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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).

Importance People who smoked cigarettes may experience respiratory symptoms without spirometric airflow obstruction. These individuals are typically excluded from chronic obstructive pulmonary disease (COPD) trials and lack evidence-based therapies. Objective To define the natural history of persons with tobacco exposure and preserved spirometry (TEPS) and symptoms (symptomatic TEPS). Design, Setting, and Participants SPIROMICS II was an extension of SPIROMICS I, a multicenter study of persons aged 40 to 80 years who smoked cigarettes (>20 pack-years) with or without COPD and controls without tobacco exposure or airflow obstruction. Participants were enrolled in SPIROMICS I and II from November 10, 2010, through July 31, 2015, and followed up through July 31, 2021. Exposures Participants in SPIROMICS I underwent spirometry, 6-minute walk distance testing, assessment of respiratory symptoms, and computed tomography of the chest at yearly visits for 3 to 4 years. Participants in SPIROMICS II had 1 additional in-person visit 5 to 7 years after enrollment in SPIROMICS I. Respiratory symptoms were assessed with the COPD Assessment Test (range, 0 to 40; higher scores indicate more severe symptoms). Participants with symptomatic TEPS had normal spirometry (postbronchodilator ratio of forced expiratory volume in the first second [FEV 1 ] to forced vital capacity >0.70) and COPD Assessment Test scores of 10 or greater. Participants with asymptomatic TEPS had normal spirometry and COPD Assessment Test scores of less than 10. Patient-reported respiratory symptoms and exacerbations were assessed every 4 months via phone calls. Main Outcomes and Measures The primary outcome was assessment for accelerated decline in lung function (FEV 1 ) in participants with symptomatic TEPS vs asymptomatic TEPS. Secondary outcomes included development of COPD defined by spirometry, respiratory symptoms, rates of respiratory exacerbations, and progression of computed tomographic–defined airway wall thickening or emphysema. Results Of 1397 study participants, 226 had symptomatic TEPS (mean age, 60.1 [SD, 9.8] years; 134 were women [59%]) and 269 had asymptomatic TEPS (mean age, 63.1 [SD, 9.1] years; 134 were women [50%]). At a median follow-up of 5.76 years, the decline in FEV 1 was −31.3 mL/y for participants with symptomatic TEPS vs −38.8 mL/y for those with asymptomatic TEPS (between-group difference, −7.5 mL/y [95% CI, −16.6 to 1.6 mL/y]). The cumulative incidence of COPD was 33.0% among participants with symptomatic TEPS vs 31.6% among those with asymptomatic TEPS (hazard ratio, 1.05 [95% CI, 0.76 to 1.46]). Participants with symptomatic TEPS had significantly more respiratory exacerbations than those with asymptomatic TEPS (0.23 vs 0.08 exacerbations per person-year, respectively; rate ratio, 2.38 [95% CI, 1.71 to 3.31], P < .001). Conclusions and Relevance Participants with symptomatic TEPS did not have accelerated rates of decline in FEV 1 or increased incidence of COPD vs those with asymptomatic TEPS, but participants with symptomatic TEPS did experience significantly more respiratory exacerbations over a median follow-up of 5.8 years.

Background: Adults with cardiovascular co-morbidities and risk factors are at greater risk of severe COVID-19. These same risk factors may also be associated with an attenuated antibody response to COVID-19 vaccines, although studies in diverse, U.S. population-based cohorts have been limited. Methods: The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) conducted a serosurvey for SARS-CoV-2 antibodies via dried blood spot (DBS) in 14 U.S. cohorts. IgG antibodies to SARS-CoV-2 spike subunit 1 (S1) and nucleocapsid (N) were measured from DBS using a semi-quantitative microsphere immunoassay and reported as median fluorescence intensity (MFI). Multivariable adjusted linear models regressed log-transformed anti-S1 MFI on age, sex, race/ethnicity, education attainment, self-reported diabetes, hypertension, cardiovascular disease (CVD), chronic kidney disease, smoking history, body mass index (BMI), asthma, obstructive lung diseases, DBS batch, anti-N MFI, vaccine type, time between vaccine and DBS, and vaccine dose at time of DBS collection. Results are presented as the percent difference in anti-S1 MFI compared with a reference group. Results: There were 6614 vaccinated participants prior to booster regimens and DBS collection (April 2021-July 2022) with 50%, 48%, and 2% of the cohort who received BNT162b2, mRNA-1273, or other vaccines, respectively. The mean (SD) time between vaccination and DBS was 3.8 (1.8) months. Over 10% of the cohort had self-reported a history of diabetes, 55% had hypertension, and 74% had a BMI>25 kg/m 2 . Anti-S1 MFI decreased as the time between vaccine dose and DBS collection increased. Diabetes was associated with a 16.1% lower anti-S1 MFI (95%CI:-22.4,-9.5) whereas neither hypertension (-3.8%;95%CI:-9.3,2.1), nor cardiovascular disease history (-5.3%;95%CI:-16.3,7.4) were associated with anti-S1 MFI. Former and current smoking history were each associated with a lower anti-S1 MFI: (-6.6%;95%CI:-12.1,-0.8) and (-16.1%;95%CI:-24.7,-6.6), respectively. Participants with a BMI 25-29.9 kg/m 2 had a 7.6% higher anti-S1 MFI (95%CI:0.3,15.4) whereas those with a BMI of 30-35 kg/m 2 and >35 kg/m 2 had 6.2% (95%CI:-2.4,15.5) higher and 8.9% lower (95%CI:-17.6,0.7) MFI levels, respectively. Older age and male sex were each associated with a lower anti-S1 MFI and mRNA-1273 vaccine, Asian subgroup, higher anti-N titer, and prior COVID-19 infection were each associated with higher anti-S1 MFI. Chronic kidney disease, education attainment, and lung disease were not associated with anti-S1 MFI. Conclusions: Several traditional cardiovascular disease risk factors were associated with diminished humoral responses to the initial COVID-19 vaccine regimens in a diverse U.S. population-based cohort and may have implications on strategies to improve vaccine responses.

Abstract Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to match proteomes to genomes for 2,812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We were able to correctly match 90%-95% of proteomes to their correct genome and for 95%-99% we could match the proteome to the 1% most likely genome. The accuracy of matching in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. When serial proteomes are available, the matching algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1,000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

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.