Genetic variation in genes regulating skeletal muscle regeneration and tissue remodelling associated with weight loss in chronic obstructive pulmonary disease

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally. COPD patients with cachexia or weight loss have increased risk of death independent of body mass index (BMI) and lung function. We tested the hypothesis genetic variation is associated with weight loss in COPD using a genome-wide association study approach. METHODS: Participants with COPD (N = 4308) from three studies (COPDGene, ECLIPSE, and SPIROMICS) were analysed. Discovery analyses were performed in COPDGene with replication in SPIROMICS and ECLIPSE. In COPDGene, weight loss was defined as self-reported unintentional weight loss > 5% in the past year or low BMI (BMI < 20 kg/m2). In ECLIPSE and SPIROMICS, weight loss was calculated using available longitudinal visits. Stratified analyses were performed among African American (AA) and Non-Hispanic White (NHW) participants with COPD. Single variant and gene-based analyses were performed adjusting for confounders. Fine mapping was performed using a Bayesian approach integrating genetic association results with linkage disequilibrium and functional annotation. Significant gene networks were identified by integrating genetic regions associated with weight loss with skeletal muscle protein–protein interaction (PPI) data. RESULTS: At the single variant level, only the rs35368512 variant, intergenic to GRXCR1 and LINC02383, was associated with weight loss (odds ratio = 3.6, 95% confidence interval = 2.3–5.6, P = 3.2 × 10−8) among AA COPD participants in COPDGene. At the gene level in COPDGene, EFNA2 and BAIAP2 were significantly associated with weight loss in AA and NHW COPD participants, respectively. The EFNA2 association replicated among AA from SPIROMICS (P = 0.0014), whereas the BAIAP2 association replicated in NHW from ECLIPSE (P = 0.025). The EFNA2 gene encodes the membrane-bound protein ephrin-A2 involved in the regulation of developmental processes and adult tissue homeostasis such as skeletal muscle. The BAIAP2 gene encodes the insulin-responsive protein of mass 53 kD (IRSp53), a negative regulator of myogenic differentiation. Integration of the gene-based findings participants with PPI data revealed networks of genes involved in pathways such as Rho and synapse signalling. CONCLUSIONS: The EFNA2 and BAIAP2 genes were significantly associated with weight loss in COPD participants. Collectively, the integrative network analyses indicated genetic variation associated with weight loss in COPD may influence skeletal muscle regeneration and tissue remodelling

Impact of bronchiectasis on the frequency and severity of respiratory exacerbations in COPD

Wassim W Labaki, MeiLan K Han Division of Pulmonary and Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI, USAIn a recent issue of the International Journal of Chronic Obstructive Pulmonary Disease, Kawamatawong et al reported an association between chest CT-detected bronchiectasis and frequent or severe respiratory exacerbations in 72 Thai patients with COPD (adjusted odds ratio [OR] 4.99; 95% CI 1.31&ndash;18.94; p=0.018).1 Frequent exacerbations were defined as two or more events per year, and severe ones as those requiring hospitalization. The results of this study are consistent with those previously reported by Mart&iacute;nez-Garc&iacute;a et al who found bronchiectasis to be independently asso&not;ciated with severe COPD exacerbations in 92 subjects (OR 3.07; 95% CI 1.07&ndash;8.77; p=0.037).2View the original paper by Kawamatawong and colleagues

Can CAPTURE be used to identify undiagnosed patients with mild-to-moderate COPD likely to benefit from treatment?

Nancy K Leidy,1 Fernando J Martinez,2 Karen G Malley,1 David M Mannino,3 MeiLan K Han,4 Elizabeth D Bacci,5 Randall W Brown,6 Julia F Houfek,7 Wassim W Labaki,4 Barry J Make,8 Catherine A Meldrum,4 Wilson Quezada,9 Stephen Rennard,10 Byron Thomashow,9 Barbara P Yawn11 1Evidera, Patient-Centered Research, Bethesda, MD, USA; 2Weill Cornell Medicine, Joan &amp; Sanford Weill Department of Medicine, New York, NY, USA; 3University of Kentucky, Preventive Medicine &amp; Environmental Health, Lexington, KY, USA; 4University of Michigan, Division of Pulmonary &amp; Critical Care Medicine, Ann Arbor, MI, USA; 5Evidera, Patient-Centered Research, Seattle, WA, USA; 6University of Michigan, Department of Health Behavior &amp; Health Education, School of Public Health, Ann Arbor, MI, USA; 7University of Nebraska Medical Center College of Nursing, Omaha, NE, USA; 8National Jewish Health, Department of Medicine, Division of Pulmonary, Critical Care &amp; Sleep Medicine, Denver, CO, USA; 9Columbia University Medical Center, Division of Pulmonary, Allergy, &amp; Critical Care, New York, NY, USA; 10AstraZeneca, IMED Biotech Unit, Cambridge, UK &amp; University of Nebraska Medical Center, Department of Medicine, Omaha, NE, USA; 11University of Minnesota, Department of Family &amp; Community Health, Minneapolis, MN &amp; COPD Foundation, Miami, FL, USA Background: COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE&trade;) uses five questions and peak expiratory flow (PEF) thresholds (males &le;350 L/min; females &le;250 L/min) to identify patients with a forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) &lt;0.70 and FEV1 &lt;60% predicted or exacerbation risk requiring further evaluation for COPD. This study tested CAPTURE&rsquo;s ability to identify symptomatic patients with mild-to-moderate COPD (FEV1 60%&ndash;80% predicted) who may also benefit from diagnosis and treatment. Methods: Data from the CAPTURE development study were used to test its sensitivity (SN) and specificity (SP) differentiating mild-to-moderate COPD (n=73) from no COPD (n=87). SN and SP for differentiating all COPD cases (mild to severe; n=259) from those without COPD (n=87) were also estimated. The modified Medical Research Council (mMRC) dyspnea scale and COPD Assessment Test (CAT&trade;) were used to evaluate symptoms and health status. Clinical Trial Registration: NCT01880177, https://ClinicalTrials.gov/ct2/show/NCT01880177?term=NCT01880177&amp;rank=1. Results: Mean age (+SD): 61 (+10.5) years; 41% male. COPD: FEV1/FVC=0.60 (+0.1), FEV1% predicted=74% (+12.4). SN and SP for differentiating mild-to-moderate and non-COPD patients (n=160): Questionnaire: 83.6%, 67.8%; PEF (&le;450 L/min; &le;350 L/min): 83.6%, 66.7%; CAPTURE (Questionnaire+PEF): 71.2%, 83.9%. COPD patients whose CAPTURE results suggested that diagnostic evaluation was warranted (n=52) were more likely to be symptomatic than patients whose results did not (n=21) (mMRC &gt;2: 37% vs 5%, p&lt;0.01; CAT&gt;10: 86% vs 57%, p&lt;0.01). CAPTURE differentiated COPD from no COPD (n=346): SN: 88.0%, SP: 83.9%. Conclusion: CAPTURE (450/350) may be useful for identifying symptomatic patients with mild-to-moderate airflow obstruction in need of diagnostic evaluation for COPD. Keywords: COPD, case-finding, undiagnosed COPD, screening tool, peak expiratory flo

Clinical Trajectory Analysis With Longitudinal Validation in COPD: A COPDGene Study

RATIONALE: Chronic obstructive pulmonary disease (COPD) is heterogeneous in its clinical phenotypes (e.g. chronic bronchitis, emphysema) and trajectories of disease progression. Analysis of large high-dimensional datasets presents a key opportunity to address the gap in our understanding of COPD phenotypes and progression. Clinical trajectory analysis (ClinTrajAn), based on the concept of the branching principal tree, simultaneously phenotypes and determines patient trajectories within cross-sectional clinical data. Our aim was to apply ClinTrajAn to map prominent subtypes and trajectories in a large population of participants, covering the whole range of COPD severity and at-risk profiles, and validate proposed trajectories using longitudinal data. METHODS: Cross-sectional data for 8972 participants from Phase 1 of the COPDGene longitudinal study were utilized for model training, with 4585/8972 (51%) of participants having Phase 2 data (∼5 years later). Participants included current and former smokers with COPD (GOLD 1-4), normal spirometry (GOLD 0), and preserved ratio impaired spirometry (PRISm). 30 features were selected for training, covering demographics, exposure, pulmonary function, and CT imaging. The Phase 1 data matrix (8972x30) contained 2302 missing values (< 1%), which were imputed via single value decomposition (SVD). Principal component analysis (PCA) was applied to this completed matrix to reduce dimensionality to the first six principal components. A bifurcating principal tree fitting this reduced data was computed by averaging over 100 iteratively grown trees fitting random 95% samples. Longitudinal displacement was determined via projection of SVD imputed Phase 2 data using Phase 1 PCA results. RESULTS: The averaged tree contained six terminal segments and two notable bridging segments (Figure 1 A). Terminal segments divided emphysema dominant COPD by sex, identified mild-to severe COPD participants with bronchodilator reversibility (BDR), chronic bronchitis dominance, healthy aged participants, and PRISm dominance. Bridging segments divided healthy aged and PRISm participants from COPD, and mild COPD or chronic bronchitis from severe COPD or participants with COPD and BDR. Trajectories were defined as paths starting from a root among GOLD 0 participants. Longitudinal analysis showed most participants (69%) stayed on the same segment after 5 years, with segment displacements on average moving away from the root, and a notable increase in displacement for cases with accelerated decline leading to a COPD subtype or PRISm terminal (Figure 1 B). CONCLUSIONS: We have applied ClinTrajAn in a large longitudinal study population to model phenotypes and trajectories in COPD, and validated prediction of progression pathways through observation of projected displacements over 5 years. </p