Quantification and standardization of epicardial adipose tissue in diagnostic of coronary artery disease

Introduction. Cardiovascular diseases are the main cause of death worldwide. Coronary artery disease (CAD) is responsible for approximately 7.4 million deaths per annum across the globe. The search for novel cardiovascular risk factors, which significantly contribute to CAD, continues to this day. Cardiovascular diseases are associated with excessive adiposity. Visceral adipose tissue surrounding the heart muscle, which is directly connected to coronary arteries, is named epicardial adipose tissue (EAT). EAT has emerged as a new modifiable cardiometabolic risk factor besides abdominal depots of visceral adipose tissue. Increase in the size of EAT is associated with increased calcium score in coronary arteries, CAD, subclinical atherosclerosis, metabolic syndrome, cardiac arrhythmias, lipotoxic cardiomyopathy. Excessive EAT produces large quantities of different proinflammatory cytokines and vasoactive peptides. All those substances independently facilitate the production of atheromatous plaques in coronary vessels and thus, the progress of CAD. The size of EAT depots may be measured using different radiological techniques: echocardiography, computed tomography and cardiac magnetic resonance (CMR). Nevertheless, recent literature has identified CMR as the golden standard in evaluation of the size of EAT. EAT depots are estimated by measuring EAT thickness on different sites and volume. Because there is no guideline-advocated technique for EAT quantification, individual studies are subject to authors discretion and experience. There is a lack of research in terms of the comparison between different quantification methods of EAT in prediction of CAD. It is not clear whether EAT thickness measured at a single point would be comparable with the volumetric assessment of epicardial fat. Furthermore, the representation of small study populations in published studies is doubtful to determine reference values of the EAT size in prediction of CAD. Fewer studies are available on the topic of EAT indexation. The effect of anthropometric EAT variability is assumed to play an important role in identifying individuals with increased cardiovascular risk. Besides different non-indexed EAT depots measurements, some authors derived EAT amounts, indexed to body surface area (BSA) or to body mass index (BMI). Goals of research. The aim of this study was to compare predictive powers of various EAT measurement techniques in identification of patients with CAD. Moreover, this study aimed to answer whether indexed EAT measurements bring advantage in comparison to non-indexed counterparts in prediction of CAD. Materials and methods. This retrospective case-control study involved 760 adult individuals who received CMR scan in the past due to different medical conditions. Exclusion criteria included absence of necessary CMR sequences, pericardial effusion, presence of coronary bypass or stent, absence of medical records, repeated CMR scan. 630 study subjects entered the final analysis. Height and weight of patients were recorded from the CMR safety screening questionnaire. These variables were used to calculate BMI and BSA. EAT volume was calculated on CMR images by using modified Simpson rule with integration over the image slices. Altogether, there were 11 EAT thickness measurements conducted: three measurements over the right ventricular free wall (RVFW), three over the left ventricular free wall (LVFW), single measurement in the superior interventricular groove (SIVG), inferior interventricular groove (IIVG), anterior interventricular groove (AIVG), right atrioventricular groove (RAVG) and left atrioventricular groove (LAVG). In the final analysis, separate EAT thickness measurements as well as mean values of EAT thickness were used as follows: overall mean thickness from measurements in 11 locations, mean thickness of groove measurements (5 locations), mean thickness of measurements over both ventricular free walls (6 locations), mean thickness of measurements over the RVFW (3 locations) and mean thickness of measurements over the LVFW (3 locations). Finally, interventricular septum length (ISL) was manually measured using a distance measurement tool on CMR images. All CAD patients received coronary angiography either in our or in an external institution. Overall, patients with the angiographic evidence of ≥1 stenosis of ≥50% in diameter in a major coronary artery were assigned to CAD group. CAD group additionally included patients who suffered from the acute myocardial infarction as a result of the atherothrombotic CAD. The severity of CAD was assessed with two grading scales – Canadian Cardiovascular Society grading of angina pectoris and Gensini Score. A binary logistic regression analysis was conducted for the identification of possible CAD predictors. All EAT thickness measurements and EAT volume were indexed by BMI, BSA and ISL. Receiver operating characteristic curve analysis was performed in order to identify the most suitable EAT measurement, as well as reference values for identification of patients with CAD. Area under the receiver operating characteristic curve (AUC) was assessed to determine the accuracy of indexed and non-indexed EAT measurements to predict CAD. Finally, sensitivity and specificity were calculated for cut-off EAT measurements with the highest AUC value. Analysis of variance (ANOVA) and Pearson correlation coefficient were applied to evaluate the connection between the most feasible EAT measurements in regard to the CAD severity. Results. EAT proved to independently predict CAD along with other established CAD risk factors – higher age, male gender and obesity. Non-indexed EAT measurements showed outstanding performance in identification of CAD patients. Indexation of EAT measurements did not provide a significant benefit in identification of CAD patients. Non-indexed EAT volume demonstrated the best efficiency in classification of subjects with CAD. CAD was correctly identified in subjects with the non-indexed EAT volume above 93.62 cm3 with sensitivity of 84% and specificity of 80%. Non-indexed average all location EAT thickness above 7 mm correctly characterized CAD patients with sensitivity of 79% and specificity of 77%. Although with a very good ability to identify CAD patients, EAT measurements failed to show any potential to determine the severity of CAD. Discussion. Non-indexed EAT thickness as well as non-indexed and BSA-indexed EAT volume were numerous times confirmed to be independent predictors of CAD. The association between EAT measurements and the severity of CAD remained unclear because of discrepant results in published literature. A possible reason could be the opposing effect of accompanying heart failure with the consecutive gradual reduction of epicardial fat depots. Only occasional studies compared the predictive power of EAT measurements in regard to CAD, the comparison always involved not more than 3 measurement techniques. For the majority, pericoronary EAT thickness predicted CAD better than EAT thickness measurements over the RVFW or LVFW. Since none of the published studies compared the prediction power between EAT volume and thickness measurements in terms of a significant coronary stenosis, our work is the first one to report these results. Our study is the first one to extensively compare all most commonly reported ways of quantifying epicardial fat depots in regard to CAD. Our research can be defined as the largest single center CMR study about the association between EAT and CAD. Moreover, our study is the first one to index EAT thickness measurements. For the first time we indexed the size of epicardial fat depots with anthropometric heart measurements such as ISL. Finally, our study is the first one to examine the association between EAT measurements and the severity of CAD, measured with the Canadian Cardiovascular Society grading of angina pectoris. Conclusions. Non-indexed volumetric quantification of epicardial fat depots proved to be the most suitable EAT measurement in prediction of the presence of CAD, but not of its severity. Standardization of EAT measurements did not provide a significant benefit in identification of CAD patients.

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