Cardiac magnetic resonance based evaluation of aortic stiffness and epicardial fat volume in patients with hypertension,diabetes mellitus,and myocardial infarction

Abstract

Background

Aortic stiffness and epicardial fat relate to cardiovascular risk. Their relationship with each other and their role with hypertension, diabetes mellitus (DM), and myocardial infarction (MI) can be evaluated by cardiac magnetic resonance (CMR).

Purpose

To explore an association between aortic stiffness and epicardial as well as paracardial fat volume (EFV and ParaFV, respectively) in hypertensive patients and to relate the results to the presence of DM and MI.

Material and Methods

A total of 156 hypertensive and 20 non-hypertensive participants were examined at 1.5 Tesla. A 2D-velocity-encoded sequence was acquired to assess aortic pulse wave velocity (PWV in m/s) as a measure of aortic stiffness. A 3D-Dixon sequence was used to determine EFV and ParaFV.

Results

PWV correlated with EFV (R = 0.474; P < 0.001), but not with ParaFV. Fat volumes (in mL/m²) and PWV were lower in non-hypertensive controls compared to hypertensive patients. EFV and PWV were significantly higher in diabetic hypertensive patients without MI (n = 19; PWV: 10.4 ± 2.9; EFV: 92.5 ± 19.3) compared to hypertension-only patients (n = 84 [no DM or MI]; EFV: 64.8 ± 25.1, PWV: 9.0 ± 2.6; P < 0.05). Logistic regression analysis showed a significant association between the presence of a MI and a higher EFV (P < 0.05), but not with PWV (P = 0.060) or ParaFV (P = 0.375).

Conclusion

A relationship between aortic stiffness and EFV was found in hypertensive patients. Both were increased in the presence of DM; however, only EFV was increased in the presence of MI. This may relate to the PWV lowering effect of the antihypertensive medication used by hypertensive patients and underscores the benefit of EFV assessment in this regard.

Keywords

Epicardial fat aortic stiffness hypertension diabetes mellitus myocardial infarction cardiac magnetic resonance

Introduction

Pericardial fat has been related to atherosclerosis and cardiovascular risk factors in general (1); (pro)-inflammatory and metabolic mechanisms are ascribed to this tissue. Pericardial fat is divided into epicardial fat, which surrounds the heart and is covered by the pericardium, and paracardial fat, which surrounds the heart outside this region (1). Aortic pulse wave velocity (PWV)—a measure of aortic stiffness—is also a marker for increased cardiovascular risk and a strong predictor of cardiovascular events (2,3). Antihypertensive medication is known to have a lowering effect on PWV (2,4).

Associations between epicardial fat and aortic stiffness have recently been observed (5,6). However, the relationship of epicardial and paracardial fat, which have different origins and biochemical activities (1), with aortic stiffness and their role in hypertensive patients using antihypertensive medications, as well as their role regarding cardiovascular endpoints, such as myocardial infarction (MI), have not been fully elucidated. Different techniques for their measurements, such as computed tomography (CT), for the accurate evaluation epicardial fat volume (EFV) or tonometric devices for the evaluation of PWV are disadvantageous. Here, cardiac magnetic resonance (CMR) has the advantage that it allows for both measurements in a single exam. Accurate evaluation of EFV and paracardial fat volumes (ParaFV) can be performed using an electrocardiography triggered, respiratory navigator gated three-dimensional (3D) gradient echo pulse sequence and aortic stiffness can be determined by velocity-encoded CMR to calculate the aortic PWV (5,7,8).

Therefore, the aim of the CMR study was to, first, explore the association between EFV, ParaFV, and aortic PWV in patients with treated hypertension and, second, to relate the results to the presence of diabetes mellitus (DM) and MI.

Material and Methods

This prospective study was approved by the local ethic committee. All examinations were performed on a 1.5 T MR system (Ingenia, Philips Healthcare, Best, The Netherlands) with a maximum gradient strength of 45 mT/m and a maximum slew rate of 120 mT/m/ms. A 32-channel torso coil with digital interface was used for signal reception. Written informed consent was obtained from all study participants prior to CMR.

Study population

A total of 176 men and women were examined. In total, 156 were patients with treated hypertension. Twenty participants had no hypertension, no DM, and no history of cardiovascular disease; they served as controls. According to clinical history and CMR findings and in order to explore relationships with EFV, ParaFV, and aortic PWV, hypertensive patients were grouped in those with MI (n = 53) and without MI (n = 103). Both groups were also sub-grouped according to the presence of DM. Exclusion criteria included general contraindications for CMR (e.g. pacemaker, claustrophobia), arrhythmias (e.g. atrial fibrillation), a left ventricular ejection fraction lower than 50%, and relevant valve diseases.

Magnetic resonance imaging (MRI) acquisition

Functional imaging: ECG-gated SSFP-cine images in the four-chamber view, two-chamber view, left ventricular outflow tract, and short axis were obtained in breath-hold for wall motion and functional analysis. Following sequence parameters were used: field of view (FOV) = 350 × 350 mm², slice thickness =8 mm, pixel size = 1.7 × 1.7 mm², reconstructed to 1 × 1 mm², repetition time (TR) = 3.1 ms, echo time (TE) = 1.6 ms, flip angle (α) = 60°, parallel imaging factor (SENSE) = 2.5, and number of cardiac phases reconstructed = 40. The presence of a MI was detected by late gadolinium enhancement (LGE) using a 3D-segmented inversion-recovery gradient-echo sequences in the short axis and the two- and four-chamber views (9).

Dixon chemical shift imaging: An electrocardiography triggered and respiratory navigator gated magnetization prepared 3D-gradient echo pulse sequence was used for cardiac modified Dixon imaging (7). The “gate and track” option of the scanner software was used, i.e. navigator gating was combined with prospective motion correction (10). Trigger delay was set to end-diastole and optimized according to the cine MRI data. Sequence parameters were: FOV = 350 × 302 × 180 mm³, voxel size = 1.5 × 1.5 × 3.0 mm³, reconstructed voxel size = 1.0 × 1.0 × 1.5 mm³, TR = 5.4 ms, TE₁/TE₂ = 1.8 ms /4.0 ms; α = 20°, parallel imaging factor (SENSE) = 1.5 in both phase encoding directions, water fat shift = 0.16 pixel, arrhythmia rejection was applied, T2 preparation = 50 ms, acquisition window = 100–156 ms (selected based on cine MRI data). In-phase (IP), opposed-phase (OP), water only (W), and fat only (F) images were reconstructed online at the scanner console using an algorithm described in reference (11).

Aortic PWV: PWV was assessed based on velocity encoded (VE) MRI. For planning of the VE sequence, ECG-gated SSFP-cine images with oblique sagittal view of the aortic arch including the ascending (AA) and the proximal descending aorta (DA) were acquired. A 2D velocity-encoded sequence was planned perpendicular through the AA and DA at the level of the pulmonary artery bifurcation with the following parameters: FOV = 300 × 225 mm², slice thickness = 8 mm, pixel size = 1.7 × 1.7 mm², reconstructed to 1.2 ×1.2 mm², TR = 6.5 ms, TE = 2.2 ms, α = 15°, velocity encoding: 150 cm/s; number of heart phases per cycle = 100 (5,8).

Image analysis

Cardiac parameters: Left ventricular end systolic and end diastolic volumes (LVESV and LVEDV, respectively) and LV ejection fraction (LVEF) were determined offline using dedicated software (ViewForum, Philips Healthcare) by manual tracing of the endocardial borders. Papillary muscles were included in the left ventricular cavity volume. Left ventricular volumes were additionally normalized to the body surface area (BSA) yielding a LVEDV index (LVEDVi).

Epicardial and paracardial fat volumes: Dixon images were analyzed offline on a personal computer using dedicated software written in MATLAB (The MathWorks, Inc., Natick, MA, USA) as described before (7). Briefly, fat volumes were measured between the bifurcation of the pulmonary artery and the most inferior transversal slice of the myocardium by manually defining two 3D regions of interest (ROI). One ROI contoured the epicardial fat border in each slice to measure EFV. A second ROI included the whole pericardial fat (epicardial and paracardial). Fat-fraction (FF) maps were computed based on the fat- and water-only images. Appropriate noise- and FF-thresholds accounting for relaxation effects were applied to identify voxels predominantly containing fat. The fat volumes were finally determined by multiplying the number of fat voxels inside the three-dimensional ROIs by the voxel size. ParaFV was calculated by subtracting the EFV from the whole pericardial fat volume. Fat volumes were normalized to the BSA.

Aortic PWV: PWV quantification was performed using a tool implemented in the software Segment (Segment, version 1.9, R3918; http://segment.heiberg.se). The time interval between the arrival of the velocity waveform at the section of the AA and at the section of the proximal DA (transit time or TT) was determined by contour-drawing in the aortic velocity maps. TT is measured as the time between the intercept of the two calculated tangents with the time axis. The path length of the aortic arch (aortic length [AL]) between the section through the AA and through the proximal DA, was measured between the center of the cross-sections of AA and proximal DA and the aortic PWV was finally calculated by PWV = AL/TT (5,8).

Statistical analysis

Continuous variables were expressed as mean ±standard deviation and categorical data as absolute frequencies and percentages. Spearman rank correlation was used to test the associations between two ranked variables. Continuous variables were tested for normal distribution. Results were adjusted for age, gender, and BMI. Regression models, the independent two-sample Student’s t test or the Mann–Whitney U test were used. Analysis was performed using Stata version 13.1 (StataCorp LP, College Station, TX, USA).

Results

The Dixon and velocity-encoded sequences for the assessment of PWV, EFV, and ParaFV have been acquired successfully in 176/176 study participants. There was a correlation between aortic PWV and EFV (R = 0.474, P < 0.001) and between aortic PWV and age (R = 0.55, P < 0.001) in all study participants (n = 176). The significant association between EFV and aortic PWV was confirmed after adjustment for age, BMI, and gender (P = 0.013). There was no significant correlation between aortic PWV and ParaFV.

The non-hypertensive controls had a lower EFV (EFV: 49.4 ± 19.9 versus 64.8 ± 25.1 mL/m², P =0.017; ParaFV: 66.1 ± 30.8 versus 81.4 ± 41.0 mL/m², P =0.106) and a somewhat lower PWV (7.9 ± 2.6 versus 9.0 ± 2.6 m/s, P = 0.089) than hypertensive only patients (without DM or MI). As expected, the hypertensive patients also showed a higher interventricular septal thickness compared to non-hypertensive controls (8.6 ± 1.3 versus 10.5 ± 2.6 mm, P = 0.044). There were no significant differences regarding age, BMI, or gender (Table 1).

Table 1.

Clinical characteristics and results of controls (with no hypertension, DM, or cardiovascular disease) and of hypertensive patients without DM or MI (HTN-only).

Parameter	Controls	HTN-only	P value
n	20	84
Age (years)	60.3 ± 7.6	61.2 ± 13.1	0.750
BMI (kg/m²)	27.0 ± 4.5	27.8 ± 4.7	0.502
Male	11 (55%)	46 (55%)	0.709
EF (%)	65.6 ± 4.3	63.3 ± 7.2	0.168
LVEDVi (mL/m²)	66.8 ± 12.9	69.8 ± 20.9	0.536
IVSD (mm)	8.6 ± 1.3	10.5 ± 2.6	0.044
PWV (m/s)	7.9 ± 2.6	9.0 ± 2.6	0.089
EFV (mL/m²)	49.4 ± 19.9	64.8 ± 25.1	0.017
ParaFV (mL/m²)	66.1 ± 30.8	81.4 ± 41.0	0.106

BMI, body mass index; EF, ejection fraction; LVEDV, left ventricular end-diastolic volume; LVEDVi, left ventricular end-diastolic volume index = LVEDV/body surface area; IVSD, interventricular septal diameter; EFV, epicardial fat volume; ParaFV, paracardial fat volume; PWV, aortic pulse wave velocity.

Compared to hypertensive patients without MI, the presence of a MI was associated with a higher EFV (69.3 ± 26.3 versus 84.0 ± 27.8, P = 0.011) and an only somewhat higher PWV (9.3 ± 2.6 versus 10.0 ± 3.1, P = 0.059). The results are displayed in Table 2.

Table 2.

Clinical characteristics and results of hypertensive patients divided into patients with and without MI.

	Hypertensive patients
Parameter	No MI	MI	P value
n	103	53
Age (years)	61.5 ± 14.4	62.3 ± 11.2	0.604
BMI (kg/m²)	28.8 ± 5.5	29.0 ± 4.5	0.802
Male	62	43	0.010
DM	19	14	0.251
Medication
ß-inh.	62 (60%)	44 (83%)	0.004
ACE-inh.	73 (71%	49 (92%)	0.000
CCh-inh.	19 (18%)	13 (25%)	0.360
Diuretics	34 (33%)	24 (45%)	0.188
Statins	50 (49%)	50 (94%)	0.000
ASA	48 (47%)	52 (98%)	0.000
EF (%)	63.6 ± 7.2	57.3 ± 14.3	0.000
LVEDVi (mL/m²)	69.8 ± 19.8	78.3 ± 21.7	0.020
IVSD (mm)	10.6 ± 2.5	11.2 ± 2.3	0.120
PWV (m/s)	9.3 ± 2.6	10.0 ± 3.1	0.059*
EFV (mL/m²)	69.3 ± 26.3	84.0 ± 27.8	0.011*
ParaFV (mL/m²)	89.4 ± 43.7	103.7 ± 51.4	0.349*

After adjustment of age, BMI, and gender.

BMI, body mass index; DM, diabetes mellitus; ß-inh., beta-receptor inhibitors; ACE-inh., angiotensin-converting-enzyme inhibitors; CCh-Inh., calcium-channel inhibitors; ASA, acetylsalicylic acid; EF, ejection fraction; LVEDVi, left ventricular end-diastolic volume index = LVEDV/body surface area; IVSD, interventricular septal diameter; EFV, epicardial fat volume; ParaFV, paracardial fat volume; PWV, aortic pulse wave velocity.

In the hypertensive cohort without MI, participants with DM had higher fat volumes (EFV: 64.8 ± 25.1 versus 92.5 ± 19.3 mL/m², P = 0.014 and ParaFV: 81.4 ± 41.0 versus 125.3 ± 37.9 mK/m², P = 0.066) and a higher PWV (9.0 ± 2.6 versus 10.4 ± 2.9 m/s, P =0.017) than those without DM (Table 3).

Table 3.

Results of the hypertensive patients sub-grouped in those with and without diabetes mellitus (DM).

	Hypertensive patients without DM*			Hypertensive patients + DM^†			Hypertensive patients without MI^‡
Parameter	1) No MI	2) +MI	P value	3) No MI	4) +MI	P value	P value 1 vs. 3: [HTN] vs. [HTN + DM]
n	84	39		19	14
Age (years)	61.2 ± 13.1	62.3 ± 11.9	0.655	62.9 ± 8.6	63.1 ± 9.2	0.876	0.565
BMI (kg/m²)	27.8 ± 4.7	28.7 ± 4.6	0.321	32.8 ± 6.1	30.0 ± 4.1	0.095	0.001
Male	49	33	0.004	13	10	0.858	0.247
EF (%)	63.3 ± 7.2	57.5 ± 11.4	0.001	65.1 ± 7.1	58.1 ± 8.5	0.016	0.322
LVEDVi (mL/m²)	69.8 ± 20.9	78.2 ± 21.7	0.043	70.6 ± 14.3	77.4 ± 22.1	0.250	0.835
IVSD (mm)	10.5 ± 2.6	11.1 ± 2.3	0.451	10.8 ± 2.1	11.4 ± 2.5	0.432	0.626
PWV (m/s)	9.0 ± 2.6	10.2 ± 3.3	0.066^§	10.4 ± 2.9	9.9 ± 3.0	0.166^§	0.017^§
EFV (mL/m²)	64.8 ± 25.1	87.0 ± 39.3	0.024^§	92.5 ± 19.3	87.2 ± 25.4	0.222^§	0.014^§
ParaFV (mL/m²)	81.4 ± 41.0	109.4 ± 63.1	0.308^§	125.3 ± 37.9	97.6 ± 33.5	0.494^§	0.066^§

Displays the comparison between non-diabetic hypertensive patients and ^†between diabetic hypertensive patients with and without myocardial infarction (MI).

‡

Displays the P values of the comparison between diabetic and non-diabetic hypertensive patients free from MI.

After adjustment of age, BMI and gender.

BMI, body mass index; EF, ejection fraction; LVEDVi, left ventricular end-diastolic volume index = LVEDV/body surface area; IVSD, interventricular septal diameter; EFV, epicardial fat volume; ParaFV, paracardial fat volume; PWV, aortic pulse wave velocity.

In the non-diabetic hypertensive cohort, it was observed that patients with a MI had a higher EFV than those patients without a MI. However, there were no differences regarding ParaFV or PWV. In diabetic patients, there were no significant MI-related differences regarding EFV, ParaFV, or PWV relative to diabetic patients without MI (Table 3).

A logistic regression analysis with odds ratio (OR) calculation adjusted for age, BMI, sex, and DM confirmed the results, revealing that only EFV was significantly increased in the presence of a MI, but not ParaFV or PWV (EFV per 10 mL/m²: 1.20 ± 0.10, P = 0.019; ParaFV per 10 mL/m²: 1.04 ± 0.04, P =0.375; PWV per m/s: 1.10 ± 0.09, P = 0.060).

Examples of the fat volume and aortic PWV measurements are shown in Figs. 1 and 2.

Fig. 1.

EFV and ParaFV. (a) Reconstructed fat only image with the pericardial outlines (arrows) for the measurement of the EFV in a 57-year-old male patient with hypertension and DM but no MI. (b) Same patient as in (a) showing the segmented fat voxels based on fat- and water-only images with the transferred ROIs. The inner dashed circle indicates the ROI for determination of the EFV and the outer dashed circle indicates the ROI including the whole pericardial fat. (c) Reconstructed fat-only image in a 64-year-old male patient with hypertension and MI and (d) in a 51-year-old male patient without hypertension or DM.

Fig. 2.

PWV quantification in a 51-year-old male, using a software tool (Segment, version 1.9, R3918; http://segment.heiberg.se). (a) The path length of the aortic arch (aortic length = AL) is the distance between the section through the ascending (AA) and proximal descending aorta (DA). (b) ROI in AA and in DA for flow measurements. (c) Flow curves along with their respective calculated tangents. PWV = AL/TT, with TT being the transit time between the two tangents.

Discussion

Atherosclerosis is the leading cause of death in the Western world (12). Thus, identification and exploration of related parameters which can easily be integrated into a routine imaging examination—as done in this study—is desirable. In this CMR study a relationship between aortic stiffness and EFV was observed in hypertensive patients. Both, epicardial fat and aortic stiffness were increased in hypertensive patients with additional DM; however, only epicardial fat was found to be significantly increased in the presence of a MI.

In hypertension, an increased aortic stiffness occurs earlier in age as a result of structural changes due to aortic wall stretching and an accelerated development of atherosclerosis with consecutive arterial wall thickening (2,3). DM may have an additive effect. Possible mechanisms may relate to an associated dyslipidemia, disturbances in insulin sensitivity and endothelial function eventually leading to vascular changes, and vessel wall damage. An increased inflammatory burden may also play a role (13,14). Inflammatory mechanisms are known to be involved in the development of atherosclerosis (15). These, as well as unfavorable metabolic activities, are also related to increased amounts of epicardial fat (1,16,17). When increased, the beneficial functions of that fatty tissue, such as the production of anti-atherogenic and anti-inflammatory adipokines or the vascular flow regulation by vasocrine mechanisms, may get lost or reduced (1,18). When dysfunctional, hypertrophied, and hypoxic, the epicardial fat attracts a variety of inflammatory cells and factors and may further shift towards an unfavorable metabolic and (pro)-inflammatory state. This, in turn, may promote arterial wall inflammation and progression of atherosclerosis (1,16,19 –21). Accordingly, aortic PWV—as a measure of aortic stiffness—correlated with EFV (5,6). However, no correlation was found with ParaFV, possibly due to embryological and biochemical differences as well as differences in inflammatory and metabolic activities between the two fat compartments, which are known to be higher for epicardial fat (1,18). Finally, the same epicardial fat related (pro)-inflammatory activity may also act locally at the level of the aortic root and may partly explain the relationship between aortic PWV and epicardial fat.

Epicardial fat amounts were increased in hypertensive patients and patients with DM. Epicardial fat, which has direct contact with coronary arteries and shares the same blood supply, eventually contributes to development of local atherosclerosis through the above mechanisms (1). Higher amounts of epicardial fat have been related to unstable plaques and MI. Furthermore, in perivascular epicardial fat increased amounts of inflammatory markers involved in coronary atherogenesis have been observed, especially at the sites of thin cap fibroatheromas (19,21 –25). Harada et al. observed increased amounts of epicardial fat in patients with lipid-rich plaques and acute coronary events but not in patients with stable angina pectoris (23). Schlett et al. demonstrated increased EFVs in patients with a high-risk coronary artery lesion morphology compared to patients with no high-risk morphology (24). Accordingly, in our study EFV was increased in the presence of a MI. However, no increased risk was found for aortic stiffness, possibly due to the fact that all hypertensive patients received antihypertensive medication. Antihypertensive medications, such as beta-receptor inhibitors or diuretics, are indeed known to reduce the PWV and this may explain the lack of a relation between aortic PWV and the presence of a MI (2,4). As a consequence, in hypertensive patients who receive such medications, the determination of PWV may be of limited value. Therefore, the present study underscores the role of EFV as a valuable measure in the assessment of cardiovascular risk in such patients.

The fact that significantly more patients with MI had statins in their medication might be regarded as a limitation since statins have also been shown to decrease epicardial adipose tissue (26). However, even with statins these patients had significantly higher EFVs. A second limitation is the relatively low number of study subjects when sub-grouped into one disease group, particularly in patients with DM and/or MI. Therefore, the results cannot be generalized and further studies with higher numbers of patients in these different groups are needed. The observational and explorative study design did not allow for the determination of the exact causality or pathogenesis of the described findings neither the effect of the different medications on aortic stiffness. At this point, the role of the investigated CMR-based parameters for prediction of future cardiovascular event remains unclear and therefore future follow-up studies are needed.

In conclusion, the presented CMR study revealed a relationship between aortic stiffness and EFV in hypertensive patients, presumably through similar inflammatory and metabolic mechanisms. There was no relationship between aortic stiffness and ParaFV. Both parameters, EFV and aortic stiffness, were increased in the presence of DM; however, only EFV was found to be increased in the presence of a MI. This may relate to the PWV lowering effect of antihypertensive medication, and, in this regard, underscores the benefit of EFV assessment.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Talman

Psaltis

Cameron

et al.

Epicardial adipose tissue: far more than a fat depot. Cardiovasc Diagn Ther 2014; 4: 416–429.

Laurent

Cockcroft

Van Bortel

et al.

Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J 2006; 27: 2588–2605.

Baulmann

Homsi

Uen

et al.

Pulse wave velocity is increased in patients with transient myocardial ischemia. J Hypertens 2006; 24: 2085–2090.

Dudenbostel

Glasser

. Effects of antihypertensive drugs on arterial stiffness. Cardiol Rev 2012; 20: 259–263.

Homsi

Thomas

Gieseke

et al.

Epicardial fat volume and aortic stiffness in healthy individuals: a quantitative cardiac magnetic resonance study. Rofo 2016; 188: 853–858.

Kim

Kang

. Echocardiographic epicardial fat thickness is associated with coronary artery calcification- results from the CAESAR Study. Circ J 2015; 79: 818–824.

Homsi

Meier-Schroers

Gieseke

et al.

3D-Dixon MRI based volumetry of peri- and epicardial fat. Int J Cardiovasc Imaging 2016; 32: 291–299.

Wentland

Grist

Wieben

. Review of MRI-based measurements of pulse wave velocity: a biomarker of arterial stiffness. Cardiovasc Diagn Ther 2014; 4: 193–206.

Look

Locker

. Time saving in measurement of NMR and EPR relaxation times. Review of Scientific Instruments 1970; 41: 250–251.

10.

McConnell

Khasgiwala

Savord

et al.

Prospective adaptive navigator correction for breath-hold MR coronary angiography. Magn Reson Med 1997; 37: 148–152.

11.

Eggers

Brendel

Duijndam

et al.

Dual-echo Dixon imaging with flexible choice of echo times. Magn Reson Med 2011; 65: 96–107.

12.

Yusuf

Hawken

Ounpuu

et al.

Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004; 364: 937–952.

13.

Lyons

Basu

. Biomarkers in diabetes: hemoglobin A1c, vascular and tissue markers. Transl Res 2012; 159: 303–312.

14.

Saely

Rein

Vonbank

et al.

Type 2 diabetes and the progression of visualized atherosclerosis to clinical cardiovascular events. Int J Cardiol 2013; 167: 776–780.

15.

Libby

. Inflammation in atherosclerosis. Nature 2002; 420: 868–874.

16.

Iacobellis

Bianco

. Epicardial adipose tissue: emerging physiological, pathophysiological and clinical features. Trends Endocrinol Metab 2011; 22: 450–457.

17.

Ferreira

Snijder

Twisk

et al.

Central fat mass versus peripheral fat and lean mass: opposite (adverse versus favorable) associations with arterial stiffness? The Amsterdam Growth and Health Longitudinal Study. J Clin Endocrinol Metab 2004; 89: 2632–2639.

18.

Marchington

Pond

. Site-specific properties of pericardial and epicardial adipose tissue: the effects of insulin and high-fat feeding on lipogenesis and the incorporation of fatty acids in vitro. Int J Obes 1990; 14: 1013–1022.

19.

Shimabukuro

Hirata

Tabata

et al.

Epicardial adipose tissue volume and adipocytokine imbalance are strongly linked to human coronary atherosclerosis. Arterioscler Thromb Vasc Biol 2013; 33: 1077–1084.

20.

Karastergiou

Evans

Ogston

et al.

Epicardial adipokines in obesity and coronary artery disease induce atherogenic changes in monocytes and endothelial cells. Arterioscler Thromb Vasc Biol 2010; 30: 1340–1346.

21.

Mazurek

Zhang

Zalewski

et al.

Human epicardial adipose tissue is a source of inflammatory mediators. Circulation 2003; 108: 2460–2466.

22.

Hirata

Tabata

Kurobe

et al.

Coronary atherosclerosis is associated with macrophage polarization in epicardial adipose tissue. J Am Coll Cardiol 2011; 58: 248–255.

23.

Harada

Uetani

et al.

The different association of epicardial fat with coronary plaque in patients with acute coronary syndrome and patients with stable angina pectoris: analysis using integrated backscatter intravascular ultrasound. Atherosclerosis 2014; 236: 301–306.

24.

Schlett

Ferencik

Kriegel

et al.

Association of pericardial fat and coronary high-risk lesions as determined by cardiac CT. Atherosclerosis 2012; 222: 129–134.

25.

Homsi

Sprinkart

Gieseke

et al.

3D-Dixon cardiac magnetic resonance detects an increased epicardial fat volume in hypertensive men with myocardial infarction. Eur J Radiol 2016; 85: 936–942.

26.

Park

Kim

et al.

Effects of statins on the epicardial fat thickness in patients with coronary artery stenosis underwent percutaneous coronary intervention: comparison of atorvastatin with simvastatin/ezetimibe. J Cardiovasc Ultrasound 2010; 18: 121–126.