BACKGROUND: Limited data are available regarding marijuana smokings impact on the development or progression of chronic obstructive pulmonary disease (COPD) in middle-aged or older adults with a variable history of tobacco cigarette smoking. METHODS: We divided ever-tobacco smoking participants in the SubPopulations and InteRmediate Outcomes In COPD Study (SPIROMICS) into 3 groups based on self-reported marijuana use: current, former, or never marijuana smokers (CMSs, FMSs or NMSs, respectively). Longitudinal data were analyzed in participants with ≥2 visits over a period of ≥52 weeks. MEASUREMENTS: We compared CMSs, FMSs, and NMSs, and those with varying amounts of lifetime marijuana use. Mixed effects linear regression models were used to analyze changes in spirometry, symptoms, health status, and radiographic metrics; zero-inflated negative binomial models were used for exacerbation rates. All models were adjusted for age, sex, race, baseline tobacco smoking amount, and forced expiratory volume in 1 second (FEV1) %predicted. RESULTS: Most participants were followed for ≥4 years. Annual rates of change in FEV1, incident COPD, respiratory symptoms, health status, radiographic extent of emphysema or air trapping, and total or severe exacerbations were not different between CMSs or FMSs versus NMSs or between those with any lifetime amount of marijuana use versus NMSs. CONCLUSIONS: Among SPIROMICS participants with or without COPD, neither former nor current marijuana smoking of any lifetime amount was associated with evidence of COPD progression or its development. Because of our studys limitations, these findings underscore the need for further studies to better understand longer-term effects of marijuana smoking in COPD.

Background

Electronic cigarettes (e-cigarettes) are battery-operated nicotine-delivery devices used by some smokers as a cessation tool as well as by never smokers.

Objective

To determine the usage of e-cigarettes in older adults at risk for or with chronic obstructive pulmonary disease (COPD).

Design

Prospective cohorts.

Participants

COPDGene (N = 3536) and SPIROMICS (N = 1060) subjects who were current or former smokers aged 45-80.

Main measures

Participants were surveyed to determine whether e-cigarette use was associated with longitudinal changes in COPD progression or smoking habits.

Key results

From 2010 to 2016, participants who had ever used e-cigarettes steadily increased to 12-16%, but from 2014 to 2016 current use was stable at ~5%. E-cigarette use in African-Americans (AA) and whites was similar; however, AA were 1.8-2.9 times as likely to use menthol-flavored e-cigarettes. Current e-cigarette and conventional cigarette users had higher nicotine dependence and consumed more nicotine than those who smoked only conventional cigarettes. E-cigarette users had a heavier conventional cigarette smoking history and worse respiratory health, were less likely to reduce or quit conventional cigarette smoking, had higher nicotine dependence, and were more likely to report chronic bronchitis and exacerbations. Ever e-cigarette users had more rapid decline in lung function, but this trend did not persist after adjustment for persistent conventional cigarette smoking.

Conclusions

E-cigarette use, which is common in adults with or at risk for COPD, was associated with worse pulmonary-related health outcomes, but not with cessation of smoking conventional cigarettes. Although this was an observational study, we find no evidence supporting the use of e-cigarettes as a harm reduction strategy among current smokers with or at risk for COPD.

Background

Bronchial airway expression profiling has identified inflammatory subphenotypes of asthma, but the invasiveness of this technique has limited its application to childhood asthma.

Objectives

We sought to determine whether the nasal transcriptome can proxy expression changes in the lung airway transcriptome in asthmatic patients. We also sought to determine whether the nasal transcriptome can distinguish subphenotypes of asthma.

Methods

Whole-transcriptome RNA sequencing was performed on nasal airway brushings from 10 control subjects and 10 asthmatic subjects, which were compared with established bronchial and small-airway transcriptomes. Targeted RNA sequencing nasal expression analysis was used to profile 105 genes in 50 asthmatic subjects and 50 control subjects for differential expression and clustering analyses.

Results

We found 90.2% overlap in expressed genes and strong correlation in gene expression (ρ = .87) between the nasal and bronchial transcriptomes. Previously observed asthmatic bronchial differential expression was strongly correlated with asthmatic nasal differential expression (ρ = 0.77, P = 5.6 × 10(-9)). Clustering analysis identified TH2-high and TH2-low subjects differentiated by expression of 70 genes, including IL13, IL5, periostin (POSTN), calcium-activated chloride channel regulator 1 (CLCA1), and serpin peptidase inhibitor, clade B (SERPINB2). TH2-high subjects were more likely to have atopy (odds ratio, 10.3; P = 3.5 × 10(-6)), atopic asthma (odds ratio, 32.6; P = 6.9 × 10(-7)), high blood eosinophil counts (odds ratio, 9.1; P = 2.6 × 10(-6)), and rhinitis (odds ratio, 8.3; P = 4.1 × 10(-6)) compared with TH2-low subjects. Nasal IL13 expression levels were 3.9-fold higher in asthmatic participants who experienced an asthma exacerbation in the past year (P = .01). Several differentially expressed nasal genes were specific to asthma and independent of atopic status.

Conclusion

Nasal airway gene expression profiles largely recapitulate expression profiles in the lung airways. Nasal expression profiling can be used to identify subjects with IL13-driven asthma and a TH2-skewed systemic immune response.

Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from -0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies.

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.