Epicardial adipose tissue is associated with cardiorespiratory fitness and hemodynamics among Japanese individuals of various ages and of both sexes

Epicardial adipose tissue may affect hemodynamics and cardiorespiratory fitness as it is a metabolically active visceral adipose tissue and a source of inflammatory bioactive substances that can substantially modulate cardiovascular morphology and function. However, the associations between epicardial adipose tissue and hemodynamics and cardiorespiratory fitness remain unclear. This cross-sectional study aimed to examine the association between epicardial adipose tissue volume and hemodynamics, and cardiorespiratory fitness among Japanese individuals of various ages and of both sexes. Epicardial adipose tissue volume was measured in 120 participants (age, 21–85 years) by cardiac magnetic resonance imaging. To evaluate cardiorespiratory fitness, peak oxygen uptake was measured by cardiopulmonary exercise testing. Peak cardiac output and arteriovenous oxygen difference were calculated by impedance cardiography. The epicardial adipose tissue volume was significantly increased in middle-aged and older women. The epicardial adipose tissue volume was significantly and negatively correlated to peak cardiac output and peak oxygen uptake, regardless of age and sex; furthermore, epicardial adipose tissue showed a strong negative correlation with peak heart rate. Epicardial adipose tissue and peak cardiac output were significantly associated (β = -0.359, 95% confidence interval, -0.119 to -0.049, p < 0.001), even after multivariate adjustment (R2 = 0.778). However, in the multiple regression analysis with peak oxygen uptake as a dependent variable, the epicardial adipose tissue volume was not an independent predictor. These data suggest that increased epicardial adipose tissue volume may be correlated with decreased peak oxygen uptake, which might have mediated the abnormal hemodynamics among Japanese people of various ages and of both sexes. Interventions targeting epicardial adipose tissue could potentially improve hemodynamics and cardiorespiratory fitness.

Deep Learning-Based Computed Tomography Images for Quantitative Measurement of the Correlation between Epicardial Adipose Tissue Volume and Coronary Heart Disease

The epicardial adipose tissue volume (EATV) was quantitatively measured by deep learning-based computed tomography (CT) images, and its correlation with coronary heart disease (CHD) was investigated in this study. 150 patients who underwent coronary artery CT examination in hospital were taken as research objects. Besides, patients from the observation group (group A) suffered from vascular stenosis, while patients from the control group (group B) had no vascular stenosis. The deep convolutional neural network model was applied to construct deep learning algorithm, and deep learning-based CT images were adopted to quantitatively measure EATV. The results showed that the sensitivity, specificity, accuracy, and area under the curve (AUC) of the deep learning algorithm were 0.8512, 0.9899, 0.9623, and 0.9813, respectively. By comparison, the correlation results of the traditional George algorithm, Aslani algorithm, and Lahiri algorithm were all lower than those of the deep learning algorithm, and the difference was statistically substantial ( P < 0.05 ). The epicardial adipose tissue volume of the observation group (114.23 ± 55.46) was higher markedly than the volume of the control group (92.65 ± 43.28), with a statistically huge difference ( P < 0.05 ). The r values of EATV with plaque properties and the number of stenosed coronary vessels were 0.232 and 0.268 in turn, both showing significant positive correlation. In conclusion, the sensitivity and other index values of deep learning algorithm were improved greatly compared with traditional algorithm. CT images based on deep learning algorithm achieved good blood vessel segmentation effects. In addition, EATV was closely related to the development of CHD.