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Impaired single breath carbon monoxide diffusing capacity (DLCO) is associated with emphysema. Small airways disease (SAD) may be a precursor lesion to emphysema, but the relationship between SAD and DLCO is undescribed. We hypothesized that in mild COPD, functional SAD (fSAD) defined by computed tomography (CT) and Parametric Response Mapping methodology would correlate with impaired DLCO. Using data from ever-smokers in the COPDGene cohort, we established that fSAD correlated significantly with lower DLCO among both non-obstructed and GOLD 1-2 subjects. The relationship between DLCO with CT-defined emphysema was present in all GOLD stages, but most prominent in severe disease. TRIAL REGISTRATION: NCT00608764. Registry: COPDGene. Registered 06 February 2008, retrospectively registered.

Rationale: The airway microbiome has the potential to shape chronic obstructive pulmonary disease (COPD) pathogenesis, but its relationship to outcomes in milder disease is unestablished. Objectives: To identify sputum microbiome characteristics associated with markers of COPD in participants of the Subpopulations and Intermediate Outcome Measures of COPD Study (SPIROMICS). Methods: Sputum DNA from 877 participants was analyzed using 16S ribosomal RNA gene sequencing. Relationships between baseline airway microbiota composition and clinical, radiographic, and mucoinflammatory markers, including longitudinal lung function trajectory, were examined. Measurements and Main Results: Participant data represented predominantly milder disease (Global Initiative for Chronic Obstructive Lung Disease stage 0–2 obstruction in 732 of 877 participants). Phylogenetic diversity (i.e., range of different species within a sample) correlated positively with baseline lung function, decreased with higher Global Initiative for Chronic Obstructive Lung Disease stage, and correlated negatively with symptom burden, radiographic markers of airway disease, and total mucin concentrations (P < 0.001). In covariate-adjusted regression models, organisms robustly associated with better lung function included Alloprevotella, Oribacterium, and Veillonella species. Conversely, lower lung function, greater symptoms, and radiographic measures of small airway disease were associated with enrichment in members of Streptococcus, Actinobacillus, Actinomyces, and other genera. Baseline sputum microbiota features were also associated with lung function trajectory during SPIROMICS follow-up (stable/improved, decline, or rapid decline groups). The stable/improved group (slope of FEV1 regression ⩾66th percentile) had greater bacterial diversity at baseline associated with enrichment in Prevotella, Leptotrichia, and Neisseria species. In contrast, the rapid decline group (FEV1 slope ⩽33rd percentile) had significantly lower baseline diversity associated with enrichment in Streptococcus species. Conclusions: In SPIROMICS, baseline airway microbiota features demonstrate divergent associations with better or worse COPD-related outcomes. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to anatomically correlate ventilation defects with regions of air trapping by whole lung, lung lobe, and airway segment in the context of airway mucus plugging in asthma. Methods: A total of 34 asthmatics [13M:21F, 13 mild/moderate, median age (range) of 49.5 (36.8-53.3) years and 21 severe, 56.1 (47.1-62.6) years] and 4 healthy subjects [1M:3F, 38.5 (26.6-52.2) years] underwent HP ³He MRI and CT imaging. HP ³He MRI was assessed for ventilation defects using a semi-automated k-means clustering algorithm. Inspiratory and expiratory CTs were analyzed using parametric response mapping (PRM) to quantify markers of emphysema and functional small airways disease (fSAD). Segmental and lobar lung masks were obtained from CT and registered to HP ³He MRI in order to localize ventilation defect percent (VDP), at the lobar and segmental level, to regions of fSAD and mucus plugging. Spearman's correlation was utilized to compare biomarkers on a global and lobar level, and a multivariate analysis was conducted to predict segmental fSAD given segmental VDP (sVDP) and mucus score as variables in order to further understand the functional relationships between regional measures of obstruction. Results: On a global level, fSAD was correlated with whole lung VDP (r = 0.65, p < 0.001), mucus score (r = 0.55, p < 0.01), and moderately correlated (-0.60 ≤ r ≤ -0.56, p < 0.001) to percent predicted (%p) FEV1, FEF25-75 and FEV1/FVC, and more weakly correlated to FVC%p (-0.38 ≤ r ≤ -0.35, p < 0.001) as expected from previous work. On a regional level, lobar VDP, mucus scores, and fSAD were also moderately correlated (r from 0.45-0.66, p < 0.01). For segmental colocalization, the model of best fit was a piecewise quadratic model, which suggests that sVDP may be increasing due to local airway obstruction that does not manifest as fSAD until more extensive disease is present. sVDP was more sensitive to the presence of a mucus plugs overall, but the prediction of fSAD using multivariate regression showed an interaction in the presence of a mucus plugs when sVDP was between 4% and 10% (p < 0.001). Conclusion: This multi-modality study in asthma confirmed that areas of ventilation defects are spatially correlated with air trapping at the level of the airway segment and suggests VDP and fSAD are sensitive to specific sources of airway obstruction in asthma, including mucus plugs. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV1) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease (GOLD 0), or with mild-to-moderate (GOLD 1-2) COPD. We trained multiple models to predict rapid FEV1 decline using demographic, clinical and radiologic biomarker data. Training and internal validation data were obtained from the COPDGene study and prediction models were validated against the SPIROMICS cohort. Methods: We used GOLD 0-2 participants (n = 3,821) from COPDGene (60.0 ± 8.8 years, 49.9% male) for variable selection and model training. Accelerated lung function decline was defined as a mean drop in FEV1% predicted of > 1.5%/year at 5-year follow-up. We built logistic regression models predicting accelerated decline based on 22 chest CT imaging biomarker, pulmonary function, symptom, and demographic features. Models were validated using n = 885 SPIROMICS subjects (63.6 ± 8.6 years, 47.8% male). Results: The most important variables for predicting FEV1 decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV1% predicted (FEV1.pp.post), and CT-derived expiratory lung volume; among GOLD 1 and 2 subjects, they were BDR, age, and PRMlower lobes fSAD. In the validation cohort, GOLD 0 and GOLD 1-2 full variable models had significant predictive performance with AUCs of 0.620 ± 0.081 (p = 0.041) and 0.640 ± 0.059 (p < 0.001). Subjects with higher model-derived risk scores had significantly greater odds of FEV1 decline than those with lower scores. Conclusion: Predicting FEV1 decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts. [ABSTRACT FROM AUTHOR]