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Abstract 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.

Abstract Background Quantitative interstitial abnormalities (QIA) are an automated computed tomography (CT) finding of early parenchymal lung disease, associated with worse lung function, reduced exercise capacity, increased respiratory symptoms, and death. The metabolomic perturbations associated with QIA are not well known. We sought to identify plasma metabolites associated with QIA in smokers. We also sought to identify shared and differentiating metabolomics features between QIA and emphysema, another smoking-related advanced radiographic abnormality. Methods In 928 former and current smokers in the Genetic Epidemiology of COPD cohort, we measured QIA and emphysema using an automated local density histogram method and generated metabolite profiles from plasma samples using liquid chromatography–mass spectrometry (Metabolon). We assessed the associations between metabolite levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, pack-years, and inhaled corticosteroid use, at a Benjamini–Hochberg False Discovery Rate p-value of ≤ 0.05. Using multinomial regression models adjusted for these covariates, we assessed the associations between metabolite levels and the following CT phenotypes: QIA-predominant, emphysema-predominant, combined-predominant, and neither- predominant. Pathway enrichment analyses were performed using MetaboAnalyst. Results We found 85 metabolites significantly associated with QIA, with overrepresentation of the nicotinate and nicotinamide, histidine, starch and sucrose, pyrimidine, phosphatidylcholine, lysophospholipid, and sphingomyelin pathways. These included metabolites involved in inflammation and immune response, extracellular matrix remodeling, surfactant, and muscle cachexia. There were 75 metabolites significantly different between QIA-predominant and emphysema-predominant phenotypes, with overrepresentation of the phosphatidylethanolamine, nicotinate and nicotinamide, aminoacyl-tRNA, arginine, proline, alanine, aspartate, and glutamate pathways. Conclusions Metabolomic correlates may lend insight to the biologic perturbations and pathways that underlie clinically meaningful quantitative CT measurements like QIA in smokers.

Abstract The consequences of returning infectious pathogen test results identified incidentally in research studies have not been well-studied. Concerns include identification of an important health issue for individuals, accuracy of research test results, public health impact, potential emotional distress for participants, and need for IRB permissions. Blood RNA-sequencing analysis for non-human RNA in 3984 participants from the COPDGene study identified 228 participants with evidence suggestive for hepatitis C virus (HCV) infection. We hypothesized that incidentally discovered HCV results could be effectively returned to COPDGene participants with attention to the identified concerns. In conjunction with a COPDGene Participant Advisory Panel, we developed and obtained IRB approval for a process of returning HCV research results and an HCV Follow-Up Study questionnaire to capture information about previous HCV diagnosis and treatment information and participant reactions to return of HCV results. During phone calls following the initial HCV notification letter, 84 of 124 participants who could be contacted (67.7%) volunteered that they had been previously diagnosed with HCV infection. Thirty-one of these 124 COPDGene participants were enrolled in the HCV Follow-Up Study. Five of the 31 HCV Follow-Up Study participants did not report a previous diagnosis of HCV. For four of these participants, subsequent clinical HCV testing confirmed HCV infection. Thus, 30/31 Follow-Up Study participants had confirmed HCV diagnoses, supporting the accuracy of the HCV research test results. However, the limited number of participants in the Follow-Up Study precludes an accurate assessment of the false-positive and false-negative rates of the research RNA sequencing evidence for HCV. Most HCV Follow-Up Study participants (29/31) were supportive of returning HCV research results, and most participants found the process for returning HCV results to be informative and not upsetting. Newly diagnosed participants were more likely to be pleased to learn about a potentially curable infection (p = 0.027) and showed a trend toward being more frightened by the potential health risks of HCV (p = 0.11). We conclude that HCV results identified incidentally during transcriptomic research studies can be successfully returned to research study participants with a carefully designed process.

Abstract Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90–95% of proteomes to their correct genome and for 95–99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

Abstract Accelerated progression of chronic obstructive pulmonary disease (COPD) is associated with increased risks of hospitalization and death. Prognostic insights into mechanisms and markers of progression could facilitate development of disease-modifying therapies. Although individual biomarkers exhibit some predictive value, performance is modest and their univariate nature limits network-level insights. To overcome these limitations and gain insights into early pathways associated with rapid progression, we measured 1305 peripheral blood and 48 bronchoalveolar lavage proteins in individuals with COPD [n = 45, mean initial forced expiratory volume in one second (FEV1) 75.6 ± 17.4% predicted]. We applied a data-driven analysis pipeline, which enabled identification of protein signatures that predicted individuals at-risk for accelerated lung function decline (FEV1 decline ≥ 70 mL/year) ~ 6 years later, with high accuracy. Progression signatures suggested that early dysregulation in elements of the complement cascade is associated with accelerated decline. Our results propose potential biomarkers and early aberrant signaling mechanisms driving rapid progression in COPD.

Abstract Flavored electronic cigarettes (ECs) present a serious health challenge globally. Currently, it is unknown whether the addition of highly popular menthol flavoring to e-liquid is associated with changes in the number of aerosolized particles generated or altered lung function. Here, we first performed preclinical studies using our novel robotic platform Human Vaping Mimetic Real-Time Particle Analyzer (HUMITIPAA). HUMITIPAA generates fresh aerosols for any desired EC in a very controlled and user-definable manner and utilizes an optical sensing system to quantitate and analyze sub-micron and microparticles from every puff over the course of vaping session in real-time while emulating clinically relevant breathing mechanics and vaping topography. We discovered that addition of menthol flavoring to freshly prepared e-liquid base propylene glycol–vegetable glycerin leads to enhanced particle counts in all tested size fractions, similar to the effect of adding vitamin E acetate to e-liquid we previously reported. Similarly, we found that menthol vs. non-menthol (tobacco) flavored pods from commercially available ECs leads to generation of significantly higher quantities of 1–10 µm particles upon inhalation. We then retrospectively analyzed data from the COPDGene study and identified an association between the use of menthol flavored ECs and reduced FEV1% predicted and FEV1/FVC independent of age, gender, race, pack-years of smoking, and use of nicotine or cannabis-containing vaping products. Our results reveal an association between enhanced inhaled particle due to menthol addition to ECs and worse lung function indices. Detailed causal relation remains to be demonstrated in future large-scale prospective clinical studies. Importantly, here we demonstrate utility of the HUMITIPAA as a predictive enabling technology to identify inhalation toxicological potential of emerging ECs as the chemical formulation of e-liquid gets modified.

Rationale Lung volumes identify the “susceptible smokers” who progress to develop spirometric COPD. However, among susceptible smokers, development of spirometric COPD seems to be heterogeneous, suggesting the presence of different pathological mechanisms during early establishment of spirometric COPD. The objective of the present study was to determine the differential patterns of radiographic pathologies among susceptible smokers. Methods We categorised smokers with preserved spirometry (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 0) in the Genetic Epidemiology of COPD (COPDGene) cohort based on tertiles (low, intermediate and high) of lung volumes (either total lung capacity (TLC), functional residual capacity FRC or FRC/TLC) at baseline visit. We then examined the differential patterns of change in spirometry and the associated prevalence of computed tomography measured pathologies of emphysema and airway disease with those categories of lung volumes. Results The pattern of spirometric change differed when participants were categorised by TLC versus FRC/TLC: those in the high TLC tertile showed stable forced expiratory volume in 1 s (FEV1), but enlarging forced vital capacity (FVC), while those in the high FRC/TLC tertile showed decline in both FEV1 and FVC. When participants from the high TLC and high FRC/TLC tertiles were partitioned into mutually exclusive groups, compared to those with high TLC, those with high FRC/TLC had lesser emphysema, but greater air trapping, more self-reported respiratory symptoms and exacerbation episodes and higher likelihood of progressing to more severe spirometric disease (GOLD stages 2–4 versus GOLD stage 1). Conclusions Lung volumes identify distinct physiological and radiographic phenotypes in early disease among susceptible smokers and predict the rate of spirometric disease progression and the severity of symptoms in early COPD.

Abstract Background There is a strong need for biomarkers to better characterize individuals with COPD and to take into account the heterogeneity of COPD. The blood protein sRAGE has been put forward as promising biomarker for COPD in general and emphysema in particular. Here, we measured plasma sRAGE levels using quantitative LC–MS and assessed whether the plasma sRAGE levels associate with (changes in) lung function, radiological emphysema parameters, and radiological subtypes of emphysema. Methods Three hundred and twenty-four COPD patients (mean FEV1: 63%predicted) and 185 healthy controls from the COPDGene study were selected. Plasma sRAGE was measured by immunoprecipitation in 96-well plate methodology to enrich sRAGE, followed by targeted quantitative liquid chromatography-mass spectrometry. Spirometry and HRCT scans (inspiration and expiration) with a 5-year follow-up were used; both subjected to high quality control standards. Results Lower sRAGE values significantly associated with the presence of COPD, the severity of airflow obstruction, the severity of emphysema on HRCT, the heterogeneous distribution of emphysema, centrilobular emphysema, and 5-year progression of emphysema. However, sRAGE values did not associate with airway wall thickness or paraseptal emphysema. Conclusions Rather than being a general COPD biomarker, sRAGE is especially a promising biomarker for centrilobular emphysema. Follow-up studies should elucidate whether sRAGE can be used as a biomarker for other COPD phenotypes as well.

Extracellular superoxide dismutase (EC-SOD) is highly expressed in the lung and vasculature. A common human single nucleotide polymorphism (SNP) in the matrix binding region of EC-SOD leads to a single amino acid substitution, R213G, and alters EC-SOD tissue binding affinity. The change in tissue binding affinity redistributes EC-SOD from tissue to extracellular fluids. Mice (R213G mice) expressing a knock-in of this EC-SOD SNP exhibit elevated plasma and reduced lung EC-SOD content and activity and are protected against bleomycin-induced lung injury and inflammation. It is unknown how the redistribution of EC-SOD alters site-specific redox-regulated molecules relevant for protection. In this study, we tested the hypothesis that the change in the local EC-SOD content would influence not only the extracellular redox microenvironment where EC-SOD is localized but also protect the intracellular redox status of the lung. Mice were treated with bleomycin and harvested 7 days post-treatment. Superoxide levels, measured by electron paramagnetic resonance (EPR), were lower in plasma and Bronchoalveolar lavage fluid (BALF) cells in R213G mice compared to wild-type (WT) mice, while lung cellular superoxide levels in R213G mice were not elevated post-bleomycin compared to WT mice despite low lung EC-SOD levels. Lung glutathione redox potential (EhGSSG), determined by HPLC and fluorescence, was more oxidized in WT compared to R213G mice. In R213G mice, lung mitochondrial oxidative stress was reduced shown by mitochondrial superoxide level measured by EPR in lung and the resistance to bleomycin-induced cardiolipin oxidation. Bleomycin treatment suppressed mitochondrial respiration in WT mice. Mitochondrial function was impaired at baseline in R213G mice but did not exhibit further suppression in respiration post-bleomycin. Collectively, the results indicate that R213G variant preserves intracellular redox state and protects mitochondrial function in the setting of bleomycin-induced inflammation.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.

Abstract Background The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression. Methods We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants. Results We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections. Conclusions These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.

Abstract Background Soluble receptor for advanced glycation end products (sRAGE) is a proposed emphysema and airflow obstruction biomarker; however, previous publications have shown inconsistent associations and only one study has investigate the association between sRAGE and emphysema. No cohorts have examined the association between sRAGE and progressive decline of lung function. There have also been no evaluation of assay compatibility, receiver operating characteristics, and little examination of the effect of genetic variability in non-white population. This manuscript addresses these deficiencies and introduces novel data from Pittsburgh COPD SCCOR and as well as novel work on airflow obstruction. A meta-analysis is used to quantify sRAGE associations with clinical phenotypes. Methods sRAGE was measured in four independent longitudinal cohorts on different analytic assays: COPDGene (n = 1443); SPIROMICS (n = 1623); ECLIPSE (n = 2349); Pittsburgh COPD SCCOR (n = 399). We constructed adjusted linear mixed models to determine associations of sRAGE with baseline and follow up forced expiratory volume at one second (FEV1) and emphysema by quantitative high-resolution CT lung density at the 15th percentile (adjusted for total lung capacity). Results Lower plasma or serum sRAGE values were associated with a COPD diagnosis (P

Abstract Background There is a lack of mechanism-driven, clinically relevant biomarkers in chronic obstructive pulmonary disease (COPD). Mitochondrial dysfunction, a proposed disease mechanism in COPD, is associated with the release of mitochondrial DNA (mtDNA), but plasma cell-free mtDNA has not been previously examined prospectively for associations with clinical COPD measures. Methods P-mtDNA, defined as copy number of mitochondrially-encoded NADH dehydrogenase-1 (MT-ND1) gene, was measured by real-time quantitative PCR in 700 plasma samples from participants enrolled in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) cohort. Associations between p-mtDNA and clinical disease parameters were examined, adjusting for age, sex, smoking status, and for informative loss to follow-up. Results P-mtDNA levels were higher in participants with mild or moderate COPD, compared to smokers without airflow obstruction, and to participants with severe COPD. Baseline increased p-mtDNA levels were associated with better CAT scores in female smokers without airflow obstruction and female participants with mild or moderate COPD on 1-year follow-up, but worse 6MWD in females with severe COPD. Higher p-mtDNA levels were associated with better 6MWD in male participants with severe COPD. These associations were no longer significant after adjusting for informative loss to follow-up. Conclusion In this study, p-mtDNA levels associated with baseline COPD status but not future changes in clinical COPD measures after accounting for informative loss to follow-up. To better characterize mitochondrial dysfunction as a potential COPD endotype, these results should be confirmed and validated in future studies. Trial Registration: ClinicalTrials.gov NCT01969344 (SPIROMICS)

Abstract Chronic obstructive pulmonary disease (COPD) is heterogeneous in development, progression, and phenotypes. Little is known about the lung microbiome, sampled by bronchoscopy, in milder COPD and its relationships to clinical features that reflect disease heterogeneity (lung function, symptom burden, and functional impairment). Using bronchoalveolar lavage fluid collected from 181 never-smokers and ever-smokers with or without COPD (GOLD 0-2) enrolled in the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS), we find that lung bacterial composition associates with several clinical features, in particular bronchodilator responsiveness, peak expiratory flow rate, and forced expiratory flow rate between 25 and 75% of FVC (FEF25–75). Measures of symptom burden (COPD Assessment Test) and functional impairment (six-minute walk distance) also associate with disparate lung microbiota composition. Drivers of these relationships include members of the Streptococcus, Prevotella, Veillonella, Staphylococcus, and Pseudomonas genera. Thus, lung microbiota differences may contribute to airway dysfunction and airway disease in milder COPD.

Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGM to weighted graphical Lasso (wGlasso) and network-based gene set analysis (Netgsa) and have demonstrated the advantages of incorporating PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, we extended the Netgsa approach and developed an augmented high-dimensional graphical Lasso (AhGlasso) method to incorporate edge weights in known PPI with omics data for global network learning. This new method outperforms weighted graphical Lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. The total runtime of AhGlasso is approximately five times faster than weighted Glasso methods when the graph size ranges from 1,000 to 3,000 with a fixed sample size (n = 300). The runtime difference between AhGlasso and weighted Glasso increases when the graph size increases. Using proteomic data from a study on chronic obstructive pulmonary disease, we demonstrate that AhGlasso improves protein network inference compared to the Netgsa approach by incorporating PPI information.

Chronic obstructive pulmonary disease (COPD) is mainly caused by cigarette smoking and characterized by chronic inflammation in vulnerable individuals. However, it is unknown how genetic factors may shape chronic inflammation in COPD. To understand how hedgehog interacting protein, encoded by HHIP gene identified in the genome-wide association study in COPD, plays a role in inflammation, we utilized Hhip+/– mice that present persistent inflammation and emphysema upon aging similar to that observed in human COPD. By performing single-cell RNA sequencing of the whole lung from mice at different ages, we found that Hhip+/– mice developed a cytotoxic immune response with a specific increase in killer cell lectin-like receptor G1–positive CD8+ T cells with upregulated Ifnγ expression recapitulating human COPD. Hhip expression was restricted to a lung fibroblast subpopulation that had increased interaction with CD8+ T lymphocytes in Hhip+/– compared with Hhip+/+ during aging. Hhip-expressing lung fibroblasts had upregulated IL-18 pathway genes in Hhip+/– lung fibroblasts, which was sufficient to drive increased levels of IFN-γ in CD8+ T cells ex vivo. Our finding provides insight into how a common genetic variation contributes to the amplified lymphocytic inflammation in COPD.

In COPD, anaemia is associated with increased morbidity, but the relationship between haemoglobin over its entire observed range and morbidity is poorly understood. Such an understanding could guide future therapeutic targeting of haemoglobin in COPD management. Leveraging the COPDGene study, we conducted a cross-sectional analysis of haemoglobin from COPD participants, examining symptoms, quality of life, functional performance, and acute exacerbations of COPD (AECOPD). Haemoglobin was analysed both as a continuous variable and categorised into anaemia, normal haemoglobin, and polycythaemia groups. Fractional polynomial modelling was used for continuous analyses; categorical models were multivariable linear or negative binomial regressions. Covariates included demographics, comorbidities, emphysema, diffusing capacity, and airflow obstruction. From 2539 participants, 366 (14%) were identified as anaemic and 125 (5%) as polycythaemic. Compared with normal haemoglobin, anaemia was significantly associated with increased symptoms (COPD Assessment Test score: p=0.006, modified Medical Research Council (mMRC) Dyspnoea Score: p=0.001); worse quality of life (St. George's Respiratory Questionnaire (SGRQ) score: p

Abstract Background Although cigarette smoking (CS) is by far the most important risk factor of chronic obstructive pulmonary disease (COPD), repeated and sustained infections are clearly linked to disease pathogenesis and are responsible for acute inflammatory flares (i.e. COPD exacerbations). We have previously identified Fatty Acid Binding Protein 5 (FABP5) as an important anti-inflammatory protein in primary airway epithelial cells. Results In this study we found decreased FABP5 mRNA and protein levels in peripheral blood mononuclear cells (PBMCs) of COPD patients, especially among those who reported episodes of COPD exacerbations. Using wildtype (WT) and FABP5−/− mice, we examined the effects of FABP5 on CS and infection-induced inflammatory responses. Similarly to what we saw in airway epithelial cells, infection increased FABP5 expression while CS decreased FABP5 expression in mouse lung tissues. CS-exposed and P. aeruginosa-infected FABP5−/− mice had significantly increased inflammation as shown by increased lung histopathological score, cell infiltration and inflammatory cytokine levels. Restoration of FABP5 in alveolar macrophages using a lentiviral approach attenuated the CS- and bacteria-induced pulmonary inflammation. And finally, while P. aeruginosa infection increased PPARγ activity, CS or FABP5 knockdown greatly reduced PPARγ activity. Conclusions These findings support a model in which CS-induced FABP5 inhibition contributes to increased inflammation in COPD exacerbations. It is interesting to speculate that the increased inflammation is a result of decreased PPARγ activity.

Chronic obstructive pulmonary disease (COPD) is a complex heterogeneous disease state with multiple phenotypic presentations that include chronic bronchitis and emphysema. Although COPD is a lung disease, it has systemic manifestations that are associated with a dysregulated metabolome in extrapulmonary compartments (e.g., blood and urine). In this scoping review of the COPD metabolomics literature, we identified 37 publications with a primary metabolomics investigation of COPD phenotypes in human subjects through Google Scholar, PubMed, and Web of Science databases. These studies consistently identified a dysregulation of the TCA cycle, carnitines, sphingolipids, and branched-chain amino acids. Many of the COPD metabolome pathways are confounded by age and sex. The effects of COPD in young versus old and male versus female need further focused investigations. There are also few studies of the metabolome’s association with COPD progression, and it is unclear whether the markers of disease and disease severity are also important predictors of disease progression.

Chronic obstructive pulmonary disease (COPD) is a disease with marked metabolic disturbance. Previous studies have shown the association between single metabolites and lung function for COPD, but whether a combination of metabolites could predict phenotype is unknown. We developed metabolomic severity scores using plasma metabolomics from the Metabolon platform from two US cohorts of ever-smokers: the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) (n = 648; training/testing cohort; 72% non-Hispanic, white; average age 63 years) and the COPDGene Study (n = 1120; validation cohort; 92% non-Hispanic, white; average age 67 years). Separate adaptive LASSO (adaLASSO) models were used to model forced expiratory volume at one second (FEV1) and MESA-adjusted lung density using 762 metabolites common between studies. Metabolite coefficients selected by the adaLASSO procedure were used to create a metabolomic severity score (metSS) for each outcome. A total of 132 metabolites were selected to create a metSS for FEV1. The metSS-only models explained 64.8% and 31.7% of the variability in FEV1 in the training and validation cohorts, respectively. For MESA-adjusted lung density, 129 metabolites were selected, and metSS-only models explained 59.0% of the variability in the training cohort and 17.4% in the validation cohort. Regression models including both clinical covariates and the metSS explained more variability than either the clinical covariate or metSS-only models (53.4% vs. 46.4% and 31.6%) in the validation dataset. The metabolomic pathways for arginine biosynthesis; aminoacyl-tRNA biosynthesis; and glycine, serine, and threonine pathway were enriched by adaLASSO metabolites for FEV1. This is the first demonstration of a respiratory metabolomic severity score, which shows how a metSS can add explanation of variance to clinical predictors of FEV1 and MESA-adjusted lung density. The advantage of a comprehensive metSS is that it explains more disease than individual metabolites and can account for substantial collinearity among classes of metabolites. Future studies should be performed to determine whether metSSs are similar in younger, and more racially and ethnically diverse populations as well as whether a metabolomic severity score can predict disease development in individuals who do not yet have COPD.

Background Heavy smokers perform worse on neuropsychological assessment than age‐matched peers. However, traditional pulmonary measures of airflow limitation and hypoxemia explain only a modest amount of variance in cognition. The current objective was to determine whether carotid artery stiffness is associated with cognition in former smokers beyond the effects of amount of smoking and pulmonary function. Methods and Results Eighty‐four former smokers including individuals across a spectrum of airflow limitation severity were included: 30 without chronic obstructive pulmonary disease (Global Initiative for Chronic Obstructive Lung Disease [GOLD] 0 with normal spirometry and lung computed tomography), 31 with mild‐moderate chronic obstructive pulmonary disease (GOLD 1–2), and 23 with severe‐very severe chronic obstructive pulmonary disease (GOLD 3–4). Participants completed questionnaires, spirometry, carotid ultrasonography, and neuropsychological testing. Multiple linear regression was used to determine whether carotid artery stiffness is associated with neuropsychological performance in 4 cognitive domains after adjusting for age, sex, pack‐years of smoking, estimated premorbid intellectual functioning, and airflow limitation. Higher carotid artery β‐stiffness index was associated with reduced executive functioning‐processing speed in the fully adjusted model (β=−0.49, SE=0.14; P=0.001). Lower premorbid intellectual function, male sex, and presence of airflow limitation (GOLD 1 or 2 and GOLD 3 or 4) were also associated with worse executive functioning‐processing speed. β‐Stiffness index was not significantly associated with performance in other cognitive domains. Conclusions Carotid artery stiffness is associated with worse performance on executive functioning‐processing speed in former smokers beyond the effects of aging, amount of past smoking, severity of airflow limitation, and hypoxemia. Future research should examine whether carotid stiffness can be used to identify former smokers at risk for subsequent cognitive impairment.

Abstract Background Thrombocytosis has been associated with COPD prevalence and increased all-cause mortality in patients with acute exacerbation of COPD (AECOPD); but whether it is associated with morbidity in stable COPD is unknown. This study aims to determine the association of thrombocytosis with COPD morbidity including reported AECOPD, respiratory symptoms and exercise capacity. Methods Participants with COPD were included from two multi-center observational studies (SPIROMICS and COPDGene). Cross-sectional associations of thrombocytosis (platelet count ≥350 × 109/L) with AECOPD during prior year (none vs. any), exertional dyspnea (modified Medical Research Council (mMRC) score ≥ 2), COPD Assessment Test (CAT) score ≥ 10, six-minute-walk distance (6MWD), and St. George Respiratory questionnaire (SGRQ) were modeled using multivariable logistic or linear regression. A pooled effect estimate for thrombocytosis was produced using meta-analysis of data from both studies. Results Thrombocytosis was present in 124/1820 (6.8%) SPIROMICS participants and 111/2185 (5.1%) COPDGene participants. In meta-analysis thrombocytosis was associated with any AECOPD (adjusted odds ratio [aOR] 1.5; 95% confidence interval [95% CI]: 1.1–2.0), severe AECOPD (aOR 1.5; 95% CI: 1.1–2.2), dyspnea (mMRC ≥ 2 aOR 1.4; 95% CI: 1.0–1.9), respiratory symptoms (CAT ≥ 10 aOR 1.6; 95% CI: 1.1–2.4), and higher SGRQ score (β 2.7; 95% CI: 0.5, 5). Thrombocytosis was also associated with classification into Global Initiative for Chronic Obstructive Lung Disease (GOLD) group D (aOR 1.7 95% CI: 1.2–2.4). Conclusions Thrombocytosis was associated with higher likelihood of prior exacerbation and worse symptoms. Platelet count, a commonly measured clinical assay, may be a biomarker for moderate-severe COPD symptoms, guide disease classification and intensity of treatment. Future longitudinal studies investigating the role of platelets in COPD progression may be warranted. Trial registration ClinicalTrials.gov: NCT01969344 (SPIROMICS) and NCT00608764 (COPDGene).

Abstract Background Blood biomarkers are increasingly used to stratify high risk chronic obstructive pulmonary disease (COPD) patients; however, there are fewer studies that have investigated multiple biomarkers and replicated in multiple large well-characterized cohorts of susceptible current and former smokers. Methods We used two MSD multiplex panels to measure 9 cytokines and chemokines in 2123 subjects from COPDGene and 1117 subjects from SPIROMICS. These biomarkers included: interleukin (IL)-2, IL-6, IL-8, IL-10, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, eotaxin/CCL-11, eotaxin-3/CCL-26, and thymus and activation-regulated chemokine (TARC)/CCL-17. Regression models adjusted for clinical covariates were used to determine which biomarkers were associated with the following COPD phenotypes: airflow obstruction (forced expiratory flow at 1 s (FEV1%) and FEV1/forced vital capacity (FEV1/FVC), chronic bronchitis, COPD exacerbations, and emphysema. Biomarker-genotype associations were assessed by genome-wide association of single nucleotide polymorphisms (SNPs). Results Eotaxin and IL-6 were strongly associated with airflow obstruction and accounted for 3–5% of the measurement variance on top of clinical variables. IL-6 was associated with progressive airflow obstruction over 5 years and both IL-6 and IL-8 were associated with progressive emphysema over 5 years. None of the biomarkers were consistently associated with chronic bronchitis or COPD exacerbations. We identified one novel SNP (rs9302690 SNP) that was associated with CCL17 plasma measurements. Conclusion When assessing smoking related pulmonary disease, biomarkers of inflammation such as IL-2, IL-6, IL-8, and eotaxin may add additional modest predictive value on top of clinical variables alone. Trial registration COPDGene (ClinicalTrials.gov Identifier: NCT02445183 ). Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS) ( ClinicalTrials.gov Identifier: NCT 01969344 ).

Abstract This observational study catalogues the overlap in metabolites between matched bronchoalveolar lavage fluid (BALF) and plasma, identifies the degree of congruence between these metabolomes in human and mouse, and determines how molecules may change in response to cigarette smoke (CS) exposure. Matched BALF and plasma was collected from mice (ambient air or CS-exposed) and humans (current or former smokers), and analyzed using mass spectrometry. There were 1155 compounds in common in all 4 sample types; fatty acyls and glycerophospholipids strongly overlapped between groups. In humans and mice, more than half of the metabolites present in BALF were also present in plasma. Mouse BALF and human BALF had a strong positive correlation with 2040 metabolites in common, suggesting that mouse models can be used to interrogate human lung metabolome changes. While power was affected by small sample size in the mouse study, the BALF metabolome appeared to be more affected by CS than plasma. CS-exposed mice showed increased plasma and BALF glycerolipids and glycerophospholipids. This is the first report cataloguing the metabolites present across mouse and human, BALF and plasma. Findings are relevant to translational studies where mouse models are used to examine human disease, and where plasma may be interrogated in lieu of BALF or lung tissue.

Abstract Background Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by multiple subtypes and variable disease progression. Blood biomarkers have been variably associated with subtype, severity, and disease progression. Just as combined clinical variables are more highly predictive of outcomes than individual clinical variables, we hypothesized that multiple biomarkers may be more informative than individual biomarkers to predict subtypes, disease severity, disease progression, and mortality. Methods Fibrinogen, C-Reactive Protein (CRP), surfactant protein D (SP-D), soluble Receptor for Advanced Glycation Endproducts (sRAGE), and Club Cell Secretory Protein (CC16) were measured in the plasma of 1465 subjects from the COPDGene cohort and 2746 subjects from the ECLIPSE cohort. Regression analysis was performed to determine whether these biomarkers, individually or in combination, were predictive of subtypes, disease severity, disease progression, or mortality, after adjustment for clinical covariates. Results In COPDGene, the best combinations of biomarkers were: CC16, sRAGE, fibrinogen, CRP, and SP-D for airflow limitation (p

Susceptibility and progression of lung disease, as well as response to treatment, often differ by sex, yet the metabolic mechanisms driving these sex-specific differences are still poorly understood. Women with chronic obstructive pulmonary disease (COPD) have less emphysema and more small airway disease on average than men, though these differences become less pronounced with more severe airflow limitation. While small studies of targeted metabolites have identified compounds differing by sex and COPD status, the sex-specific effect of COPD on systemic metabolism has yet to be interrogated. Significant sex differences were observed in 9 of the 11 modules identified in COPDGene. Sex-specific associations by COPD status and emphysema were observed in 3 modules for each phenotype. Sex stratified individual metabolite associations with COPD demonstrated male-specific associations in sphingomyelins and female-specific associations in acyl carnitines and phosphatidylethanolamines. There was high preservation of module assignments in SPIROMICS (SubPopulations and InteRmediate Outcome Measures In COPD Study) and similar female-specific shift in acyl carnitines. Several COPD associated metabolites differed by sex. Acyl carnitines and sphingomyelins demonstrate sex-specific abundances and may represent important metabolic signatures of sex differences in COPD. Accurately characterizing the sex-specific molecular differences in COPD is vital for personalized diagnostics and therapeutics.

Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein−metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein−metabolite network consisting of 13 proteins and 7 metabolites which had a -0.34 correlation (p-value = 2.5 × 10−28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a -0.27 correlation (p-value = 2.6 × 10−17) to percent emphysema. Protein−metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.

Smoking causes chronic obstructive pulmonary disease (COPD). Though recent studies identified a COPD metabolomic signature in blood, no large studies examine the metabolome in bronchoalveolar lavage (BAL) fluid, a more direct representation of lung cell metabolism. We performed untargeted liquid chromatography−mass spectrometry (LC−MS) on BAL and matched plasma from 115 subjects from the SPIROMICS cohort. Regression was performed with COPD phenotypes as the outcome and metabolites as the predictor, adjusted for clinical covariates and false discovery rate. Weighted gene co-expression network analysis (WGCNA) grouped metabolites into modules which were then associated with phenotypes. K-means clustering grouped similar subjects. We detected 7939 and 10,561 compounds in BAL and paired plasma samples, respectively. FEV1/FVC (Forced Expiratory Volume in One Second/Forced Vital Capacity) ratio, emphysema, FEV1 % predicted, and COPD exacerbations associated with 1230, 792, eight, and one BAL compounds, respectively. Only two plasma compounds associated with a COPD phenotype (emphysema). Three BAL co-expression modules associated with FEV1/FVC and emphysema. K-means BAL metabolomic signature clustering identified two groups, one with more airway obstruction (34% of subjects, median FEV1/FVC 0.67), one with less (66% of subjects, median FEV1/FVC 0.77; p < 2 × 10−4). Associations between metabolites and COPD phenotypes are more robustly represented in BAL compared to plasma.

Background: Electronic nicotine delivery systems (ENDS) are driving an epidemic of vaping. Identifying biomarkers of vaping and dual use (concurrent vaping and smoking) will facilitate studies of the health effects of vaping. To identify putative biomarkers of vaping and dual use, we performed association analysis in an observational cohort of 3,892 COPDGene study participants with blood transcriptomics and/or plasma proteomics data and self-reported current vaping and smoking behavior. Methods: Biomarkers of vaping and dual use were identified through differential expression analysis and related to prospective health events over six years of follow-up. To assess the predictive accuracy of multi-biomarker panels, we constructed predictive models for vaping and smoking categories and prospective health outcomes. Results: We identified three transcriptomic and three proteomic associations with vaping, and 90 transcriptomic and 100 proteomic associations to dual use. Many of these vaping or dual use biomarkers were significantly associated with prospective health outcomes, such as FEV1 decline (three transcripts and 62 proteins), overall mortality (18 transcripts and 73 proteins), respiratory mortality (two transcripts and 23 proteins), respiratory exacerbations (13 proteins) and incident cardiovascular disease (24 proteins). Multimarker models showed good performance discriminating between vaping and smoking behavior and produced informative, modestly powerful predictions of future FEV1 decline, mortality, and respiratory exacerbations. Conclusions: In summary, vaping and dual use are associated with RNA and protein blood-based biomarkers that are also associated with adverse health outcomes.