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Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (

BACKGROUND: Emerging data from longitudinal studies suggest that preserved ratio impaired spirometry (PRISm), defined by proportionate reductions in FEV(1) and FVC, is a heterogeneous population with frequent transitions to other lung function categories relative to individuals with normal and obstructive spirometry. Controversy regarding the clinical significance of these transitions exists (eg, whether transitions merely reflect measurement variability or noise). RESEARCH QUESTION: Are individuals with PRISm enriched for transitions associated with substantial changes in lung function? STUDY DESIGN AND METHODS: Current and former smokers enrolled in the Genetic Epidemiology of COPD (COPDGene) study with spirometry available in phases 1 through 3 (enrollment, 5-year follow-up, and 10-year follow-up) were analyzed. Postbronchodilator lung function categories were as follows: PRISm (FEV(1) < 80% predicted with FEV(1)/FVC ratio ≥ 0.7), Global Initiative for Chronic Obstructive Lung Disease grade 0 (FEV(1) ≥ 80% predicted and FEV(1)/FVC ≥ 0.7), and obstruction (FEV(1)/FVC < 0.7). Significant transition status was affirmative if a subject belonged to two or more spirometric categories and had > 10% change in FEV(1) % predicted and/or FVC % predicted between consecutive visits. Ever-PRISm was present if a subject had PRISm at any visit. Logistic regression examined the association between significant transitions and ever-PRISm status, adjusted for age, sex, race, FEV(1) % predicted, current smoking, pack-years, BMI, and ever-positive bronchodilator response. RESULTS: Among subjects with complete data (N = 1,775) over 10.1 ± 0.4 years of follow-up, the prevalence of PRISm remained consistent (10.4%-11.3%) between phases 1 through 3, but nearly one-half of subjects with PRISm transitioned into or out of PRISm at each visit. Among all subjects, 19.7% had a significant transition; ever-PRISm was a significant predictor of significant transitions (unadjusted OR, 10.3; 95% CI, 7.9-13.5; adjusted OR, 14.9; 95% CI, 10.9-20.7). Results were similar with additional adjustment for radiographic emphysema and gas trapping, when lower limit of normal criteria were used to define lung function categories, and when FEV(1) alone (regardless of change in FVC % predicted) was used to define significant transitions. INTERPRETATION: PRISm is an unstable group, with frequent significant transitions to both obstruction and normal spirometry over time. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; No.: NCT000608764; URL: www.clinicaltrials.gov

RATIONALE: The course of lung function, respiratory symptoms, and functional status over time in people who smoke cigarettes is still incompletely understood. The COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease [COPD]) study provides a unique cohort to examine these trajectories, and now 10-year follow-up data are available. OBJECTIVES: This study aims to provide insight into the progression of spirometric parameters, respiratory symptoms, and functional capacity over 10 years in current and former cigarette smokers. METHODS: We analyzed available longitudinal data for COPDGene participants who did not change smoking status over three visits spanning approximately 10 years of follow-up. Change in postbronchodilator forced expiratory volume in 1 second (FEV(1)), St. George’s Respiratory Questionnaire (SGRQ), and 6-minute walk distance (6MWD) from Phase 1 to Phase 3 were examined using linear mixed models. Terms were included in the models to estimate mean progression separately for current and former cigarette smokers. Models were stratified by baseline Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometry stages as well as by new 2019 COPDGene classification. RESULTS: The mean age at enrollment of the 9,103 participants in this analysis was 59.8 years (SD = 9.2 yr); 46.4% were women, and 32.6% were African American. In all GOLD COPD groups, including participants with normal spirometry, and all groups categorized by 2019 COPDGene classification, FEV(1) decreased, SGRQ increased (indicating higher symptom burden), and 6MWD decreased over the 10-year follow-up period. Current smokers exhibited a greater mean loss of FEV(1) over the study period than former smokers for all groups except those with preserved ratio impaired spirometry. For both SGRQ and 6MWD, rates of progression tended to be similar for former and current smokers except for 6MWD in the highest severity groups, in which former smokers had greater progression. However, this could be impacted by some current smokers with faster progression who had quit smoking and were dropped from analyses. CONCLUSIONS: Progression in FEV(1), SGRQ, and 6MWD overall appears to be slow, and the change over time in groups traditionally characterized as not having disease closely mirrors that of the groups with COPD at all GOLD stages. Current cigarette smokers had greater loss of FEV(1) than former smokers, whereas SGRQ and 6MWD changes were more similar between current and former cigarette smokers.

While variation in emphysema between COPD patients is well-recognized, clinically applicable definitions of emphysema-predominant (EPD) and non-emphysema-predominant (NEPD) subtypes have not been established. To study the clinical relevance of EPD and NEPD subtypes, we tested the association of these subtypes to prospective FEV1 decline and mortality in 3,427 subjects with GOLD spirometric grade 2-4 COPD at baseline in the COPDGene Study, an ongoing national multicenter study that started in 2007. NEPD was defined as airflow obstruction with =10% CT emphysema. Mixed effects models for FEV1 demonstrated larger average annual FEV1 loss in EPD versus NEPD subjects (-10.2 ml/yr, p 0.9. NEPD and EPD COPD subtypes capture important aspects of COPD heterogeneity and are associated with different rates of disease progression and mortality.

Supplementary Figure 1 from Increased Manganese Superoxide Dismutase Expression or Treatment with Manganese Porphyrin Potentiates Dexamethasone-Induced Apoptosis in Lymphoma Cells

Supplementary Figure Legend from Increased Manganese Superoxide Dismutase Expression or Treatment with Manganese Porphyrin Potentiates Dexamethasone-Induced Apoptosis in Lymphoma Cells

Background The association between genetic variants on the X chromosome to risk of COPD has not been fully explored. We hypothesize that the X chromosome harbors variants important in determining risk of COPD related phenotypes and may drive sex differences in COPD manifestations. Methods Using X chromosome data from three COPD-enriched cohorts of adult smokers, we performed X chromosome specific quality control, imputation, and testing for association with COPD case–control status, lung function, and quantitative emphysema. Analyses were performed among all subjects, then stratified by sex, and subsequently combined in meta-analyses. Results Among 10,193 subjects of non-Hispanic white or European ancestry, a variant near TMSB4X, rs5979771, reached genome-wide significance for association with lung function measured by FEV1/FVC ($$\beta$$ β 0.020, SE 0.004, p 4.97 × 10–08), with suggestive evidence of association with FEV1 ($$\beta$$ β 0.092, SE 0.018, p 3.40 × 10–07). Sex-stratified analyses revealed X chromosome variants that were differentially trending in one sex, with significantly different effect sizes or directions. Conclusions This investigation identified loci influencing lung function, COPD, and emphysema in a comprehensive genetic association meta-analysis of X chromosome genetic markers from multiple COPD-related datasets. Sex differences play an important role in the pathobiology of complex lung disease, including X chromosome variants that demonstrate differential effects by sex and variants that may be relevant through escape from X chromosome inactivation. Comprehensive interrogation of the X chromosome to better understand genetic control of COPD and lung function is important to further understanding of disease pathology. Trial registration Genetic Epidemiology of COPD Study (COPDGene) is registered at ClinicalTrials.gov, NCT00608764 (Active since January 28, 2008). Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints Study (ECLIPSE), GlaxoSmithKline study code SCO104960, is registered at ClinicalTrials.gov, NCT00292552 (Active since February 16, 2006). Genetics of COPD in Norway Study (GenKOLS) holds GlaxoSmithKline study code RES11080, Genetics of Chronic Obstructive Lung Disease.

Background Parameters from maximal expiratory flow-volume curves (MEFVC) have been linked to CT-based parameters of COPD. However, the association between MEFVC shape and phenotypes like emphysema, small airways disease (SAD) and bronchial wall thickening (BWT) has not been investigated. Research question We analyzed if the shape of MEFVC can be linked to CT-determined emphysema, SAD and BWT in a large cohort of COPDGene participants. Study design and methods In the COPDGene cohort, we used principal component analysis (PCA) to extract patterns from MEFVC shape and performed multiple linear regression to assess the association of these patterns with CT parameters over the COPD spectrum, in mild and moderate-severe COPD. Results Over the entire spectrum, in mild and moderate-severe COPD, principal components of MEFVC were important predictors for the continuous CT parameters. Their contribution to the prediction of emphysema diminished when classical pulmonary function test parameters were added. For SAD, the components remained very strong predictors. The adjusted R2 was higher in moderate-severe COPD, while in mild COPD, the adjusted R2 for all CT outcomes was low; 0.28 for emphysema, 0.21 for SAD and 0.19 for BWT. Interpretation The shape of the maximal expiratory flow-volume curve as analyzed with PCA is not an appropriate screening tool for early disease phenotypes identified by CT scan. However, it contributes to assessing emphysema and SAD in moderate-severe COPD.

Background: The heterogeneous nature of chronic obstructive pulmonary disease (COPD) complicates the identification of the predictors of disease progression. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features. Methods: We included 4496 smokers with available data from their enrollment and 5-year follow-up visits in the COPD Genetic Epidemiology (COPDGene(®)) study. We constructed linear regression (LR) and supervised random forest models to predict 5-year progression in forced expiratory in 1 second (FEV(1)) from 46 baseline features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit. Results: Predicting the change in FEV(1) over time is more challenging than simply predicting the future absolute FEV(1) level. For random forest, R-squared was 0.15 and the area under the receiver operator characteristic (ROC) curves for the prediction of participants in the top quartile of observed progression was 0.71 (testing) and respectively, 0.10 and 0.70 (validation). Random forest provided slightly better performance than LR. The accuracy was best for Global initiative for chronic Obstructive Lung Disease (GOLD) grades 1–2 participants, and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD. Conclusion: Random forest, along with deep phenotyping, predicts FEV(1) progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.