Impact of metabolic syndrome and its individual components on the presence and severity of angiographic coronary artery disease

Abstract

Purpose: Metabolic syndrome (MS) has been reported as a potential risk factor of coronary artery disease (CAD). The aims of this study were to assess whether there was a relationship between MS score and CAD angiographic severity, and to assess the predictive value of individual components of MS for CAD.

Materials and methods: We retrospectively enrolled 632 patients who underwent coronary angiography for suspected CAD (394 men, 61.0 +/- 10.6 years of age). MS was defined by the National Cholesterol Education Program criteria with the waist criterion modified into a body mass index (BMI) of more than 25 kg/m(2). The MS score defined as the number of MS components. CAD was defined as > 50% luminal diameter stenosis of at least one major epicardial coronary artery. CAD angiographic severity was evaluated with a Gensini scoring system.

Results: Of the patients, 497 (78.6%) had CAD and 283 (44.8%) were diagnosed with MS. The MS score was significantly related to the Gensini score. High fasting blood glucose (FBG) was the only predictive factor for CAD. A cluster including high FBG, high blood pressure (BP), and low high-density lipoprotein cholesterol (HDL-C) showed the highest CAD risk.

Conclusion: The MS score correlates with the angiographic severity of CAD. The predictive ability of MS for CAD was carried almost completely by high FBG, and individual traits with high BP and low HDLC may act synergistically as risk factors for CAD.

Fig. 1

The relationship between the metabolic syndrome score and coronary artery disease severity assessed by the Gensini score.

Fig. 2

Odds ratio plots for metabolic syndrome and its components as a predictor of the presence of angiographic coronary artery disease. MS, metabolic syndrome; FBG, fasting blood glucose; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; BP, blood pressure; BMI, body mass index; UV, univariate analysis; MV, multivariate analysis.