Relationship between epicardial adipose tissue, coronary artery disease and adiponectin in a Mexican population.

BACKGROUND: The amount of epicardial adipose tissue (EAT) around the heart has been identified as an independent predictor of coronary artery disease (CAD), potentially through local release of inflammatory cytokines. Ethnic differences have been observed, but no studies have investigated this relationship in the Mexican population. The objective of the present study was to evaluate whether a relationship exist between EAT thickness assessed via echocardiography with CAD and adiponectin levels in a Mexican population. METHODS: We studied 153 consecutive patients who underwent coronary angiography and transthoracic echocardiography (TTE). EAT thickness on the free wall of the right ventricle was measured at the end of systole from parasternal long and short axis views of three consecutive cardiac cycles. Coronary angiograms were analyzed for the presence, extent and severity of CAD. Serum adiponectin, lipids, glucose, C-reactive protein and fibrinogen were determined. RESULTS: EAT thickness was greater in patients with CAD than in those without CAD from both parasternal long (5.39 ± 1.75 mm vs 4.00 ± 1.67 mm p<0.0001) and short-axis views (5.23 ± 1.67 vs 4.12 ± 1.77, p=0.001). EAT thickness measured from parasternal long and short-axis showed a statistically significant positive correlation with age (r=0.354, p<0.001; r=0.286, p<0.001 respectively), and waist circumference (r=0.189, p=0.019; r=0.217, p=0.007 respectively). A significant negative correlation between EAT thickness from the parasternal long axis with cholesterol-HDL was observed (r=-0.163, p=0.045). No significant correlation was found between epicardial fat thickness and serum adiponectin or with the severity of CAD. CONCLUSIONS: EAT thickness was greater in patients with CAD. However, no correlation was observed with the severity of the disease or with serum adiponectin levels. EAT thickness measured by echocardiography might provide additional information for risk assessment and prediction of CAD.