Deepak Subramanian

Assessment of pulmonary neutrophilic inflammation in emphysema by quantitative positron emission tomography

RATIONALE Neutrophilic inflammation is understood to be of pathogenetic importance in chronic obstructive pulmonary disease (COPD) and may be quantified using 18-fluorodeoxyglucose positron emission tomography-computed tomography ((18)FDG PET-CT) as a noninvasive, spatially informative biomarker. OBJECTIVES To assess the potential usefulness of (18)FDG PET-CT as a surrogate measure of pulmonary neutrophilic inflammation in patients with usual COPD and α(1)-antitrypsin deficiency (AATD). METHODS (18)FDG PET-CT imaging was performed in 10 patients with usual COPD, 10 patients with AATD, and 10 healthy control subjects. Pulmonary (18)FDG uptake was estimated by three-dimensional Patlak graphical analysis as an indicator of pulmonary neutrophilic glycolytic activity. Patients with AATD were treated with 12 weekly intravenous infusions of AAT augmentation therapy before repeat imaging. (18)FDG uptake, lung physiology, lung density, and systemic markers of inflammation were compared for all groups at baseline and, in patients with AATD, at baseline and on treatment. MEASUREMENTS AND MAIN RESULTS (18)FDG uptake in the upper lung of patients with usual COPD was greater compared with the healthy control group (P = 0.009) and correlated with measures of disease severity (FEV(1)% predicted, r = -0.848, P = 0.001; FEV(1)/FVC, r = -0.918, P < 0.001; Kco% predicted, r = -0.624, P = 0.027; 15th percentile point, r = -0.709, P = 0.011). No significant difference was observed between measurements at baseline and on treatment in patients with AATD. CONCLUSIONS Quantitative (18)FDG PET-CT has a potential role as an imaging biomarker in mechanistic and interventional studies in patients with usual COPD. The data support previous evidence of distinct functional characteristics of neutrophils in COPD. Clinical trial registered with https://eudract.ema.europa.eu/index.html (EudraCT 2007-004869-18).

Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome

Background Changes in microbial community composition in the lung of patients suffering from moderate to severe COPD have been well documented. However, knowledge about specific microbiome structures in the human lung associated with CT defined abnormalities is limited. Methods Bacterial community composition derived from brush samples from lungs of 16 patients suffering from different CT defined subtypes of COPD and 9 healthy subjects was analyzed using a cultivation independent barcoding approach applying 454-pyrosequencing of 16S rRNA gene fragment amplicons. Results We could show that bacterial community composition in patients with changes in CT (either airway or emphysema type changes, designated as severe subtypes) was different from community composition in lungs of patients without visible changes in CT as well as from healthy subjects (designated as mild COPD subtype and control group) (PC1, Padj = 0.002). Higher abundance of Prevotella in samples from patients with mild COPD subtype and from controls and of Streptococcus in the severe subtype cases mainly contributed to the separation of bacterial communities of subjects. No significant effects of treatment with inhaled glucocorticoids on bacterial community composition were detected within COPD cases with and without abnormalities in CT in PCoA. Co-occurrence analysis suggests the presence of networks of co-occurring bacteria. Four communities of positively correlated bacteria were revealed. The microbial communities can clearly be distinguished by their associations with the CT defined disease phenotype. Conclusion Our findings indicate that CT detectable structural changes in the lung of COPD patients, which we termed severe subtypes, are associated with alterations in bacterial communities, which may induce further changes in the interaction between microbes and host cells. This might result in a changed interplay with the host immune system.

The EvA study: aims and strategy

The EvA study is a European Union-funded project under the Seventh Framework Programme (FP7), which aims at defining new markers for chronic obstructive pulmonary disease (COPD) and its subtypes. The acronym is derived from emphysema versus airway disease, indicating that the project targets these two main phenotypes of the disease. The EvA study is based on the concept that emphysema and airway disease are governed by different pathophysiological processes, are driven by different genes and have differential gene expression in the lung. To define these genes, patients and non-COPD controls are recruited for clinical examination, lung function analysis and computed tomography (CT) of the lung. CT scans are used to define the phenotypes based on lung density and airway wall thickness. This is followed by bronchoscopy in order to obtain samples from the airways and the alveoli. These tissue samples, along with blood samples, are then subjected to genome-wide expression and association analysis and markers linked to the phenotypes are identified. The population of the EvA study is different from other COPD study populations, since patients with current oral glucocorticoids, antibiotics and exacerbations or current smokers are excluded, such that the signals detected in the molecular analysis are due to the distinct inflammatory process of emphysema and airway disease in COPD.

Emphysema- and airway-dominant COPD phenotypes defined by standardised quantitative computed tomography

EvA (Emphysema versus Airway disease) is a multicentre project to study mechanisms and identify biomarkers of emphysema and airway disease in chronic obstructive pulmonary disease (COPD). The objective of this study was to delineate objectively imaging-based emphysema-dominant and airway disease-dominant phenotypes using quantitative computed tomography (QCT) indices, standardised with a novel phantom-based approach. 441 subjects with COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1–3) were assessed in terms of clinical and physiological measurements, laboratory testing and standardised QCT indices of emphysema and airway wall geometry. QCT indices were influenced by scanner non-conformity, but standardisation significantly reduced variability (p<0.001) and led to more robust phenotypes. Four imaging-derived phenotypes were identified, reflecting “emphysema-dominant”, “airway disease-dominant”, “mixed” disease and “mild” disease. The emphysema-dominant group had significantly higher lung volumes, lower gas transfer coefficient, lower oxygen (PO2) and carbon dioxide (PCO2) tensions, higher haemoglobin and higher blood leukocyte numbers than the airway disease-dominant group. The utility of QCT for phenotyping in the setting of an international multicentre study is improved by standardisation. QCT indices of emphysema and airway disease can delineate within a population of patients with COPD, phenotypic groups that have typical clinical features known to be associated with emphysema-dominant and airway-dominant disease. Standardisation of quantitative CT improves delineation of emphysema and airway phenotypes in a multicentre study http://ow.ly/10zjhV

Quantification of lung PET images: challenges and opportunities.

Millions of people are affected by respiratory diseases, leading to a significant health burden globally. Because of the current insufficient knowledge of the underlying mechanisms that lead to the development and progression of respiratory diseases, treatment options remain limited. To overcome this limitation and understand the associated molecular changes, noninvasive imaging techniques such as PET and SPECT have been explored for biomarker development, with 18F-FDG PET imaging being the most studied. The quantification of pulmonary molecular imaging data remains challenging because of variations in tissue, air, blood, and water fractions within the lungs. The proportions of these components further differ depending on the lung disease. Therefore, different quantification approaches have been proposed to address these variabilities. However, no standardized approach has been developed to date. This article reviews the data evaluating 18F-FDG PET quantification approaches in lung diseases, focusing on methods to account for variations in lung components and the interpretation of the derived parameters. The diseases reviewed include acute respiratory distress syndrome, chronic obstructive pulmonary disease, and interstitial lung diseases such as idiopathic pulmonary fibrosis. Based on review of prior literature, ongoing research, and discussions among the authors, suggested considerations are presented to assist with the interpretation of the derived parameters from these approaches and the design of future studies.

Temporal assessment of airway remodeling in severe asthma using quantitative computed tomography.

To the Editor: Heterogeneity in asthma is evident in every aspect of the disease process (1–3). Quantitative computed tomography (QCT) has emerged as a reliable, noninvasive tool for assessment of proximal airway remodeling and air trapping in asthma (4). We have identified three asthma clusters based on QCT indices, using factor and cluster analysis (3). Subjects in clusters 1 and 3, with more severe asthma, had distinct patterns of proximal airway remodeling: cluster 1 showing a dilated right upper lobe apical segmental bronchus (RB1) lumen with wall thickening and cluster 3 had no wall thickening and markedly narrowed lumen. Subjects in cluster 2 had milder asthma, and there was a lack of proximal airway remodeling. It remains elusive whether airway structural changes reflect cause or effect; namely, are they a consequence of asthma and represent different stages of disease progression or the distinct remodeling changes that are fundamental to the pathogenesis of asthma, representing distinct asthma endotypes (5)? Our aim was to assess the temporal pattern of proximal airway remodeling in QCT-derived asthma clusters. Some of the results of this study have been previously reported in the form of an abstract (6). Twenty-two patients with severe asthma of mean (SEM) disease duration 28.6 (4) years, who were in the placebo arm of a previous study (7), were included in the analysis. All 22 patients had undergone two inspiratory thoracic CT scans to image RB1 and further inspiratory and expiratory full thoracic CT scans as part of research studies at our institute (3, 7). All CT scans were performed after administration of long-acting β2-agonist. The mean (range) duration between the first (baseline) and second CT scan was 1.6 (0.9–2.7) years and between the second and third was 2.6 (1.9–3.7) years. QCT-derived asthma clusters were determined on the basis of full thoracic paired inspiratory and expiratory CT scans obtained at time point 3 (3). Only inspiratory scans were used for the current analysis. Informed consent was obtained from all subjects and the studies were approved by the Leicestershire, Northamptonshire, and Rutland Research Ethics Committees. Fully automated software (VIDA Pulmonary Workstation, version 2.0; VIDA Diagnostics, Coralville, IA) was used for quantitative airway morphometry as described previously (3). RB1 wall area (WA)/body surface area (BSA) demonstrated a significant increase over time (mean [SEM]: first CT, 14.3 [0.9]; second CT, 14.7 [0.9]; third CT, 16.5 [1.3] mm2/m2; repeated measure analysis of variance [ANOVA], P = 0.008). No significant change was seen in RB1 lumen area (LA)/BSA (mean [SEM]: first CT, 9.1 [1.0]; second CT, 9.6 [1.0]; third CT, 9.9 [0.9]; repeated measure ANOVA, P = 0.4). There was an increase in RB1 length at the time of the third CT (mean [SEM]: first CT, 11.3 [0.8]; second CT, 11.0 [0.7]; third CT, 13.1 [0.6] mm; repeated measure ANOVA, P < 0.01). The change in RB1 WA/BSA (ΔRB1 WA/BSA = RB1 WA/BSA third CT − RB1 WA/BSA first CT) negatively correlated with change in RB1 length (Pearson correlation, −0.5; P = 0.03). When the subjects with severe asthma were split into previously described QCT-derived clusters (3), the mean (SEM) change in interval normalized RB1 WA/BSA and LA/BSA, respectively, was as follows: cluster 1 (n = 3), 3.6 (0.8) mm2/m2/year, 1.7 (1.1) mm2/m2/year; cluster 2 (n = 9), 1.0 (0.5) mm2/m2/year, −0.02 (0.4) mm2/m2/year; cluster 3 (n = 10), −0.1 (0.3) mm2/m2/year, 0.1 (0.4) mm2/m2/year (Figure 1). A one-way between-groups analysis of covariance (ANCOVA) was performed to compare the differences between clusters (independent variable), of airway mophometry at the time of the second and third CT (dependent variables) after controlling for airway morphometry at the time of first CT (covariate). After adjusting for airway morphometry (first CT), there were significant differences between the three clusters for RB1 WA/BSA (third CT) [F(2, 18) = 21, P < 0.001, partial η2 = 0.70] and for RB1 LA/BSA (third CT) [F(2, 18) = 32, P < 0.001, partial η2 = 0.78]. No significant difference was seen between the three clusters for RB1 WA/BSA (second CT) and RB1 LA/BSA (second CT) (data not shown). A comparison of airway morphometry in healthy control subjects at time point 3 with airway morphometry in severe asthma clusters at time points 1, 2, and 3 is presented in Table 1. Figure 1. Temporal assessment of airway remodeling in asthma clusters. Asthma clusters were determined on the basis of data from the third computed tomography (CT). Retrospective scans were available for temporal assessment of RB1 (right upper lobe apical segmental ... Table 1. RB1 Dimensions of Subjects with Severe Asthma and Healthy Subjects The subjects did not show any significant change in postbronchodilator FEV1% predicted (mean [SEM] change from baseline, −1.8 [2.7]; paired sample t test, P = 0.5), postbronchodilator FEV1/FVC (%) (mean [SEM] change from baseline, −0.7 [1.3]; paired sample t test, P = 0.6), asthma quality of life questionnaire (AQLQ) score (mean [SEM] change from baseline, 0.07 [1.3]; paired sample t test, P = 0.7), and sputum neutrophils (mean [SEM] change from baseline, 5.4 [7.1]; paired sample t test, P = 0.5) at the time of third CT scan compared with baseline. There was a statistically significant increase in the asthma control questionnaire (ACQ) (mean [SEM] change from baseline, 0.4 [0.2]; paired sample t test, P = 0.03). The change in RB1 QCT indices (LA/BSA, WA/BSA, and length) between third and first CT did not show any significant correlation with change in postbronchodilator FEV1% predicted, postbronchodilator FEV1/FVC%, ACQ, and AQLQ. Previous longitudinal studies have demonstrated a significant decrease in proximal airway wall dimensions after use of inhaled corticosteroids (ICS) (8, 9), an ICS/long-acting β2 agonist (LABA) combination (10), and anti-IgE treatment (11). In contrast, Brillet and colleagues found no change in CT-assessed airway dimensions in subjects with poorly controlled asthma treated for 12 weeks with inhaled LABA and ICS despite improvement in physiological measures of airway obstruction and air trapping (12). A follow-up of subjects with asthma on ICS from a previous study (8) for a mean duration of 4.2 years did not show any significant change in airway dimensions, with a reported mean (SEM) change in interval-normalized RB1 WA/BSA of −0.27 (0.59) mm2/m2/year (13). We have previously shown a decrease in RB1 WA/BSA in subjects with severe asthma after 1 year of treatment with anti–IL-5 compared with placebo, with an approximately 10% between-group change (7). In the current analysis subjects with severe asthma demonstrate a small, albeit significant temporal increase in RB1 WA/BSA but no change in RB1 LA/BSA. These varied patterns of airway remodeling exhibited by subjects with asthma may be explained by the heterogeneous nature of the disease, and differences in patient selection and duration of treatment and/or follow-up. A longitudinal study in subjects with severe asthma has demonstrated that in a multivariate regression model baseline %WA was a predictor of subsequent airway remodeling (14). In our analysis after adjusting for the RB1dimensions at time of first CT, significant differences were found in RB1 dimensions between severe asthma QCT-derived clusters at the time of third CT but not at the time of second CT. Patients with severe asthma, when grouped on the basis of QCT-derived clusters, show a differential temporal pattern of airway remodeling, particularly patients in cluster 3, where no significant change in airway wall or lumen dimensions was demonstrated over a period of 2.6 years. This suggests that the mechanism of lumen narrowing in this asthma phenotype may be due to decreased compliance of the airway wall or alteration between intrinsic and extrinsic airway wall properties (15), rather than thickened airway wall encroaching on the lumen. Mathematical modeling studies (16, 17) have also shown that thickening of the adventitia can uncouple the airway smooth muscle (ASM) from the lung’s elastic recoil forces, abating the airway–parenchymal interdependence. QCT based phenotyping could thus help us unravel novel asthma subtypes which may have distinct pathophysiological mechanisms. The inverse correlation between the change in RB1 WA/BSA and RB1 length suggests that despite bronchodilation, ASM shortening resulting in shortening of airway length may contribute to QCT-assessed airway wall thickening. We acknowledge that QCT-derived clusters were determined on the basis of full thoracic paired inspiratory (third CT in the current analysis) and expiratory CT scans as part of a recent study (3) and that temporal CT (first and second CT in the current analysis) data were obtained from retrospective scans. We therefore are unable to assess the stability of CT-derived phenotypes. Moreover, data are lacking in the current literature on the temporal stability of airway morphometry in healthy subjects. Temporal assessment was possible only in a small number of subjects in each cluster, with only three subjects in cluster 1, and therefore further verification of these findings is required by large longitudinal studies. Despite this limitation, temporal analysis may provide useful insight into the natural history of airway remodeling.