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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]

Rationale: Emerging data demonstrate that the smallest conducting airways, terminal bronchioles, are the early site of tissue destruction in chronic obstructive pulmonary disease (COPD) and are reduced by as much as 41% by the time someone is diagnosed with mild (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stage 1) COPD. Objectives: To develop a single-cell atlas that describes the structural, cellular, and extracellular matrix alterations underlying terminal bronchiole loss in COPD. Methods: This cross-sectional study of 262 lung samples derived from 34 ex-smokers with normal lung function (n = 10) or GOLD stage 1 (n = 10), stage 2 (n = 8), or stage 4 (n = 6) COPD was performed to assess the morphology, extracellular matrix, single-cell atlas, and genes associated with terminal bronchiole reduction using stereology, micro-computed tomography, nonlinear optical microscopy, imaging mass spectrometry, and transcriptomics. Measurements and Main Results: The lumen area of terminal bronchioles progressively narrows with COPD severity as a result of the loss of elastin fibers within alveolar attachments, which was observed before microscopic emphysematous tissue destruction in GOLD stage 1 and 2 COPD. The single-cell atlas of terminal bronchioles in COPD demonstrated M1-like macrophages and neutrophils located within alveolar attachments and associated with the pathobiology of elastin fiber loss, whereas adaptive immune cells (naive, CD4, and CD8 T cells, and B cells) are associated with terminal bronchiole wall remodeling. Terminal bronchiole pathology was associated with the upregulation of genes involved in innate and adaptive immune responses, the interferon response, and the degranulation of neutrophils. Conclusions: This comprehensive single-cell atlas highlights terminal bronchiole alveolar attachments as the initial site of tissue destruction in centrilobular emphysema and an attractive target for disease modification. [ABSTRACT FROM AUTHOR]

The article presents a study which determined whether PRM is increased in military personnel with constrictive bronchiolitis (CB) (MPCB) relative to healthy subjects and assessed the severity of small airway disease by comparing it with the parametric response mapping (PRM) detected in subjects with varying severities of chronic obstructive pulmonary disease (COPD).

Advanced bronchoscopic lung volume reduction treatment (BLVR) is now a routine care option for treating patients with severe emphysema. Patterns of low attenuation clusters indicating emphysema and functional small airway disease (fSAD) on paired CT, which may provide additional insights to the target selection of the segmental or subsegmental lobe of the treatments, require further investigation. The low attenuation clusters (LACS) were segmented to identify the scalar and spatial distribution of the lung destructions, in terms of 10 fractions scales of low attenuation density (LAD) located in upper lobes and lower lobes. The LACs of functional small airway disease (fSAD) were delineated by applying the technique of parametric response map (PRM) on the co-registered CT image data. Both emphysematous LACs of inspiratory CT and fSAD LACs on expiratory CT were used to derive the coefficients of the predictive model for estimating the airflow limitation. The voxel-wise severity is then predicted using the regional LACs on the co-registered CT to indicate the functional localization, namely, the bullous parametric response map (BPRM). A total of 100 subjects, 88 patients with mild to very severe COPD and 12 control participants with normal lung functions (FEV1/FVC % > 70%), were evaluated. Pearson's correlations between FEV1/FVC% and LAV%HU-950 of severe emphysema are − 0.55 comparing to − 0.67 and − 0.62 of LAV%HU-856 of air-trapping and LAV%fSAD respectively. Pearson's correlation between FEV1/FVC% and FEV1/FVC% predicted by the proposed model using LAD% of HU-950 and fSAD on BPRM is 0.82 (p < 0.01). The result of the Bullous Parametric Response Map (BPRM) is capable of identifying the less functional area of the lung, where the BLVR treatment is aimed at removing from a hyperinflated area of emphysematous regions. [ABSTRACT FROM AUTHOR]

Purpose: In the literature on automated phenotyping of chronic obstructive pulmonary disease (COPD), there is a multitude of isolated classical machine learning and deep learning techniques, mostly investigating individual phenotypes, with small study cohorts and heterogeneous meta‐parameters, e.g., different scan protocols or segmented regions. The objective is to compare the impact of different experimental setups, i.e., varying meta‐parameters related to image formation and data representation, with the impact of the learning technique for subtyping automation for a variety of phenotypes. The identified associations of these parameters with automation performance and their interactions might be a first step towards a determination of optimal meta‐parameters, i.e., a meta‐strategy. Methods: A clinical cohort of 981 patients (53.8 ± 15.1 years, 554 male) was examined. The inspiratory CT images were analyzed to automate the diagnosis of 13 COPD phenotypes given by two radiologists. A benchmark feature set that integrates many quantitative criteria was extracted from the lung and trained a variety of learning algorithms on the first 654 patients (two thirds) and the respective algorithm retrospectively assessed the remaining 327 patients (one third). The automation performance was evaluated by the area under the receiver operating characteristic curve (AUC). 1717 experiments were conducted with varying meta‐parameters such as reconstruction kernel, segmented regions and input dimensionality, i.e., number of extracted features. The association of the meta‐parameters with the automation performance was analyzed by multivariable general linear model decomposition of the automation performance in the contributions of meta‐parameters and the learning technique. Results: The automation performance varied strongly for varying meta‐parameters. For emphysema‐predominant phenotypes, an AUC of 93%–95% could be achieved for the best meta‐configuration. The airways‐predominant phenotypes led to a lower performance of 65%–85%, while smooth kernel configurations on average were unexpectedly superior to those with sharp kernels. The performance impact of meta‐parameters, even that of often neglected ones like the missing‐data imputation, was in general larger than that of the learning technique. Advanced learning techniques like 3D deep learning or automated machine learning yielded inferior automation performance for non‐optimal meta‐configurations in comparison to simple techniques with suitable meta‐configurations. The best automation performance was achieved by a combination of modern learning techniques and a suitable meta‐configuration. Conclusions: Our results indicate that for COPD phenotype automation, study design parameters such as reconstruction kernel and the model input dimensionality should be adapted to the learning technique and may be more important than the technique itself. To achieve optimal automation and prediction results, the interaction between input those meta‐parameters and the learning technique should be considered. This might be particularly relevant for the development of specific scan protocols for novel learning algorithms, and towards an understanding of good study design for automated phenotyping. [ABSTRACT FROM AUTHOR]

Our starting point is that relatively new findings into the pathogenesis and pathophysiology of airway disease in smokers that lead to chronic obstructive pulmonary disease (COPD) need to be reassessed as a whole and integrated into "mainstream" thinking along with traditional concepts which have stood the test of time. Such a refining of the accepted disease paradigm is urgently needed as thinking on therapeutic targets is currently under active reconsideration. We feel that generalised airway wall "inflammation" is unduly over-emphasised, and highlight the patchy and variable nature of the pathology (with the core being airway remodelling). In addition, we present evidence for airway wall disease in smokers/COPD as including a hypocellular, hypovascular, destructive, fibrotic pathology, with a likely spectrum of epithelial-mesenchymal transition states as significant drivers of this remodelling. Furthermore, we present data from a number of research modalities and integrate this with the aetiology of lung cancer, the role of chronic airway luminal colonisation/infection by a specific group of "respiratory" bacteria in smokers (which results in luminal inflammation) and the central role for oxidative stress on the epithelium. We suggest translation of these insights into more focus on asymptomatic smokers and early COPD, with the potential for fresh preventive and therapeutic approaches. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network to learn from CT data and derive tissue pattern-clusters jointly. We then applied exploratory factor analysis (EFA) to discover the unobserved latent traits (factors) among pattern-clusters. CT images at total lung capacity (TLC) and residual volume (RV) of 541 former smokers and 59 healthy non-smokers from the cohort of the SubPopulations and Intermediate Outcome Measures in the COPD Study (SPIROMICS) were analyzed. TLC and RV images were registered to calculate the Jacobian (determinant) values for all the voxels in TLC images. 3D Regions of interest (ROIs) with two data channels of CT intensity and Jacobian value were randomly extracted from training images and were fed to the 3D CAE-FC model. 80 pattern-clusters and 7 factors were identified. Factor scores computed for individual subjects were able to predict spirometry-measured pulmonary functions. Two factors which correlated with various emphysema subtypes, parametric response mapping (PRM) metrics, airway variants, and airway tree to lung volume ratio were discriminants of patients across all severity stages. Our findings suggest the potential of developing factor-based surrogate markers for new COPD phenotypes. [ABSTRACT FROM AUTHOR]

Objectives: Chronic obstructive pulmonary disease (COPD) is characterized by variable contributions of emphysema and airway disease on computed tomography (CT), and still little is known on their temporal evolution. We hypothesized that quantitative CT (QCT) is able to detect short-time changes in a cohort of patients with very severe COPD.Methods: Two paired in- and expiratory CT each from 70 patients with avg. GOLD stage of 3.6 (mean age = 66 ± 7.5, mean FEV1/FVC = 35.28 ± 7.75) were taken 3 months apart and analyzed by fully automatic software computing emphysema (emphysema index (EI), mean lung density (MLD)), air-trapping (ratio expiration to inspiration of mean lung attenuation (E/I MLA), relative volume change between - 856 HU and - 950 HU (RVC856-950)), and parametric response mapping (PRM) parameters for each lobe separately and the whole lung. Airway metrics measured were wall thickness (WT) and lumen area (LA) for each airway generation and the whole lung.Results: The average of the emphysema parameters (EI, MLD) increased significantly by 1.5% (p < 0.001) for the whole lung, whereas air-trapping parameters (E/I MLA, RVC856-950) were stable. PRMEmph increased from 34.3 to 35.7% (p < 0.001), whereas PRMNormal decrased from 23.6% to 22.8% (p = 0.012). WT decreased significantly from 1.17 ± 0.18 to 1.14 ± 0.19 mm (p = 0.036) and LA increased significantly from 25.08 ± 4.49 to 25.84 ± 4.87 mm2 (p = 0.041) for the whole lung. The generation-based analysis showed heterogeneous results.Conclusion: QCT detects short-time progression of emphysema in severe COPD. The changes were partly different among lung lobes and airway generations, indicating that QCT is useful to address the heterogeneity of COPD progression.Key Points: • QCT detects short-time progression of emphysema in severe COPD in a 3-month period. • QCT is able to quantify even slight parenchymal changes, which were not detected by spirometry. • QCT is able to address the heterogeneity of COPD, revealing inconsistent changes individual lung lobes and airway generations. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a devastating lung disease with a high personal and societal burden. Exposure to toxic particles and gases, including cigarette smoke, is the main risk factor for COPD. Together with smoking cessation, current treatment strategies of COPD aim to improve symptoms and prevent exacerbations, but there is no disease-modifying treatment. The biggest drawback of today's COPD treatment regimen is the 'one size fits all' pharmacological intervention, mainly based on disease severity and symptoms and not the individual's disease pathology. To halt the worrying increase in the burden of COPD, disease management needs to be advanced with a focus on personalized treatment. The main pathological feature of COPD includes a chronic and abnormal inflammatory response within the lungs, which results in airway and alveolar changes in the lung as reflected by (small) airways disease and emphysema. Here we discuss recent developments related to the abnormal inflammatory response, ECM and age-related changes, structural changes in the small airways and the role of sex-related differences, which are all relevant to explain the individual differences in the disease pathology of COPD and improve disease endotyping. Furthermore, we will discuss the most recent developments of new treatment strategies using biologicals to target specific pathological features or disease endotypes of COPD. [ABSTRACT FROM AUTHOR]

Target lung tissue selection remains a challenging task to perform for treating severe emphysema with lung volume reduction (LVR). In order to target the treatment candidate, the percentage of low attenuation volume (LAV%) representing the proportion of emphysema volume to whole lung volume is measured using computed tomography (CT) images. Although LAV% have shown to have a correlation with lung function in patients with chronic obstructive pulmonary disease (COPD), similar measurements of LAV% in whole lung or lobes may have large variations in lung function due to emphysema heterogeneity. The functional information of regional emphysema destruction is required for supporting the choice of optimal target. The purpose of this study is to develop an emphysema heterogeneity descriptor for the three-dimensional emphysematous bullae according to the size variations of emphysematous density (ED) and their spatial distribution. The second purpose is to derive a predictive model of airflow limitation based on the regional emphysema heterogeneity. Deriving the bullous representation and grouping them into four scales in the upper and lower lobes, a predictive model is computed using the linear model fitting to estimate the severity of lung function. A total of 99 subjects, 87 patients with mild to very severe COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage I~IV) and 12 control participants with normal lung functions (forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) > 0.7) were evaluated. The final model was trained with stratified cross-validation on randomly selected 75% of the dataset (n = 76) and tested on the remaining dataset (n = 23). The dispersed cases of LAV% inconsistent with their lung function outcome were evaluated, and the correlation study suggests that comparing to LAV of larger bullae, the widely spread smaller bullae with equivalent LAV has a larger impact on lung function. The testing dataset has the correlation of r = −0.76 (p < 0.01) between the whole lung LAV% and FEV1/FVC, whereas using two ED % of scales and location-dependent variables to predict the emphysema-associated FEV1/FVC, the results shows their correlation of 0.82 (p < 0.001) with clinical FEV1/FVC. [ABSTRACT FROM AUTHOR]

Background: Pulmonary and systemic inflammation are central features of chronic obstructive pulmonary disease (COPD). Previous studies have demonstrated relationships between biologically active extracellular matrix components, or matrikines, and COPD pathogenesis. We studied the relationships between the matrikine acetyl-proline-glycine-proline (AcPGP) in sputum and plasma and clinical features of COPD.Methods: Sputum and plasma samples were obtained from COPD participants in the SPIROMICS cohort at enrollment. AcPGP was isolated using solid phase extraction and measured by mass spectrometry. Demographics, spirometry, quality of life questionnaires, and quantitative computed tomography (CT) imaging with parametric response mapping (PRM) were obtained at baseline. Severe COPD exacerbations were recorded at 1-year of prospective follow-up. We used linear and logistic regression models to measure associations between AcPGP and features of COPD, and Kaplan-Meier analyses to measure time-to-first severe exacerbation.Results: The 182 COPD participants in the analysis were 66 ± 8 years old, 62% male, 84% White race, and 39% were current smokers. AcPGP concentrations were 0.61 ± 1.89 ng/mL (mean ± SD) in sputum and 0.60 ± 1.13 ng/mL in plasma. In adjusted linear regression models, sputum AcPGP was associated with FEV1/FVC, spirometric GOLD stage, PRM-small airways disease, and PRM-emphysema. Sputum AcPGP also correlated with severe AECOPD, and elevated sputum AcPGP was associated with shorter time-to-first severe COPD exacerbation. In contrast, plasma AcPGP was not associated with symptoms, pulmonary function, or severe exacerbation risk.Conclusions: In COPD, sputum but not plasma AcPGP concentrations are associated with the severity of airflow limitation, small airways disease, emphysema, and risk for severe AECOPD at 1-year of follow-up.Trial Registration: ClinicalTrials.gov: NCT01969344 (SPIROMICS). [ABSTRACT FROM AUTHOR]

Rationale: Chronic obstructive pulmonary disease (COPD) has been associated with numerous genetic variants, yet the extent to which its genetic risk is mediated by variation in lung structure remains unknown.Objectives: To characterize associations between a genetic risk score (GRS) associated with COPD susceptibility and lung structure on computed tomography (CT).Methods: We analyzed data from MESA Lung (Multi-Ethnic Study of Atherosclerosis Lung Study), a U.S. general population-based cohort, and SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study). A weighted GRS was calculated from 83 SNPs that were previously associated with lung function. Lung density, spatially matched airway dimensions, and airway counts were assessed on full-lung CT. Generalized linear models were adjusted for age, age squared, sex, height, principal components of genetic ancestry, smoking status, pack-years, CT model, milliamperes, and total lung volume.Measurements and Main Results: MESA Lung and SPIROMICS contributed 2,517 and 2,339 participants, respectively. Higher GRS was associated with lower lung function and increased COPD risk, as well as lower lung density, smaller airway lumens, and fewer small airways, without effect modification by smoking. Adjustment for CT lung structure, particularly small airway measures, attenuated associations between the GRS and FEV1/FVC by 100% and 60% in MESA and SPIROMICS, respectively. Lung structure (P < 0.0001), but not the GRS (P > 0.10), improved discrimination of moderate-to-severe COPD cases relative to clinical factors alone.Conclusions: A GRS associated with COPD susceptibility was associated with CT lung structure. Lung structure may be an important mediator of heritability and determinant of personalized COPD risk. [ABSTRACT FROM AUTHOR]

Rationale: The small conducting airways are the major site of airflow obstruction in chronic obstructive pulmonary disease and may precede emphysema development.Objectives: We hypothesized a novel computed tomography (CT) biomarker of small airway disease predicts FEV1 decline.Methods: We analyzed 1,508 current and former smokers from COPDGene with linear regression to assess predictors of change in FEV1 (ml/yr) over 5 years. Separate models for subjects without and with airflow obstruction were generated using baseline clinical and physiologic predictors in addition to two novel CT metrics created by parametric response mapping (PRM), a technique pairing inspiratory and expiratory CT images to define emphysema (PRM(emph)) and functional small airways disease (PRM(fSAD)), a measure of nonemphysematous air trapping.Measurements and Main Results: Mean (SD) rate of FEV1 decline in ml/yr for GOLD (Global Initiative for Chronic Obstructive Lung Disease) 0-4 was as follows: 41.8 (47.7), 53.8 (57.1), 45.6 (61.1), 31.6 (43.6), and 5.1 (35.8), respectively (trend test for grades 1-4; P < 0.001). In multivariable linear regression, for participants without airflow obstruction, PRM(fSAD) but not PRM(emph) was associated with FEV1 decline (P < 0.001). In GOLD 1-4 participants, both PRM(fSAD) and PRM(emph) were associated with FEV1 decline (P < 0.001 and P = 0.001, respectively). Based on the model, the proportional contribution of the two CT metrics to FEV1 decline, relative to each other, was 87% versus 13% and 68% versus 32% for PRM(fSAD) and PRM(emph) in GOLD 1/2 and 3/4, respectively.Conclusions: CT-assessed functional small airway disease and emphysema are associated with FEV1 decline, but the association with functional small airway disease has greatest importance in mild-to-moderate stage chronic obstructive pulmonary disease where the rate of FEV1 decline is the greatest. Clinical trial registered with www.clinicaltrials.gov (NCT 00608764). [ABSTRACT FROM AUTHOR]

The article offers no resistance to airflow in healthy subjects but becomes the site of airflow obstruction in various pulmonary diseases remains integral to understanding of airway remodeling in chronic obstructive pulmonary disease (COPD). Topics include reports that realization has fueled extensive research into sensitive techniques for detecting early signs of disease accumulation in the small airways, leading to the development of numerous nonimaging methods such as forced oscillation.