main Results: The study cohort's mean age was 63 years, BMI was 28 kg/m2, and forced expiratory volume in 1 second (FEV1) was 74.8% predicted. The majority were male (55%), White (77%), former smokers (59%), and had COPD (63%). A high-risk OSA score was reported in 36% and 61% using DIS and BSQ respectively. There was a 9% increased odds of a high-risk DIS score (odds ratio [OR]=1.09, 95% confidence interval [CI]:1.03-1.14) and nominally increased odds of a high-risk BSQ score for every 10% decrease in FEV1 %predicted (OR=1.04, 95%CI: 0.998-1.09). Lung function-OSA risk associations persisted after additionally adjusting for lung structure measurements (%emphysema, %air trapping, parametric response mapping for functional small airways disease, , mean segmental wall area, tracheal %wall area, dysanapsis) for DIS (OR=1.12, 95%CI:1.03-1.22) and BSQ (OR=1.09, 95%CI:1.01-1.18). Conclusions: Lower lung function independently associates with having high risk for OSA in current and former smokers. Lung structural elements, especially dysanapsis, functional small airways disease, and tracheal %wall area strengthened the effects on OSA risk.

Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema.

Rationale: Emphysema is a chronic obstructive pulmonary disease phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. Objectives: To discover blood omics biomarkers for chest computed tomography-quantified emphysema and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training sample of 2,370 COPDGene participants with available blood RNA sequencing, plasma proteomics, and clinical data. Internal validation was conducted in a COPDGene testing sample (n = 1,016), and external validation was done in the ECLIPSE study (n = 526). Because low body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models with bootstrapping were also developed in the training sample sequentially using clinical, blood cell proportions, RNA-sequencing, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed by the area under the receiver operating characteristic curves for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: Totals of 3,829 genes, 942 isoforms, 260 exons, and 714 proteins were significantly associated with emphysema (false discovery rate, 5%) and yielded 11 biological pathways. Seventy-four percent of these genes and 62% of these proteins showed mediation by BMI. Our prediction models demonstrated reasonable predictive performance in both COPDGene and ECLIPSE. The highest-performing model used clinical, blood cell, and protein data (area under the receiver operating characteristic curve in COPDGene testing, 0.90; 95% confidence interval, 0.85-0.90). Conclusions: Blood transcriptome and proteome-wide analyses revealed key biological pathways of emphysema and enhanced the prediction of emphysema.

The Regulation of Fatty Acid Synthase by Exosomal miR-143-5p and miR-342-5p in Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease caused by an aberrant repair of injured alveolar epithelial cells. The maintenance of the alveolar epithelium and its regeneration after the damage is fueled by alveolar type II (ATII) cells. Injured cells release exosomes containing microRNAs (miRNAs), which can alter the recipient cells' function. Lung tissue, ATII cells, fibroblasts, plasma, and exosomes were obtained from naive patients with IPF, patients with IPF taking pirfenidone or nintedanib, and control organ donors. miRNA expression was analyzed to study their impact on exosome-mediated effects in IPF. High miR-143-5p and miR-342-5p levels were detected in ATII cells, lung tissue, plasma, and exosomes in naive patients with IPF. Decreased FASN (fatty acid synthase) and ACSL-4 (acyl-CoA-synthetase long-chain family member 4) expression was found in ATII cells. miR-143-5p and miR-342-5p overexpression or ATII cell treatment with IPF-derived exosomes containing these miRNAs lowered FASN and ACSL-4 levels. Also, this contributed to ATII cell injury and senescence. However, exosomes isolated from patients with IPF taking nintedanib or pirfenidone increased FASN expression in ATII cells compared with naive patients with IPF. Furthermore, fibroblast treatment with exosomes obtained from naive patients with IPF increased SMAD3, CTGF, COL3A1, and TGFß1 expression. Our results suggest that IPF-derived exosomes containing miR-143-5p and miR-342-5p inhibited the de novo fatty acid synthesis pathway in ATII cells. They also induced the profibrotic response in fibroblasts. Pirfenidone and nintedanib improved ATII cell function and inhibited fibrogenesis. This study highlights the importance of exosomes in IPF pathophysiology.