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      Epicardial fat from echocardiography: a new method for visceral adipose tissue prediction.

      Obesity research

      Viscera, Adipose Tissue, Regression Analysis, ultrasonography, Pericardium, Middle Aged, Male, Magnetic Resonance Imaging, Linear Models, Humans, Female, Echocardiography, Body Constitution, Body Composition, Aged, Adult

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          To validate transthoracic echocardiography as an easy and reliable imaging method for visceral adipose tissue (VAT) prediction. VAT is recognized as an important indicator of high cardiovascular and metabolic risk. Several methods are applied to estimate VAT, with different results. We selected 60 healthy subjects (29 women, 31 men, 49.5 +/- 16.2 years) with a wide range of body mass indexes. Each subject underwent transthoracic echocardiogram and magnetic resonance imaging (MRI) to measure epicardial fat thickness on the right ventricle. Measurements of epicardial adipose tissue thickness were obtained from the same echocardiographic and MRI views and points. MRI was also used to measure VAT cross-sectional areas at the level of L4 to L5. Anthropometric indexes were also measured. Subjects with predominant visceral fat accumulation showed higher epicardial adipose tissue thickness than subjects with predominant peripheral fat distribution: 9.97 +/- 2.88 vs. 4.34 +/- 1.98 (p = 0.005) and 7.19 +/- 2.74 vs. 3.43 +/- 1.64 (p = 0.004) in men and women, respectively. Simple linear regression analysis showed an excellent correlation between epicardial adipose tissue and waist circumference (r = 0.895, p = 0.01) and MRI abdominal VAT (r = 0.864, p = 0.01). Multiple regression analysis showed that epicardial adipose tissue thickness (r(2) = 0.442, p = 0.02) was the strongest independent variable correlated to MRI VAT. Bland test confirmed the good agreement between the two methods. Epicardial adipose tissue showed a strong correlation with anthropometric and imaging measurements of VAT. Hence, transthoracic echocardiography could be an easy and reliable imaging method for VAT prediction.

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