Different Predictors of Right and Left Ventricular Metabolism in Healthy Middle-Aged Men

Dysfunction of the right ventricle (RV) plays a crucial role in the outcome of various cardiovascular diseases. Previous studies on RV metabolism are sparse although evidence implies it may differ from left ventricular (LV) metabolism. Therefore, the aims of this study were (1) to determine predictors of RV glucose uptake (GU) and free fatty acid uptake (FFAU) and (2) to compare them to predictors of LV metabolism in healthy middle-aged men. Altogether 28 healthy, sedentary, middle-aged (40-55 years) men were studied. Insulin-stimulated GU and fasting FFAU were measured by positron emission tomography and RV and LV structural and functional parameters by cardiac magnetic resonance. Several parameters related to whole-body health were also measured. Predictors of RV and LV metabolism were determined by pairwise correlation analysis, lasso regression models, and variable clustering using heatmap. RVGU was most strongly predicted by age and moderately by RV ejection fraction (EF). The strongest determinants of RVFFAU were exercise capacity (peak oxygen uptake), resting heart rate, LVEF, and whole body insulin stimulated glucose uptake rate. When considering LV metabolism, age and RVEF were associated also with LVGU. In addition, LVGU was strongly, and negatively, influenced by whole-body insulin-stimulated glucose uptake rate. LVFFAU was predicted only by LVEF. This study shows that while RV and LV metabolism have shared characteristics, they also have unique properties. Age of the subject should be taken into account when measuring myocardial glucose utilization. Ejection fraction is related to myocardial metabolism, and even so that RVEF may be more closely related to GU of both ventricles and LVEF to FFAU of both ventricles, a finding supporting the ventricular interdependence. However, only RV fatty acid utilization associates with exercise capacity so that better physical fitness in a relatively sedentary population is related with decreased RV fat metabolism. To conclude, this study highlights the need for further study designed specifically on less known RV as the results on LV metabolism and physiology may not be directly applicable to the RV.</p

Association of maximal stress ergometry performance with troponin T and abdominal aortic calcification score in advanced chronic kidney disease

Background: Cardiac biomarkers Troponin T (TnT) and N-terminal pro-B-type natriuretic peptide (proBNP) and abdominal aortic calcification score (AAC) are associated with cardiovascular events and mortality in patients with chronic kidney disease (CKD). The effects of cardiac biomarkers and AAC on maximal exercise capacity in CKD are unknown and were studied.Methods: One hundred seventy-four CKD 4-5 patients not on maintenance dialysis underwent maximal bicycle ergometry stress testing, lateral lumbar radiograph to study AAC, echocardiography and biochemical assessments.Results: The subjects with proportional maximal ergometry workload (WMAX%) less than 50% of the expected values had higher TnT, proBNP, AAC, left ventricular end-diastolic diameter, left ventricular mass index, E/e' and pulse pressure, and lower global longitudinal strain compared to the better performing patients. TnT (beta = - 0.09, p = 0.02), AAC (beta = - 1.67, p Conclusions: TnT and AAC are independently associated with maximal ergometry stress test workload in patients with advanced CKD.</p

Activity recognition in smart homes with self verification of assignments

Activity recognition in smart homes provides valuable benefits in the field of health and elderly care by remote monitoring of patients. In health care, capabilities of both performing the correct recognition and reducing the wrong assignments are of high importance. The novelty of the proposed activity recognition approach lies in being able to assign a category to the incoming activity, while measuring the confidence score of the assigned category that reduces the false positives in the assignments. Multiple sensors deployed at different locations of a smart home are used for activity observations. For multi-class activity classification, we propose a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments. We obtain the confidence score of each assignment by estimating the activity distribution within each class such that the assignments with low confidence are separated for further investigation by a human operator. The proposed approach is evaluated using a comprehensive performance evaluation metrics. Experimental results obtained from nine publicly available smart home datasets demonstrate a better performance of the proposed approach compared to the state of the art

Quantification in cardiac MRI: advances in image acquisition and processing

Cardiac magnetic resonance (CMR) imaging enables accurate and reproducible quantification of measurements of global and regional ventricular function, blood flow, perfusion at rest and stress as well as myocardial injury. Recent advances in MR hardware and software have resulted in significant improvements in image quality and a reduction in imaging time. Methods for automated and robust assessment of the parameters of cardiac function, blood flow and morphology are being developed. This article reviews the recent advances in image acquisition and quantitative image analysis in CMR

Monitoring Nocturnal Heart Rate with Bed Sensor

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems"