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Additional file 4: Figure S1. Immunofluorescence data showing increased expression of heme oxygenase 1 (HO1) protein by esomeprazole in primary human lung epithelial cells. The cells were treated with various concentrations of esomeprazole for 24 hours (1-100 μM) prior to staining with mouse anti-HO1 antibody (shown in red). The cell membrane was stained with Alexa Fluor 488-conjugated phalloidin and is shown in green. Figure S2. Western blot data showing nuclear translocation of nuclear factor-like 2 (Nrf2) in human lung endothelial cells. The cells were treated for 24 hours with esomeprazole (1-100 μM) or vehicle control (water) prior to isolation of nuclear protein. Data is representative of five independent experiments. Histone H3 is used as a loading control. Densitometric quantification of the protein bands relative to the housekeeping control protein histone H3 is shown in the lower panel. Figure S3. Quantitative RT-PCR (qRT-PCR) data showing dose-dependent upregulation of NADPH quinone oxidoreductase 1 (NQO1) by esomeprazole in human IPF lung fibroblasts. The cells were treated with various concentrations of esomeprazole for 6 hours prior to isolation of RNA for qRT-PCR. Data is Mean ± SEM from triplicate experiments. *p

BackgroundChronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality. Present-day diagnostic criteria are largely based solely on spirometric criteria. Accumulating evidence has identified a substantial number of individuals without spirometric evidence of COPD who suffer from respiratory symptoms and/or increased morbidity and mortality. There is a clear need for an expanded definition of COPD that is linked to physiologic, structural (computed tomography [CT]) and clinical evidence of disease. Using data from the COPD Genetic Epidemiology study (COPDGene®), we hypothesized that an integrated approach that includes environmental exposure, clinical symptoms, chest CT imaging and spirometry better defines disease and captures the likelihood of progression of respiratory obstruction and mortality.MethodsFour key disease characteristics - environmental exposure (cigarette smoking), clinical symptoms (dyspnea and/or chronic bronchitis), chest CT imaging abnormalities (emphysema, gas trapping and/or airway wall thickening), and abnormal spirometry - were evaluated in a group of 8784 current and former smokers who were participants in COPDGene® Phase 1. Using these 4 disease characteristics, 8 categories of participants were identified and evaluated for odds of spirometric disease progression (FEV1 > 350 ml loss over 5 years), and the hazard ratio for all-cause mortality was examined.ResultsUsing smokers without symptoms, CT imaging abnormalities or airflow obstruction as the reference population, individuals were classified as Possible COPD, Probable COPD and Definite COPD. Current Global initiative for obstructive Lung Disease (GOLD) criteria would diagnose 4062 (46%) of the 8784 study participants with COPD. The proposed COPDGene® 2019 diagnostic criteria would add an additional 3144 participants. Under the new criteria, 82% of the 8784 study participants would be diagnosed with Possible, Probable or Definite COPD. These COPD groups showed increased risk of disease progression and mortality. Mortality increased in patients as the number of their COPD characteristics increased, with a maximum hazard ratio for all cause-mortality of 5.18 (95% confidence interval [CI]: 4.15-6.48) in those with all 4 disease characteristics.ConclusionsA substantial portion of smokers with respiratory symptoms and imaging abnormalities do not manifest spirometric obstruction as defined by population normals. These individuals are at significant risk of death and spirometric disease progression. We propose to redefine the diagnosis of COPD through an integrated approach using environmental exposure, clinical symptoms, CT imaging and spirometric criteria. These expanded criteria offer the potential to stimulate both current and future interventions that could slow or halt disease progression in patients before disability or irreversible lung structural changes develop.

Rationale: Anxiety is a common comorbidity of chronic obstructive pulmonary disease (COPD) that is associated with higher morbidity and mortality. We evaluated three anxiety screening questionnaires: the Generalized Anxiety Disorder 7-Item Scale (GAD-7), the Hospital Anxiety and Depression Scale Anxiety subscale (HADS-A), and the Anxiety Inventory for Respiratory Disease (AIR).Objectives: To evaluate and compare the test performance characteristics of three anxiety screening questionnaires, using the Mini-International Neuropsychiatric Interview (MINI), version 7.0, as the "gold standard."Methods: Individuals with COPD were recruited at 16 centers. The MINI and questionnaires were administered by trained research coordinators at an in-person visit and readministered by telephone 2-4 weeks later. A composite score for the presence of any Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-V) anxiety disorder was computed, based on the MINI as the gold standard, compared with a participant screening positive on self-report measures for these analyses.Results: Two hundred and twenty eligible individuals with COPD were enrolled; 219 completed the study. Eleven percent were identified as having a DSM-V anxiety disorder, based on the MINI. Elevated anxiety symptoms based on questionnaires were 38% for the AIR, 30% for the GAD-7, and 20% for the HADS-A. Area under the receiver operating characteristic curve (AUC) was highest for the GAD-7 (0.78; 95% confidence interval [CI], 0.69-0.87), followed by the HADS-A (0.74; 95% CI, 0.64-0.84) and the AIR (0.66; 95% CI, 0.56-0.76). The AUC for the GAD-7 was significantly greater than for the AIR (P = 0.014). Sensitivity was not statistically different among the questionnaires: 77% for the GAD-7, 63% for the HADS-A, and 66% for the AIR. The HADS-A had the highest specificity, 85%, which was significantly higher than that of the GAD-7 (77%; P

BackgroundCOPD patients have a great burden of comorbidity. However, it is not well established whether this is due to shared risk factors such as smoking, if they impact patients exercise capacity and quality of life, or whether there are racial disparities in their impact on COPD.MethodsWe analyzed data from 10,192 current and ex-smokers with (cases) and without COPD (controls) from the COPDGene® cohort to establish risk for COPD comorbidities adjusted for pertinent covariates. In adjusted models, we examined comorbidities prevalence and impact in African-Americans (AA) and Non-Hispanic Whites (NHW).ResultsComorbidities are more common in COPD compared to those with normal spirometry (controls), and the risk persists after adjustments for covariates including pack-years smoked. After adjustment for confounders, eight conditions were independently associated with worse exercise capacity, quality of life and dyspnea. There were racial disparities in the impact of comorbidities on exercise capacity, dyspnea and quality of life, presence of osteoarthritis and gastroesophageal reflux disease having a greater negative impact on all three outcomes in AAs than NHWs (p

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10−10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group., Author Summary Precision medicine is an emerging approach that takes into account variability in genes, gene and protein expression, environment and lifestyle. Recent advances in high-throughput genome-wide genotyping, genomics, and proteomics coupled with the creation of large, highly-phenotyped clinical cohorts now allows for integration of these molecular data sets at the individual level. Here we use genome-wide genotyping and blood measurements of 88 biomarkers in 1,340 subjects from two large NIH-supported clinical cohorts of smokers (SPIROMICS and COPDGene) to identify more than 300 novel DNA variants that influence measurement of blood protein levels (pQTLs). We find that many DNA variants explain a large portion of the variability of measured protein expression in blood. Furthermore, we show that integration of DNA variants with blood biomarker levels can improve the ability of predictive models to reflect the relationship between biomarker and disease features (e.g., emphysema) within chronic obstructive pulmonary disease (COPD).

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10^−10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10^−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.

Importance Comorbidities are common in COPD, but quantifying their burden is difficult. Currently there is a COPD-specific comorbidity index to predict mortality and another to predict general quality of life. We sought to develop and validate a COPD-specific comorbidity score that reflects comorbidity burden on patient-centered outcomes. Materials and Methods Using the COPDGene study (GOLD II-IV COPD), we developed comorbidity scores to describe patient-centered outcomes employing three techniques: 1) simple count, 2) weighted score, and 3) weighted score based upon statistical selection procedure. We tested associations, area under the Curve (AUC) and calibration statistics to validate scores internally with outcomes of respiratory disease-specific quality of life (St. George's Respiratory Questionnaire, SGRQ), six minute walk distance (6MWD), modified Medical Research Council (mMRC) dyspnea score and exacerbation risk, ultimately choosing one score for external validation in SPIROMICS. Results Associations between comorbidities and all outcomes were comparable across the three scores. All scores added predictive ability to models including age, gender, race, current smoking status, pack-years smoked and FEV1 (p