Zusammenfassung !Zielsetzung: Ziel der Studie war es zu untersuchen, ob die automatisierte Quantifizierung des perfundierten Lungenblutvolumens (PBV) in der dual-energy CT Pulmonalis-Angiografie (DE-CTPA) zur Beurteilung des Schweregrades einer chronisch-thromboembolischen pulmonalen Hypertonie (CTEPH) genutzt werden kann. Methoden: Die automatisierte Quantifizierung des PBV wurde bei 25 konsekutiven Patienten mit CTEPH durchgeführt, die mittels DE-CTPA untersucht wurden. Die PBV-Werte wurden mit Parametern aus Rechtsherzkatheteruntersuchung (pulmonalarterieller Druck, Herzindex und pulmonalem Gefäßwiderstand) sowie der 6-Minuten-Gehstrecke korreliert. Hierbei wurde der Korrelationskoeffizient nach Pearson verwendet und mittels multivariater linearer Regression für Alter und Geschlecht adjustiert. Ergebnisse: Die aus der DE-CTPA ermittelten PBVWerte korrelierten negativ mit dem systolischen (r = -0,64, p = 0,001) und mittleren (r = -0,57, p = 0,004) pumonalarteriellen Blutdruck. Es zeigte sich ein Trend zu einer negativen Korrelation zwischen PBV-Werten und dem pulmonalen Gefäßwiderstand (r = -0,20, p = 0,35). Zwischen PBV und Herzindex sowie 6-Minuten-Gehstrecke wurden keine signifikanten Korrelationen gefunden. Durch multivariate lineare Regression wurde bestätigt, dass diese Korrelationen von Alter und Geschlecht unabhänig waren. Schlussfolgerung: Die DE-CTPA kann bei Patienten mit CTEPH für eine automatisierte Quantifizierung des perfundierten Lungenblutvolumens genutzt werden. Deren Ergebnisse korrelieren invers mit systolischem und mittlerem pulmonalarteriellen Druck und können daher den Schweregrad der pulmonalen Hypertonie bei diesen Patienten abschätzen. Abstract !Objectives: The aim of the study was to determine whether automated quantification of pulmonary perfused blood volume (PBV) in dual-energy computed tomography pulmonary angiography (DE-CTPA) can be used to assess the severity of chronic thromboembolic pulmonary hypertension (CTEPH). Methods: Automated quantification of PBV was performed in 25 consecutive CTEPH patients undergoing DE-CTPA. PBV values were correlated with cardiac index and pulmonary vascular resistance quantified by right heart catheterization and walking distance in the 6-minute walk test using Pearson's correlation coefficient and multivariate linear regression analysis to control for age and gender. Results: DE-CTPA derived PBV values inversely correlated with systolic (r = -0.64, p = 0.001) and mean (r = -0.57, p = 0.004) pulmonary arterial pressure. There was a trend for PBV values to inversely correlate with pulmonary vascular resistance (r = -0.20, p = 0.35). No significant correlation was found between PBV values and cardiac index or 6-minute walking distance. These correlations were confirmed to be independent of age and gender on multivariate linear regression analysis. Conclusion: DE-CTPA can be used for an automated quantification of pulmonary PBV in chronic thromboembolic pulmonary hypertension. PBV values correlate inversely with systolic and mean pulmonary arteria...

Purpose Differences in resting energy expenditure (REE) between men and women mainly result from sex-related differences in lean body mass (LBM). So far, a little is known about whether REE and LBM are reflected by a distinct human metabolite profile. Therefore, we aimed to identify plasma and urine metabolite patterns that are associated with REE and LBM of healthy subjects. Methods We investigated 301 healthy male and female subjects (18-80 years) under standardized conditions in the crosssectional KarMeN (Karlsruhe Metabolomics and Nutrition) study. REE was determined by indirect calorimetry and LBM by dual X-ray absorptiometry. Fasting blood and 24 h urine samples were analyzed by targeted and non-targeted metabolomics methods using GC × GC-MS, GC-MS, LC-MS, and NMR. Data were evaluated by predictive modeling of combined data using different machine learning algorithms, namely SVM, glmnet, and PLS. Results When evaluating data of men and women combined, we were able to predict REE and LBM with high accuracy (> 90%). This, however, was a clear effect of sex, which is supported by the high degree of overlap in identified important metabolites for LBM, REE, and sex, respectively. The applied machine learning algorithms did not reveal a metabolite pattern predictive of REE or LBM, when analyzing data for men and women, separately. Conclusions We could not identify a sex independent predictive metabolite pattern for REE or LBM. REE and LBM have no impact on plasma and urine metabolite profiles in the KarMeN Study participants. Studies applying metabolomics in healthy humans need to consider sex specific data evaluation.

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