Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium

BACKGROUND

The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression.

METHODS

We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants.

RESULTS

We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections.

CONCLUSIONS

These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.

Mucus Plugs and Emphysema in the Pathophysiology of Airflow Obstruction and Hypoxemia in Smokers

Rationale: The relative roles of mucus plugs and emphysema in mechanisms of airflow limitation and hypoxemia in smokers with chronic obstructive pulmonary disease (COPD) are uncertain.Objectives: To relate image-based measures of mucus plugs and emphysema to measures of airflow obstruction and oxygenation in patients with COPD.Methods: We analyzed computed tomographic (CT) lung images and lung function in participants in the Subpopulations and Intermediate Outcome Measures in COPD Study. Radiologists scored mucus plugs on CT lung images, and imaging software automatically quantified emphysema percentage. Unadjusted and adjusted relationships between mucus plug score, emphysema percentage, and lung function were determined using regression.Measurements and Main Results: Among 400 smokers, 229 (57%) had mucus plugs and 207 (52%) had emphysema, and subgroups could be identified with mucus-dominant and emphysema-dominant disease. Only 33% of smokers with high mucus plug scores had mucus symptoms. Mucus plug score and emphysema percentage were independently associated with lower values for FEV1 and peripheral oxygen saturation (P < 0.001). The relationships between mucus plug score and lung function outcomes were strongest in smokers with limited emphysema (P < 0.001). Compared with smokers with low mucus plug scores, those with high scores had worse COPD Assessment Test scores (17.4 ± 7.7 vs. 14.4 ± 13.3), more frequent annual exacerbations (0.75 ± 1.1 vs. 0.43 ± 0.85), and shorter 6-minute-walk distance (329 ± 115 vs. 392 ± 117 m) (P < 0.001).Conclusions: Symptomatically silent mucus plugs are highly prevalent in smokers and independently associate with lung function outcomes. These data provide rationale for targeting patients with mucus-high/emphysema-low COPD in clinical trials of mucoactive treatments.Clinical trial registered with www.clinicaltrials.gov (NCT01969344).

Adiposity and Interstitial Lung Abnormalities in Community-Dwelling Adults: The MESA Cohort Study

BACKGROUND

Obesity is associated with restrictive ventilatory defects and a faster rate of decline in FVC. This association is not exclusively mediated by mechanical factors and may reflect direct pulmonary injury by adipose-derived mediators.

RESEARCH QUESTION

Is adipose tissue involved in the pathogenesis of interstitial lung disease (ILD)?

STUDY DESIGN AND METHODS

We evaluated the association of CT measures of pericardial, abdominal visceral, and abdominal subcutaneous adipose tissue with high-attenuation areas (HAAs) and interstitial lung abnormalities (ILAs) in a large multicenter cohort study of community-dwelling adults, using multivariable-adjusted models. We secondarily evaluated the association of adipose depot size with FVC and biomarkers of obesity and inflammation.

RESULTS

In fully adjusted models, every doubling in pericardial adipose tissue volume was associated with a 63.4-unit increase in HAA (95% CI, 55.5-71.3), 20% increased odds of ILA (95% CI, -2% to 50%), and a 5.5% decrease in percent predicted FVC (95% CI, -6.8% to -4.3%). IL-6 levels accounted for 8% of the association between pericardial adipose tissue and HAA. Every doubling in visceral adipose tissue area was associated with a 41.5-unit increase in HAA (95% CI, 28.3-54.7), 30% increased odds of ILA (95% CI, -10% to 80%), and a 5.4% decrease in percent predicted FVC (95% CI, -6.6% to -4.3%). IL-6 and leptin accounted for 17% and 18%, respectively, of the association between visceral adipose tissue and HAA.

INTERPRETATION

Greater amounts of pericardial and abdominal visceral adipose tissue were associated with CT measures of early lung injury and lower FVC in a cohort of community-dwelling adults. Adipose tissue may represent a modifiable risk factor for ILD.

Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review

Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.

Spatiotemporal adaptive evolution of an MHC immune gene in a frog-fungus disease system

Genetic diversity of major histocompatibility complex (MHC) genes is linked to reduced pathogen susceptibility in amphibians, but few studies also examine broad spatial and temporal patterns of MHC and neutral genetic diversity. Here, we characterized range-wide MHC diversity in the Northern leopard frog, Rana pipiens, a species found throughout North America that is experiencing disease-related declines. We used previously sequenced neutral markers (mitochondrial DNA and microsatellites), sequenced an expressed MHC class IIß gene fragment, and measured infection prevalence and intensity of the global fungal pathogen Batrachochytrium dendrobatidis (Bd) across 14 populations. Four populations were sampled across two decades, enabling temporal comparisons of selection and demography. We recovered 37 unique MHC alleles, including 17 that were shared across populations. Phylogenetic and population genetic patterns between MHC and neutral markers were incongruent, and five MHC codon positions associated with peptide binding were under positive selection. MHC heterozygosity, but not neutral marker heterozygosity, was a significant factor explaining spatial patterns of Bd prevalence, whereas only environmental variables predicted Bd intensity. MHC allelic richness (AR) decreased significantly over time but microsatellite-based AR did not, highlighting a loss of functional immunogenetic diversity that may be associated with Bd selective pressures. MHC supertype 4 was significantly associated with an elevated risk of Bd infection, whereas one supertype 2 allele was associated with a nearly significant reduced risk of Bd. Taken together, these results provide evidence that positive selection contributes to MHC class IIß evolution in R. pipiens and suggest that functional MHC differences across populations may contribute to disease adaptation.

Quantitative CT-Based Methods for Bone Microstructural Measures and Their Relationships With Vertebral Fractures in a Pilot Study on Smokers

Osteoporosis causes fragile bone, and bone microstructural quality is a critical determinant of bone strength and fracture risk. This study pursues technical validation of novel CT-based methods for assessment of peripheral bone microstructure together with a human pilot study examining relationships between bone microstructure and vertebral fractures in smokers. To examine the accuracy and reproducibility of the methods, repeat ultra-high-resolution (UHR) CT and micro-CT scans of cadaveric ankle specimens were acquired. Thirty smokers from the University of Iowa COPDGene cohort were recruited at their 5-year follow-up visits. Chest CT scans, collected under the parent study, were used to assess vertebral fractures. UHR CT scans of distal tibia were acquired for this pilot study to obtain peripheral cortical and trabecular bone (Cb and Tb) measures. UHR CT-derived Tb measures, including volumetric bone mineral density (BMD), network area, transverse trabecular density, and mean plate width, showed high correlation (r > 0.901) with their micro-CT-derived values over small regions of interest (ROIs). Both Cb and Tb measures showed high reproducibility-intra-class correlation (ICC) was greater than 0.99 for all Tb measures except erosion index and greater than 0.97 for all Cb measures. Female sex was associated with lower transverse Tb density (p < 0.1), higher Tb spacing (p < 0.05), and lower cortical thickness (p < 0.001). Participants with vertebral fractures had significantly degenerated values (p < 0.05) for all Tb measures except thickness. There were no statistically significant differences for Cb measures between non-fracture and fracture groups. Vertebral fracture-group differences of Tb measures remained significant after adjustment with chronic obstructive pulmonary disease (COPD) status. Although current smokers at baseline had more fractures-81.8% versus 63.2% for former smokers-the difference was not statistically significant. This pilot cross-sectional human study demonstrates CT-based peripheral bone microstructural differences among smokers with and without vertebral fractures. © 2021 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Generation-based study of airway remodeling in smokers with normal-looking CT with normalization to control inter-subject variability

PURPOSE

With the help of quantitative computed tomography (QCT), it is possible to identify smoking-associated airway remodeling. However, there is currently little information on whether QCT-based airway metrics are sensitive to early airway wall remodeling in subclinical phases of smoking-associated airway disease. This study aimed to evaluate a predictive model that normalized airway parameters and investigate structural airway alterations in smokers with normal-looking CT using the normalization scheme.

METHODS

In this retrospective analysis, 222 non-smokers (male 97, female 125) and 69 smokers (male 66, female 3) from January 2014 to December 2016 were included, and airway parameters were quantitatively analyzed. To control inter-subject variability, multiple linear regressions of tracheal wall thickness (WT), diameter (D), and luminal area (LA) were performed, adjusted for age, sex, and height. Using this normalization scheme, airway parameters with matched generation were compared between smokers and non-smokers.

RESULTS

Using the normalization scheme, it was possible to assess generation-based structural alterations of the airways in subclinical smokers. Smokers showed diffuse luminal narrowing of airways for most generations (P < 0.05, except 3rd generation), no change in wall thickness of the proximal bronchi (1st-3rd generation), and a thinning of distal airways (P <0.05, ≥4th generation).

CONCLUSION

QCT assessment for subclinical smokers can help identify minimal structural changes in airways induced by smoking.

Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images

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.

Lung microbiota associations with clinical features of COPD in the SPIROMICS cohort

Chronic obstructive pulmonary disease (COPD) is heterogeneous in development, progression, and phenotypes. Little is known about the lung microbiome, sampled by bronchoscopy, in milder COPD and its relationships to clinical features that reflect disease heterogeneity (lung function, symptom burden, and functional impairment). Using bronchoalveolar lavage fluid collected from 181 never-smokers and ever-smokers with or without COPD (GOLD 0-2) enrolled in the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS), we find that lung bacterial composition associates with several clinical features, in particular bronchodilator responsiveness, peak expiratory flow rate, and forced expiratory flow rate between 25 and 75% of FVC (FEF_25–75). Measures of symptom burden (COPD Assessment Test) and functional impairment (six-minute walk distance) also associate with disparate lung microbiota composition. Drivers of these relationships include members of the Streptococcus, Prevotella, Veillonella, Staphylococcus, and Pseudomonas genera. Thus, lung microbiota differences may contribute to airway dysfunction and airway disease in milder COPD.

Ratio of FEV1/Slow Vital Capacity of < 0.7 Is Associated With Clinical, Functional, and Radiologic Features of Obstructive Lung Disease in Smokers With Preserved Lung Function

BACKGROUND

Mild expiratory flow limitation may not be recognized using traditional spirometric criteria based on the ratio of FEV₁/FVC.

RESEARCH QUESTION

Does slow vital capacity (SVC) instead of FVC increase the sensitivity of spirometry to identify patients with early or mild obstructive lung disease?

STUDY DESIGN AND METHODS

We included 854 current and former smokers from the Subpopulations and Intermediate Outcome Measures in COPD Study cohort with a postbronchodilator FEV₁/FVC ≥ 0.7 and FEV₁ % predicted of ≥ 80% at enrollment. We compared baseline characteristics, chest CT scan features, exacerbations, and progression to COPD (postbronchodilator FEV₁/FVC, < 0.7) during the follow-up period between 734 participants with postbronchodilator FEV₁/SVC of ≥ 0.7 and 120 with postbronchodilator FEV₁/SVC < 0.7 at the enrollment. We performed multivariate linear and logistic regression models and negative binomial and interval-censored proportion hazards regression models adjusted for demographics and smoking exposure to examine the association of FEV₁/SVC < 0.7 with those characteristics and outcomes.

RESULTS

Participants with FEV₁/SVC < 0.7 were older and had lower FEV₁ and more emphysema than those with FEV₁/SVC ≥ 0.7. In adjusted analysis, individuals with postbronchodilator FEV₁/SVC < 0.7 showed a greater percentage of emphysema by 0.45% (95% CI, 0.09%-0.82%), percentage of gas trapping by 2.52% (95% CI, 0.59%-4.44%), and percentage of functional small airways disease based on parametric response mapping by 2.78% (95% CI, 0.72%-4.83%) at baseline than those with FEV₁/SVC ≥ 0.7. During a median follow-up time of 1,500 days, an FEV₁/SVC < 0.7 was not associated with total exacerbations (incident rate ratio [IRR], 1.61; 95% CI, 0.97-2.64), but was associated with severe exacerbations (IRR, 2.60; 95% CI, 1.04-4.89). An FEV₁/SVC < 0.7 was associated with progression to COPD during a 3-year follow-up even after adjustment for demographics and smoking exposure (hazard ratio, 3.93; 95% CI, 2.71-5.72). We found similar results when we examined the association of prebronchodilator FEV₁/SVC < 0.7 or FEV₁/SVC less than the lower limit of normal with chest CT scan features and progression to COPD.

INTERPRETATION

Low FEV₁ to SVC in current and former smokers with normal spirometry results can identify individuals with CT scan features of COPD who are at risk for severe exacerbations and is associated with progression to COPD in the future.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT01969344T4; URL: www.clinicaltrials.gov.

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia

Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of [Formula: see text] mm and Dice coefficient of [Formula: see text]. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of $0.495 \pm 0.309$ mm and Dice coefficient of $0.985 \pm 0.011$. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

Associations of ω-3 Fatty Acids With Interstitial Lung Disease and Lung Imaging Abnormalities Among Adults

Docosahexaenoic acid (DHA), an ω-3 polyunsaturated fatty acid, attenuates interstitial lung disease (ILD) in experimental models, but human studies are lacking. We examined associations of circulating levels of DHA and other polyunsaturated fatty acids with hospitalization and death due to ILD over 12 years in the Multi-Ethnic Study of Atherosclerosis (MESA; n = 6,573). We examined cross-sectional associations with CT lung abnormalities in MESA (2000-2012; n = 6,541), the Framingham Heart Study (2005-2011; n = 3,917), and the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-Reykjavik) (2002-2006; n = 1,106). Polyunsaturated fatty acid levels were determined from fasting blood samples and extracted from plasma phospholipids (MESA and AGES-Reykjavik) or red blood cell membranes (Framingham Heart Study). Higher DHA levels were associated with a lower risk of hospitalization due to ILD (per standard-deviation increment, adjusted rate ratio = 0.69, 95% confidence interval (CI): 0.48, 0.99) and a lower rate of death due to ILD (per standard-deviation increment, adjusted hazard ratio = 0.68, 95% CI: 0.47, 0.98). Higher DHA was associated with fewer interstitial lung abnormalities on computed tomography (per natural log increment, pooled adjusted odds ratio = 0.65, 95% CI: 0.46, 0.91). Higher DHA levels were associated with a lower risk of hospitalization and death due to ILD and fewer lung abnormalities on computed tomography in a meta-analysis of data from population-based cohort studies.

Regional Lung Perfusion Analysis in Experimental ARDS by Electrical Impedance and Computed Tomography

Electrical impedance tomography is clinically used to trace ventilation related changes in electrical conductivity of lung tissue. Estimating regional pulmonary perfusion using electrical impedance tomography is still a matter of research. To support clinical decision making, reliable bedside information of pulmonary perfusion is needed. We introduce a method to robustly detect pulmonary perfusion based on indicator-enhanced electrical impedance tomography and validate it by dynamic multidetector computed tomography in two experimental models of acute respiratory distress syndrome. The acute injury was induced in a sublobar segment of the right lung by saline lavage or endotoxin instillation in eight anesthetized mechanically ventilated pigs. For electrical impedance tomography measurements, a conductive bolus (10% saline solution) was injected into the right ventricle during breath hold. Electrical impedance tomography perfusion images were reconstructed by linear and normalized Gauss-Newton reconstruction on a finite element mesh with subsequent element-wise signal and feature analysis. An iodinated contrast agent was used to compute pulmonary blood flow via dynamic multidetector computed tomography. Spatial perfusion was estimated based on first-pass indicator dilution for both electrical impedance and multidetector computed tomography and compared by Pearson correlation and Bland-Altman analysis. Strong correlation was found in dorsoventral (r = 0.92) and in right-to-left directions (r = 0.85) with good limits of agreement of 8.74% in eight lung segments. With a robust electrical impedance tomography perfusion estimation method, we found strong agreement between multidetector computed and electrical impedance tomography perfusion in healthy and regionally injured lungs and demonstrated feasibility of electrical impedance tomography perfusion imaging.

A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning

Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.

Polycythemia is Associated with Lower Incidence of Severe COPD Exacerbations in the SPIROMICS Study

Secondary polycythemia has long been recognized as a consequence of chronic pulmonary disease and hypoxemia and is associated with lower mortality and fewer hospitalizations among individuals with chronic obstructive pulmonary disease (COPD)-prescribed long-term oxygen therapy. This study investigates the association of polycythemia with COPD severity, phenotypic features, and respiratory exacerbations in a contemporary and representative sample of individuals with COPD. Current and former smokers with COPD (forced expiratory volume in 1 second [FEV₁] to forced vital capacity [FVC] ratio <70%) without a history of hematologic/oncologic disorders were selected from the SubPopulations and InteRmediate Outcomes Measures In COPD Study (SPIROMICS), a multi-center observational cohort. Participants with polycythemia (hemoglobin ≥15g/dL [females] or ≥17g/dL [males]), were compared to individuals without anemia (hemoglobin ≥12g/dL [females] or ≥13g/dL [males]). Cross-sectional outcomes including percent predicted FEV₁, respiratory symptoms, quality of life, exercise tolerance, and percentage and distribution of emphysema (voxels<-950 Hounsfield units [HU] at total lung capacity) were evaluated using linear or logistic regression. Longitudinal acute exacerbation of COPD (AECOPD) and severe AECOPD (requiring an emergency department visit or hospitalization) were assessed using zero-inflated negative binomial models. Among 1261 participants, 148 (11.7%) had polycythemia. Average follow-up was 4.2±1.7 years and did not differ by presence of polycythemia. In multivariate analysis, compared to participants with normal hemoglobin, polycythemia was associated with a reduced rate of severe AECOPD (adjusted incidence rate ratio 0.57, 95% CI: 0.33-0.98), lower percent predicted FEV₁, lower resting oxygen saturation, increased upper to lower lobe ratio of emphysema, and a greater degree of emphysema, though the latter was attenuated after adjusting for lung function. There were no significant differences in total AECOPD, patient-reported outcomes, or exercise tolerance. These findings suggest that polycythemia, while associated with less favorable physiologic parameters, is not independently associated with symptoms, and is associated with fewer severe exacerbations. Future studies should explore the potentially protective role of increased hemoglobin beyond the correction of anemia.

The Reversion of cg05575921 Methylation in Smoking Cessation: A Potential Tool for Incentivizing Healthy Aging

Smoking is the largest preventable cause of mortality and the largest environmental driver of epigenetic aging. Contingency management-based strategies can be used to treat smoking but require objective methods of verifying quitting status. Prior studies have suggested that cg05575921 methylation reverts as a function of smoking cessation, but that it can be used to verify the success of smoking cessation has not been unequivocally demonstrated. To test whether methylation can be used to verify cessation, we determined monthly cg05575921 levels in a group of 67 self-reported smokers undergoing biochemically monitored contingency management-based smoking cessation therapy, as part of a lung imaging protocol. A total of 20 subjects in this protocol completed three months of cotinine verified smoking cessation. In these 20 quitters, the reversion of cg05575921 methylation was dependent on their initial smoking intensity, with methylation levels in the heaviest smokers reverting to an average of 0.12% per day over the 3-month treatment period. In addition, we found suggestive evidence that some individuals may have embellished their smoking history to gain entry to the study. Given the prominent effect of smoking on longevity, we conclude that DNA methylation may be a useful tool for guiding and incentivizing contingency management-based approaches for smoking cessation.

Relative Regional Air Volume Change Maps at the Acinar Scale Reflect Variable Ventilation in Low Lung Attenuation of COPD patients

OBJECTIVES

The purpose of this study was to investigate regional air volume changes at the acinar scale of the lung in chronic obstructive pulmonary disease (COPD) patients using an image registration technique.

MATERIALS AND METHODS

Thirty-four emphysema patients and 24 subjects with normal chest CT and pulmonary function test (PFT) results were included in this retrospective study for which informed consent was waived by the institutional review board. After lung segmentation, a mass-preserving image registration technique was used to compute relative regional air volume changes (RRAVCs) between inspiration and expiration CT scans. After determining the appropriate thresholds of RRAVCs for low ventilation areas (LVAs), they were displayed and analyzed using color maps on the background inspiration CT image, and compared with the low attenuation area (LAA) map. Correlations between quantitative CT parameters and PFTs were assessed using Pearson's correlation test, and parameters were compared between emphysema and normal-CT patients using the Student's t-test.

RESULTS

LVA percentage with an RRAVC threshold of 0.5 (%LVA_0.5) showed the strongest correlations with FEV₁/FVC (r = -0.566), FEV₁ (r = -0.534), %LAA_-950insp (r = 0.712), and %LAA_-856exp (r = 0.775). %LVA_0.5 was significantly higher (P < 0.001) in COPD patients than normal subjects. Despite the identical appearance of emphysematous lesions on the LAA_-950insp map, the RRAVC map depicted a wide range of ventilation differences between these LAA clusters.

CONCLUSION

RRAVC-based %LVA_0.5 correlated well with FEV₁/FVC, FEV₁, %LAA_-950insp and %LAA_-856exp. RRAVC holds the potential for providing additional acinar scale functional information for emphysematous LAAs in inspiratory CT images, providing the basis for a novel set for emphysematous phenotypes.

Lack of airway submucosal glands impairs respiratory host defenses

Submucosal glands (SMGs) are a prominent structure that lines human cartilaginous airways. Although it has been assumed that SMGs contribute to respiratory defense, that hypothesis has gone without a direct test. Therefore, we studied pigs, which have lungs like humans, and disrupted the gene for ectodysplasin (EDA-KO), which initiates SMG development. EDA-KO pigs lacked SMGs throughout the airways. Their airway surface liquid had a reduced ability to kill bacteria, consistent with SMG production of antimicrobials. In wild-type pigs, SMGs secrete mucus that emerges onto the airway surface as strands. Lack of SMGs and mucus strands disrupted mucociliary transport in EDA-KO pigs. Consequently, EDA-KO pigs failed to eradicate a bacterial challenge in lung regions normally populated by SMGs. These in vivo and ex vivo results indicate that SMGs are required for normal antimicrobial activity and mucociliary transport, two key host defenses that protect the lung.

Quantitative CT-based image registration metrics provide different ventilation and lung motion patterns in prone and supine positions in healthy subjects

Background

Previous studies suggested that the prone position (PP) improves oxygenation and reduces mortality among patients with acute respiratory distress syndrome (ARDS). However, the mechanism of this clinical benefit of PP is not completely understood. The aim of the present study was to quantitatively compare regional characteristics of lung functions in the PP with those in the supine position (SP) using inspiratory and expiratory computed tomography (CT) scans.

Methods

Ninety subjects with normal pulmonary function and inspiration and expiration CT images were included in the study. Thirty-four subjects were scanned in PP, and 56 subjects were scanned in SP. Non-rigid image registration-based inspiratory-expiratory image matching assessment was used for regional lung function analysis. Tissue fractions (TF) were computed based on the CT density and compared on a lobar basis. Three registration-derived functional variables, relative regional air volume change (RRAVC), volumetric expansion ratio (J), and three-dimensional relative regional displacement (s*) were used to evaluate regional ventilation and deformation characteristics.

Results

J was greater in PP than in SP in the right middle lobe (P = 0 .025), and RRAVC was increased in the upper and right middle lobes (P < 0.001). The ratio of the TF on inspiratory and expiratory scans, J, and RRAVC at the upper lobes to those at the middle and lower lobes and that ratio at the upper and middle lobes to those at the lower lobes of were all near unity in PP, and significantly higher than those in SP (0.98–1.06 vs 0.61–0.94, P < 0.001).

Conclusion

We visually and quantitatively observed that PP not only induced more uniform contributions of regional lung ventilation along the ventral-dorsal axis but also minimized the lobar differences of lung functions in comparison with SP. This may help in the clinician’s search for an understanding of the benefits of the application of PP to the patients with ARDS or other gravitationally dependent pathologic lung diseases.

Trial registration

Retrospectively registered.

Lung-Specific Risk Factors Associated With Incident Hip Fracture in Current and Former Smokers

Hip fractures are associated with significant morbidity and mortality in smokers with lung disease, but whether lung-specific factors are associated with fracture risk is unknown. Our goal was to determine whether lung-specific factors associate with incident hip fracture and improve risk discrimination of traditional fracture risk models in smokers. The analysis consisted of a convenience sample of 9187 current and former smokers (58,477 participant follow-up years) participating in the Genetic Epidemiology of chronic obstructive pulmonary disease (COPD) longitudinal observational cohort study. Participants were enrolled between 2008 and 2011 with follow-up data collection through July 2018. Traditional risk factors associated with incident hip fracture (n = 361) included age, female sex, osteoporosis, prevalent spine and hip fracture, rheumatoid arthritis, and diabetes. Lung-specific risk factors included post-bronchodilator percent forced expiratory volume in 1 s (FEV₁ %) predicted (OR, 0.95; 95% CI, 0.92-0.99 for each 10% increase), Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification (OR, 1.09; 95% CI, 1.002-1.19 for each higher stage), presence of CT-determined emphysema (OR, 1.34; 95% CI, 1.06-1.69), symptom scores (OR, 1.10; 95% CI, 1.03-1.19 for each higher unit score), 6-min walk distance (OR, 0.92; 95% CI, 0.90-0.95 for each 30-m increase), body mass index, airflow obstruction, dyspnea, and exercise (BODE) index (OR, 1.07; 95% CI, 1.01-1.13 for each higher unit score), total exacerbations (OR, 1.13; 95% CI, 1.10-1.16 per exacerbation), and annual exacerbations (OR, 1.37; 95% CI, 1.21-1.55 per exacerbation). In multivariable modeling, age, black race, osteoporosis, prevalent hip and spine fracture, rheumatoid arthritis, and diabetes were associated with incident hip fracture. The presence of emphysema, 6-min walk distance, and total number of exacerbations added to traditional models improved risk discrimination (integrated discrimination improvement [IDI] values 0.001 [95% CI, 0.0003-0.002], 0.001 [95% CI, 0.0001-0.002], and 0.008 [95% CI, 0.003-0.013], corresponding to relative IDIs of 12.8%, 6.3%, and 34.6%, respectively). These findings suggest that the incorporation of lung-specific risk factors into fracture risk assessment tools may more accurately predict fracture risk in smokers. © 2020 American Society for Bone and Mineral Research.

Lung function of primary cooks using LPG or biomass and the effect of particulate matter on airway epithelial barrier integrity

BACKGROUND

Cooks exposed to biomass fuel experience increased risk of respiratory disease and mortality. We sought to characterize lung function and environmental exposures of primary cooking women using two fuel-types in southeastern India, as well as to investigate the effect of particulate matter (PM) from kitchens on human airway epithelial (HAE) cells in vitro.

METHODS

We assessed pre- and post-bronchodilator lung function on 25 primary female cooks using wood biomass or liquified petroleum gas (LPG), and quantified exposures from 34 kitchens (PM_2.5, PM < 40 μm, black carbon, endotoxin, and PM metal and bacterial content). We then challenged HAE cells with PM, assessing its cytotoxicity to small-airway cells (A549) and its effect on: transepithelial conductance and macromolecule permeability (NuLi cells), and antimicrobial activity (using airway surface liquid, ASL, from primary HAE cells).

RESULTS

Lung function was impaired in cooks using both fuel-types. 60% of participants in both fuel-types had respiratory restriction (post bronchodilator FEV₁/FVC>90). The remaining 40% in the LPG group had normal spirometry (post FEV₁/FVC = 80-90), while only 10% of participants in the biomass group had normal spirometry, and the remaining biomass cooks (30%) had respiratory obstruction (post FEV₁/FVC<80). Significant differences were found in environmental parameters, with biomass kitchens containing greater PM_2.5, black carbon, zirconium, arsenic, iron, vanadium, and endotoxin concentrations. LPG kitchens tended to have more bacteria (p = 0.14), and LPG kitchen PM had greater sulphur concentrations (p = 0.02). In vitro, PM induced cytotoxicity in HAE A549 cells in a dose-dependent manner, however the effect was minimal and there were no differences between fuel-types. PM from homes of participants with a restrictive physiology increased electrical conductance of NuLi HAE cells (p = 0.06) and decreased macromolar permeability (p ≤ 0.05), while PM from homes of those with respiratory obstruction tended to increase electrical conductance (p = 0.20) and permeability (p = 0.07). PM from homes of participants with normal spirometry did not affect conductance or permeability. PM from all homes tended to inhibit antimicrobial activity of primary HAE cell airway surface liquid (p = 0.06).

CONCLUSIONS

Biomass cooks had airway obstruction, and significantly greater concentrations of kitchen environmental contaminants than LPG kitchens. PM from homes of participants with respiratory restriction and obstruction altered airway cell barrier function, elucidating mechanisms potentially responsible for respiratory phenotypes observed in biomass cooks.