Person:

Hersh, Craig

Background: Gas trapping quantified on chest CT scans has been proposed as a surrogate for small airway disease in COPD. We sought to determine if measurements using paired inspiratory and expiratory CT scans may be better able to separate gas trapping due to emphysema from gas trapping due to small airway disease. Methods: Smokers with and without COPD from the COPDGene Study underwent inspiratory and expiratory chest CT scans. Emphysema was quantified by the percent of lung with attenuation < −950HU on inspiratory CT. Four gas trapping measures were defined: (1) Exp−856, the percent of lung < −856HU on expiratory imaging; (2) E/I MLA, the ratio of expiratory to inspiratory mean lung attenuation; (3) RVC856-950, the difference between expiratory and inspiratory lung volumes with attenuation between −856 and −950 HU; and (4) Residuals from the regression of Exp−856 on percent emphysema. Results: In 8517 subjects with complete data, Exp−856 was highly correlated with emphysema. The measures based on paired inspiratory and expiratory CT scans were less strongly correlated with emphysema. Exp−856, E/I MLA and RVC856-950 were predictive of spirometry, exercise capacity and quality of life in all subjects and in subjects without emphysema. In subjects with severe emphysema, E/I MLA and RVC856-950 showed the highest correlations with clinical variables. Conclusions: Quantitative measures based on paired inspiratory and expiratory chest CT scans can be used as markers of small airway disease in smokers with and without COPD, but this will require that future studies acquire both inspiratory and expiratory CT scans.

Rationale: Chronic obstructive pulmonary disease (COPD) is a phenotypically heterogeneous disease. In COPD, the presence of emphysema is associated with increased mortality and risk of lung cancer. High resolution computed tomography (HRCT) scans are useful in quantifying emphysema but are associated with radiation exposure and high incidence of false positive findings (i.e., nodules). Using a comprehensive biomarker panel, we sought to determine if there was a peripheral blood biomarker signature of emphysema. Methods: 114 plasma biomarkers were measured using a custom assay in 588 individuals enrolled in the COPDGene study. Quantitative emphysema measurements included percent low lung attenuation (%LAA) ≤ −950 HU, ≤ − 910 HU and mean lung attenuation at the 15th percentile on lung attenuation curve (LP15A). Multiple regression analysis was performed to determine plasma biomarkers associated with emphysema independent of covariates age, gender, smoking status, body mass index and FEV1. The findings were subsequently validated using baseline blood samples from a separate cohort of 388 subjects enrolled in the Treatment of Emphysema with a Selective Retinoid Agonist (TESRA) study. Results: Regression analysis identified multiple biomarkers associated with CT-assessed emphysema in COPDGene, including advanced glycosylation end-products receptor (AGER or RAGE, p < 0.001), intercellular adhesion molecule 1 (ICAM, p < 0.001), and chemokine ligand 20 (CCL20, p < 0.001). Validation in the TESRA cohort revealed significant associations with RAGE, ICAM1, and CCL20 with radiologic emphysema (p < 0.001 after meta-analysis). Other biomarkers that were associated with emphysema include CDH1, CDH 13 and SERPINA7, but were not available for validation in the TESRA study. Receiver operating characteristics analysis demonstrated a benefit of adding a biomarker panel to clinical covariates for detecting emphysema, especially in those without severe airflow limitation (AUC 0.85). Conclusions: Our findings, suggest that a panel of blood biomarkers including sRAGE, ICAM1 and CCL20 may serve as a useful surrogate measure of emphysema, and when combined with clinical covariates, may be useful clinically in predicting the presence of emphysema compared to just using covariates alone, especially in those with less severe COPD. Ultimately biomarkers may shed light on disease pathogenesis, providing targets for new treatments. Electronic supplementary material The online version of this article (doi:10.1186/s12931-014-0127-9) contains supplementary material, which is available to authorized users.

Background: The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes. Results: We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches. Conclusion: Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.

Background: Pulmonary function decline is a major contributor to morbidity and mortality among smokers. Post bronchodilator FEV1 and FEV1/FVC ratio are considered the standard assessment of airflow obstruction. We performed a genome-wide association study (GWAS) in 9919 current and former smokers in the COPDGene study (6659 non-Hispanic Whites [NHW] and 3260 African Americans [AA]) to identify associations with spirometric measures (post-bronchodilator FEV1 and FEV1/FVC). We also conducted meta-analysis of FEV1 and FEV1/FVC GWAS in the COPDGene, ECLIPSE, and GenKOLS cohorts (total n = 13,532). Results: Among NHW in the COPDGene cohort, both measures of pulmonary function were significantly associated with SNPs at the 15q25 locus [containing CHRNA3/5, AGPHD1, IREB2, CHRNB4] (lowest p-value = 2.17 × 10−11), and FEV1/FVC was associated with a genomic region on chromosome 4 [upstream of HHIP] (lowest p-value = 5.94 × 10−10); both regions have been previously associated with COPD. For the meta-analysis, in addition to confirming associations to the regions near CHRNA3/5 and HHIP, genome-wide significant associations were identified for FEV1 on chromosome 1 [TGFB2] (p-value = 8.99 × 10−9), 9 [DBH] (p-value = 9.69 × 10−9) and 19 [CYP2A6/7] (p-value = 3.49 × 10−8) and for FEV1/FVC on chromosome 1 [TGFB2] (p-value = 8.99 × 10−9), 4 [FAM13A] (p-value = 3.88 × 10−12), 11 [MMP3/12] (p-value = 3.29 × 10−10) and 14 [RIN3] (p-value = 5.64 × 10−9). Conclusions: In a large genome-wide association study of lung function in smokers, we found genome-wide significant associations at several previously described loci with lung function or COPD. We additionally identified a novel genome-wide significant locus with FEV1 on chromosome 9 [DBH] in a meta-analysis of three study populations. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0299-4) contains supplementary material, which is available to authorized users.

Background: Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. Methods: A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets. Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV1 associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV1 and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression. Conclusions: Network based approaches are promising tools to identify potential biomarkers for COPD. Trial registration The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960 Electronic supplementary material The online version of this article (doi:10.1186/s12931-017-0558-1) contains supplementary material, which is available to authorized users.

Background: In the National Emphysema Treatment Trial (NETT), marked variability in response to lung volume reduction surgery (LVRS) was observed. We sought to identify genetic differences which may explain some of this variability. Methods: In 203 subjects from the NETT Genetics Ancillary Study, four outcome measures were used to define response to LVRS at six months: modified BODE index, post-bronchodilator FEV1, maximum work achieved on a cardiopulmonary exercise test, and University of California, San Diego shortness of breath questionnaire. Sixty-four single nucleotide polymorphisms (SNPs) were genotyped in five genes previously shown to be associated with chronic obstructive pulmonary disease susceptibility, exercise capacity, or emphysema distribution. Results: A SNP upstream from glutathione S-transferase pi (GSTP1; p = 0.003) and a coding SNP in microsomal epoxide hydrolase (EPHX1; p = 0.02) were each associated with change in BODE score. These effects appeared to be strongest in patients in the non-upper lobe predominant, low exercise subgroup. A promoter SNP in EPHX1 was associated with change in BODE score (p = 0.008), with the strongest effects in patients with upper lobe predominant emphysema and low exercise capacity. One additional SNP in GSTP1 and three additional SNPs in EPHX1 were associated (p < 0.05) with additional LVRS outcomes. None of these SNP effects were seen in 166 patients randomized to medical therapy. Conclusion: Genetic variants in GSTP1 and EPHX1, two genes encoding xenobiotic metabolizing enzymes, were predictive of response to LVRS. These polymorphisms may identify patients most likely to benefit from LVRS.

Abstract Background: There have been a number of candidate gene association studies of cancer cachexia‐related traits, but no genome‐wide association study (GWAS) has been published to date. Cachexia presents in patients with a number of complex traits, including both cancer and COPD. The objective of the current investigation was to search for a shared genetic aetiology for change in body mass index (ΔBMI) among cancer and COPD by using GWAS data in the Framingham Heart Study. Methods: A linear mixed effects model accounting for age, sex, and change in smoking status was used to calculate ΔBMI in participants over 40 years of age with three consecutive BMI time points (n = 4162). Four GWAS of ΔBMI using generalized estimating equations were performed among 1085 participants with a cancer diagnosis, 204 with gastrointestinal (GI) cancer, 112 with lung cancer, and 237 with COPD to test for association with 418 365 single‐nucleotide polymorphisms (SNPs). Results: Two SNPs reached a level of genome‐wide significance (P < 5 × 10−8) with ΔBMI: (i) rs41526344 within the CNTN4 gene, among COPD cases (β = 0.13, P = 4.3 × 10−8); and (ii) rs4751240 in the gene Dedicator of Cytokinesis 1 (DOCK1) among GI cancer cases (β = 0.10, P = 1.9 × 10−8). The DOCK1 SNP association replicated in the ΔBMI GWAS among COPD cases (β meta‐analyis = 0.10, P meta‐analyis = 9.3 × 10−10). The DOCK1 gene codes for the dedicator of cytokinesis 1 protein, which has a role in myoblast fusion. Conclusions: In sum, one statistically significant common variant in the DOCK1 gene was associated with ΔBMI in GI cancer and COPD cases providing support for at least partially shared aetiology of ΔBMI in complex diseases.

Background: Despite inhaled medications that decrease exacerbation risk, some COPD patients experience frequent exacerbations. We determined prospective risk factors for exacerbations among subjects in the COPDGene Study taking inhaled medications. Methods: 2113 COPD subjects were categorized into four medication use patterns: triple therapy with tiotropium (TIO) plus long-acting beta-agonist/inhaled-corticosteroid (ICS ± LABA), tiotropium alone, ICS ± LABA, and short-acting bronchodilators. Self-reported exacerbations were recorded in telephone and web-based longitudinal follow-up surveys. Associations with exacerbations were determined within each medication group using four separate logistic regression models. A head-to-head analysis compared exacerbation risk among subjects using tiotropium vs. ICS ± LABA. Results: In separate logistic regression models, the presence of gastroesophageal reflux, female gender, and higher scores on the St. George’s Respiratory Questionnaire were significant predictors of exacerbator status within multiple medication groups (reflux: OR 1.62–2.75; female gender: OR 1.53 - OR 1.90; SGRQ: OR 1.02–1.03). Subjects taking either ICS ± LABA or tiotropium had similar baseline characteristics, allowing comparison between these two groups. In the head-to-head comparison, tiotropium users showed a trend towards lower rates of exacerbations (OR = 0.69 [95 % CI 0.45, 1.06], p = 0.09) compared with ICS ± LABA users, especially in subjects without comorbid asthma (OR = 0.56 [95 % CI 0.31, 1.00], p = 0.05). Conclusions: Each common COPD medication usage group showed unique risk factor patterns associated with increased risk of exacerbations, which may help clinicians identify subjects at risk. Compared to similar subjects using ICS ± LABA, those taking tiotropium showed a trend towards reduced exacerbation risk, especially in subjects without asthma. Trial registration ClinicalTrials.gov NCT00608764, first received 1/28/2008. Electronic supplementary material The online version of this article (doi:10.1186/s12890-016-0191-7) contains supplementary material, which is available to authorized users.

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.