IJCS | Volume 33, Nº1, January / February 2019

61 linear regression model including all cardiovascular risk factors (Table 2). Variables significantly associated with obstructive CAD, defined by CCTA, included age, male sex, and diabetes; hypertension was of marginal significance for outcome definition (p = 0.08). Obesitywas not correlated with obstructive CAD (p = 0.10) when the other variables were maintained constant. Discussion The present study showed that, although the prevalence of obstructive CAD was not different Figure 2 - Prevalence of obstructive coronary artery disease (CAD) and coronary artery calcium score by body mass index (BMI). BMI < 30 kg/m 2 BMI >30 kg/m 2 Non-obstructive CAD (%) Obstructive CAD (%) 81.6 81.6 18.4 18.4 1.4 14.7 between obese and non-obese patients, coronary artery calcium scores were significantly lower in non-obese than obese patients. Obesity is believed to have a direct effect onmetabolic health, since proinflammatory cytokines released by the adipose tissue can lead to subclinical inflammation at long-term, even if counterbalanced by anti-inflammatory cytokines. This condition is characterized by a gradual increase in inflammatory markers, such as C-reactive protein, TNF-alpha and interleukin-6, which have a direct relationshipwith insulin resistance, hepatic steatosis and endothelial dysfunction, leading to atherosclerosis. 14 Pereira et al. Obesity and coronary artery disease Int J Cardiovasc Sci. 2020;33(1):57-64 Original Article Table 2 - Obesity and risk factors as predictors of obstructive coronary arterial disease according to coronary computed tomography angiography Obstructive coronary artery disease Coef. Standard error 95%CI p* Obesity -0.035 0.021 -0.077 – 0.007 0.102 Age 0.005 0.0008 0.003 – 0.0065 < 0.001 Sex 0.046 0.01 0.027 – 0.066 < 0.001 Diabetes 0.065 0.024 0.019 – 0.11 0.006 Hypertension 0.034 0.020 -0.004 – 0.073 0.08 Dyslipidemia 0.012 0.020 -0.027 – 0.05 0.548 Smoking 0.015 0.015 -0.014 – 0.04 0.308 Family history of CAD -0.022 0.013 -0.048 – 0.004 0.105 * p-values by multiple linear regression model.