Development, Progression, and Mortality of Suspected Interstitial Lung Disease in COPDGene.

RATIONALE: Some with interstitial lung abnormalities (ILA) have suspected interstitial lung disease (ILD), a subgroup with adverse outcomes. Rates of development and progression of suspected ILD and their effect on mortality are unknown. OBJECTIVES: To determine rates of development and progression of suspected ILD and assess effects of individual ILD and progression criteria on mortality. METHODS: Participants from COPDGene were included. ILD was defined as ILA and fibrosis and/or FVC <80% predicted. Prevalent ILD was assessed at enrollment, incident ILD and progression at 5-year follow-up. CT progression was assessed visually and FVC decline as relative change. Multivariable Cox regression tested associations between mortality and ILD groups. RESULTS: Of 9,588 participants at enrollment, 267 (2.8%) had prevalent ILD. Those with prevalent ILD had 52% mortality after median 10.6 years, which was higher than ILA (33%; HR=2.0; p<0.001). The subgroup of prevalent ILD with fibrosis only had worse mortality (59%) than ILA (HR=2.2; p<0.001). 97 participants with prevalent ILD completed 5-year follow-up: 32% had stable CT and relative FVC decline <10%, 6% FVC decline ≥10% only, 39% CT progression only, and 22% both CT progression and FVC decline ≥10%. Mortality rates were 32%, 50%, 45%, and 46% respectively; those with CT progression only had worse mortality than ILA (HR=2.6; p=0.005). At 5-year follow-up, incident ILD occurred in 168/4,843 participants without prevalent ILD and had worse mortality than ILA (HR=2.5; p<0.001). CONCLUSION: Rates of mortality and progression are high among those with suspected ILD in COPDGene; fibrosis and radiologic progression are important predictors of mortality.

Rheumatoid arthritis and changes on spirometry by smoking status in two prospective longitudinal cohorts.

OBJECTIVE: To compare longitudinal changes in spirometric measures between patients with rheumatoid arthritis (RA) and non-RA comparators. METHODS: We analysed longitudinal data from two prospective cohorts: the UK Biobank and COPDGene. Spirometry was conducted at baseline and a second visit after 5-7 years. RA was identified based on self-report and disease-modifying antirheumatic drug use; non-RA comparators reported neither. The primary outcomes were annual changes in the per cent-predicted forced expiratory volume in 1 s (FEV1%) and per cent predicted forced vital capacity (FVC%). Statistical comparisons were performed using multivariable linear regression. The analysis was stratified based on baseline smoking status and the presence of obstructive pattern (FEV1/FVC <0.7). RESULTS: Among participants who underwent baseline and follow-up spirometry, we identified 233 patients with RA and 37 735 non-RA comparators. Among never-smoking participants without an obstructive pattern, RA was significantly associated with more FEV1% decline (ß=-0.49, p=0.04). However, in ever smokers with ≥10 pack-years, those with RA exhibited significantly less FEV1% decline than non-RA comparators (ß=0.50, p=0.02). This difference was more pronounced among those with an obstructive pattern at baseline (ß=1.12, p=0.01). Results were similar for FEV1/FVC decline. No difference was observed in the annual FVC% change in RA versus non-RA. CONCLUSIONS: Smokers with RA, especially those with baseline obstructive spirometric patterns, experienced lower FEV1% and FEV1/FVC decline than non-RA comparators. Conversely, never smokers with RA had more FEV1% decline than non-RA comparators. Future studies should investigate potential treatments and the pathogenesis of obstructive lung diseases in smokers with RA.

Preacinar Arterial Dilation Mediates Outcomes of Quantitative Interstitial Abnormalities in COPDGene.

RATIONALE: Quantitative interstitial abnormalities (QIA) are a computed tomography (CT) measure of early parenchymal lung disease associated with worse clinical outcomes including exercise capacity and symptoms. The presence of pulmonary vasculopathy in QIA and its role in the QIA-outcome relationship is unknown. OBJECTIVES: To quantify radiographic pulmonary vasculopathy in quantitative interstitial abnormalities (QIA) and determine if this vasculopathy mediates the QIA-outcome relationship. METHODS: Ever-smokers with QIA, outcome, and pulmonary vascular mediator data were identified from the COPDGene cohort. CT-based vascular mediators were: right ventricle-to-left ventricle ratio (RV/LV), pulmonary artery-to-aorta ratio (PA/Ao), and pre-acinar intraparenchymal arterial dilation (PA volume 5-20mm2 in cross-sectional area, normalized to total arterial volume). Outcomes were: six-minute walk distance (6MWD) and modified Medical Council Research Council (mMRC) Dyspnea score ≥2. Adjusted causal mediation analyses were used to determine if the pulmonary vasculature mediated the QIA effect on outcomes. Associations of pre-acinar arterial dilation with select plasma biomarkers of pulmonary vascular dysfunction were examined. MAIN RESULTS: Among 8,200 participants, QIA burden correlated positively with vascular damage measures including pre-acinar arterial dilation. Pre-acinar arterial dilation mediated 79.6% of the detrimental impact of QIA on 6MWD (56.2-100%, p<0.001). PA/Ao was a weak mediator and RV/LV was a suppressor. Similar results were observed in the QIA-mMRC relationship. Pre-acinar arterial dilation correlated with increased pulmonary vascular dysfunction biomarker levels including angiopoietin-2 and NT-proBNP. CONCLUSIONS: Parenchymal quantitative interstitial abnormalities (QIA) deleteriously impact outcomes primarily through pulmonary vasculopathy. Pre-acinar arterial dilation may be a novel marker of pulmonary vasculopathy in QIA.

Association of Acute Respiratory Disease Events with Quantitative Interstitial Abnormality Progression at CT in Individuals with a History of Smoking.

Background Acute respiratory disease (ARD) events are often thought to be airway-disease related, but some may be related to quantitative interstitial abnormalities (QIAs), which are subtle parenchymal abnormalities on CT scans associated with morbidity and mortality in individuals with a smoking history. Purpose To determine whether QIA progression at CT is associated with ARD and severe ARD events in individuals with a history of smoking. Materials and Methods This secondary analysis of a prospective study included individuals with a 10 pack-years or greater smoking history recruited from multiple centers between November 2007 and July 2017. QIA progression was assessed between baseline (visit 1) and 5-year follow-up (visit 2) chest CT scans. Episodes of ARD were defined as increased cough or dyspnea lasting 48 hours and requiring antibiotics or corticosteroids, whereas severe ARD episodes were those requiring an emergency room visit or hospitalization. Episodes were recorded via questionnaires completed every 3 to 6 months. Multivariable logistic regression and zero-inflated negative binomial regression models adjusted for comorbidities (eg, emphysema, small airway disease) were used to assess the association between QIA progression and episodes between visits 1 and 2 (intercurrent) and after visit 2 (subsequent). Results A total of 3972 participants (mean age at baseline, 60.7 years ± 8.6 [SD]; 2120 [53.4%] women) were included. Annual percentage QIA progression was associated with increased odds of one or more intercurrent (odds ratio [OR] = 1.29 [95% CI: 1.06, 1.56]; P = .01) and subsequent (OR = 1.26 [95% CI: 1.05, 1.52]; P = .02) severe ARD events. Participants in the highest quartile of QIA progression (≥1.2%) had more frequent intercurrent ARD (incidence rate ratio [IRR] = 1.46 [95% CI: 1.14, 1.86]; P = .003) and severe ARD (IRR = 1.79 [95% CI: 1.18, 2.73]; P = .006) events than those in the lowest quartile (≤-1.7%). Conclusion QIA progression was independently associated with higher odds of severe ARD events during and after radiographic progression, with higher frequency of intercurrent severe events in those with faster progression. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Little in this issue.

Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study.

INTRODUCTION/RATIONALE: Protein biomarkers may help enable the prediction of incident interstitial features on chest CT. METHODS: We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p<0.001), and which of those were associated with interstitial features longitudinally (multivariable mixed effects model FDR p<0.05). To predict incident interstitial features, we trained four random forest classifiers in a two-thirds random subset of COPDGene: (1) imaging and demographic information, (2) univariate screen biomarkers, (3) multivariable confirmation biomarkers and (4) multivariable confirmation biomarkers available in a separate testing cohort (Pittsburgh Lung Screening Study (PLuSS)). We evaluated classifier performance in the remaining one-third of COPDGene, and, for the final model, also in PLuSS. RESULTS: In COPDGene, 1305 biomarkers were available and 20 differed between those with and without interstitial features at baseline. Of these, 11 were associated with feature progression over a mean of 5.5 years of follow-up, and of these 4 were available in PLuSS, (angiopoietin-2, matrix metalloproteinase 7, macrophage inflammatory protein 1 alpha) over a mean of 8.8 years of follow-up. The area under the curve (AUC) of classifiers using demographics and imaging features in COPDGene and PLuSS were 0.69 and 0.59, respectively. In COPDGene, the AUC of the univariate screen classifier was 0.78 and of the multivariable confirmation classifier was 0.76. The AUC of the final classifier in COPDGene was 0.75 and in PLuSS was 0.76. The outcome for all of the models was the development of incident interstitial features. CONCLUSIONS: Multiple novel and previously identified proteomic biomarkers are associated with interstitial features on chest CT and may enable the prediction of incident interstitial diseases such as idiopathic pulmonary fibrosis.

Cluster Analysis of World Trade Center Related Lower Airway Diseases.

ABSTRACT: Introduction: Cluster analysis can classify without a priori assumptions the heterogeneous chronic lower airway diseases found in former workers at the World Trade Center (WTC) disaster site. Methods: We selected the first available chest computed tomography scan with quantitative computed tomography measurements on 311 former WTC workers with complete clinical, and spirometric data from their closest surveillance visit. We performed a nonhierarchical iterative algorithm K-prototype cluster analysis, using gap measure. Results: A five-cluster solution was most satisfactory. Cluster 5 had the healthiest individuals. In cluster 4, smoking was most prevalent and intense but there was scant evidence of respiratory disease. Cluster 3 had symptomatic subjects with reduced forced vital capacity impairment (low FVC). Clusters 1 and 2 had less dyspneic subjects, but more functional and quantitative computed tomography evidence of chronic obstructive pulmonary disease (COPD) in cluster 1, or low FVC in cluster 2. Clusters 1 and 4 had the highest proportion of rapid first-second forced expiratory volume decliners. Conclusions: Cluster analysis confirms low FVC and COPD/pre-COPD as distinctive chronic lower airway disease phenotypes on long-term surveillance of the WTC workers.

Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction.

Background CT attenuation is affected by lung volume, dosage, and scanner bias, leading to inaccurate emphysema progression measurements in multicenter studies. Purpose To develop and validate a method that simultaneously corrects volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT in a longitudinal multicenter study. Materials and Methods In this secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, lung function data were obtained from participants who completed baseline and 5-year follow-up visits from January 2008 to August 2017. CT emphysema progression was measured with volume-adjusted lung density (VALD) and compared with the joint volume-noise-bias-adjusted lung density (VNB-ALD). Reproducibility was studied under change of dosage protocol and scanner model with repeated acquisitions. Emphysema progression was visually scored in 102 randomly selected participants. A stratified analysis of clinical characteristics was performed that considered groups based on their combined lung density change measured by VALD and VNB-ALD. Results A total of 4954 COPDGene participants (mean age, 60 years ± 9 [SD]; 2511 male, 2443 female) were analyzed (1329 with repeated reduced-dose acquisition in the follow-up visit). Mean repeatability coefficients were 30 g/L ± 0.46 for VALD and 14 g/L ± 0.34 for VNB-ALD. VALD measurements showed no evidence of differences between nonprogressors and progressors (mean, -5.5 g/L ± 9.5 vs -8.6 g/L ± 9.6; P = .11), while VNB-ALD agreed with visual readings and showed a difference (mean, -0.67 g/L ± 4.8 vs -4.2 g/L ± 5.5; P < .001). Analysis of progression showed that VNB-ALD progressors had a greater decline in forced expiratory volume in 1 second (-42 mL per year vs -32 mL per year; Tukey-adjusted P = .002). Conclusion Simultaneously correcting volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT improved repeatability analyses and agreed with visual readings. It distinguished between progressors and nonprogressors and was associated with a greater decline in lung function metrics. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Goo in this issue.

Dysanapsis is differentially related to lung function trajectories with distinct structural and functional patterns in COPD and variable risk for adverse outcomes.

Background: Abnormal lung function trajectories are associated with increased risk of chronic obstructive pulmonary disease (COPD) and premature mortality; several risk factors for following these trajectories have been identified. Airway under-sizing dysanapsis (small airway lumens relative to lung size), is associated with an increased risk for COPD. The relationship between dysanapsis and lung function trajectories at risk for adverse outcomes of COPD is largely unexplored. We test the hypothesis that dysanapsis differentially affects distinct lung function trajectories associated with adverse outcomes of COPD. Methods: To identify lung function trajectories, we applied Bayesian trajectory analysis to longitudinal FEV1 and FVC Z-scores in the COPDGene Study, an ongoing longitudinal study that collected baseline data from 2007 to 2012. To ensure clinical relevance, we selected trajectories based on risk stratification for all-cause mortality and prospective exacerbations of COPD (ECOPD). Dysanapsis was measured in baseline COPDGene CT scans as the airway lumen-to-lung volume (a/l) ratio. We compared a/l ratios between trajectories and evaluated their association with trajectory assignment, controlling for previously identified risk factors. We also assigned COPDGene participants for whom only baseline data is available to their most likely trajectory and repeated our analysis to further evaluate the relationship between trajectory assignment and a/l ratio measures. Findings: We identified seven trajectories: supranormal, reference, and five trajectories at increased risk for mortality and exacerbations. Three at-risk trajectories are characterized by varying degrees of concomitant FEV1 and FVC impairments and exhibit airway predominant COPD patterns as assessed by quantitative CT imaging. These trajectories have lower a/l ratio values and increased risk for mortality and ECOPD compared to the reference trajectory. Two at-risk trajectories are characterized by disparate levels of FEV1 and FVC impairment and exhibit mixed airway and emphysema COPD patterns on quantitative CT imaging. These trajectories have markedly lower a/l ratio values compared to both the reference trajectory and airway-predominant trajectories and are at greater risk for mortality and ECOPD compared to the airway-predominant trajectories. These findings were observed among the participants with baseline-only data as well. Interpretation: The degree of dysanapsis appears to portend patterns of progression leading to COPD. Assignment of individuals-including those without spirometric obstruction-to distinct trajectories is possible in a clinical setting and may influence management strategies. Strategies that combine CT-assessed dysanapsis together with spirometric measures of lung function and smoke exposure assessment are likely to further improve trajectory assignment accuracy, thereby improving early detection of those most at risk for adverse outcomes. Funding: United States National Institute of Health, COPD Foundation, and Brigham and Women's Hospital.

Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study.

Rationale: Quantitative interstitial abnormalities (QIAs) are early measures of lung injury automatically detected on chest computed tomography scans. QIAs are associated with impaired respiratory health and share features with advanced lung diseases, but their biological underpinnings are not well understood. Objectives: To identify novel protein biomarkers of QIAs using high-throughput plasma proteomic panels within two multicenter cohorts. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (COPDGene [Genetic Epidemiology of Chronic Obstructive Pulmonary Disease]) and 2,925 participants in a younger population cohort (CARDIA [Coronary Artery Disease Risk in Young Adults]) using the SomaLogic SomaScan assays. We measured QIAs using a local density histogram method. We assessed the associations between proteomic biomarker concentrations and QIAs using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg false discovery rate-corrected P ⩽ 0.05). Measurements and Main Results: In total, 852 proteins were significantly associated with QIAs in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including biological processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; cellular components in extracellular regions; and molecular functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIAs in an older, smoking population with a higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.

QUANTITATIVE BIOMARKERS REPRODUCIBILITY USING GENERATIVE ADVERSARIAL APPROACHES IN REDUCED TO CONVENTIONAL DOSE CT.

In recent years, several techniques for image-to-image translation by means of generative adversarial neural networks (GAN) have been proposed to learn mapping characteristics between a source and a target domain. In particular, in the medical imaging field conditional GAN frameworks with paired samples (cGAN) and unconditional cycle-consistent GANs with unpaired data (CycleGAN) have been demonstrated as a powerful scheme to model non-linear mappings that produce realistic target images from different modality sources. When proposing the usage and adaptation of these frameworks for medical image synthesis, quantitative and qualitative validation are usually performed by assessing the similarity between synthetic and target images in terms of metrics such as mean absolute error (MAE) or structural similarity (SSIM) index. However, an evaluation of clinically relevant markers showing that diagnostic information is not overlooked in the translation process is often missing. In this work, we aim at demonstrating the importance of validating medical image-to-image translation techniques by assessing their effect on the measurement of clinically relevant metrics and biomarkers. We implemented both a conditional and an unconditional approach to synthesize conventional dose chest CT scans from reduced dose CT and show that while both visually and in terms of traditional metrics the network appears to successfully minimize perceptual discrepancies, these methods are not reliable to systematically reproduce quantitative measurements of various chest biomarkers.