Labaki, Wassim W.

Abstract Rationale: We have previously shown reduced total IgG, IgG subclasses, and levels/function of pneumococcal-specific antibodies are associated with increased risk for exacerbations of COPD (ECOPD). The binding of IgG to its receptor, the Fc receptor for IgG (FcγR) on immune cells, has a critical role in determining antibody levels and function. In addition to our research demonstrating that IgG heavy chain gene (IGHG) variants are associated with ECOPD, single nucleotide variants (SNVs) in genes encoding FcγR (FCGR1-3, which encode receptor subtypes FcγRI, FcγRIIa/b/c, and FcγRIIIa/b) may also result in antibody deficiency and increased exacerbation risk. Due to technical challenges in accurately identifying SNVs in FCGR, this region has not been well-characterized. The limited data that exist are from people of European descent, and prior studies have not included African American participants. We therefore sought to investigate the hypothesis that distinct SNVs in genes encoding FcγR are associated with disease severity and history of exacerbations in African American people with COPD. Methods: We developed a novel pipeline to identify SNVs in the coding (CDS) regions of chromosome 1 using short-read whole genome sequence data. The pipeline was benchmarked using paired long-read samples from Genome in a Bottle. SNVs were called within CDS regions and analyzed in a meta-analysis of self-reported African American participants in SPIROMICS and COPDGene (n=3063). Genetic variants were tested for association with COPD severity (GOLD spirometry stage 1-2 vs 3-4) as well as COPD exacerbations, adjusting for age, sex and principal components controlling for population structure. Results: Eight SNVs were significantly associated with frequent ECOPD (≥2 in one year) in the meta-analysis of African American COPDGene and SPIROMICS participants (Table). Seven of these 8 SNVs were found in FCGR3; 4 in the activating FCGR3A subtype, and 3 in FCGR3B. Seven of 11 SNVs associated with COPD severity were in FCGR3 (4 in FCGR3A; 3 in FCGR3B). One SNV (rs371849) was associated with both frequent exacerbations (P=0.002) and COPD severity (P=0.044). Conclusions: We used a cutting-edge variant calling pipeline to identify multiple SNVs in FCGR, primarily involving FcγRIII receptor subtypes, associated with COPD severity and exacerbations in African American participants in SPIROMICS and COPDGene. FcγRIII variants are known to cause impaired antibody levels and function in people of European descent. The structural and functional importance of newly recognized variants in African American cohorts require further study, as they may represent immunogenetic risk factors for antibody deficiency and exacerbations.

Abstract Rationale Among tobacco-exposed persons with preserved spirometry (TEPSs), we previously demonstrated that different lung volume indices—specifically, elevated total lung capacity (TLC) versus elevated ratio of functional residual capacity to TLC (FRC/TLC)—identify different lung disease characteristics in the COPDGene cohort. Objective We sought to determine differential disease characteristics and trajectories associated with lung volume indices among TEPSs in the SPIROMICS cohort. Methods We categorized TEPSs (n = 814) by tertiles (low, intermediate, and high) of TLC or residual volume–to-TLC ratio (RV/TLC) derived from baseline computed tomography images and then examined clinical and spirometric disease trajectories in mutually exclusive categories of participants with high TLC without high RV/TLC ([TLC]high) versus high RV/TLC without high TLC ([RV/TLC]high). We examined differences in computed tomography–measured emphysema (Hounsfield units [HU] ⩽−950; parametric response mapping [PRM] of emphysema), air trapping (HU⩽−856; PRM of functional small airway disease; a disease probability measure for non-emphysematous gas trapping), airway geometry (the mean square root of wall area of a hypothetical airway with 10 mm internal perimeter), respiratory symptoms (on the modified Medical Research Council Dyspnea Scale; COPD Assessment Test [CAT]; St. George’s Respiratory Questionnaire [SGRQ]; and Short Form-12 [SF12]), and outcomes (annualized exacerbation rate) between the two categories at baseline and over follow-up time up to 8.5 years, using regression modeling adjusted for age, sex, height, weight, and smoking status (current vs. former smoker) and burden (pack-years). Results In TEPSs, the pattern of spirometric disease progression differed between participants with [TLC]high and those with [RV/TLC]high: There was increased forced vital capacity with stable forced expiratory volume in 1 second in participants with [TLC]high, versus unchanged forced vital capacity but nominally decreased forced expiratory volume in 1 second in those with [RV/TLC]high. Compared with participants with [TLC]high, TEPSs with [RV/TLC]high had less emphysema (by HU ⩽−950) but more airway disease (by HU ⩽−856; PRM of functional small airway disease; disease probability measure for gas trapping, and mean square root of wall area of a hypothetical airway with 10 mm internal perimeter), more respiratory symptoms (on the modified Medical Research Council Dyspnea Scale, CAT, SGRQ, and SF12), and more severe exacerbations at baseline. Over an average follow-up of 4.1 ± 2.4 years (range = 0.5–8.5 yr), TEPSs with [RV/TLC]high also had a higher likelihood of developing more severe spirometric disease (preserved ratio impaired spirometry or Global Initiative for Chronic Obstructive Lung Disease Classification 2) and worsening of their respiratory symptoms (on the CAT and SGRQ). Although the incidence rates of respiratory exacerbations, hospitalizations, and mortality were not significantly different between the two categories over the follow-up period, TEPSs with [RV/TLC]high were more likely to have been prescribed a respiratory inhaler at their last follow-up visit. Conclusions In these TEPSs from the SPIROMICS cohort, lung volume stratification by TLC versus RV/TLC identifies two pre-COPD phenotypes with distinct respiratory symptoms, radiographic features, and clinical trajectories. The characteristics of these pre-COPD phenotypes match those previously described in the COPDGene cohort using TLC versus FRC/TLC stratification.

Abstract Study Objectives Sleep is an important dimension in the care of chronic obstructive pulmonary disease (COPD), but its relevance to exacerbations is unclear. We wanted to assess whether sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) is associated with an increased risk of COPD exacerbations and does this differ by socio-environmental exposures. Methods We included 1647 current and former smokers with spirometrically confirmed COPD from the SPIROMICS cohort. We assessed incidence rate ratios for exacerbation using zero-inflated negative binomial regression adjusting for demographics, medical comorbidities, and multiple metrics of disease severity, including respiratory medications, airflow obstruction, and symptom burden. Our final model adjusted for socio-environmental exposures using the Area Deprivation Index, a composite measure of contemporary neighborhood quality, and Adversity–Opportunity Index, a composite measure of individual-level historic and current socioeconomic indicators. We used a pre-determined threshold of 20% missingness to undertake multiple imputation by chained equations. As sensitivity analyses, we repeated models in those with complete data and after controlling for prior exacerbations. As an exploratory analysis, we considered an interaction between socio-environmental condition and sleep quality. Results After adjustment for all co-variates, increasing PSQI scores (range 0–21) were associated with a 5% increased risk for exacerbation per point (p = .001) in the imputed dataset. Sensitivity analyses using complete cases and after controlling for prior exacerbation history were similar. Exploratory analysis suggested less effect among those who lived in poor-quality neighborhoods (p-for-interaction = .035). Conclusions Poor sleep quality may contribute to future exacerbations among patients with COPD. This represents one target for improving disease control. Clinical Trial Registration Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS). ClinicalTrials.gov Identifier# NCT01969344. Registry URL: https://clinicaltrials.gov/ct2/show/.

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.