Obesity is becoming more and more prevalent especially in Western industrial nations. The understanding of adipose tissue as an endocrine organ as well as the detection of adipocytokines – hormones that are secreted from the adipose tissue – gave reason to examine the interactions between adipose tissue and target organs. These efforts have been intensified especially in the context of bariatric surgery as promising weight loss therapy. Interactions between the lung and adipose tissue have rarely been investigated and are not well understood. There are obvious mechanical effects of obesity on lung function explaining the associations between obesity and lung diseases, in particular obesity hypoventilation syndrome, obstructive sleep apnea syndrome, asthma, and chronic obstructive pulmonary disease. The rise in the prevalence of obesity affects the epidemiology of pulmonary diseases as well. The aim of this review is to summarize the current knowledge on interactions, associations, and consequences of obesity and weight loss on lung function and lung diseases. Based on these data, areas for future research are identified.
Background Progranulin represents an adipokine putatively mediating insulin resistance and inflammation. Data in humans are sparse, and the source of circulating progranulin in obesity is unknown. Objectives Serum progranulin concentrations and subcutaneous (sc) as well as visceral (vis) adipose tissue (AT) progranulin expression were quantified in a large cohort of patients with obesity undergoing bariatric surgery (BS) (n = 153) or a low‐calorie diet (LCD) (n = 121). Cohorts and methods Paired serum and AT mRNA samples were obtained from patients with severe obesity undergoing BS (ROBS cohort; Research in Obesity and Bariatric Surgery). Serum progranulin was measured by ELISA in both cohorts, and AT mRNA expression was analysed by quantitative real‐time PCR in bariatric patients. Results There was no gender‐specific effect in serum progranulin or AT progranulin expression. Importantly, circulating progranulin was independent from adipose tissue gene expression in paired samples. sc AT progranulin expression was higher than in vis AT (P = 0.027), and there was a positive correlation between sc AT and vis AT gene expression (P < 0.001; r = +0.34). Serum progranulin strongly and rapidly increased after BS within 3 days and remained elevated up to 12 months. Serum progranulin was strongly correlated with serum CTRP‐3 levels. Conclusions The present study provides detailed progranulin gene expression data in sc and vis AT in a large, prospective and observational cohort of patients with severe obesity. Serum progranulin concentrations are not predicted by sc or vis AT progranulin gene expression. Thus, AT seems not to be the main source of circulating progranulin levels in obesity.
There are similarities and differences between chronic obstructive pulmonary disease (COPD) and asthma patients in terms of computed tomography (CT) disease-related features. Our objective was to determine the optimal subset of CT imaging features for differentiating COPD and asthma using machine learning.COPD and asthma patients were recruited from Heidelberg University Hospital. CT was acquired and 93 features were extracted (VIDA Diagnostics): percentage of low-attenuating-areas below −950HU (LAA950), LAA950 hole count, estimated airway-wall-thickness for a 10 mm internal perimeter airway (Pi10), total-airway-count (TAC), as well as inner/outer perimeter/areas and wall thickness for each of five segmental airways, and the average of those five airways. Hybrid feature selection was used to select the optimum number of features, and support vector machine was used to classify COPD and asthma.Ninety-five participants were included (n=48 COPD; n=47 asthma); there were no differences between COPD and asthma for age (p=0.25) or FEV1 (p=0.31). In a model including all CT features, the accuracy and F1-score was 80% and 81%, respectively. The top features were: LAA950, LAA950 hole count, average outer and inner airway perimeter, outer and inner airway area RB1, and TAC. In the model with only airway features, the accuracy and F1-score were 66% and 68%, respectively. The top features were: inner area RB1, wall thickness RB1, outer area LB1, TAC LB10, average outer/inner perimeter, Pi10, and TAC.In conclusions, COPD and asthma can be differentiated using machine learning with moderate-high accuracy by a subset of only 7 CT features.
Endoscopic lung volume reduction procedure with valves is a well-studied treatment option for advanced lung emphysema to target lung hyperinflation in carefully selected patients with COPD. Before valve implantation, collateral ventilation (CV) of the target lobe needs to be assessed to obtain an optimal treatment effect. The analysis of CV according to current standards occurs via an in vivo assessment with the Chartis®system (PulmonX Inc., Redwood City, CA, USA) and a computed tomography (CT) scan of the thorax with interlobar fissure analysis. The focus of this review is to provide detailed information about the Chartis®procedure and interpretation of Chartis® phenotypes. As a main tool in the assessment of CV and being a safe procedure, the Chartis® assessment should be performed by default to confirm interlobar fissure analysis in most emphysema patients. Based on the obtained results, lung volume reduction therapy options should be discussed in an interdisciplinary emphysema conference.
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