Progression of Emphysema and Small Airways Disease in Cigarette Smokers

Tobacco smoking is associated with combined pulmonary fibrosis and emphysema and worse outcomes in interstitial lung disease

Tobacco smoking is an established cause of pulmonary disease whose contribution to interstitial lung disease (ILD) is incompletely characterized. We hypothesized that compared with nonsmokers, subjects who smoked tobacco would differ in their clinical phenotype and have greater mortality. We performed a retrospective cohort study of tobacco smoking in ILD. We evaluated demographic and clinical characteristics, time to clinically meaningful lung function decline (LFD), and mortality in patients stratified by tobacco smoking status (ever vs. never) within a tertiary center ILD registry (2006-2021) and replicated mortality outcomes across four nontertiary medical centers. Data were analyzed by two-sided t tests, Poisson generalized linear models, and Cox proportional hazard models adjusted for age, sex, forced vital capacity (FVC), diffusion capacity of the lung for carbon monoxide (DLCO), ILD subtype, antifibrotic therapy, and hospital center. Of 1,163 study participants, 651 were tobacco smokers. Smokers were more likely to be older, male, have idiopathic pulmonary fibrosis (IPF), coronary artery disease, CT honeycombing and emphysema, higher FVC, and lower DLCO than nonsmokers (P < 0.01). Time to LFD in smokers was shorter (19.7 ± 20 mo vs. 24.8 ± 29 mo; P = 0.038) and survival time was decreased [10.75 (10.08-11.50) yr vs. 20 (18.67-21.25) yr; adjusted mortality HR = 1.50, 95%CI 1.17-1.92; P < 0.0001] compared with nonsmokers. Smokers had 12% greater odds of death for every additional 10 pack yr of smoking (P < 0.0001). Mortality outcomes remained consistent in the nontertiary cohort (HR = 1.51, 95%CI = 1.03-2.23; P = 0.036). Tobacco smokers with ILD have a distinct clinical phenotype strongly associated with the syndrome of combined PF and emphysema, shorter time to LFD, and decreased survival. Smoking prevention may improve ILD outcomes.NEW & NOTEWORTHY Smoking in ILD is associated with combined pulmonary fibrosis and emphysema and worse clinical outcomes.

CT-Based Commercial Software Applications: Improving Patient Care Through Accurate COPD Subtyping

Chronic obstructive pulmonary disease (COPD) is heterogenous in its clinical manifestations and disease progression. Patients often have disease courses that are difficult to predict with readily available data, such as lung function testing. The ability to better classify COPD into well-defined groups will allow researchers and clinicians to tailor novel therapies, monitor their effects, and improve patient-centered outcomes. Different modalities of assessing these COPD phenotypes are actively being studied, and an area of great promise includes the use of quantitative computed tomography (QCT) techniques focused on key features such as airway anatomy, lung density, and vascular morphology. Over the last few decades, companies around the world have commercialized automated CT software packages that have proven immensely useful in these endeavors. This article reviews the key features of several commercial platforms, including the technologies they are based on, the metrics they can generate, and their clinical correlations and applications. While such tools are increasingly being used in research and clinical settings, they have yet to be consistently adopted for diagnostic work-up and treatment planning, and their full potential remains to be explored.

CD19 and POU2AF1 are Potential Immune-Related Biomarkers Involved in the Emphysema of COPD: On Multiple Microarray Analysis

Purpose

Emphysema is the main cause of the progression of chronic obstructive pulmonary disease (COPD). This study aimed to identify the key genes involved in COPD-related emphysema.

Patients and Methods

GSE76925 was downloaded from Gene Expression Omnibus database. Protein–protein interaction networks of differentially expressed genes (DEGs) between control and COPD groups were constructed to identify hub genes using Cytoscape. Diagnostic performance of hub genes was evaluated using receiver operating characteristic analysis. Correlation analysis was performed to identify the key genes by analyzing the relationship between the hub genes and lung function and computed tomography (CT) indexes of emphysema. COPD patients were then divided into two groups based on the median expression of key genes and DEGs between these two groups were identified. Enrichment analysis of DEGs and correlation analysis between key genes and the infiltration of the immune cells were also analyzed. Finally, the role of key genes was evaluated in a lung tissues dataset (GSE47460) and a blood dataset (GSE76705). Additionally, the expression of key genes was validated by quantitative real-time polymerase chain reaction and immunohistochemistry.

Results

CD19 and POU2AF1 had diagnostic efficacy for COPD and were significantly correlated with lung function and CT indexes of emphysema. Enrichment and immune analyses revealed that CD19 and POU2AF1 were correlated with the B cells in COPD. These results were consistent in GSE47460. The expression of CD19 and POU2AF1 in blood was the opposite of that in lung tissues, and CD19 and POU2AF1 were both significantly upregulated in COPD lung tissues at both the mRNA and protein levels.

Conclusion

CD19 and POU2AF1 may serve as key regulators of emphysema and contribute to the progression of COPD by regulating the B-cell immunology. Targeting B cells may be a promising strategy for treating COPD.

Spirometry parameters used to define small airways obstruction in population-based studies: systematic review

Background

The assessment of small airways obstruction (SAO) using spirometry is practiced in population-based studies. However, it is not clear what are the most used parameters and cut-offs to define abnormal results.

Methods

We searched three databases (Medline, Web of Science, Google Scholar) for population-based studies, published by 1 May 2021, that used spirometry parameters to identify SAO and/or provided criteria for defining SAO. We systematically reviewed these studies and summarised evidence to determine the most widely used spirometry parameter and criteria for defining SAO. In addition, we extracted prevalence estimates and identified associated risk factors. To estimate a pooled prevalence of SAO, we conducted a meta-analysis and explored heterogeneity across studies using meta regression.

Results

Twenty-five studies used spirometry to identify SAO. The most widely utilised parameter (15 studies) was FEF_25–75, either alone or in combination with other measurements. Ten studies provided criteria for the definition of SAO, of which percent predicted cut-offs were the most common (5 studies). However, there was no agreement on which cut-off value to use. Prevalence of SAO ranged from 7.5% to 45.9%. As a result of high heterogeneity across studies (I² = 99.3%), explained by choice of spirometry parameter and WHO region, we do not present a pooled prevalence estimate.

Conclusion

There is a lack of consensus regarding the best spirometry parameter or defining criteria for identification of SAO. The value of continuing to measure SAO using spirometry is unclear without further research using large longitudinal data.

PROSPERO registration number CRD42021250206

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-01990-2.