This was a cross-sectional study analyzing single-center data collected from 3,159 consecutive symptomatic South Korean subjects who underwent cCTA evaluation with 64-slice multidetector computed tomography (MDCT) at Yonsei Cardiovascular Hospital between January 2005 and April 2009. All participants were referred for evaluation of CAD who had at least one of the symptoms, including typical angina, atypical angina, dyspnea, and excessive fatigue, but were not patients with acute coronary syndrome who required emergent coronary intervention or surgery. Subjects who were younger than 30 years (n = 26), or had an estimated modification of diet in renal disease (MDRD) glomerular filtration rate (GFR) <60 mL/min/1.73m² (n = 264) were excluded from the present study according to the study protocol. As a result, 2,869 participants were finally included. All patients provided written informed consent, and ethical approval was obtained from the Institutional Review Board of Severance Hospital, Yonsei University Health System.

Subjects with an initial heart rate ≥65 beats/min before MDCT examination received a single oral dose of 50 mg metoprolol tartrate (Betaloc, Yuhan, Seoul, South Korea) 1 h before CT examination unless beta-adrenergic blocking agents were contraindicated owing to overt heart failure, atrioventricular conduction abnormalities, and bronchial asthma. In patients with atrial fibrillation, patients with a mean heart rate >100 beats/min received beta-adrenergic blocking agents orally 1 h before cCTA. If the mean heart rate remained >100 beats/min at the time of scanning, we withdraw the scan. A contrast-enhanced volume data set was acquired with retrospective electrocardiogram gating without using tube current modulation to allow reconstructions during all phases of the cardiac cycle. In subjects with a mild allergy to the contrast material such as drug eruption or urticaria, we used a prophylactic IV steroid. However, we did not allow a scan for the patients with severe allergic reactions such as shock or laryngeal edema.

Imaging was performed for all the subjects using a 64-slice CT scanner (Sensation 64; Siemens Medical System, Forchheim, Germany). All CT examinations were performed during breath holding in inspiration. Initially, a non-enhanced prospective electrocardiogram-gated scan to evaluate CACS was performed with the following parameters: rotation time of 330 ms, slice collimation of 0.6 mm, slice width of 3.0 mm, tube voltage of 100–120 kV, tube current of 50 mA, and table feed/scan of 18 mm. Thereafter, cCTA was performed using retrospective electrocardiographic gating with the following scan parameters: rotation time of 330 ms, slice collimation of 64 × 0.6 mm, tube voltage of 100–120 kV, tube current of 400–800 mA (depending on patient size), table feed/scan of 3.8 mm, and pitch factor of 0.2. ECG-based tube current modulation was applied to 65% of the R–R interval. A real-time bolus-tracking technique was applied to trigger scan initiation. The total estimated average radiation dose for the multislice CT protocol was 8.8 ± 1.6 mSv. Contrast enhancement was achieved with 60 mL iopamidol (370 mg/mL iodine, Iopamiro; Bracco, Milan, Italy) injected at 5 mL/s, followed by an injection of 30 mL of diluted contrast medium (saline-to-contrast agent ratio, 7:3) and then 30 mL saline at 5 mL/s, with a power injector (Envision CT; Medrad, Indianola, PA) via an antecubital vein. The estimated volume CT dose index (CTDIvol) was recorded for each patient. The product of CTDIvol and scanning length (dose–length product, mGy × cm) was calculated, and effective dose was estimated using a normalization factor for the adult chest (0.017 mSv × mGy⁻¹ × cm⁻¹). Image reconstruction was performed in the scanner workstation using commercially available software (Wizard, Siemens Medical Solutions). Axial images were reconstructed retrospectively at 65% of the R–R interval for each cardiac cycle. If artifacts were present, additional data sets were obtained for various points of the cardiac cycle, and the data set with the minimum artifact was selected for further analysis. The reconstructed image data sets were transferred to an off-line workstation (Aquar-ius Workstation, TeraRecon, Inc., San Mateo, CA). Each lesion identified was examined using maximum intensity projection and multiplanar reconstruction techniques on a short axis and along multiple longitudinal axes. Lesions were classified by the maximal stenosis of luminal diameter observed on any plane.

All cCTA were evaluated by 2 experienced cardiac radiologists (Y.J.K. and B.W.C., who respectively had 6 and 9 years of experience in cardiac CT). In case of disagreement, a joint reading was performed to reach a consensus. This study primarily evaluated the presence of any plaque, obstructive plaque, and a CACS >100. Both any plaque and obstructive plaque were divided into 2 subtypes according to the presence of coronary calcification as follows: calcified or mixed plaque (CMP) and non-calcified plaque (NCP). CACS was measured with the scoring system using a previously described method [17]. Because the frequency of CACS >100 in the Asian population is known to be low compared with that in Caucasians, African-Americans, and Hispanics [18], and few subjects had a CACS >400 (5%) in the present study, consequently, we used CACS >100 as the parameter for estimating severe coronary calcification. Plaque was defined as structures >1 mm² within and/or adjacent to the vessel lumen that were clearly distinguished from the lumen and surrounding pericardial tissue; obstructive plaque was defined as plaque with ≥50% luminal diameter stenosis. CAD was defined as the presence of any coronary plaque identified in cCTA (both non-obstructive and obstructive lesions, including non-calcified plaques).

Information on medical history of hypertension, diabetes, and smoking status were systematically collected. Height, weight, and blood pressure were measured during hospital visits. All blood samples were obtained after a minimum 8-h fast and analyzed for triglycerides, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and glucose levels. Body mass index (BMI) was calculated as weight (kg) ÷ height (m²), and obesity was defined as a BMI ≥25 kg/m². Kidney function was assessed based on the estimated GFR calculated using the formula validated in the Modification of Diet in Renal Disease study [19]. Current smoking history was considered present if the subject consistently smoked or smoked within 1 month before the study. MetS was defined as the presence of ≥3 of the following: (a) blood pressure ≥130 mmHg systolic or ≥85 mmHg diastolic, or on antihypertensive treatment; (b) HDL cholesterol level <40 mg/dL in men or <50 mg/dL in women; (c) fasting triglycerides level ≥150 mg/dL; (d) BMI ≥25 kg/m²; and (e) impaired fasting glucose, defined as a fasting glucose level ≥100 mg/dL, a referral diagnosis of diabetes, or diabetes treatment according to the National Cholesterol Education Program–Adult Treatment Panel III definition [1]. Diabetes was defined as a fasting glucose level ≥126 mg/dL, undergoing an antidiabetic treatment, or a referral diagnosis of diabetes.

Clinical, biochemical, and coronary characteristics are described according to the presence of MetS and diabetes. Values are expressed as mean ± SD or n (%). Continuous variables were compared using the Student t-test, and categorical variables were compared using the χ² test. The associations between the individual MetS components and the coronary parameters, namely plaque, obstructive plaque, and CACS >100, were analyzed in the subjects with and without diabetes after adjusting for confounding risk factors. Univariate and multivariate logistic regression analyses for identifying the association between MetS and coronary parameters were performed according to diabetes status. These analyses were also performed to identify the association between the increase in number of MetS components (MetSN) and coronary parameters according to diabetes status. The covariate-adjusted odds ratios (OR) and 95% confidence intervals (CI) for each coronary parameter were calculated. Statistical Package for the Social Sciences version 18 (SPSS Inc., Chicago, IL) was used for all the analyses. All the statistical tests were 2-tailed, and P <0.05 was considered significant.

The clinical characteristics of the 2,869 subjects (58 ± 9 years, 51% male) are listed in Table 1. Of the subjects, 2,308 were non-diabetic subjects (80%) and 561 were diabetic subjects (20%). The prevalence of MetS was significantly higher in the diabetic subjects than in the non-diabetic subjects (69% vs. 34%, P <0.001). The incidence of any plaque (64% vs. 43%, P <0.001), obstructive plaque (26% vs. 13%, P = 0.006), and CACS >100 (23% vs. 12%, P = 0.012) was also significantly higher in diabetic subjects than in non-diabetic subjects (Figure 1). The incidence of any plaque (48% vs. 39%, P <0.001), obstructive plaque (15% vs. 11%, P = 0.006), and CACS >100 (14% vs. 10%, P = 0.012), was significantly higher in the non-diabetic subjects with MetS than in those without MetS. However, the incidence of all these parameters was not significantly different in the diabetic subjects according to the presence of MetS (Figure 2).

The association between the individual MetS components and the coronary parameters according to the presence of diabetes is presented in Table 2. After adjusting for age, sex, current smoking status, LDL, and GFR, obesity was significantly associated with any plaque (OR 1.27, 95% CI 1.05–1.55, P = 0.016). Increased blood pressure and increased triglycerides were significantly associated with any plaque (increased blood pressure: OR 1.25, 95% CI 1.01–1.55, P = 0.045; increased triglycerides: OR 1.37, 95% CI 1.12–1.68, P = 0.002) and obstructive plaque (increased blood pressure: OR 1.59, 95% CI 1.14–2.23, P = 0.006; increased triglycerides: OR 1.56, 95% CI 1.18–2.06, P = 0.002). Decreased HDL level was significantly associated with any plaque (OR 1.35, 95% CI 1.09–1.68, P = 0.007), obstructive plaque (OR 1.55, 95% CI 1.14–2.10, P = 0.005), and CACS >100 (OR 1.57, 95% CI 1.12–2.19, P = 0.008) in the non-diabetic subjects. However, no MetS components were significantly associated with all the coronary parameters in the diabetic subjects.

The association between MetS, the MetSN, and the coronary parameters, including any plaque, obstructive plaque, and CACS >100 according to the presence of diabetes are presented in Table 3. The univariate logistic regression analysis revealed that MetS was significantly associated with increased risks of all coronary parameters in the non-diabetic subjects (any plaque: OR 1.44, 95% CI 1.21–1.72, P <0.001; obstructive plaque: OR 1.42, 95% CI 1.10–1.82, P = 0.007; CACS >100: OR 1.40, 95% CI 1.08–1.82, P = 0.012). However, MetS was not significantly associated with any of these parameters in the diabetic subjects. The MetSN was significantly associated with increased risks of all coronary parameters in the non-diabetic subjects (any plaque: OR 1.19, 95% CI 1.11–1.28, P <0.001; obstructive plaque: OR 1.22, 95% CI 1.10–1.35, P <0.001; CACS >100: OR 1.18, 95% CI 1.06–1.32, P = 0.002) but not in the diabetic subjects. After adjusting for age, sex, current smoking status, LDL, and GFR, MetS was significantly associated with increased risks of all the coronary parameters in the non-diabetic subjects (any plaque: OR 1.55, 95% CI 1.27–1.89, P <0.001; obstructive plaque: OR 1.52, 95% CI 1.15–2.01, P = 0.003; CACS >100: OR 1.46, 95% CI 1.08–1.99, P = 0.015). However, MetS was not significantly associated with any of these parameters in the diabetic subjects. The MetSN was significantly associated with increased risks of all the coronary parameters in the non-diabetic subjects (any plaque: OR 1.22, 95% CI 1.12–1.32, P <0.001; obstructive plaque: OR 1.25, 95% CI 1.11–1.41, P <0.001; CACS >100: OR 1.21, 95% CI 1.06–1.37, P = 0.004) but not in the diabetic subjects (Figure 3).