Validation of cardiovascular magnetic resonance assessment of pericardial adipose tissue volume

Abstract

Abstract Background Pericardial adipose tissue (PAT) has been shown to be an independent predictor of coronary artery disease. To date its assessment has been restricted to the use of surrogate echocardiographic indices such as measurement of epicardial fat thickness over the right ventricular free wall, which have limitations. Cardiovascular magnetic resonance (CMR) offers the potential to non-invasively assess total PAT, however like other imaging modalities, CMR has not yet been validated for this purpose. Thus, we sought to describe a novel technique for assessing total PAT with validation in an ovine model. Methods 11 merino sheep were studied. A standard clinical series of ventricular short axis CMR images (1.5T Siemens Sonata) were obtained during mechanical ventilation breath-holds. Beginning at the mitral annulus, consecutive end-diastolic ventricular images were used to determine the area and volume of epicardial, paracardial and pericardial adipose tissue. In addition adipose thickness was measured at the right ventricular free wall. Following euthanasia, the paracardial adipose tissue was removed from the ventricle and weighed to allow comparison with corresponding CMR measurements. Results There was a strong correlation between CMR-derived paracardial adipose tissue volume and ex vivo paracardial mass (R2 = 0.89, p ex vivo paracardial mass (R2 = 0.003, p = 0.878). Conclusion In this ovine model, CMR-derived paracardial adipose tissue volume, but not the corresponding and conventional measure of paracardial adipose thickness over the RV free wall, accurately reflected paracardial adipose tissue mass. This study validates for the first time, the use of clinically utilised CMR sequences for the accurate and reproducible assessment of pericardial adiposity. Furthermore this non-invasive modality does not use ionising radiation and therefore is ideally suited for future studies of PAT and its role in cardiovascular risk prediction and disease in clinical practice.