Emerging metabolomic tools have created the opportunity to establish metabolic signatures of myocardial injury. We applied a mass spectrometry–based metabolite profiling platform to 36 patients undergoing alcohol septal ablation treatment for hypertrophic obstructive cardiomyopathy, a human model of planned myocardial infarction (PMI). Serial blood samples were obtained before and at various intervals after PMI, with patients undergoing elective diagnostic coronary angiography and patients with spontaneous myocardial infarction (SMI) serving as negative and positive controls, respectively. We identified changes in circulating levels of metabolites participating in pyrimidine metabolism, the tricarboxylic acid cycle and its upstream contributors, and the pentose phosphate pathway. Alterations in levels of multiple metabolites were detected as early as 10 minutes after PMI in an initial derivation group and were validated in a second, independent group of PMI patients. A PMI-derived metabolic signature consisting of aconitic acid, hypoxanthine, trimethylamine N-oxide, and threonine differentiated patients with SMI from those undergoing diagnostic coronary angiography with high accuracy, and coronary sinus sampling distinguished cardiac-derived from peripheral metabolic changes. Our results identify a role for metabolic profiling in the early detection of myocardial injury and suggest that similar approaches may be used for detection or prediction of other disease states.

Background— Despite successful surgical correction, morbidity of patients with coarctation of the aorta is increased. It is well known that these patients have impaired elastic properties of the prestenotic arteries. To find out whether these abnormalities are primarily present or develop later, we studied 17 newborns before and early after surgical repair. Methods and Results— Aortic wall stiffness index and distensibility were calculated using ascending and abdominal aortic diameters determined by M-mode echocardiography and noninvasive estimation of aortic pulse pressure in the right arm and leg. Seventeen patients with aortic coarctation (mean age, 20±26 days) were compared with 17 normal neonates (mean age, 13±7 days) preoperatively and postoperatively (10±6 days after surgery). Ascending aortic distensibility in patients was significantly reduced preoperatively (79±58 versus 105±36; P =0.03) and postoperatively (65±24 versus 105±36; P <0.005). Preoperative and postoperative ascending aortic stiffness index was higher in patients (preoperative, 5.2±4.4 versus 2.7±0.9; P =0.04; postoperative, 4.0±1.6 versus 2.7±0.9; P <0.005). Elastic properties of the descending aorta did not differ preoperatively or postoperatively compared with those in normal subjects. Conclusions— Elastic properties of the prestenotic aorta of patients with coarctation seem to be impaired primarily, even in neonates, and remain unchanged early after successful operation. Surgical correction does not resolve inborn pathology of the prestenotic aortic vascular bed.

Motivation: Prostate cancer is the most prevalent tumor in males and its incidence is expected to increase as the population ages. Prostate cancer is treatable by excision if detected at an early enough stage. The challenges of early diagnosis require the discovery of novel biomarkers and tools for prostate cancer management. Results: We developed a novel feature selection algorithm termed as associative voting (AV) for identifying biomarker candidates in prostate cancer data measured via targeted metabolite profiling MS/MS analysis. We benchmarked our algorithm against two standard entropy-based and correlation-based feature selection methods [Information Gain (IG) and ReliefF (RF)] and observed that, on a variety of classification tasks in prostate cancer diagnosis, our algorithm identified subsets of biomarker candidates that are both smaller and show higher discriminatory power than the subsets identified by IG and RF. A literature study confirms that the highest ranked biomarker candidates identified by AV have independently been identified as important factors in prostate cancer development. Availability: The algorithm can be downloaded from the following http://biomed.umit.at/page.cfm?pageid=516 Contact:melanie.osl@umit.at

Motivation: Alcoholic fatty liver disease (AFLD) and non-AFLD (NAFLD) can progress to severe liver diseases such as steatohepatitis, cirrhosis and cancer. Thus, the detection of early liver disease is essential; however, minimal invasive diagnostic methods in clinical hepatology still lack specificity. Results: Ion molecule reaction mass spectrometry (IMR-MS) was applied to a total of 126 human breath gas samples comprising 91 cases (AFLD, NAFLD and cirrhosis) and 35 healthy controls. A new feature selection modality termed Stacked Feature Ranking (SFR) was developed to identify potential liver disease marker candidates in breath gas samples, relying on the combination of different entropy- and correlation-based feature ranking methods including statistical hypothesis testing using a two-level architecture with a suggestion and a decision layer. We benchmarked SFR against four single feature selection methods, a wrapper and a recently described ensemble method, indicating a significantly higher discriminatory ability of up to 10–15% for the SFR selected gas compounds expressed by the area under the ROC curve (AUC) of 0.85–0.95. Using this approach, we were able to identify unexpected breath gas marker candidates in liver disease of high predictive value. A literature study further supports top-ranked markers to be associated with liver disease. We propose SFR as a powerful tool for biomarker search in breath gas and other biological samples using mass spectrometry. Availability: The algorithm SFR and IMR-MS datasets are available under http://biomed.umit.at/page.cfm?pageid=526 Contact:michalel.netzer@umit.at; christian.baumgartner@umit.at