Int J Cardiovasc Imaging (2014) 30:195–204 DOI 10.1007/s10554-013-0308-5
ORIGINAL PAPER
Epicardial adipose tissue: relationship between measurement location and metabolic syndrome Ju-Hye Chung • Beom-June Kwon • Sang-Wook Song • Sun-Myeong Ock Whan-Seok Choi • Se-Hong Kim
•
Received: 25 June 2013 / Accepted: 5 October 2013 / Published online: 30 November 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract Epicardial adipose tissue (EAT) is a contributing factor of metabolic syndrome (MS) and coronary artery disease (CAD). However, it is still unclear which measurement location of EAT area best reflects its cardiometabolic risk. The purpose of our study was to investigate the distribution of EAT and its relationship to the total EAT volume and MS. To assess volume and cross-sectional areas of EAT, coronary CT angiography were obtained in 256 asymptomatic subjects. The EAT areas within the threshold range of -190 to -30 Hounsfield units were measured at six representative slices. Correlations between single slice EAT areas and total EAT volumes were high across all measurement locations (correlation coefficient r [ 0.80). The receiver–operator characteristic curves demonstrated EAT area at left main coronary artery (LMCA) was well discriminative for MS (AUC 0.82, p \ 0.001) and CAD (AUC 0.76, p \ 0.001). EAT areas across all measurement
locations were significantly increased linearly with increasing number of MS components. EAT areas were significantly associated with MS at all measurement locations; the highest odds ratio (OR) between EAT area and MS was at the LMCA level (OR 5.86, p \ 0.001). The OR between EAT area and coronary artery calcium was also significant in LMCA locations (OR 1.56, p = 0.042). We demonstrated that the single-slice EAT area measurement is a simple and reliable method compared with time-consuming volumetric measurements. The EAT area at LMCA level was the best single slice representing the risk of metabolic syndrome and coronary atherosclerosis. Keywords Epicardial adipose tissue Metabolic syndrome Measurement location Computed tomography
Introduction J.-H. Chung Department of Family Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 271, Cheon Bo-ro, Uijeong bu-si, Gyeonggi-do 480-717, Korea B.-J. Kwon Division of Cardiology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea S.-W. Song S.-H. Kim (&) Department of Family Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 93-6 Ji-dong, Paldal-gu, Suwon, Kyonggi-do 442-723, Korea e-mail: [email protected] S.-M. Ock W.-S. Choi Department of Family Medicine, St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 505 Banpo-Dong, Seocho-Gu, Seoul 137-701, Korea
Metabolic syndrome (MS) is a cluster of risk factors for type 2 diabetes and cardiovascular disease and is associated with increased mortality [1, 2]. Although visceral adipose tissue (VAT) has been recognized as a key determinant of MS and is presumed to play an important role in its development [3, 4], epicardial adipose tissue (EAT) is also considered to be a contributing factor of MS and coronary artery disease (CAD) [5–7]. EAT is an ectopic fat surrounding the heart, within the boundary of the pericardium. EAT is in direct contact with the major branches of the coronary vessels, and the rate of lipolysis and lipogenesis is significantly higher in EAT than in other adipose tissue of the body [8]. The availability of high-resolution imaging modalities, such as computed tomography (CT) and magnetic resonance
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imaging (MRI), has provided more accurate quantification of EAT compared with echocardiography. Especially, the volumetric measurement of EAT by cardiac CT is a wellestablished validated method for the estimation of MS [9, 10] and CAD [5, 11]. However, the application of multislice imaging with CT is a relatively time-consuming process and is limited by the amount of radiation exposure. Whereas a volumetric measurement with CT is generally considered the reference for estimating EAT, little attention has been paid to the EAT area measurement at a single crosssectional image, which is optimal for the determination of cardiometabolic risk. Although EAT area measured in a single cross-sectional image was correlated with total EAT volume in previous studies of small samples [12, 13], the correlation of EAT area with cardiometabolic risk factors and MS was inconsistent [14, 15]. Thus, it is still unclear which measurement location of EAT area best reflects its metabolic risk and total EAT volume in asymptomatic population. The purpose of our study was: (1) to investigate the distribution of EAT and its relationship to cardiometabolic risk factors; (2) to determine whether a single-slice EAT area can represent the total EAT volume; and (3) to evaluate the impact of EAT area measurement location on coronary atherosclerosis and MS.
Methods Subjects A total of 256 asymptomatic subjects (113 male and 143 female) were retrospectively evaluated from the outpatient clinics at St. Paul’s Hospital and St. Vincent’s Hospital in South Korea. The study subjects were self-referred or referred by their primary physician for various reasons such as abnormal EKG findings or multiple CAD risk factors. All subjects received comprehensive health screening and underwent cardiac and abdominal CT scans for screening purposes between September 2009 and March 2012. Subjects who underwent cardiac or abdominal surgeries affecting the epicardial and visceral fat distribution were excluded from the study. This study was approved by the Research Ethical Committee of the College of Medicine, the Catholic University of Korea, and written informed consent was obtained from all patients. Risk factor assessment The anthropometric, clinical and laboratory investigations were performed on all subjects. The height of each participant was determined using a fixed wall-scale measuring device and was measured to the nearest 0.1 cm. The body weight was measured to the nearest 0.1 kg using a digital scale calibrated prior to each measurement. The body mass
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index (BMI, kg/m2) was calculated as the weight in kilograms (kg) divided by the square of height in meters. Waist circumference (WC) was measured twice to the nearest centimeter at the end of normal expiration in a horizontal plane immediately superior to the left iliac crest according to the National Health and Nutrition Examination Survey protocols. If the variation between these two measurements was greater than 2 cm, a third measurement was taken and the mean was calculated using the two closest measurements. Seated blood pressures were measured using a mercury sphygmomanometer after a 10 min rest period. Two blood pressures measurements were taken from all subjects with a 5 min interval and were averaged for analysis. Fasting plasma glucose, total cholesterol, triglyceride, and HDL-cholesterol levels were measured after a 12-h fasting using an auto-analyzer (Hitachi 747 auto-analyzer, Tokyo, Japan). Smoking status and alcohol use were also investigated. Criteria for metabolic syndrome MS was defined according to the revised NCEP-ATP III criteria [16] with an ethnic-specific cutoff point for abdominal obesity [17]. Diagnosis of MS was based on the presence of three or more of the following clinical criteria: (1) WC C90 cm for men or C85 cm for women; (2) TG levels C150 mg/dL; (3) HDL-cholesterol levels \40 mg/dL for men or\50 mg/dL for women; (4) SBP C130 mmHg or DBP C85 mmHg, or the use of antihypertensive medication; and (5) FBS C100 mg/dL, or the use of anti-diabetics or insulin. CT scan protocol Contrast-enhanced coronary CT angiography was performed with a 128-slice dual source CT scanner (SOMATOM Definition Flash, Siemens Medical Solutions, Forchheim, Germany). The prospective ECG-triggered scanning with the flexible padding technique was performed, and the mean dose–length product (DLP) was 295 ± 134.48 mGy-cm (range 100–595 mGy-cm) for all participants. An average of 250 contiguous slices were taken with a coronary CT angiography protocol (64 9 0.6 mm slice collimation, 280 ms gantry rotation time, 0.32 pitch, 120 kV tube voltage, 800 mA tube current, image matrix of 512 9 512 pixels, temporal resolution 0.075 s, and slice thickness 0.75 mm). At the time of scanning, a bolus of 80 mL of contrast agent (Iopromide, Ultravist 370; Bayer-Schering, Berlin, Germany) was intravenously injected at a rate of 5 mL per second, followed by 50 mL of saline solution administered intravenously at a rate of 5 mL per second. On the same day, approximately 4–5 continuous transverse images (120 kV,
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Fig. 1 Measurement location of epicardial adipose tissue (EAT) area. a Right pulmonary artery (RPA). b Origin of the left main coronary artery (LMCA). c Origin of the right coronary artery (RCA). d Mitral valve level (MV). e Origin of the coronary sinus (CS). f Distal right coronary artery level (dRCA). The measurement of EAT was
performed by tracing the pericardium in axial slices (yellow line). After region of interest (ROI) drawing, pixels with a threshold range of -190 to -30 Hounsfield units (HU) were identified as the EAT area (blue color)
200 mA, scanning time of 2 s, field of view of 380 mm, and slice thickness 5 mm) were obtained at the L4-5 intervertebral space using a CT scanner (LightSpeed, GE Healthcare, Milwaukee, WI) to assess abdominal fat distribution.
quantify total EAT volume, we traced 30–40 slices in each patient, depending on heart size. For the measurement of EAT area, we selected six representative slices on axial images. We used the center of the right pulmonary artery (RPA), the origin of the left main coronary artery (LMCA), the origin of the right coronary artery (RCA), the mitral valve level (MV), the origin of the coronary sinus (CS) and the distal right coronary artery (dRCA) level as anatomical landmarks (Fig. 1). The mitral valve level was defined as the location where the aortic valve disappeared and the four cardiac chambers were well visualized. The level of the distal right coronary artery was regarded as the slice demonstrating the distal right coronary artery and the inferior wall of the right and left ventricles at the bottom of heart. The intra- and inter-observer reproducibility for EAT measurements was evaluated on a random sample of 20 participants by the intra-class correlation coefficient. For the inter-observer reproducibility test, two independent readers measured EAT. For the intra-observer reproducibility test, a second EAT measurement was taken at least 2 weeks later by a single reader. The visceral adipose regions were obtained at the L4-5 intervertebral space by manual tracing, and the pixels with a threshold range of -190 to -30 Hounsfield units (HU) were calculated for adipose tissue area. All coronary CT angiography results were interpreted for the presence of CAD. The patient with CAD was defined as [50 % luminal diameter stenosis in at least one major coronary
Adipose tissue measurement The total volume and cross-sectional areas of EAT were measured by one experienced observer blinded to the clinical information of study subjects. All CT analyses were performed using a dedicated offline workstation (Rapidia, software version 2.8, Infinitt, Seoul, Korea). EAT was defined as the adipose tissue between the outer surface of the myocardium and the visceral layer of the pericardium. We set the slice containing the center of the right pulmonary artery to be the upper slice limit and the last axial slice including any portion of the heart to be the lower slice limit. The segmentation of the axial images into EAT was performed on every fifth slice within this range using semi-automated method. One expert reader set multiple seed points along the pericardium in each axial slice and these points were connected automatically to create a ROI (region of interest). After the ROI drawing, the pixels with a threshold range of -190 to -30 Hounsfield units (HU) were identified as the EAT area in cm2. The EAT volumes were calculated as the sum of the EAT volume of each slice from the superior to inferior boundary and reported in cm3. To
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artery or its branches, otherwise as normal coronary arteries. The coronary artery calcium (CAC) was quantified in Agatston score [18], and the presence of calcified coronary plaque was defined as an Agatston score [ 0.
Int J Cardiovasc Imaging (2014) 30:195–204 Table 1 General characteristics of study subjects (n = 256) Mean ± SD or median (IQR) Age (years)
59.08 ± 8.96
Glucose (mg/dL)
104 (95–121)
Statistical analysis
WC (cm)
87.36 ± 9.02
HDL cholesterol (mg/dL)
47.96 ± 13.65
Data were analyzed using SPSS version 13.0 (SPSS Inc., Chicago, IL) and presented as mean ± standard deviation (SD). The values of fasting plasma glucose and triglyceride were highly skewed, thus, log-transformed for all analyses. The differences between EAT areas at 6 different levels were compared using a repeated measures ANOVA. Ageadjusted partial correlation coefficients were used to assess simple correlations between the EAT areas at various locations and the EAT volume. The cut off value of EAT with the best sensitivity and specificity to predict MS and CAD was determined by using receiver–operator characteristics (ROC) curves. One-way analysis of variance (ANOVA) with a post hoc multiple comparisons LSD test was used to compare the EAT area according to the number of metabolic syndrome risk factors. Multiple logistic regression analysis was performed to assess the relationship of EAT area with metabolic syndrome (MS) and coronary artery calcium (CAC). The odds ratio (OR) for the presence of MS and CAC were based on a 1-SD increase in each of the EAT areas or volumes. A two sided p value \0.05 was considered statistically significant.
Triglyceride (mg/dL)
111 (83–168)
SBP (mmHg)
123.76 ± 14.88
DBP (mmHg)
77.83 ± 8.79
BMI (kg/m2)
24.75 ± 4.13
Coronary calcium score
31 (0–233)
Visceral adipose tissue (cm2)
112.77 ± 56.77
Subcutaneous adipose tissue (cm2) EAT volume (cm3)
164.62 ± 3.39 110.83 ± 45.96
EAT area (cm2) RPA
9.24 ± 5.56
LMCA
13.56 ± 6.68
RCA
11.47 ± 5.10
MV
9.12 ± 4.31
CS
12.05 ± 5.85
dRCA
15.04 ± 7.45 n (%)
Female
143 (55.8)
Metabolic syndrome
110 (42.9)
Male/female
42(37.7)/68(47.5)
Obesity (BMI C 25 kg/m2)
142 (55.5)
CAD
64 (25)
Smoking status Never/ex-smokers
Results Table 1 summarizes the baseline characteristics of the study participants. The mean age was 59.08 ± 8.96 years (58.38 ± 9.09 in males and 59.63 ± 8.84 in females). MS was present in 42.9 % of the study population and was more prevalent in women than in men (47.5 and 37.7 %, respectively). Obesity (BMI C 25 kg/m2) was present in 55.5 % of the study population, and was more prevalent in women than men (76.1 and 49.5 %). 64 (25 %) patients had CAD (stenosis [50 %) on coronary CT angiography, while the remaining 192 subjects (75 %) had normal coronary arteries. The mean EAT area was lowest at the mitral valve level (9.12 ± 4.31 cm2) and highest at the dRCA level (15.04 ± 7.45 cm2). The intra- and inter-observer reproducibility was excellent for the EAT volume measurements (intra-class correlation coefficient of 0.99 and 0.95, respectively). The reproducibility for quantification of the EAT area was also good (intra-class correlation coefficient r [ 0.8 at all measurement locations; p \ 0.001) (Table 2).
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Current smokers
167(65.2)/31(12.1) 58 (22.6)
Alcohol use
102 (39.8)
Hypertension
165 (64.4)
Diabetes mellitus
73 (28.52)
Values are presented as the mean ± SD (standard deviation), medians with interquartile ranges (IQR; 25th and 75th percentiles) and n (%); coronary artery disease (CAD) was defined as [50 % luminal diameter stenosis. n number of persons, WC waist circumference, HDL high-density lipoprotein, SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, EAT epicardial adipose tissue, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
The pattern of EAT distribution according to BMI is shown in Fig. 2. Differences in the EAT areas between measurement sites were significant across all comparisons (p \ 0.001, for both normal and obese groups by a repeated measures ANOVA). The EAT area was increased more stiffly in the obese group (BMI C 25 kg/m2) from the mitral valve level to the caudal and cranial position compared with the normal group. The ratio of the EAT area at the LMCA to
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Table 2 Reproducibility of the EAT measurements Inter-observer reliability
Intra-observer reliability
0.955 (0.333, 0.989)
0.999 (0.973, 0.999)
RPA
0.914 (0.799, 0.965)
0.947 (0.874, 0.979)
LMCA
0.963 (0.778, 0.989)
0.996 (0.990, 0.998)
RCA
0.944 (0.666, 0.983)
0.999 (0.998, 1.000)
Mitral valve
0.940 (0.853, 0.976)
0.973 (0.933, 0.989)
EAT volume EAT area
CS
0.916 (0.617, 0.973)
0.995 (0.987, 0.998)
dRCA
0.892 (0.549, 0.965)
0.986 (0.962, 0.994)
Values are presented as the intra-class correlation coefficient with 95 % confidence interval, p \ 0.001 EAT epicardial adipose tissue, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, CS coronary sinus, dRCA distal right coronary artery
Measurement location
RPA LMCA
ratio (MV/CS) in the obese group was lower than in the nonobese group (0.744 ± 0.15 vs. 0.815 ± 0.17, p = 0.001). Correlations between single-slice EAT areas and EAT volumes were generally high across all measurement locations (correlation coefficient r [ 0.80). The anatomic location of the single slice area which had the highest correlation with EAT volume was the mitral valve level (r = 0.897). The area under the ROC curve (AUC) demonstrated EAT area at LMCA was well discriminative for MS (AUC 0.822, 95 % CI 0.769–0.874, p \ 0.001) and CAD (AUC 0.760, 95 % CI 0.694–0.825, p \ 0.001). EAT area was superior to EAT volume in identifying the presence of MS (AUC 0.822 vs. 0.806, p \ 0.001) and CAD (AUC 0.760 vs. 0.723, p \ 0.001). The cut-off value for EAT volume using the ROC curve was 108.7 cm3 for predicting a CAD (sensitivity 70.3 %, specificity 60.9 %, p \ 0.001). On the other hand, the cut-off value of EAT area at LMCA was 13.59 cm2 for CAD (sensitivity 73.4 %, specificity 63.5 %, p \ 0.001) and 12.73 cm2 for MS (sensitivity 77.9 %, specificity 70.0 %, p \ 0.001) (Fig. 3).
RCA
obese group (BMI>25) MV
Association between EAT areas with MS and CAC
normal
CS dRCA 0
5
10
15
20
Epicardial adipose tissue area (cm2)
Fig. 2 EAT distribution at various measurement locations according to BMI. BMI body mass index, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
the MV (LMCA/MV) in the obese group was higher than in the non-obese group (1.60 ± 0.38 vs. 1.437 ± 0.41, p = 0.003). The ratio of the EAT area at the MV to the CS
We divided subjects into 4 groups according to the presence of metabolic syndrome risk factors (none, one, two and three or more risk factors). The EAT areas across all measurement locations were significantly increased linearly with an increasing number of MS components (Fig. 4). The association between EAT areas and MS was assessed by multivariate logistic regression models (Table 3). The EAT areas were significantly associated with MS at all measurement locations after adjusting for age, smoking and alcohol (Model 1). The highest OR between EAT area and metabolic syndrome was at the LMCA level (OR 5.866; 95 % CI 3.474–9.906; p \ 0.001), which was higher than the total EAT volume (OR 4.766; CI
Fig. 3 Receiver-operating characteristics (ROC) curve to determine cut-off value of epicardial adipose tissue for the prediction of CAD (a) and MS (b)
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Int J Cardiovasc Imaging (2014) 30:195–204 Table 4 Multiple logistic regression analysis for epicardial adipose tissue with coronary artery calcium OR
95 % CI
p value
Model 1: age adjusted VAT
1.378
1.017–1.867
0.039
EAT volume
1.635
1.161–2.304
0.005
RPA
1.425
1.028–1.975
0.034
LMCA
1.603
1.144–2.244
0.006
RCA MV
1.328 1.517
0.970–1.819 1.080–2.130
0.077 0.016
CS
1.314
0.952–1.814
0.097
dRCA
1.061
0.774–1.455
0.713
EAT area
Fig. 4 Epicardial adipose tissue area according to the number of metabolic syndrome risk factors. *Statistically significant difference between adjacent groups, p \ 0.01. EAT epicardial adipose tissue, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
Table 3 Multiple logistic regression analysis for epicardial adipose tissue with metabolic syndrome OR
95 % CI
Model 1: age, smoking and alcohol adjusted VAT
5.966
3.553–10.019
EAT volume
4.766
2.962–7.669
EAT area RPA
3.415
2.274–5.128
LMCA
5.866
3.474–9.906
RCA MV
3.398 3.975
2.249–5.134 2.570–6.147
CS
5.494
3.311–9.118
dRCA
4.079
2.591–6.422
Model 2: age, smoking, alcohol and BMI adjusted VAT
4.316
2.471–7.536
EAT volume
3.563
2.140–5.931
RPA
2.492
1.562–3.976
LMCA
4.368
2.515–7.589
RCA
2.888
1.813–4.602
MV
3.018
1.889–4.823
CS
3.994
2.300–6.93
dRCA
3.075
1.851–5.108
EAT area
All ORs were significant at p \ 0.001 Odds ratios (ORs) and 95 % confidence intervals (CIs) are presented as the odds of metabolic syndrome with a 1-SD increase in adipose tissue VAT visceral adipose tissue, EAT epicardial adipose tissue, BMI body mass index, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
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Model 2: Age, smoking, alcohol and BMI adjusted VAT
1.361
0.931–1.990
0.112
EAT volume
1.536
1.015–2.326
0.042
RPA
1.223
0.832–1.798
0.307
LMCA
1.560
1.016–2.395
0.042
RCA
1.261
0.877–1.812
0.211
MV
1.328
0.902–1.955
0.151
CS
1.132
0.770–1.664
0.527
dRCA
0.903
0.625–1.306
0.589
EAT area
Odds ratios (ORs) and 95 % confidence intervals (CIs) are presented as the odds of coronary artery calcium with a 1-SD increase in adipose tissue VAT visceral adipose tissue, EAT epicardial adipose tissue, BMI body mass index, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
2.962–7.669; p \ 0.001). This association remained unchanged and consistent after further adjustment for BMI (Model 2). While the OR for MS was higher in VAT than in EAT, the OR for CAC was higher in EAT than in VAT (Table 4). The OR between EAT area and CAC was significant at the RPA, LMCA and mitral valve levels in age-adjusted models. However, the OR for CAC was significant only at the LMCA level after additional adjustments for smoking, alcohol and BMI (OR 1.560; CI 1.016–2.395; p = 0.042).
Discussion In this study, we demonstrated that measuring EAT area at a single cross-sectional CT image is a reliable method for predicting the epicardial fat volume and cardiometabolic risk. EAT areas at the specific level of axial CT images were an independent risk factor for metabolic syndrome
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and coronary artery calcium. Furthermore, EAT were somewhat site specific, and EAT area obtained from the LMCA level was the best single slices representing the epicardial fat burden. To the best of our knowledge, this is the first study evaluating the impact of EAT area measurement location on cardiometabolic risk factors. It is well known EAT is more lipogenic than other fat tissues of the body, and secretes numerous inflammatory cytokines [7, 19–22]. Thus, EAT is presumed to be a contributing factor in the pathogenesis of MS and coronary artery disease [23–26]. Earlier studies have measured the epicardial fat thickness as an index of EAT mass using echocardiography [27, 28]. However, Echocardiographic measurement of the epicardial fat thickness has several limitations for quantification of EAT, including the lack of an adequate window around all cardiac segments and low reproducibility [14, 29, 30]. Compared with echocardiographic measurements of EAT thickness, the availability of computed tomography (CT) has provided a more accurate quantification of epicardial fat distribution [14]. Therefore, most studies on EAT were performed by the measurement of adipose tissue volume or thickness using CT. Many previous studies have demonstrated that EAT volume measured by CT is strongly associated with cardiometabolic risk factors [5], coronary calcium [31, 32], metabolic syndrome [11] and the severity of CAD [33, 34]. However, measuring EAT volume with multislice CT is limited by radiation exposure and timeconsuming process [35]. In addition, the measurement of EAT thickness showed low reproducibility compared with volumetric EAT measurement [14, 36, 37]. Thus, we measured single-slice fat area for quantification of EAT in this study, and found that the epicardial fat area at a crosssectional CT image strongly correlated with the total epicardial fat volume, metabolic syndrome and coronary artery calcium. Only a few studies on EAT areas have been reported so far, and the results are inconsistent according to measurement location and study populations. Early studies of small (50 * 70 subjects) selected samples have reported that single-slice epicardial fat area measurement is correlated with the epicardial fat volume [12, 38, 39]. In contrast, in other studies, either lower reproducibility of single-slice areas compared with EAT volume were found [14], or the epicardial fat measured by single MRI images was not associated with cardiovascular disease risk [15]. This conflicting result might be associated with the choosing the measurement location. Although Gorter et al. [14] measured EAT area at the base of the ventricles, they did not describe the exact location of measurement site. In the present study, single-slice EAT area was correlated with total epicardial fat volume and cardio-metabolic risk across all measurement locations. In multivariate logistic
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regression, EAT area was significantly associated with MS after adjusting for age, smoking and alcohol use. Consistent with a previous study reporting that the BMI and the total epicardial fat were not correlated [40], the association between EAT area and MS was unchanged and consistent even after adjustment for BMI. This finding suggests that EAT may be a contributing factor of MS independent of overall adiposity. Our results on the EAT distribution demonstrated a different pattern of epicardial fat deposition according to BMI. The ratio of EAT area at the LMCA to the MV (LMCA/MV) and the CS to the MV (CS/MV) was higher in the obese group (BMI C 25 kg/m2) than in the nonobese group, suggesting the tendency of EAT deposition to increase more stiffly from the mitral valve level to the caudal and cranial position. This finding may be explained by the anatomical order of epicardial fat deposition. EAT initially accumulates around the proximal coronary vessels in the atrioventricular groove rather than in the ventricular wall [41]. Because a cross-sectional image at the LMCA or the CS level includes a large portion of the proximal coronary vessels in the groove, EAT areas at these levels may increase more rapidly with weight gain compared with the MV level. One recent study demonstrated a regional variation in the association between EAT measurements and cardiometabolic risk factors [42]. Consistent with this study, we demonstrated that the EAT measurement site had an impact on the magnitude of the risk for MS. Indeed, EAT area obtained from the LMCA level resulted in the highest OR for MS, which was higher than the OR of total EAT volume. After further adjusting for BMI, even the OR of VAT for metabolic syndrome was lower than the OR of EAT area at the LMCA image. When looking at individual metabolic risk factors, images obtained at the LMCA level provided correlation coefficients that were consistently high in magnitude for most risk factors. Additionally, EAT area at the LMCA level was also associated with calcified coronary plaque in this study. The OR between EAT and CAC was significant at the LMCA locations after adjustments for age, smoking, alcohol and BMI. Interestingly, while the OR for MS was higher in VAT than EAT, the OR for CAC was higher in EAT than VAT, consistent with a previous study [5]. Taken together, these findings suggest that EAT has a site-specific effect on cardiometabolic risk factors according to the location of EAT deposition. Therefore, an image located at the LMCA level, rather than the total EAT volume, would be a better predictor for MS and coronary atherosclerosis. Consistent with our results, Oyama et al. [13] reported that single-slice epicardial fat area at the LMCA level is significantly correlated with the epicardial fat volume. However, this study measured epicardial fat depositions only in small non-obese people, and
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did not investigate the clinical data of cardiometabolic risk factors. Considering that the association between EAT and MS or CAD might differ according to BMI, our finding provides more robust evidence for using single CT images at the LMCA level in assessing EAT. In this study, we measured EAT using contrastenhanced coronary CT angiography images, which result in higher acquisition times and radiation doses than nonenhanced CT. In addition, CT coronary angiography is not recommended in asymptomatic persons for the assessment of occult CAD. However, in clinically selected, intermediate and low-risk patients, it may be reasonable to measure the coronary artery calcium score using CT to refine clinical risk prediction and to select patients for more aggressive target values for lipid-lowering therapies [43]. Non-enhanced CT scanning for calcium scoring is sufficient to measure EAT amount without additional contrastenhanced CT scanning. Furthermore, EAT area at specific level such as LMCA can be measured with narrow upper and lower boundary of scanning without whole cardiac scan. Thus, our study suggest the possibility that EAT measurement using CT may be used for risk assessment of the clinically selected asymptomatic individual. There are some limitations in this study. First, our findings may not be applicable to the general population because the participants of this study were self referred or referred by their primary physician, suggesting high-risk subjects might be included rather than the healthier general population. This selection bias could influence the relatively high prevalence of CAD in this study, and the results may not be generalizable to an asymptomatic population. Furthermore, the subjects with CAD in this study were limited in small sample size and validation of the derived cut-off values in a larger cohort is needed. Second, although we selected six cross-sectional images with easily discernible vascular landmarks, we did not analyze entire continuous scans. Thus, the exact location of the slice with the strongest correlation with MS could not be identified. Third, we performed a cross-sectional study, and we could not determine causality between EAT and MS. However, our study design has strength in allowing for an exact analysis of EAT distribution across multiple levels with a relatively large sample size, which was different than earlier studies in which EAT was measured using few slice levels with a relatively small sample size. Considering slightly different level may suggest interval change in serial measurement, further prospective studies are required to evaluate whether measurement site influences the relationship between changes in EAT and metabolic risk factors over time to determine the optimal location for the quantification of EAT. In conclusion, we demonstrated that a single-slice EAT area measurement is a simple and reliable method
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compared with time-consuming volumetric measurements and may be used for the quantification of EAT. The epicardial fat area at the LMCA level was the best single slice representing the risk of metabolic syndrome and coronary atherosclerosis. Our findings provide evidence for the advantage of using the EAT area at a single-slice image instead of total EAT volume and suggest that monitoring the changes of EAT deposition at the LMCA level might be used as a therapeutic target for weight-loss interventions or epidemiologic studies. Acknowledgments The authors thank to Moon-young Kim (INFINITT healthcare Inc) for applications support. We also thankful to S. Chen, PhD (Keck School of Medicine, University of Southern California) for the review of our report and Kyung-Mi Nam (The Catholic University of Korea) for data collection. Conflict of interest
None.
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Int J Cardiovasc Imaging (2014) 30:195–204 cardiac computed tomography: a scientific statement from the American Heart Association Committee on Cardiovascular Imaging and Intervention, Council on Cardiovascular Radiology and Intervention, and Committee on Cardiac Imaging, Council on Clinical Cardiology. Circulation 114:1761–1791
ORIGINAL PAPER
Epicardial adipose tissue: relationship between measurement location and metabolic syndrome Ju-Hye Chung • Beom-June Kwon • Sang-Wook Song • Sun-Myeong Ock Whan-Seok Choi • Se-Hong Kim
•
Received: 25 June 2013 / Accepted: 5 October 2013 / Published online: 30 November 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract Epicardial adipose tissue (EAT) is a contributing factor of metabolic syndrome (MS) and coronary artery disease (CAD). However, it is still unclear which measurement location of EAT area best reflects its cardiometabolic risk. The purpose of our study was to investigate the distribution of EAT and its relationship to the total EAT volume and MS. To assess volume and cross-sectional areas of EAT, coronary CT angiography were obtained in 256 asymptomatic subjects. The EAT areas within the threshold range of -190 to -30 Hounsfield units were measured at six representative slices. Correlations between single slice EAT areas and total EAT volumes were high across all measurement locations (correlation coefficient r [ 0.80). The receiver–operator characteristic curves demonstrated EAT area at left main coronary artery (LMCA) was well discriminative for MS (AUC 0.82, p \ 0.001) and CAD (AUC 0.76, p \ 0.001). EAT areas across all measurement
locations were significantly increased linearly with increasing number of MS components. EAT areas were significantly associated with MS at all measurement locations; the highest odds ratio (OR) between EAT area and MS was at the LMCA level (OR 5.86, p \ 0.001). The OR between EAT area and coronary artery calcium was also significant in LMCA locations (OR 1.56, p = 0.042). We demonstrated that the single-slice EAT area measurement is a simple and reliable method compared with time-consuming volumetric measurements. The EAT area at LMCA level was the best single slice representing the risk of metabolic syndrome and coronary atherosclerosis. Keywords Epicardial adipose tissue Metabolic syndrome Measurement location Computed tomography
Introduction J.-H. Chung Department of Family Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 271, Cheon Bo-ro, Uijeong bu-si, Gyeonggi-do 480-717, Korea B.-J. Kwon Division of Cardiology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea S.-W. Song S.-H. Kim (&) Department of Family Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 93-6 Ji-dong, Paldal-gu, Suwon, Kyonggi-do 442-723, Korea e-mail: [email protected] S.-M. Ock W.-S. Choi Department of Family Medicine, St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 505 Banpo-Dong, Seocho-Gu, Seoul 137-701, Korea
Metabolic syndrome (MS) is a cluster of risk factors for type 2 diabetes and cardiovascular disease and is associated with increased mortality [1, 2]. Although visceral adipose tissue (VAT) has been recognized as a key determinant of MS and is presumed to play an important role in its development [3, 4], epicardial adipose tissue (EAT) is also considered to be a contributing factor of MS and coronary artery disease (CAD) [5–7]. EAT is an ectopic fat surrounding the heart, within the boundary of the pericardium. EAT is in direct contact with the major branches of the coronary vessels, and the rate of lipolysis and lipogenesis is significantly higher in EAT than in other adipose tissue of the body [8]. The availability of high-resolution imaging modalities, such as computed tomography (CT) and magnetic resonance
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imaging (MRI), has provided more accurate quantification of EAT compared with echocardiography. Especially, the volumetric measurement of EAT by cardiac CT is a wellestablished validated method for the estimation of MS [9, 10] and CAD [5, 11]. However, the application of multislice imaging with CT is a relatively time-consuming process and is limited by the amount of radiation exposure. Whereas a volumetric measurement with CT is generally considered the reference for estimating EAT, little attention has been paid to the EAT area measurement at a single crosssectional image, which is optimal for the determination of cardiometabolic risk. Although EAT area measured in a single cross-sectional image was correlated with total EAT volume in previous studies of small samples [12, 13], the correlation of EAT area with cardiometabolic risk factors and MS was inconsistent [14, 15]. Thus, it is still unclear which measurement location of EAT area best reflects its metabolic risk and total EAT volume in asymptomatic population. The purpose of our study was: (1) to investigate the distribution of EAT and its relationship to cardiometabolic risk factors; (2) to determine whether a single-slice EAT area can represent the total EAT volume; and (3) to evaluate the impact of EAT area measurement location on coronary atherosclerosis and MS.
Methods Subjects A total of 256 asymptomatic subjects (113 male and 143 female) were retrospectively evaluated from the outpatient clinics at St. Paul’s Hospital and St. Vincent’s Hospital in South Korea. The study subjects were self-referred or referred by their primary physician for various reasons such as abnormal EKG findings or multiple CAD risk factors. All subjects received comprehensive health screening and underwent cardiac and abdominal CT scans for screening purposes between September 2009 and March 2012. Subjects who underwent cardiac or abdominal surgeries affecting the epicardial and visceral fat distribution were excluded from the study. This study was approved by the Research Ethical Committee of the College of Medicine, the Catholic University of Korea, and written informed consent was obtained from all patients. Risk factor assessment The anthropometric, clinical and laboratory investigations were performed on all subjects. The height of each participant was determined using a fixed wall-scale measuring device and was measured to the nearest 0.1 cm. The body weight was measured to the nearest 0.1 kg using a digital scale calibrated prior to each measurement. The body mass
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index (BMI, kg/m2) was calculated as the weight in kilograms (kg) divided by the square of height in meters. Waist circumference (WC) was measured twice to the nearest centimeter at the end of normal expiration in a horizontal plane immediately superior to the left iliac crest according to the National Health and Nutrition Examination Survey protocols. If the variation between these two measurements was greater than 2 cm, a third measurement was taken and the mean was calculated using the two closest measurements. Seated blood pressures were measured using a mercury sphygmomanometer after a 10 min rest period. Two blood pressures measurements were taken from all subjects with a 5 min interval and were averaged for analysis. Fasting plasma glucose, total cholesterol, triglyceride, and HDL-cholesterol levels were measured after a 12-h fasting using an auto-analyzer (Hitachi 747 auto-analyzer, Tokyo, Japan). Smoking status and alcohol use were also investigated. Criteria for metabolic syndrome MS was defined according to the revised NCEP-ATP III criteria [16] with an ethnic-specific cutoff point for abdominal obesity [17]. Diagnosis of MS was based on the presence of three or more of the following clinical criteria: (1) WC C90 cm for men or C85 cm for women; (2) TG levels C150 mg/dL; (3) HDL-cholesterol levels \40 mg/dL for men or\50 mg/dL for women; (4) SBP C130 mmHg or DBP C85 mmHg, or the use of antihypertensive medication; and (5) FBS C100 mg/dL, or the use of anti-diabetics or insulin. CT scan protocol Contrast-enhanced coronary CT angiography was performed with a 128-slice dual source CT scanner (SOMATOM Definition Flash, Siemens Medical Solutions, Forchheim, Germany). The prospective ECG-triggered scanning with the flexible padding technique was performed, and the mean dose–length product (DLP) was 295 ± 134.48 mGy-cm (range 100–595 mGy-cm) for all participants. An average of 250 contiguous slices were taken with a coronary CT angiography protocol (64 9 0.6 mm slice collimation, 280 ms gantry rotation time, 0.32 pitch, 120 kV tube voltage, 800 mA tube current, image matrix of 512 9 512 pixels, temporal resolution 0.075 s, and slice thickness 0.75 mm). At the time of scanning, a bolus of 80 mL of contrast agent (Iopromide, Ultravist 370; Bayer-Schering, Berlin, Germany) was intravenously injected at a rate of 5 mL per second, followed by 50 mL of saline solution administered intravenously at a rate of 5 mL per second. On the same day, approximately 4–5 continuous transverse images (120 kV,
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Fig. 1 Measurement location of epicardial adipose tissue (EAT) area. a Right pulmonary artery (RPA). b Origin of the left main coronary artery (LMCA). c Origin of the right coronary artery (RCA). d Mitral valve level (MV). e Origin of the coronary sinus (CS). f Distal right coronary artery level (dRCA). The measurement of EAT was
performed by tracing the pericardium in axial slices (yellow line). After region of interest (ROI) drawing, pixels with a threshold range of -190 to -30 Hounsfield units (HU) were identified as the EAT area (blue color)
200 mA, scanning time of 2 s, field of view of 380 mm, and slice thickness 5 mm) were obtained at the L4-5 intervertebral space using a CT scanner (LightSpeed, GE Healthcare, Milwaukee, WI) to assess abdominal fat distribution.
quantify total EAT volume, we traced 30–40 slices in each patient, depending on heart size. For the measurement of EAT area, we selected six representative slices on axial images. We used the center of the right pulmonary artery (RPA), the origin of the left main coronary artery (LMCA), the origin of the right coronary artery (RCA), the mitral valve level (MV), the origin of the coronary sinus (CS) and the distal right coronary artery (dRCA) level as anatomical landmarks (Fig. 1). The mitral valve level was defined as the location where the aortic valve disappeared and the four cardiac chambers were well visualized. The level of the distal right coronary artery was regarded as the slice demonstrating the distal right coronary artery and the inferior wall of the right and left ventricles at the bottom of heart. The intra- and inter-observer reproducibility for EAT measurements was evaluated on a random sample of 20 participants by the intra-class correlation coefficient. For the inter-observer reproducibility test, two independent readers measured EAT. For the intra-observer reproducibility test, a second EAT measurement was taken at least 2 weeks later by a single reader. The visceral adipose regions were obtained at the L4-5 intervertebral space by manual tracing, and the pixels with a threshold range of -190 to -30 Hounsfield units (HU) were calculated for adipose tissue area. All coronary CT angiography results were interpreted for the presence of CAD. The patient with CAD was defined as [50 % luminal diameter stenosis in at least one major coronary
Adipose tissue measurement The total volume and cross-sectional areas of EAT were measured by one experienced observer blinded to the clinical information of study subjects. All CT analyses were performed using a dedicated offline workstation (Rapidia, software version 2.8, Infinitt, Seoul, Korea). EAT was defined as the adipose tissue between the outer surface of the myocardium and the visceral layer of the pericardium. We set the slice containing the center of the right pulmonary artery to be the upper slice limit and the last axial slice including any portion of the heart to be the lower slice limit. The segmentation of the axial images into EAT was performed on every fifth slice within this range using semi-automated method. One expert reader set multiple seed points along the pericardium in each axial slice and these points were connected automatically to create a ROI (region of interest). After the ROI drawing, the pixels with a threshold range of -190 to -30 Hounsfield units (HU) were identified as the EAT area in cm2. The EAT volumes were calculated as the sum of the EAT volume of each slice from the superior to inferior boundary and reported in cm3. To
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artery or its branches, otherwise as normal coronary arteries. The coronary artery calcium (CAC) was quantified in Agatston score [18], and the presence of calcified coronary plaque was defined as an Agatston score [ 0.
Int J Cardiovasc Imaging (2014) 30:195–204 Table 1 General characteristics of study subjects (n = 256) Mean ± SD or median (IQR) Age (years)
59.08 ± 8.96
Glucose (mg/dL)
104 (95–121)
Statistical analysis
WC (cm)
87.36 ± 9.02
HDL cholesterol (mg/dL)
47.96 ± 13.65
Data were analyzed using SPSS version 13.0 (SPSS Inc., Chicago, IL) and presented as mean ± standard deviation (SD). The values of fasting plasma glucose and triglyceride were highly skewed, thus, log-transformed for all analyses. The differences between EAT areas at 6 different levels were compared using a repeated measures ANOVA. Ageadjusted partial correlation coefficients were used to assess simple correlations between the EAT areas at various locations and the EAT volume. The cut off value of EAT with the best sensitivity and specificity to predict MS and CAD was determined by using receiver–operator characteristics (ROC) curves. One-way analysis of variance (ANOVA) with a post hoc multiple comparisons LSD test was used to compare the EAT area according to the number of metabolic syndrome risk factors. Multiple logistic regression analysis was performed to assess the relationship of EAT area with metabolic syndrome (MS) and coronary artery calcium (CAC). The odds ratio (OR) for the presence of MS and CAC were based on a 1-SD increase in each of the EAT areas or volumes. A two sided p value \0.05 was considered statistically significant.
Triglyceride (mg/dL)
111 (83–168)
SBP (mmHg)
123.76 ± 14.88
DBP (mmHg)
77.83 ± 8.79
BMI (kg/m2)
24.75 ± 4.13
Coronary calcium score
31 (0–233)
Visceral adipose tissue (cm2)
112.77 ± 56.77
Subcutaneous adipose tissue (cm2) EAT volume (cm3)
164.62 ± 3.39 110.83 ± 45.96
EAT area (cm2) RPA
9.24 ± 5.56
LMCA
13.56 ± 6.68
RCA
11.47 ± 5.10
MV
9.12 ± 4.31
CS
12.05 ± 5.85
dRCA
15.04 ± 7.45 n (%)
Female
143 (55.8)
Metabolic syndrome
110 (42.9)
Male/female
42(37.7)/68(47.5)
Obesity (BMI C 25 kg/m2)
142 (55.5)
CAD
64 (25)
Smoking status Never/ex-smokers
Results Table 1 summarizes the baseline characteristics of the study participants. The mean age was 59.08 ± 8.96 years (58.38 ± 9.09 in males and 59.63 ± 8.84 in females). MS was present in 42.9 % of the study population and was more prevalent in women than in men (47.5 and 37.7 %, respectively). Obesity (BMI C 25 kg/m2) was present in 55.5 % of the study population, and was more prevalent in women than men (76.1 and 49.5 %). 64 (25 %) patients had CAD (stenosis [50 %) on coronary CT angiography, while the remaining 192 subjects (75 %) had normal coronary arteries. The mean EAT area was lowest at the mitral valve level (9.12 ± 4.31 cm2) and highest at the dRCA level (15.04 ± 7.45 cm2). The intra- and inter-observer reproducibility was excellent for the EAT volume measurements (intra-class correlation coefficient of 0.99 and 0.95, respectively). The reproducibility for quantification of the EAT area was also good (intra-class correlation coefficient r [ 0.8 at all measurement locations; p \ 0.001) (Table 2).
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Current smokers
167(65.2)/31(12.1) 58 (22.6)
Alcohol use
102 (39.8)
Hypertension
165 (64.4)
Diabetes mellitus
73 (28.52)
Values are presented as the mean ± SD (standard deviation), medians with interquartile ranges (IQR; 25th and 75th percentiles) and n (%); coronary artery disease (CAD) was defined as [50 % luminal diameter stenosis. n number of persons, WC waist circumference, HDL high-density lipoprotein, SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, EAT epicardial adipose tissue, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
The pattern of EAT distribution according to BMI is shown in Fig. 2. Differences in the EAT areas between measurement sites were significant across all comparisons (p \ 0.001, for both normal and obese groups by a repeated measures ANOVA). The EAT area was increased more stiffly in the obese group (BMI C 25 kg/m2) from the mitral valve level to the caudal and cranial position compared with the normal group. The ratio of the EAT area at the LMCA to
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Table 2 Reproducibility of the EAT measurements Inter-observer reliability
Intra-observer reliability
0.955 (0.333, 0.989)
0.999 (0.973, 0.999)
RPA
0.914 (0.799, 0.965)
0.947 (0.874, 0.979)
LMCA
0.963 (0.778, 0.989)
0.996 (0.990, 0.998)
RCA
0.944 (0.666, 0.983)
0.999 (0.998, 1.000)
Mitral valve
0.940 (0.853, 0.976)
0.973 (0.933, 0.989)
EAT volume EAT area
CS
0.916 (0.617, 0.973)
0.995 (0.987, 0.998)
dRCA
0.892 (0.549, 0.965)
0.986 (0.962, 0.994)
Values are presented as the intra-class correlation coefficient with 95 % confidence interval, p \ 0.001 EAT epicardial adipose tissue, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, CS coronary sinus, dRCA distal right coronary artery
Measurement location
RPA LMCA
ratio (MV/CS) in the obese group was lower than in the nonobese group (0.744 ± 0.15 vs. 0.815 ± 0.17, p = 0.001). Correlations between single-slice EAT areas and EAT volumes were generally high across all measurement locations (correlation coefficient r [ 0.80). The anatomic location of the single slice area which had the highest correlation with EAT volume was the mitral valve level (r = 0.897). The area under the ROC curve (AUC) demonstrated EAT area at LMCA was well discriminative for MS (AUC 0.822, 95 % CI 0.769–0.874, p \ 0.001) and CAD (AUC 0.760, 95 % CI 0.694–0.825, p \ 0.001). EAT area was superior to EAT volume in identifying the presence of MS (AUC 0.822 vs. 0.806, p \ 0.001) and CAD (AUC 0.760 vs. 0.723, p \ 0.001). The cut-off value for EAT volume using the ROC curve was 108.7 cm3 for predicting a CAD (sensitivity 70.3 %, specificity 60.9 %, p \ 0.001). On the other hand, the cut-off value of EAT area at LMCA was 13.59 cm2 for CAD (sensitivity 73.4 %, specificity 63.5 %, p \ 0.001) and 12.73 cm2 for MS (sensitivity 77.9 %, specificity 70.0 %, p \ 0.001) (Fig. 3).
RCA
obese group (BMI>25) MV
Association between EAT areas with MS and CAC
normal
CS dRCA 0
5
10
15
20
Epicardial adipose tissue area (cm2)
Fig. 2 EAT distribution at various measurement locations according to BMI. BMI body mass index, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
the MV (LMCA/MV) in the obese group was higher than in the non-obese group (1.60 ± 0.38 vs. 1.437 ± 0.41, p = 0.003). The ratio of the EAT area at the MV to the CS
We divided subjects into 4 groups according to the presence of metabolic syndrome risk factors (none, one, two and three or more risk factors). The EAT areas across all measurement locations were significantly increased linearly with an increasing number of MS components (Fig. 4). The association between EAT areas and MS was assessed by multivariate logistic regression models (Table 3). The EAT areas were significantly associated with MS at all measurement locations after adjusting for age, smoking and alcohol (Model 1). The highest OR between EAT area and metabolic syndrome was at the LMCA level (OR 5.866; 95 % CI 3.474–9.906; p \ 0.001), which was higher than the total EAT volume (OR 4.766; CI
Fig. 3 Receiver-operating characteristics (ROC) curve to determine cut-off value of epicardial adipose tissue for the prediction of CAD (a) and MS (b)
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Int J Cardiovasc Imaging (2014) 30:195–204 Table 4 Multiple logistic regression analysis for epicardial adipose tissue with coronary artery calcium OR
95 % CI
p value
Model 1: age adjusted VAT
1.378
1.017–1.867
0.039
EAT volume
1.635
1.161–2.304
0.005
RPA
1.425
1.028–1.975
0.034
LMCA
1.603
1.144–2.244
0.006
RCA MV
1.328 1.517
0.970–1.819 1.080–2.130
0.077 0.016
CS
1.314
0.952–1.814
0.097
dRCA
1.061
0.774–1.455
0.713
EAT area
Fig. 4 Epicardial adipose tissue area according to the number of metabolic syndrome risk factors. *Statistically significant difference between adjacent groups, p \ 0.01. EAT epicardial adipose tissue, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
Table 3 Multiple logistic regression analysis for epicardial adipose tissue with metabolic syndrome OR
95 % CI
Model 1: age, smoking and alcohol adjusted VAT
5.966
3.553–10.019
EAT volume
4.766
2.962–7.669
EAT area RPA
3.415
2.274–5.128
LMCA
5.866
3.474–9.906
RCA MV
3.398 3.975
2.249–5.134 2.570–6.147
CS
5.494
3.311–9.118
dRCA
4.079
2.591–6.422
Model 2: age, smoking, alcohol and BMI adjusted VAT
4.316
2.471–7.536
EAT volume
3.563
2.140–5.931
RPA
2.492
1.562–3.976
LMCA
4.368
2.515–7.589
RCA
2.888
1.813–4.602
MV
3.018
1.889–4.823
CS
3.994
2.300–6.93
dRCA
3.075
1.851–5.108
EAT area
All ORs were significant at p \ 0.001 Odds ratios (ORs) and 95 % confidence intervals (CIs) are presented as the odds of metabolic syndrome with a 1-SD increase in adipose tissue VAT visceral adipose tissue, EAT epicardial adipose tissue, BMI body mass index, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
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Model 2: Age, smoking, alcohol and BMI adjusted VAT
1.361
0.931–1.990
0.112
EAT volume
1.536
1.015–2.326
0.042
RPA
1.223
0.832–1.798
0.307
LMCA
1.560
1.016–2.395
0.042
RCA
1.261
0.877–1.812
0.211
MV
1.328
0.902–1.955
0.151
CS
1.132
0.770–1.664
0.527
dRCA
0.903
0.625–1.306
0.589
EAT area
Odds ratios (ORs) and 95 % confidence intervals (CIs) are presented as the odds of coronary artery calcium with a 1-SD increase in adipose tissue VAT visceral adipose tissue, EAT epicardial adipose tissue, BMI body mass index, RPA right pulmonary artery, LMCA left main coronary artery, RCA right coronary artery, MV mitral valve, CS coronary sinus, dRCA distal right coronary artery
2.962–7.669; p \ 0.001). This association remained unchanged and consistent after further adjustment for BMI (Model 2). While the OR for MS was higher in VAT than in EAT, the OR for CAC was higher in EAT than in VAT (Table 4). The OR between EAT area and CAC was significant at the RPA, LMCA and mitral valve levels in age-adjusted models. However, the OR for CAC was significant only at the LMCA level after additional adjustments for smoking, alcohol and BMI (OR 1.560; CI 1.016–2.395; p = 0.042).
Discussion In this study, we demonstrated that measuring EAT area at a single cross-sectional CT image is a reliable method for predicting the epicardial fat volume and cardiometabolic risk. EAT areas at the specific level of axial CT images were an independent risk factor for metabolic syndrome
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and coronary artery calcium. Furthermore, EAT were somewhat site specific, and EAT area obtained from the LMCA level was the best single slices representing the epicardial fat burden. To the best of our knowledge, this is the first study evaluating the impact of EAT area measurement location on cardiometabolic risk factors. It is well known EAT is more lipogenic than other fat tissues of the body, and secretes numerous inflammatory cytokines [7, 19–22]. Thus, EAT is presumed to be a contributing factor in the pathogenesis of MS and coronary artery disease [23–26]. Earlier studies have measured the epicardial fat thickness as an index of EAT mass using echocardiography [27, 28]. However, Echocardiographic measurement of the epicardial fat thickness has several limitations for quantification of EAT, including the lack of an adequate window around all cardiac segments and low reproducibility [14, 29, 30]. Compared with echocardiographic measurements of EAT thickness, the availability of computed tomography (CT) has provided a more accurate quantification of epicardial fat distribution [14]. Therefore, most studies on EAT were performed by the measurement of adipose tissue volume or thickness using CT. Many previous studies have demonstrated that EAT volume measured by CT is strongly associated with cardiometabolic risk factors [5], coronary calcium [31, 32], metabolic syndrome [11] and the severity of CAD [33, 34]. However, measuring EAT volume with multislice CT is limited by radiation exposure and timeconsuming process [35]. In addition, the measurement of EAT thickness showed low reproducibility compared with volumetric EAT measurement [14, 36, 37]. Thus, we measured single-slice fat area for quantification of EAT in this study, and found that the epicardial fat area at a crosssectional CT image strongly correlated with the total epicardial fat volume, metabolic syndrome and coronary artery calcium. Only a few studies on EAT areas have been reported so far, and the results are inconsistent according to measurement location and study populations. Early studies of small (50 * 70 subjects) selected samples have reported that single-slice epicardial fat area measurement is correlated with the epicardial fat volume [12, 38, 39]. In contrast, in other studies, either lower reproducibility of single-slice areas compared with EAT volume were found [14], or the epicardial fat measured by single MRI images was not associated with cardiovascular disease risk [15]. This conflicting result might be associated with the choosing the measurement location. Although Gorter et al. [14] measured EAT area at the base of the ventricles, they did not describe the exact location of measurement site. In the present study, single-slice EAT area was correlated with total epicardial fat volume and cardio-metabolic risk across all measurement locations. In multivariate logistic
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regression, EAT area was significantly associated with MS after adjusting for age, smoking and alcohol use. Consistent with a previous study reporting that the BMI and the total epicardial fat were not correlated [40], the association between EAT area and MS was unchanged and consistent even after adjustment for BMI. This finding suggests that EAT may be a contributing factor of MS independent of overall adiposity. Our results on the EAT distribution demonstrated a different pattern of epicardial fat deposition according to BMI. The ratio of EAT area at the LMCA to the MV (LMCA/MV) and the CS to the MV (CS/MV) was higher in the obese group (BMI C 25 kg/m2) than in the nonobese group, suggesting the tendency of EAT deposition to increase more stiffly from the mitral valve level to the caudal and cranial position. This finding may be explained by the anatomical order of epicardial fat deposition. EAT initially accumulates around the proximal coronary vessels in the atrioventricular groove rather than in the ventricular wall [41]. Because a cross-sectional image at the LMCA or the CS level includes a large portion of the proximal coronary vessels in the groove, EAT areas at these levels may increase more rapidly with weight gain compared with the MV level. One recent study demonstrated a regional variation in the association between EAT measurements and cardiometabolic risk factors [42]. Consistent with this study, we demonstrated that the EAT measurement site had an impact on the magnitude of the risk for MS. Indeed, EAT area obtained from the LMCA level resulted in the highest OR for MS, which was higher than the OR of total EAT volume. After further adjusting for BMI, even the OR of VAT for metabolic syndrome was lower than the OR of EAT area at the LMCA image. When looking at individual metabolic risk factors, images obtained at the LMCA level provided correlation coefficients that were consistently high in magnitude for most risk factors. Additionally, EAT area at the LMCA level was also associated with calcified coronary plaque in this study. The OR between EAT and CAC was significant at the LMCA locations after adjustments for age, smoking, alcohol and BMI. Interestingly, while the OR for MS was higher in VAT than EAT, the OR for CAC was higher in EAT than VAT, consistent with a previous study [5]. Taken together, these findings suggest that EAT has a site-specific effect on cardiometabolic risk factors according to the location of EAT deposition. Therefore, an image located at the LMCA level, rather than the total EAT volume, would be a better predictor for MS and coronary atherosclerosis. Consistent with our results, Oyama et al. [13] reported that single-slice epicardial fat area at the LMCA level is significantly correlated with the epicardial fat volume. However, this study measured epicardial fat depositions only in small non-obese people, and
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did not investigate the clinical data of cardiometabolic risk factors. Considering that the association between EAT and MS or CAD might differ according to BMI, our finding provides more robust evidence for using single CT images at the LMCA level in assessing EAT. In this study, we measured EAT using contrastenhanced coronary CT angiography images, which result in higher acquisition times and radiation doses than nonenhanced CT. In addition, CT coronary angiography is not recommended in asymptomatic persons for the assessment of occult CAD. However, in clinically selected, intermediate and low-risk patients, it may be reasonable to measure the coronary artery calcium score using CT to refine clinical risk prediction and to select patients for more aggressive target values for lipid-lowering therapies [43]. Non-enhanced CT scanning for calcium scoring is sufficient to measure EAT amount without additional contrastenhanced CT scanning. Furthermore, EAT area at specific level such as LMCA can be measured with narrow upper and lower boundary of scanning without whole cardiac scan. Thus, our study suggest the possibility that EAT measurement using CT may be used for risk assessment of the clinically selected asymptomatic individual. There are some limitations in this study. First, our findings may not be applicable to the general population because the participants of this study were self referred or referred by their primary physician, suggesting high-risk subjects might be included rather than the healthier general population. This selection bias could influence the relatively high prevalence of CAD in this study, and the results may not be generalizable to an asymptomatic population. Furthermore, the subjects with CAD in this study were limited in small sample size and validation of the derived cut-off values in a larger cohort is needed. Second, although we selected six cross-sectional images with easily discernible vascular landmarks, we did not analyze entire continuous scans. Thus, the exact location of the slice with the strongest correlation with MS could not be identified. Third, we performed a cross-sectional study, and we could not determine causality between EAT and MS. However, our study design has strength in allowing for an exact analysis of EAT distribution across multiple levels with a relatively large sample size, which was different than earlier studies in which EAT was measured using few slice levels with a relatively small sample size. Considering slightly different level may suggest interval change in serial measurement, further prospective studies are required to evaluate whether measurement site influences the relationship between changes in EAT and metabolic risk factors over time to determine the optimal location for the quantification of EAT. In conclusion, we demonstrated that a single-slice EAT area measurement is a simple and reliable method
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compared with time-consuming volumetric measurements and may be used for the quantification of EAT. The epicardial fat area at the LMCA level was the best single slice representing the risk of metabolic syndrome and coronary atherosclerosis. Our findings provide evidence for the advantage of using the EAT area at a single-slice image instead of total EAT volume and suggest that monitoring the changes of EAT deposition at the LMCA level might be used as a therapeutic target for weight-loss interventions or epidemiologic studies. Acknowledgments The authors thank to Moon-young Kim (INFINITT healthcare Inc) for applications support. We also thankful to S. Chen, PhD (Keck School of Medicine, University of Southern California) for the review of our report and Kyung-Mi Nam (The Catholic University of Korea) for data collection. Conflict of interest
None.
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