Methods

Coronary computed tomography angiograms (CTAs) were used from the CTRAD study (Cardiac CT’s Role in Asymptomatic Patients with DM-II) in which consecutive asymptomatic patients (n = 203) with type II DM from 3 community clinics of the University of California, Irvine, were randomly assigned to either undergo 64-slice computed tomography (CT) angiography or continue their usual care. Type II DM was defined as a fasting blood glucose of ≥126 mg/dl, a physician-documented diagnosis of DM, current treatment with oral hypoglycemic medications, and current treatment with insulin or treatment with a noninsulin injectable therapy for DM; 92 patients were identified who fell into the DM group. Patients with type I diabetes were not used in this study. These patients were matched 3:1 with age- and gender-matched nondiabetic controls (non-DM) from a CT database of healthy community volunteers that was simultaneously collected to create the total patient population for this case-control study.

Exclusion criteria included previous diagnosis of CAD, previous percutaneous coronary transluminal angioplasty, previous percutaneous coronary intervention, coronary bypass grafting, or the presence or chest pain that was felt to necessitate a cardiac workup. The Institutional Review Board of the University of California approved the study, and all study data were handled in accordance with Health Insurance Portability and Accountability Act regulations.

In preparation for image acquisition, patients without contraindications were given oral or intravenous metoprolol tartrate with the goal of reaching a heart rate <65 beats/min. One minute before imaging, patients without contraindications were administered sublingual nitroglycerin (0.4 to 0.8 mg). Scout images of the thorax were then acquired to define an imaging field that encompassed the entire cardiac volume on a 64-slice Toshiba Aquilion CT system (Toshiba Inc., Tustin, CA). Patients were then intravenously administered 64 to 93 ml (mean contrast volume 74.9 ± 3.3 ml) of iodinated CT contrast (Iohexol, Omnipaque; Amersham Health, Cork, Ireland) injected at a rate of 4 to 5 ml/s followed by a 50-ml flush of saline through an 18-gauge line.

Using a previously described imaging sequence, routine CT coronary angiography was performed followed by acquisition of a retrospective electrocardiography-gated volumetric data set during a single breath hold. Mean scan time was 9.1 ± 1.4 seconds (range of 8 to 13 seconds). Data sets were reconstructed based on a relative-delay strategy at 10% of R-R intervals.

Reconstructed CT data sets were evaluated from a remote workstation (Vitrea 2; Vital Images Inc., Minnetonka, Minnesota) by 2 independent blinded clinicians who were CTA level III certified. Images were evaluated at several electrocardiographic phases to ensure the highest diagnostic image quality by choosing the optimal data set. Identified atherosclerotic plaques in the epicardial coronary arteries were categorized as either mild to moderate if the observed stenosis was 50% to 70% or significant if there was a stenosis with >70% luminal obstruction. CAD severity was recorded based on the most severe lesion identified. If no stenosis was present with a 50% or greater luminal obstruction, then the patient was recorded as having no significant disease. CAC scores were calculated using Vitrea Enterprise Suite (Toshiba Inc.), and an overall Agatston score was recorded for each patient.

EAT volume was determined by 2 clinicians who had received dedicated training in measuring EAT and were blinded to the CAD and CAC assessment. Axial noncontrast enhanced images from the mid-diastolic phase used for calcium scoring were used to calculate the EAT volume. Tissue was defined as EAT if it was within the parietal pericardial boundary and demonstrated fat density attenuation on CT measured by Hounsfield units (−30 to −190 HU). Sequential images from the lower margin of the aortic arch to the inferior cardiac border were used (range of 35 to 40 slices per patient). The area of each pocket of epicardial fat was manually traced, and a total area of epicardial fat for each slice based on the summation of all pockets was recorded. To achieve a volumetric measurement, the area of epicardial fat was multiplied by the slice thickness (3 mm). This volumetric assessment included the epicardial coronary arteries. The volume of EAT as opposed to EAT thickness was used in this study as previous studies using EAT thickness have shown contradictory results, and EAT volume is a more accurate representation of the entire EAT burden.

Categorical variables are presented as percentages, and continuous variables are presented as mean values ± SD. All data represented by continuous variables were subjected to a normality analysis that confirmed all data were normally distributed. We evaluated the clinical characteristics of patients with versus those without DM using chi-square test for categorical variables and Student’s t test for continuous variables. Additionally, we used analysis of variance to examine CAD severity stratified into 3 separate categories (none, mild/moderate, and significant) versus patient characteristics within the DM and non-DM patient groups. Separately, we used bivariate linear regression to associate EAT volume with previously identified patient characteristics within both the DM and non-DM groups.

Finally, we examined the association between EAT and CAD severity in study participants using several multivariate logistic regression models. A cutoff defined as the presence of >120 cm ³ of EAT was defined for this analysis. This EAT volume corresponded to the sixty sixth percentile of EAT volumes measured in this study. We used a sequential modeling technique to assess contribution of additional variables to the model. The first model was adjusted for age, gender, race, diabetes, hypertension, dyslipidemia, and the use of insulin therapy. Two additional models were created adjusting for BMI, in addition to the presence of smoking. Our fourth model included adjustments for all previously used variables and CAC scores. Statistical significance was defined as a p value <0.05. All statistical analyses were performed using SAS 9.3 (SAS Institute, Cary, NC).

Results

Table 1 describes the clinical characteristics of the 362 study patients, of which 92 (25.4%) were patients with DM who had no symptoms of CAD and 270 asymptomatic patients without DM (non-DM). The mean age in the DM group and non-DM group were 56.2 ± 9.5 and 57.2 ± 9.7 years, respectively. In the DM group, 57.9% of the patients were women as were 60% of the patients in the non-DM group. The mean BMI for participants with DM and without DM was statistically significantly different (p <0.0001). There was a statistically significant difference in the ethnic makeup of the 2 groups as well (p <0.0001). Additionally, there was an expected statistically significant difference in medical co-morbidities as those with DM had a higher prevalence of hypertension and dyslipidemia (p <0.0001). Finally, there were more smokers in the non-DM group (p = 0.0008).

Among patients in the DM group, 30.4% were identified as having mild-to-moderate CAD and 27.2% (p <0.001) had significant CAD by CTA ( Table 2 ). Patients in this group who were identified as having CAD were disproportionately older and male compared with those with mild disease. EAT volumes significantly increased with increasing severity of CAD (p = 0.003). The presence of hypertension, dyslipidemia, insulin therapy, and smoking were not significantly different by severity of CAD in those with DM. Additionally, hemoglobin A1c level and increases in BMI did not significantly change with the severity of CAD.

Patients without DM had a similar burden of mild/moderate CAD (29.6%) as compared to the DM group, but they had a much lower incidence of significant CAD at 5.2%. Non-DM patients also demonstrated a statistically significant association of increasing age with increasing severity of CAD along with a male predominance. Those without DM also showed a clear relation of CAD severity with the presence of the traditional risk factors, such as hypertension, dyslipidemia, and smoking.

Mean EAT volumes for DM and non-DM patients, respectively, can be seen in Table 3 . Figure 1 illustrates the EAT distribution in both groups. Among non-DM patients, but not DM patients, age >60 years and hypertension were found to be significantly associated with an increased mean EAT volume. Male subjects in both groups had an increased EAT volume as well. EAT volume was found to be significantly increased as BMI and CAC increased in both DM and non-DM patient groups, except in those with DM and with BMI ≥40, the EAT volume was slightly lower than in those with BMI 30 to 39.9 ( Table 3 ). There was a direct relation between increasing EAT volume and worsening severity of CAD for both patients with and without DM (p = 0.003 and p = 0.0001, respectively).

Table 3

Relationship between patient characteristics and mean EAT

	Type II Diabetes Mellitus
	Yes		No
	EAT Volume (cm 3 )	p-Value	EAT Volume (cm 3 )	p-Value
Overall	118.3 ± 42.2		70.0 ± 44.0
Men	130.3 ± 48.5	0.02	78.1 ± 48.7	0.02
Women	109.9 ± 36.6		64.5 ± 39.8
Age (years)
35–60	112.2 ± 42.7	0.05	63.4 ± 41.9	0.0003
>60	131.1 ± 41.5		85.0 ± 45.2
White	136.1 ± 37.6	0.03	68.6 ± 45.9	0.43
Asian	113.8 ± 42.6		73.4 ± 32.1
Hispanic	135.0 ± 43.9		78.0 ± 42.2
Other race	69.4 ± 29.5		58.7 ± 25.6
BMI (kg/m 2 )
<25	97.6 ± 38.0	0.01	55.0 ± 37.6	<0.0001
25–29.9	103.6 ± 37.5		76.5 ± 41.1
30–39.9	131.6 ± 44.6		82.4 ± 43.9
≥40	129.2 ± 36.5		125.8 ± 89.7
Hypertension	121.5 ± 43.05	0.26	80.6 ± 47.7	0.001
Dyslipidemia	116.3 ± 37.7	0.54	78.3 ± 47.0	0.06
Smokers	130.7 ± 44.1	0.4	78.3 ± 47.0	0.06
Insulin therapy	114.3 ± 52.0	0.63	n/a	n/a
Calcium score
Zero	106.6 ± 38.4	0.03	62.6 ± 39.7	0.003
1–100	121.1 ± 47.1		71.0 ± 41.8
101–400	118.9 ± 38.9		78.6 ± 50.1
>400	141.3 ± 42.5		92.6 ± 51.5
CAD severity
None	107.0 ± 39.4	0.003	63.5 ± 40.0	0.0001
Mild to moderate	112.7 ± 37.2		79.5 ± 48.6
Significant	143.1 ± 46.0		120.0 ± 40.7

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