Kaufman, Joel D.

Abstract Rationale Sleep is an important dimension of COPD symptomology. Poor self-reported sleep quality has been independently associated with increased exacerbation risk and worse quality of life. There is limited evidence around the longitudinal sleep quality of patients with smoking-related lung disease. Methods Using the SPIROMICS longitudinal cohort of heavy smokers, we used group-based trajectory modeling to understand sleep quality over time as measured by serial retests of the Pittsburgh Sleep Quality Index (PSQI). We considered all participants of Black or Non-Hispanic White race who completed the PSQI in Visits 1-4, a span of 3 years. We considered race, gender, and social environmental exposures as time-stable risk factors for trajectory group membership. We adjusted for age, lung function, mood symptoms and self-reported COPD symptom severity were considered as time-varying factors. We selected the number of trajectory groups by minimizing Bayesian Information Criteria and polynomial order by maximizing the significance of exponential terms. For quality assurance we calculated average posterior probability of correct group assignment. Calculations were performed by complete case analysis and multiple imputation with chained equations. Results We analyzed 2,838 participants yielding 76,153 usable observations. Overall, 39.2% of participants had complete observations for all variables of interest at every measured visit, and 60.8% had at least one relevant observation missing. The largest single source of missingness in our dataset was the comorbidity count, for which 44% of participants had a missing observation. We identified two major trajectory groups. In one group, the PSQI was persistently high with little variation over time, containing 77.7% of the overall study population. The second group had low PSQI at baseline but experienced wide fluctuation over time. Risk factors for persistently poor sleep included female gender, Black race, and more adverse social conditions at either the neighborhood or individual level (Table). The average posterior probability of correct group assignment was 94.6% among the Low Variable group and 89.5% among the Persistent High group. Results were similar by complete case and imputed analysis, save that BMI did not independently predict group membership in the latter. Conclusion Historically underserved groups are at long term risk for poor sleep outcomes in smoking-related lung disease, even among those who are highly motivated and participate in clinical research. More work is needed to ensure equitable access to sleep medicine in order to improve the quality of care in COPD. This abstract is funded by: Nina Ireland Program for Lung Health

Abstract Rationale It is unknown whether air pollution is associated with radiographic features of interstitial lung disease in individuals with chronic obstructive pulmonary disease (COPD). Objectives To determine whether air pollution increases the prevalence of interstitial lung abnormalities (ILA) or percent high-attenuation areas (HAA) on computed tomography (CT) in individuals with a heavy smoking history and COPD. Methods We performed a cross-sectional study of SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), focused on current or former smokers with COPD. Ten-year exposure to particulate matter ⩽2.5 μm in aerodynamic diameter (PM2.5), nitrogen oxides (NOx), nitrogen dioxide (NO2), and ozone before enrollment CT (completed between 2010 and 2015) were estimated with validated spatiotemporal models at residential addresses. We applied adjusted multivariable modified Poisson regression and linear regression to investigate associations between pollution exposure and relative risk (RR) of ILA or increased percent HAA (between −600 and −250 Hounsfield units), respectively. We assessed for effect modification by MUC5B-promoter polymorphism (variant allele carriers GT or TT vs. GG at rs3705950), smoking status, sex, and percent emphysema. Results Among 1,272 participants with COPD assessed for HAA, 424 were current smokers, and 249 were carriers of the variant MUC5B allele. A total of 519 participants were assessed for ILA. We found no association between pollution exposure and ILA or HAA. Associations between pollutant exposures and risk of ILA were modified by the presence of MUC5B polymorphism (P value interaction term for NOx = 0.04 and PM2.5 = 0.05) and smoking status (P value interaction term for NOx = 0.05; NO2 = 0.01; and ozone = 0.05). With higher exposure to NOx and PM2.5, MUC5B variant carriers had an increased risk of ILA (RR per 26 ppb NOx, 2.41; 95% confidence interval [CI], 0.97–6.0; and RR per 4 μg ⋅ m−3 PM2.5, 1.43; 95% CI, 0.93–2.2, respectively). With higher exposure to NO2, former smokers had an increased risk of ILA (RR per 10 ppb, 1.64; 95% CI, 1.0–2.7). Conclusions Exposure to ambient air pollution was not associated with interstitial features on CT in this population of heavy smokers with COPD. MUC5B modified the association between pollution and ILA, suggesting that gene–environment interactions may influence prevalence of interstitial lung features in COPD.

Abstract Rationale Individuals with chronic obstructive pulmonary disease (COPD) have airflow obstruction and maldistribution of ventilation. For those living at high altitude, any gas exchange abnormality is compounded by reduced partial pressures of inspired oxygen. Objectives Does residence at higher altitude exposure affect COPD outcomes, including lung function, imaging characteristics, symptoms, health status, functional exercise capacity, exacerbations, and mortality? Methods From the SPIROMICS (Subpopulation and Intermediate Outcome Measures in COPD Study) cohort, we identified individuals with COPD living below 1,000 ft (305 m) elevation (n = 1,367) versus above 4,000 ft (1,219 m) elevation (n = 288). Multivariable regression models were used to evaluate associations of exposure to high altitude with COPD-related outcomes. Measurements and Main Results Living at higher altitude was associated with reduced functional exercise capacity as defined by 6-minute-walk distance (−32.3 m [95% confidence interval, −49.8 to −14.8 m]). There were no differences in patient-reported outcomes as defined by symptoms (COPD Assessment Test and modified Medical Research Council dyspnea scale), or health status (St. George’s Respiratory Questionnaire). Higher altitude was not associated with a different rate of FEV1 decline. Higher altitude was associated with lower odds of severe exacerbations (incidence rate ratio, 0.65 [95% confidence interval, 0.46 to 0.90]). There were no differences in small airway disease, air trapping, or emphysema. In longitudinal analyses, higher altitude was associated with increased mortality (hazard ratio, 1.25 [95% confidence interval, 1.0 to 1.55]); however, this association was no longer significant when accounting for air pollution. Conclusions Long-term altitude exposure is associated with reduced functional exercise capacity in individuals with COPD, but this did not translate into differences in symptoms or health status. In addition, long-term high-altitude exposure did not affect progression of disease as defined by longitudinal changes in spirometry. Clinical trial registered with www.clinicaltrials.gov (NCT 01969344).

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/.

We investigated the effect of candidate variants in AS3MT (arsenic (III) methyltransferase) with urinary arsenic metabolites and their principal components in a subset of 264 participants in the Multi-Ethnic Study of Atherosclerosis (MESA). Urinary arsenic species, including inorganic arsenic (iAs), monomethylarsonate (MMA), dimethylarsinate (DMA), and arsenobetaine (Ab), were measured using high performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICPMS) and corrected for organic sources from seafood consumption by regressing Ab on arsenic species using a validated method. Principal components of arsenic metabolism were also used as independent phenotypes. We conducted linear regression of arsenic traits with allelic dosage of candidate single nucleotide polymorphisms (SNPs) rs12768205 (G > A), rs3740394 (A > G), and rs3740393 (G > C) measured using Illumina MetaboChip. Models were stratified by non-Hispanic white vs. all other race/ethnicity and adjusted for age, sex, arsenic exposure, study site, and population stratification. Consistent with previous studies, rs12768205 showed evidence for strongest association (non-Hispanic white: iAs% −0.14 (P 0.83), MMA% −0.66 (0.49), DMA% 0.81(0.49); other race/ethnicity: 0.13 (0.71), −1.21 (0.09), 1.08 (0.20)). No association, however, passed the strict Bonferroni p-value. This was a novel study among an ethnically diverse population exposed to low arsenic levels.