Air Pollution Exposure and Interstitial Lung Features in SPIROMICS Participants with COPD.

RATIONALE: It is unknown whether air pollution is associated with radiographic features of interstitial lung disease in individuals with chronic obstructive pulmonary disease (COPD). OBJECTIVES: To determine whether air pollution increases prevalence of interstitial lung abnormalities (ILA) or percent high-attenuation area (HAA) on computed tomography (CT) in individuals with a heavy smoking history and COPD. METHODS: We performed a cross-sectional study of SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), focused on current or former smokers with COPD. 10-year exposure to particulate matter < 2.5 µm (PM2.5), nitrogen oxides (NOx), nitrogen dioxide (NO2), and ozone (O3) prior to enrollment CTs (completed between 2010-2015) were estimated with validated spatiotemporal models at residential addresses. We applied adjusted multivariable modified Poisson regression and linear regression to investigate associations between pollution exposure and relative risk of ILA or increased percent HAA (between -600 and -250 Hounsfield units) respectively. We assessed for effect modification by MUC5B-promoter polymorphism (GT/TT vs GG at rs3705950), smoking status, sex, and percent emphysema. RESULTS: Among 1272 participants with COPD assessed for HAA, 424 were current smokers, 249 were carriers of the variant MUC5B allele (GT/TT). 519 participants were assessed for ILA. We found no association between pollution exposure and ILA or HAA. Associations between pollutant exposures and risk of ILA were modified by the presence of MUC5B polymorphism (p-value interaction term for NOx = 0.04 and PM2.5 = 0.05) and smoking status (p-value interaction term for NOx = 0.05, NO2 = 0.01, and O3 = 0.05). With higher exposure to NOx and PM2.5, MUC5B variant carriers had increased risk of ILA (Relative Risk [RR] per 26ppb NOx 2.41; 95% Confidence Interval [CI] 0.97 to 6.0) and RR per 4 µg·m-3 PM2.5 1.43; 95% CI 0.93 to 2.2). With higher exposure to NO2, former smokers had increased risk of ILA (RR per 10ppb 1.64; 95% CI 1.0 to 2.7). CONCLUSIONS: Exposure to ambient air pollution was not associated with interstitial features on CT in this population of heavy smokers with COPD. MUC5B modified the association between pollution and ILA, suggesting that gene-environment interactions may influence prevalence of interstitial lung features in COPD.

Vessel and Airway Characteristics in One-Year CT-defined Rapid Emphysema Progression: SPIROMICS.

Rationale: Rates of emphysema progression vary in chronic obstructive pulmonary disease (COPD), and the relationship with vascular and airway pathophysiology remain unclear. Objective: We sought to determine if indices of peripheral (segmental and beyond) pulmonary arterial (PA) dilation measured via computed tomography (CT) are associated with a 1-year index of emphysema (EI: %voxels<-950HU) progression. Methods: 599 GOLD 0-3 former and never-smokers were evaluated from the SubPopulations and InterMediate Outcome Measures in COPD Study (SPIROMICS) cohort: rapid-emphysema-progressors (RP, n=188; 1-year ΔEI>1%), non-progressors (NP, n=301; 1-year ΔEI±0.5%) and never-smokers (NS: N=110). Segmental PA cross-sectional areas were standardized to associated airway luminal areas (Segmental : Pulmonary Artery-to-Airway Ratio: PAARseg). Full inspiratory CT scan-derived total (arteries + veins) pulmonary vascular volume (TPVV) was compared to vessel volume with radius smaller than 0.75mm (SVV.75/TPVV). Airway-to-lung ratios (an index of dysanapsis and COPD risk) were compared to TPVV-lung-volume-ratios. Results: Compared with NP, RP exhibited significantly larger PAARseg (0.73±0.29 vs. 0.67±0.23; p=0.001), lower TPVV-to-lung-volume ratio (3.21%±0.42% vs. 3.48%±0.38%; p=5.0 x 10-12), lower airway-to-lung-volume ratio (0.031±0.003 vs. 0.034±0.004; p=6.1 x 10-13) and larger SVV.75/TPVV (37.91%±4.26% vs. 35.53±4.89; p=1.9 x 10-7). In adjusted analyses, a 1-SD increment in PAARseg was associated with a 98.4% higher rate of severe exacerbations (95%CI: 29 to 206%; p = 0.002) and 79.3% higher in odds of being in the rapid emphysema progression group (95%CI: 24% to 157%; p = 0.001). At year-2 followup, the CT-defined RP group demonstrated a significant decline in post-bronchodilator-FEV1% predicted. Conclusion: Rapid one-year progression of emphysema was associated with indices indicative of higher peripheral pulmonary vascular resistance and a possible role played by pulmonary vascular-airway dysanapsis.

Impact of Bronchiectasis on COPD Severity and Alpha-1 Antitrypsin Deficiency as a Risk Factor in Individuals with a Heavy Smoking History.

Rationale: Bronchiectasis is common among those with heavy smoking histories, but risk factors for bronchiectasis, including alpha-1 antitrypsin deficiency, and its implications for COPD severity are uncharacterized in such individuals. Objectives: To characterize the impact of bronchiectasis on COPD and explore alpha-1antitrypsin as a risk factor for bronchiectasis. Methods: SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) participants (N=914; ages 40-80 years; ≥20-pack-year smoking) had high-resolution computed tomography (CT) scans interpreted visually for bronchiectasis, based on airway dilation without fibrosis or cicatrization. We performed regression-based models of bronchiectasis with clinical outcomes and quantitative CT measures. We deeply sequenced the gene encoding -alpha-1 antitrypsin, SERPINA1, in 835 participants to test for rare variants, focusing on the PiZ genotype (Glu366Lys, rs28929474). Measurements and Main Results: We identified bronchiectasis in 365 (40%) participants, more frequently in women (45% versus 36%, p=0.0045), older participants (mean age=66[standard deviation (SD)=8.3] versus 64[SD=9.1] years, p=0.0083), and those with lower lung function (forced expiratory volume in 1 second [FEV1 ] percentage predicted=66%[SD=27] versus 77%[SD=25], p<0.0001; FEV1 to forced vital capacity [FVC] ratio=0.54[0.17] versus 0.63[SD=0.16], p<0.0001). Participants with bronchiectasis had greater emphysema (%voxels ≤-950 Hounsfield units, 11%[SD=12] versus 6.3%[SD=9], p<0.0001) and parametric response mapping functional small airways disease (26[SD=15] versus 19[SD=15], p<0.0001). Bronchiectasis was more frequent in the combined PiZZ and PiMZ genotype groups compared to those without PiZ, PiS, or other rare pathogenic variants (N=21 of 40 [52%] versus N=283 of 707[40%], odds ratio [OR]=1.97; 95% confidence interval [CI]=1.002, 3.90, p=0.049), an association attributed to White individuals (OR=1.98; 95%CI = 0.9956, 3.9; p=0.051). Conclusions: Bronchiectasis was common in those with heavy smoking histories and was associated with detrimental clinical and radiographic outcomes. Our findings support alpha-1antitrypsin guideline recommendations to screen for alpha-1 antitrypsin deficiency in an appropriate bronchiectasis subgroup with a significant smoking history.

Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study.

BACKGROUND: Quantitative CT is becoming increasingly common for the characterisation of lung disease; however, its added potential as a clinical tool for predicting severe exacerbations remains understudied. We aimed to develop and validate quantitative CT-based models for predicting severe chronic obstructive pulmonary disease (COPD) exacerbations. METHODS: We analysed the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS) cohort, a multicentre study done at 12 clinical sites across the USA, of individuals aged 40-80 years from four strata: individuals who never smoked, individuals who smoked but had normal spirometry, individuals who smoked and had mild to moderate COPD, and individuals who smoked and had severe COPD. We used 3-year follow-up data to develop logistic regression classifiers for predicting severe exacerbations. Predictors included age, sex, race, BMI, pulmonary function, exacerbation history, smoking status, respiratory quality of life, and CT-based measures of density gradient texture and airway structure. We externally validated our models in a subset from the Genetic Epidemiology of COPD (COPDGene) cohort. Discriminative model performance was assessed using the area under the receiver operating characteristic curve (AUC), which was also compared with other predictors, including exacerbation history and the BMI, airflow obstruction, dyspnoea, and exercise capacity (BODE) index. We evaluated model calibration using calibration plots and Brier scores. FINDINGS: Participants in SPIROMICS were enrolled between Nov 12, 2010, and July 31, 2015. Participants in COPDGene were enrolled between Jan 10, 2008, and April 15, 2011. We included 1956 participants from the SPIROMICS cohort who had complete 3-year follow-up data: the mean age of the cohort was 63·1 years (SD 9·2) and 1017 (52%) were men and 939 (48%) were women. Among the 1956 participants, 434 (22%) had a history of at least one severe exacerbation. For the CT-based models, the AUC was 0·854 (95% CI 0·852-0·855) for at least one severe exacerbation within 3 years and 0·931 (0·930-0·933) for consistent exacerbations (defined as ≥1 acute episode in each of the 3 years). Models were well calibrated with low Brier scores (0·121 for at least one severe exacerbation; 0·039 for consistent exacerbations). For the prediction of at least one severe event during 3-year follow-up, AUCs were significantly higher with CT biomarkers (0·854 [0·852-0·855]) than exacerbation history (0·823 [0·822-0·825]) and BODE index 0·812 [0·811-0·814]). 6965 participants were included in the external validation cohort, with a mean age of 60·5 years (SD 8·9). In this cohort, AUC for at least one severe exacerbation was 0·768 (0·767-0·769; Brier score 0·088). INTERPRETATION: CT-based prediction models can be used for identification of patients with COPD who are at high risk of severe exacerbations. The newly identified CT biomarkers could potentially enable investigation into underlying disease mechanisms responsible for exacerbations. FUNDING: National Institutes of Health and the National Heart, Lung, and Blood Institute.

Tezepelumab and Mucus Plugs in Patients with Moderate-to-Severe Asthma.

Tezepelumab and Mucus Plugs in Patients with AsthmaMucus plugs in airways of asthma patients are associated with airway obstruction and the activity of inflammatory cytokines. This article reports prespecified and post hoc analyses of the effect of tezepelumab treatment on mucus plugs identified by computed tomography imaging in patients with moderate-to-severe asthma. At baseline, mucus plug scores correlated positively with levels of inflammatory biomarkers and negatively with lung function measures. Patients treated with tezepelumab had resolution of more mucus plugs than patients taking placebo.

Quantitative CT Characteristics of Cluster Phenotypes in the Severe Asthma Research Program Cohorts.

Background Clustering key clinical characteristics of participants in the Severe Asthma Research Program (SARP), a large, multicenter prospective observational study of patients with asthma and healthy controls, has led to the identification of novel asthma phenotypes. Purpose To determine whether quantitative CT (qCT) could help distinguish between clinical asthma phenotypes. Materials and Methods A retrospective cross-sectional analysis was conducted with the use of qCT images (maximal bronchodilation at total lung capacity [TLC], or inspiration, and functional residual capacity [FRC], or expiration) from the cluster phenotypes of SARP participants (cluster 1: minimal disease; cluster 2: mild, reversible; cluster 3: obese asthma; cluster 4: severe, reversible; cluster 5: severe, irreversible) enrolled between September 2001 and December 2015. Airway morphometry was performed along standard paths (RB1, RB4, RB10, LB1, and LB10). Corresponding voxels from TLC and FRC images were mapped with use of deformable image registration to characterize disease probability maps (DPMs) of functional small airway disease (fSAD), voxel-level volume changes (Jacobian), and isotropy (anisotropic deformation index [ADI]). The association between cluster assignment and qCT measures was evaluated using linear mixed models. Results A total of 455 participants were evaluated with cluster assignments and CT (mean age ± SD, 42.1 years ± 14.7; 270 women). Airway morphometry had limited ability to help discern between clusters. DPM fSAD was highest in cluster 5 (cluster 1 in SARP III: 19.0% ± 20.6; cluster 2: 18.9% ± 13.3; cluster 3: 24.9% ± 13.1; cluster 4: 24.1% ± 8.4; cluster 5: 38.8% ± 14.4; P < .001). Lower whole-lung Jacobian and ADI values were associated with greater cluster severity. Compared to cluster 1, cluster 5 lung expansion was 31% smaller (Jacobian in SARP III cohort: 2.31 ± 0.6 vs 1.61 ± 0.3, respectively, P < .001) and 34% more isotropic (ADI in SARP III cohort: 0.40 ± 0.1 vs 0.61 ± 0.2, P < .001). Within-lung Jacobian and ADI SDs decreased as severity worsened (Jacobian SD in SARP III cohort: 0.90 ± 0.4 for cluster 1; 0.79 ± 0.3 for cluster 2; 0.62 ± 0.2 for cluster 3; 0.63 ± 0.2 for cluster 4; and 0.41 ± 0.2 for cluster 5; P < .001). Conclusion Quantitative CT assessments of the degree and intraindividual regional variability of lung expansion distinguished between well-established clinical phenotypes among participants with asthma from the Severe Asthma Research Program study. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.

Mucus Plugs in Asthma at CT Associated with Regional Ventilation Defects at 3He MRI.

Background Airway mucus plugs in asthma are associated with exacerbation frequency, increased eosinophilia, and reduced lung function. The relationship between mucus plugs and spatially overlapping ventilation abnormalities observed at hyperpolarized gas MRI has not been assessed quantitatively. Purpose To assess regional associations between CT mucus plugs scored by individual bronchopulmonary segment and corresponding measurements of segmental ventilation defect percentage (VDP) at hyperpolarized helium 3 (3He) MRI. Materials and Methods In this secondary analysis of a Health Insurance Portability and Accountability Act-compliant prospective observational cohort, participants in the Severe Asthma Research Program (SARP) III (NCT01760915) between December 2012 and August 2015 underwent hyperpolarized 3He MRI to determine segmental VDP. Segmental mucus plugs at CT were scored by two readers, with segments scored as plugged only if both readers agreed independently. A linear mixed-effects model controlling for interpatient variability was then used to assess differences in VDP in plugged versus plug-free segments. Results Forty-four participants with asthma were assessed (mean age ± standard deviation, 47 years ± 15; 29 women): 19 with mild-to-moderate asthma and 25 with severe asthma. Mucus plugs were observed in 49 total bronchopulmonary segments across eight of 44 patients. Segments containing mucus plugs had a median segmental VDP of 25.9% (25th-75th percentile, 7.3%-38.3%) versus 1.4% (25th-75th percentile, 0.1%-5.2%; P < .001) in plug-free segments. Similarly, the model estimated a segmental VDP of 18.9% (95% CI: 15.7, 22.2) for mucus-plugged segments versus 5.1% (95% CI: 3.3, 7.0) for plug-free segments (P < .001). Participants with one or more mucus plugs had a median whole-lung VDP of 11.1% (25th-75th percentile, 7.1%-18.9%) versus 3.1% (25th-75th percentile, 1.1%-4.4%) in those without plugs (P < .001). Conclusion Airway mucus plugging at CT was associated with reduced ventilation in the same bronchopulmonary segment at hyperpolarized helium 3 MRI, suggesting that mucus plugging may be an important cause of ventilation defects in asthma. © RSNA, 2021 Online supplemental material is available for this article.

Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: The SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS).

PURPOSE: Quantitative computed tomography (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data. PATIENTS AND METHODS: We selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers. RESULTS: COPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%). Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity and airway narrowing over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering. CONCLUSION: qCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COPD.

BACKGROUND: Soluble receptor for advanced glycation end products (sRAGE) is a proposed emphysema and airflow obstruction biomarker; however, previous publications have shown inconsistent associations and only one study has investigate the association between sRAGE and emphysema. No cohorts have examined the association between sRAGE and progressive decline of lung function. There have also been no evaluation of assay compatibility, receiver operating characteristics, and little examination of the effect of genetic variability in non-white population. This manuscript addresses these deficiencies and introduces novel data from Pittsburgh COPD SCCOR and as well as novel work on airflow obstruction. A meta-analysis is used to quantify sRAGE associations with clinical phenotypes. METHODS: sRAGE was measured in four independent longitudinal cohorts on different analytic assays: COPDGene (n = 1443); SPIROMICS (n = 1623); ECLIPSE (n = 2349); Pittsburgh COPD SCCOR (n = 399). We constructed adjusted linear mixed models to determine associations of sRAGE with baseline and follow up forced expiratory volume at one second (FEV1) and emphysema by quantitative high-resolution CT lung density at the 15th percentile (adjusted for total lung capacity). RESULTS: Lower plasma or serum sRAGE values were associated with a COPD diagnosis (P < 0.001), reduced FEV1 (P < 0.001), and emphysema severity (P < 0.001). In an inverse-variance weighted meta-analysis, one SD lower log10-transformed sRAGE was associated with 105 ± 22 mL lower FEV1 and 4.14 ± 0.55 g/L lower adjusted lung density. After adjusting for covariates, lower sRAGE at baseline was associated with greater FEV1 decline and emphysema progression only in the ECLIPSE cohort. Non-Hispanic white subjects carrying the rs2070600 minor allele (A) and non-Hispanic African Americans carrying the rs2071288 minor allele (A) had lower sRAGE measurements compare to those with the major allele, but their emphysema-sRAGE regression slopes were similar. CONCLUSIONS: Lower blood sRAGE is associated with more severe airflow obstruction and emphysema, but associations with progression are inconsistent in the cohorts analyzed. In these cohorts, genotype influenced sRAGE measurements and strengthened variance modelling. Thus, genotype should be included in sRAGE evaluations.

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-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.

Genome-Wide Association Study of Susceptibility to Idiopathic Pulmonary Fibrosis.

Rationale: Idiopathic pulmonary fibrosis (IPF) is a complex lung disease characterized by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. Genome-wide association studies have reported signals of association implicating multiple pathways including host defense, telomere maintenance, signaling, and cell-cell adhesion.Objectives: To improve our understanding of factors that increase IPF susceptibility by identifying previously unreported genetic associations.Methods: We conducted genome-wide analyses across three independent studies and meta-analyzed these results to generate the largest genome-wide association study of IPF to date (2,668 IPF cases and 8,591 controls). We performed replication in two independent studies (1,456 IPF cases and 11,874 controls) and functional analyses (including statistical fine-mapping, investigations into gene expression, and testing for enrichment of IPF susceptibility signals in regulatory regions) to determine putatively causal genes. Polygenic risk scores were used to assess the collective effect of variants not reported as associated with IPF.Measurements and Main Results: We identified and replicated three new genome-wide significant (P < 5 × 10-8) signals of association with IPF susceptibility (associated with altered gene expression of KIF15, MAD1L1, and DEPTOR) and confirmed associations at 11 previously reported loci. Polygenic risk score analyses showed that the combined effect of many thousands of as yet unreported IPF susceptibility variants contribute to IPF susceptibility.Conclusions: The observation that decreased DEPTOR expression associates with increased susceptibility to IPF supports recent studies demonstrating the importance of mTOR signaling in lung fibrosis. New signals of association implicating KIF15 and MAD1L1 suggest a possible role of mitotic spindle-assembly genes in IPF susceptibility.

Adipose tissue quantification and primary graft dysfunction after lung transplantation: The Lung Transplant Body Composition study.

BACKGROUND: Obesity is associated with an increased risk of primary graft dysfunction (PGD) after lung transplantation. The contribution of specific adipose tissue depots is unknown. METHODS: We performed a prospective cohort study of adult lung transplant recipients at 4 U.S. transplant centers. We measured cross-sectional areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) on chest and abdominal computed tomography (CT) scans and indexed each measurement to height.2 We used logistic regression to examine the associations of adipose indices and adipose classes with grade 3 PGD at 48 or 72 hours, and Cox proportional hazards models to examine survival. We used latent class analyses to identify the patterns of adipose distribution. We examined the associations of adipose indices with plasma biomarkers of obesity and PGD. RESULTS: A total of 262 and 117 subjects had available chest CT scans and underwent protocol abdominal CT scans, respectively. In the adjusted models, a greater abdominal SAT index was associated with an increased risk of PGD (odds ratio 1.9, 95% CI 1.02-3.4, p = 0.04) but not with survival time. VAT indices were not associated with PGD risk or survival time. A greater abdominal SAT index correlated with greater pre- and post-transplant leptin (r = 0.61, p < 0.001, and r = 0.44, p < 0.001), pre-transplant IL-1RA (r = 0.25, p = 0.04), and post-transplant ICAM-1 (r = 0.25, p = 0.04). We identified 3 latent patterns of adiposity. The class defined by high thoracic and abdominal SAT had the greatest risk of PGD. CONCLUSIONS: Subcutaneous, but not visceral, adiposity is associated with an increased risk of PGD after lung transplantation.

Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS).

BACKGROUND: Quantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping. METHODS: An imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration. RESULTS: We derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema. CONCLUSIONS: QCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.

Overlap of Genetic Risk between Interstitial Lung Abnormalities and Idiopathic Pulmonary Fibrosis.

Rationale: Interstitial lung abnormalities (ILAs) are associated with the highest genetic risk locus for idiopathic pulmonary fibrosis (IPF); however, the extent to which there are unique associations among individuals with ILAs or additional overlap with IPF is not known.Objectives: To perform a genome-wide association study (GWAS) of ILAs.Methods: ILAs and a subpleural-predominant subtype were assessed on chest computed tomography (CT) scans in the AGES (Age Gene/Environment Susceptibility), COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease [COPD]), Framingham Heart, ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points), MESA (Multi-Ethnic Study of Atherosclerosis), and SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) studies. We performed a GWAS of ILAs in each cohort and combined the results using a meta-analysis. We assessed for overlapping associations in independent GWASs of IPF.Measurements and Main Results: Genome-wide genotyping data were available for 1,699 individuals with ILAs and 10,274 control subjects. The MUC5B (mucin 5B) promoter variant rs35705950 was significantly associated with both ILAs (P = 2.6 × 10-27) and subpleural ILAs (P = 1.6 × 10-29). We discovered novel genome-wide associations near IPO11 (rs6886640, P = 3.8 × 10-8) and FCF1P3 (rs73199442, P = 4.8 × 10-8) with ILAs, and near HTRE1 (rs7744971, P = 4.2 × 10-8) with subpleural-predominant ILAs. These novel associations were not associated with IPF. Among 12 previously reported IPF GWAS loci, five (DPP9, DSP, FAM13A, IVD, and MUC5B) were significantly associated (P < 0.05/12) with ILAs.Conclusions: In a GWAS of ILAs in six studies, we confirmed the association with a MUC5B promoter variant and found strong evidence for an effect of previously described IPF loci; however, novel ILA associations were not associated with IPF. These findings highlight common genetically driven biologic pathways between ILAs and IPF, and also suggest distinct ones.

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.

PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. METHODS: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). RESULTS: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures. CONCLUSIONS: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.