Evolutional change in epicardial fat and its correlation with myocardial diffuse fibrosis in heart failure patients

Journal of Clinical Lipidology (2017) -, -–-

1 2 3 4 Original Article 5 6 7 8 9 10 Q1 11 12 13 1 1 Q6 14 Jien-Jiun Chen , Hao-Yuan Tsai , Lian-Yu Lin, Mao-Yuan M. Su, Yi-Fan Wu, 15 Q2 Juey-Jen Hwang, Jiunn-Lee Lin, Cho-Kai Wu* 16 17 18 Q3 Cardiovascular Center, National Taiwan University Hospital Yun–Lin Branch, Douliou, Taiwan (Dr Chen); Division of 19 Cardiology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan (Dr Tsai); 20 Division of Cardiology, Department of Internal Medicine, National Taiwan University College of Medicine and Hospital, 21 Taipei, Taiwan (Drs L.-Y. Lin, Hwang, J.-L. Lin, and C.-K. Wu); Department of Medical Imaging, National Taiwan 22 University Hospital, Taipei, Taiwan (Dr Su); and Department of Family Medicine, Taipei City Hospital, Renai Branch, 23 Taipei, Taiwan (Dr Y.-F. Wu) 24 25 OBJECTIVES: The aim of this study was to characterize the characteristics of epicardial fat (EAT) in KEYWORDS: 26 different stage heart failure (HF) patients and its relationship between cardiac fibrosis. Heart failure; 27 BACKGROUND: EAT is visceral adipose tissue that possesses inflammatory properties. InflammaCardiac magnetic 28 tion and obesity are associated with cardiac fibrosis, but the relationship between cardiac fibrosis resonance imaging; 29 and EAT is unknown. Myocardial fibrosis; 30 METHODS: EAT volume was measured using cardiac magnetic resonance imaging (CMR) in 180 Epicardial fat 31 subjects: 58 patients with systolic HF, 63 patients with HF and preserved ejection fraction, and 59 32 patients without HF. CMR derived myocardial extracellular volume (ECV) was used for fibrosis quantification. 33 RESULTS: Patients with systolic HF had significantly more EAT compared with patients with HF 34 and preserved ejection fraction or the control group (patients without HF) (indexed EAT volume 35 2 [mL/m ], 27.0 [22.7–31.6] vs 25.6 [21.4–31.2] and 24.2 [21.0–27.6], P , .05). The adjusted EAT 36 amount was associated with ECV completely independent of age, hypertension, diabetes, etiology 37 of HF, left ventricular ejection fraction, CMR–late gadolinium enhancement (LGE), left ventricular 38 mass index, and left ventricular end-diastolic volume index (correlation coefficient: 0.49; 95% confi39 dence interval: 0.12–0.86, P , .01). Increased CMR ECV was more associated with EAT in those with 40 advanced age, male sex, LGE on magnetic resonance imaging–LGE images, and less left ventricular 41 end-diastolic volume index. 42 CONCLUSIONS: EAT volume is highly associated with CMR ECV independent of traditional risk 43 factors and left ventricular mass or volume. Whether EAT plays a role in the long-term prognosis of HF requires future investigation. 44 Ó 2017 Published by Elsevier Inc. on behalf of National Lipid Association. 45 46 47 48 1 These authors contributed equally to this work. E-mail address: [email protected] 49 * Corresponding author. Division of Cardiology, Department of Internal Submitted March 14, 2017. Accepted for publication August 27, 2017. 50 Medicine, National Taiwan University College of Medicine and Hospital, 51 No. 7, Chung-Shan South Road, Taipei 100, Taiwan.

Evolutional change in epicardial fat and its correlation with myocardial diffuse fibrosis in heart failure patients

1933-2874/Ó 2017 Published by Elsevier Inc. on behalf of National Lipid Association. http://dx.doi.org/10.1016/j.jacl.2017.08.018 FLA 5.5.0 DTD  JACL1177_proof  19 September 2017  4:46 pm

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Introduction The distribution of body fat plays a pivotal role in the development and progression of both diastolic and systolic heart failure (HF).1 Epicardial fat (EAT) is the true visceral fat depot of the heart and accounts for approximately 20% of the total heart weight.2 Pericardial fat could be divided into epicardial and paracardial adipose tissue and receives blood supply primarily from the 2 coronary arteries and partly from noncoronary sources.3 There are currently several modalities and methods for evaluating the true amount of pericardial fat, including echocardiography, computed tomography, and cardiac magnetic resonance imaging (CMR). Among all the measurements, CMR is a promising tool to evaluate the structure and function of the left ventricle (LV). A recent study had shown that CMR can quantify the accurate amount of pericardial fat.4 In patients with systolic HF (SHF), EAT has been shown to be associated with structural changes and the severity of HF.4 In SHF patients with severely reduced impaired LV function, the EAT mass/LV end-diastolic mass ratio is significantly lower compared with healthy controls.4 In addition, EAT amount is positively correlated with myocardial fat content and high EAT content can be arrhythmogenic.5 To prove this hypothesis, some recent studies had evaluated the association between pericardial fat and atrial/ventricle arrhythmia and demonstrated that pericardial fat is associated with the development and severity of atrial fibrillation (AF), poorer outcomes after AF ablation and even increased risk of ventricular arrhythmia in patients with SHF.6 Because increased EAT is associated with significant LV structural changes and can cause ventricle or atrial arrhythmia, it is reasonable to hypothesize that EAT might be associated with the amount or distribution of myocardial fibrosis especially in HF patients. CMR has been proposed to be the method to noninvasively quantify diffuse myocardial fibrosis in patients with cardiomyopathies.7 Nevertheless, CMR-T1 imaging is subject to variation and interference by the magnetic field used, acquisition timing, amount of contrast injected, and the renal function of the patients. In recent years, myocardial extracellular volume (ECV) adjusted by blood T1 time has been shown to be a more reliable method for identifying cardiac fibrosis.8 We currently enrolled a prospective cohort, which included patients with SHF, HF with preserved ejection fraction (HFpEF), and controls. All participants received CMR examination to calculate global fibrosis amount as well as EAT volume. The main purpose of this study was to investigate the distribution of EAT in different stages of HF. We also evaluated the relationship between EAT and global fibrosis (ECV).

Materials and methods Ethics statement The research was approved by the Institutional Review Board of the National Taiwan University Hospital Ethics

Committee. The study was conducted in accordance with the Declaration of Helsinki. All study participants provided written informed consent.

Patient population The study enrolled patients with chronic HF and patients at risks for HF from November 1, 2012, to July 31, 2015. These patients were relatively stable and without cardiac cachexia. Patients who met the following criteria were enrolled into the HF group: (1) HF symptoms of New York Heart Association classification functional Class II to III or a history of HF symptoms/signs by Framingham criteria9; and (2) symptoms and signs of HF persistent for more than 3 months. From the HF group, diagnosis of HFpEF was defined according to the current consensus statement of the European Society of Cardiology10–12 and the ACCF/AHA task force.13 For the diagnosis of HFpEF, all the following diagnostic criteria had to be fulfilled: (1) an echocardiographic left ventricular ejection fraction (LVEF) $50% and a left ventricular end-diastolic volume index (LVEDVi) # 97 mL/m2; and (2) evidence of abnormal left ventricular relaxation, filling, or diastolic stiffness. Pulsed-wave Doppler and tissue Doppler imaging were performed to obtain the ratio of early transmitral blood velocity (E) to early diastolic mitral annular velocity (e’). HFpEF was considered likely in patients with an E/e’ ratio . 15 and unlikely in patients with an E/e’ # 8. In intermediate cases with 15 . E/e’ $ 8, serum N-terminal prohormone of brain natriuretic peptide levels were determined and if N-terminal prohormone of brain natriuretic peptide levels exceeded 220 pg/mL, HFpEF was considered likely. Patients with LVEF below 50% were defined as SHF group. Subjects at risk for HF but without a history of symptoms/signs were recruited in the same period from our out-patient clinic and were regarded as non-HF control group. Patients were excluded from the study if they had significant valvular heart diseases indicated for percutaneous or surgical intervention, chronic AF, chronic pulmonary disease, acute myocarditis, hypertrophic cardiomyopathy, active myocardial ischemia defined by a positive stress test or unrevascularized significant (70%) stenosis in coronary arteries by angiography, or an estimated glomerular filtration rate , 30 mL/min/ 1.73 m2. A total of 200 subjects were enrolled. Among them, 20 subjects were excluded because poor magnetic resonance imaging (MRI) quality for quantification of EAT.

Baseline characteristics, anthropometric measurements The basic demographics, cardiovascular risk factors including hypertension (HTN), diabetes mellitus (DM), dyslipidemia, chronic kidney disease, defined as estimated glomerular filtration rate , 60 mL/min, medications of the included subjects and cardiovascular diseases such as coronary artery disease (CAD), old myocardial infarction (MI),

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Eat and myocardial diffuse fibrosis

stroke or peripheral arterial occlusive disease were obtained by reviewing medical records. Body mass index (BMI) was calculated by the formula: BMI (kg/m2) 5 weight (kg)/ height (m)2. Body surface area (BSA) was assessed by a variation of the Dubois and Dubois formula: BSA (m2) 5 (weight [kg]0.425 ! height [cm]0.725) ! 0.007184.14

Image acquisition MRI was performed on a 3-T MRI system (Trio; Siemens, Erlangen, Germany) with an 8-channel cardiovascular phased array torso coil. Myocardial T1 mapping was performed with an electrocardiogram (ECG)-triggered Modified Look Locker Inversion recovery (MOLLI) sequence before and 10 minutes after a 0.15 mmol/kg intravenous administration of gadolinium-based contrast agent (Omniscan; Winthrop Laboratories, GE, NJ). The MOLLI protocol used 2 Look-Locker cycles to acquire 7 images over 11 heart beats. The scanning parameters were repetition time/echo time, 1.9 ms/1.0 ms; flip angle, 35 ; minimum inversion time, 110 ms; inversion time increment, 80 ms; matrix size, 256 ! 192; slice thickness, 6 mm; spatial resolution, 1.28 mm; GRAPPA acceleration factor, 2; number of inversions, 2; images acquired after first inversion, 5; pause 4 heart beats and images acquired after second inversion, 2; 5 evenly spaced short-axis slices were acquired sequentially from the LV base to apex. After postcontrast T1 acquisition, late gadolinium enhancement (LGE) images were acquired using an ECG-triggered phase-sensitive inversion recovery prepared segmented fast gradient echo pulse sequence15 at the same short-axis slices as those in the myocardial T1 mapping to identify the focal fibrosis or scarring. We performed the same MOLLI pulse sequence before and after contrast agent administration. Cine MRI was performed using a segmented, balanced, steady-state gradient echo pulse sequence with a retrospective ECG R-wave trigger. The scanning parameters were repetition time/echo time, 3.0 ms/1.5 ms; flip angle, 46 ; matrix size, 256 ! 208; and spatial resolution, 1.21 mm. Multiple short-axis slices were prescribed from the mitral orifice to LV apex with a slice thickness of 8 mm and a gap of 2 mm. The true temporal resolution was 63 ms and 30 cardiac phases were reconstructed retrospectively for each slice level. The T1 map was reconstructed and applied with nonrigid motion correction from the scanner console. The T1 maps were reconstructed by the scanner console and we adjusted the center frequency to avoid off-resonance artifacts during the image acquisition. We excluded subjects if their images suffered from severe motion artifacts due to arrhythmia or difficulty in hold-breath.

Image analysis Myocardial ECV is an indicator of myocardial interstitial diffuse fibrosis.8 Quantitative analysis of myocardial ECV was performed on T1 maps. The regions of interest (ROI) in the blood and the myocardium of the LV were drawn in

3 the central area of LV cavity and the septal myocardium on T1 maps for each slice, respectively. If the septal myocardium showed regional hyperenhancement on the LGE images, the ROI of the myocardium was redrawn in other unenhanced myocardial regions. The averaged T1 values of the segmented ROIs were then computed. After subtracting the precontrast values from the postcontrast values, the changes of the relaxation rate (1/T1) in the blood and in the myocardium were obtained. Myocardial ECV values were calculated using the ratio of the change in relaxation rate in the myocardium to that in the blood and multiplied by (1-hematocrit). After excluding myocardial areas with LGE, we later averaged each myocardial ECV value over 5 short-axis slices for each subject.16 The ECV value was calculated from ROI in pre- and post-T1 maps. The area of ROIs was the same but the location was placed as the similar as possible for pre- and post-T1 maps. For LV function and mass analysis, endocardial and epicardial contours of the LV were determined at each slice level on cine MRI and the area enclosed by each contour was computed.17 LV volumes for each time point were then determined by the Simpson’s rule to obtain the volume-time curve of the LV. LVEDV and left ventricular end-systolic volume (LVESV) of the LV were assessed from the volume–time curve for the maximal and minimal values and were used to compute LVEF. LV mass was computed as the difference between LV epicardial volume at end-diastole and LVEDV, multiplied by the density of the myocardium, 1.05 g/cc. LV volumes and mass indexed to BSA were also measured from LVEDV (LVEDVi), LVESV (LVESVi), and left ventricular mass (LVM; LVMi) divided by BSA. LVM and LV mass index were performed during diastole. EAT volumes were measured offline by 2 blinded observers (C. K. W. and H. Y. T.) by using a customdeveloped MATLAB 7.9 (MathWorks, Natick, MA) program. Fat volumes were quantified using a previously validated technique.18 EAT was defined as regions of high signal intensity between the myoepicardium and the parietal pericardium. Areas of fat were traced on consecutive end-diastolic short-axis images and multiplied by the slice thickness to derive volume (Fig. 1). EAT was divided by BSA (indexed EAT) for normalization of different body sizes. Intraobserver and interobserver reproducibility in this study were excellent (coefficients of variation 4.5% and 5.3%, respectively).

Statistical analysis The Kolmogorov–Smirnov test was used to test the normality of the continuous variables. If the variables were not normally distributed, they were expressed as medians and interquartile ranges; otherwise, they were expressed as mean 6 standard deviation. Categorical variables were expressed as percentages. Differences in continuous variables among groups were compared by using 1-way analysis of variance test with Bonferroni correction. Continuous variables were tested by the nonparametric Kruskal–Wallis

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Journal of Clinical Lipidology, Vol -, No -, - 2017

4 test, and the Mann–Whitney U test was used for post hoc analysis for comparison of the medians between different groups. Categorical variables were compared among different groups of patients by using chi-square tests. To investigate the correlation between ECV and EAT (indexed EAT) volume, a multiple linear regression model was constructed, adjusted for potential confounders. The model was adjusted for age, sex, history of HTN and DM, etiology of HF (CAD vs non-CAD), presence of LGE on MRI LGE images, LVMi, LVEDVi, ECV, and LVEF. LVMi and LVEDVi were categorized into tertiles. A Bonferroni test was performed for variables categorized into more than 3 groups to avoid multiple comparison bias. A value of P , .05 was considered significant. Statistical analyses were performed using the SPSS software package, version 19 (SPSS, Chicago, IL).

Results Baseline characteristics A total of 180 patients were studied: 59 patients with SHF, 63 patients with HFpEF, and 58 controls. The baseline characteristics of the study subjects are listed in Table 1. Patients in the SHF group were predominantly males, whereas those in the other 2 groups were predominantly females (84.5% vs 46.0% and 46.6%). Patients in SHF and

HFpEF groups had significantly higher prevalence of MI (27.1% and 17.7%, respectively) compared with those in the control group (5.2%). More patients in the SHF group received beta-blocker therapy compared with those in HFpEF and control groups (72.7% vs 47.6% and 51.7%). The other baseline characteristics were not significantly different among groups.

EAT and HF The CMR parameters of patients with SHF, HFpEF, and controls are summarized in Table 2. SHF patients had the largest LV volumes and mass indexes (LVEDVi, LVESDi, LVMi) and also the worst LVEF, whereas HFpEF patients had similar LV volumes and mass indexes and LVEF compared with the controls. The ECV, which is a marker for diffuse interstitial fibrosis, had a dose-dependent relationship among SHF, HFpEF, and the controls (32.1 6 3.1; 30.3 6 3.0; 27.6 6 2.6, respectively, P , .05). Both the total EAT volume and the indexed EAT volume (corrected EAT volume) parallel to the change of ECV and had a dose-dependent relationship with the severity of HF (indexed EAT volume: 27.0 [22.7–31.6], 25.6 [21.4–31.2], 24.2 [21.0–27.6], P , .05) for SHF, HFpEF, and the control groups, respectively. However, if we corrected EAT volume with LV mass, patients with SHF had significant lowest EAT/LV mass ratio (Table 2).

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Figure 1 Volumetric measurement of epicardial fat (EAT) outlining the contours of electrocardiogram (ECG) in end-diastolic images of short axis covering the left ventricle and extracellular volume (ECV) fraction quantification by T1 maps in 2 subjects. Subject 1 (upper panel), a patient with less ECG amount (left figure) along with less fibrosis volume (right figure). Subject 2 (lower panel), a patient Q7 with high EAT amount (left figure) along with greater fibrosis volume (right figure). Areas of ECG are shaded in yellow. FLA 5.5.0 DTD  JACL1177_proof  19 September 2017  4:46 pm

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Chen et al

Eat and myocardial diffuse fibrosis

Table 1

Basic demographics of the studied subjects

5

Patient characteristics

SHF (n 5 58)

HFpEF (n 5 63)

Non-HF (n 5 59)

Age, y Sex (male), % BMI, kg/m2 BSA, m2 Risk factors HTN, % DM, % Dyslipidemia, % Comorbidities CKD, % Stroke, % MI, % PAOD, % CAD Valvular heart disease Etiology of HF Ischemic cardiomyopathy Medications ACEI ARB Beta-blocker

63.3 6 12.4* 84.7*,† 25.2 6 3.5† 1.75 6 0.17

68.6 6 11.5 46.0 26.0 6 4.8 1.69 6 0.17

65.5 6 9.6 46.6 27.1 6 3.5 1.73 6 0.17

83.1 20.3 49.2

84.1 38.1 34.9

86.2 25.9 51.7

6.8 5.1 27.1† 3.4 50.8 3.4

9.5 4.8 17.5† 3.2 39.7 3.2

1.7 1.7 5.2 3.4 43.1 1.7

27.1

17.5

–

5.1 59.3 72.9*,†

1.6 65.1 47.6

1.7 65.5 51.7

ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; BSA, body surface area; CAD, coronary artery disease; CKD, chronic kidney disease; DM, diabetes mellitus; HFpEF, heart failure with preserved ejection fraction; HTN, hypertension; non-HF, patients without heart failure; MI, myocardial infarction; PAOD, peripheral arterial occlusive disease; SHF, systolic heart failure. *P , .05 compared with HFpEF. †P , .05 compared with the non-HF group.

Factors associated with EAT

EAT volume. On the other hand, only enlarged LVMi (CE 5 4.662, P 5 .009) and ECV (CE 5 0.491, P 5 .01) were associated with greater indexed EAT volume. A correlation test for global ECV and EAT showed significant correlation between both ECV and EAT or ECV and indexed EAT volume (Fig. 2; ECV vs EAT, r 5 0.166, P 5 .03; ECV vs indexed EAT volume, r 5 0.256, P 5 .001).

To delineate the factors associated with EAT amount, we performed multiple linear regression analysis and adjusted for all the possible confounding factors. Male sex (correlation coefficient [CE] 5 5.207, P 5 .043), greater LVMi (CE 5 7.782, P 5 .019), and ECV (CE 5 0.72, P 5 .041) were associated with increment of absolute

Table 2

Magnetic resonance image parameters in patients with and without heart failure SHF (n 5 58) 2

LVEDVi, mL/m LVESVi, mL/m2 LVMi, g/m2 LVEF, % LGE, % ECV, % EAT volume, g Indexed EAT volume, mL/m2 EAT volume/LV mass

*,†

106.5 (81.4–131.0) 70.1 (44.8–93.0)*,† 95.4 (80.7–113.4)*,† 37.1 (28.9–43.7)*,† 70.8*,† 32.1 6 3.1† 48.2 (39.4–54.6)† 27.0 (22.7–31.6)† 0.29 (0.23–0.35)*,†

HFpEF (n 5 63)

Non-HF (n 5 59)

50.9 (42.1–65.1) 11.2 (7.2–21.1) 61.0 (51.8–81.1) 77.7 (65.4–84.2) 42.2† 30.3 6 3.0† 43.5 (36.7–56.1) 25.6 (21.4–31.2) 0.41 (0.31–0.53)

48.0 10.2 58.9 78.1 13.3 27.6 40.5 24.2 0.42

(39.2–56.8) (7.2–13.7) (51.9–74.2) (73.7–83.2) 6 2.6 (35.3–48.7) (21.0–27.6) (0.32–0.48)

EAT, epicardial fat; ECV, extracellular volume fraction; HFpEF, heart failure with preserved ejection fraction; LGE, late gadolinium enhancement; LVEDVi, left ventricular end-diastolic volume index; LVEF, left ventricular ejection fraction; LVESVi, left ventricular end-systolic volume index; LVMi, left ventricular mass index; non-HF, patients without heart failure; SHF, systolic heart failure. *P , .05 compared with HFpEF. †P , .05 compared with the non-HF group.

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Table 3

Q5

Multiple linear regression model to identify the determinants of EAT volume and indexed EAT volume

Age, y Sex (male) HTN DM Etiology of HF (CAD vs non-CAD) LGE LVMi, g/m2 59.0–83.4 vs # 59.0 .83.4 vs #59.0 LVEDVi, mL/m2 48.6–71.2 vs #48.6 .71.2 vs 48.6 LVEF, % 35–50 vs $50.0 ,35 vs $50.0 ECV, %

EAT volume

P

Indexed EAT volume

20.054 5.207 1.407 0.288 20.934 21.161

.552 .043 .612 .893 .665 .656

0.043 0.432 0.392 0.373 21.147 0.047

(20.234 to 0.126) (0.157 to 10.257) (24.066 to 6.879) (23.952 to 4.529) (25.181w to 3.313) (26.300 to 23.978)

(20.054 (22.297 (22.566 (21.919 (23.443 (22.730

to to to to to to

P 0.140) 3.162) 3.350) 2.665) 1.148) 22.825)

.382 .755 .794 .748 .325 .973

7.782 (1.299 to 14.265) 7.001 (1.782 to 12.220)

.019 .009

4.662 (1.158 to 8.166) 4.277 (1.456 to 7.098)

.009 .003

26.524 (213.889 to 0.841) 22.966 (29.538 to 3.606)

.082 .374

23.161 (27.142 to 0.819) 21.274 (24.826 to 2.278)

.119 .480

1.771 (26.118 to 9.660) 21.810 (28.648 to 5.029) 0.720 (3.006 to 1.410)

.658 .602 .041

20.778 (25.042 to 3.486) 0.381 (23.315 to 4.077) 0.491 (0.118 to 0.863)

.719 .839 .010

CAD, coronary artery disease; DM, diabetes mellitus; EAT, epicardial fat; ECV, extracellular volume; HTN, hypertension; LGE, late gadolinium enhancement; LVEDVi, left ventricular end diastolic volume index; LVMi, left ventricular mass index.

EAT and ECV Table 4 summarizes the subgroup analysis for the association between ECV and EAT after adjusting the possible confounding factors including age, sex, HTN, DM, etiology of HF, and presence of LGE on MRI-LGE images. In patients with advanced age, male sex, LGE on MRI-LGE images, and less LVEDVi, ECV had a significant positive correlation with EAT volume. After adjustment for BSA (indexed EAT volume), increased ECV was associated with EAT volume in those with advanced age, male sex, HTN, LGE on MRI-LGE images, and less LVEDVi. We also performed correlation tests for indexed EAT, ECV,

and BMI. There was no significant correlation with ECV and BMI in either groups (Fig. 3A), whereas indexed EAT was only associated with BMI significantly in the non-HF population (Fig. 3B).

Discussion In the present study, we prospectively enrolled a large HF cohort and demonstrated that the EAT amount parallels with the severity of HF. Patients with SHF have higher EAT or indexed EAT than those with DHF and the controls. In addition, we also showed that the severity of global fibrosis (ECV) has a significant relationship with EAT volume. To

Figure 2 Scatter plot of extracellular volume (ECV) vs epicardial fat (EAT) volume (A) and indexed EAT volume (B). The linear correlation coefficients and P values were also shown in the figure. FLA 5.5.0 DTD  JACL1177_proof  19 September 2017  4:46 pm

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Eat and myocardial diffuse fibrosis

7

the best of our knowledge, this is the first report relating the volumes of EAT to different stages of patients with HF, which is mediated at least partially by increasing severity of global fibrosis. Our findings are consistent with the hypothesis that a local pathogenic effect of EAT might lead to augmentation of myocardial fibrosis, which in turn influences the development of HF.

In a recent study, the authors evaluated the relationship between HF amount and LV parameters as assessed by CMR in a group of HF patients.4 Their study, in contrast to our present study, showed that EAT amount is significantly reduced in patients with HF compared with controls irrespective of the underlying etiology of HF. The reasons for this discrepancy are unclear; however, metabolic abnormalities and/or anatomic alterations because of disturbed cardiac function and geometry seem to play a key role and could be a possible explanation. EAT could serve as a source of inflammation or a metabolically active organ, which could be associated with cardiac morphology and function.22 Under physiological conditions, EAT acts as a buffering system between the myocardium and the systemic circulation. Increased EAT could serve as a scavenger of excess fatty free acids, which interfere with the generation and propagation of the contractile circle of the heart that would cause lethal arrhythmias or deterioration of heart function. Our SHF group had a mean LV ejection fraction of 37.1% and a mean New York Heart Association functional class of 2 to 3. In this situation, EAT amounts could reflect the global cardiac fibrosis amount and the inflammatory status and thus are associated with the severity or even

EAT and cardiac function There are several methods to measure to volumetrically quantify EAT including echocardiography, computed tomography, and CMR. For echocardiography, unstable image quality especially for patients with poor window (eg, obese subjects) remains a major limitation. As for the latter 2 methods, both have been applied with encouraging results.19,20 CMR is recognized as the ‘‘gold standard’’ modality for evaluating adipose tissue, also it has been validated by animal model.21 In the present study, we choice CMR as our tool to evaluate EAT because most of the study parameters were derived from CMR. EAT has previously been shown to be associated with cardiac structural changes and impaired diastolic function.

Table 4 Correlation coefficients (95% confidence interval) of extracellular volume by using EAT volume and indexed EAT volume as dependent variables in different subgroups EAT volume Age, y ,65 $65 Sex Female Male HTN No Yes DM No Yes CAD No Yes LGE No Yes LVMi, g/m2 #70.4 .70.4 LVEDVi, mL/m2 #59.0 .59.0 LVEF, % #50 .50

P

Indexed EAT volume

P

0.624 (20.547 to 0.179) 1.031 (0.033 to 2.028)

.290 .043

0.410 (20.181 to 1.00) 0.704 (0.151 to 1.258)

.170 .013

0.455 (20.606 to 1.517) 0.953 (0.048 to 1.859)

.394 .039

0.301 (20.282 to 0.883) 0.677 (0.192 to 1.162)

.306 .007

1.515 (21.258 to 4.289) 0.594 (20.143 to 1.331)

.259 .113

0.854 (20.706 to 2.415) 0.398 (0.007–0.789)

.258 .046

0.634 (20.250 to 1.517) 0.832 (20.614 to 2.278)

.158 .250

0.378 (20.010 to 0.855) 0.511 (20.287 to 1.308)

.118 .202

0.393 (20.636 to 1.422) 0.823 (20.221 to 1.867)

.449 .120

0.352 (20.218 to 0.923) 0.504 (20.028 to 1.036)

.223 .063

0.227 (20.862 to 1.317) 0.124 (0.325 to 2.146)

.679 .009

0.183 (20.392 to 0.758) 0.822 (0.317 to 1.326)

.529 .002

0.178 (20.625 to 0.982) 0.117 (20.012 to 2.356)

.660 .052

0.009 (20.339 to 0.528) 0.808 (0.173 to 1.444)

.666 .013

1.172 (0.167 to 2.177) 0.234 (20.792 to 1.260)

.023 .651

0.603 (0.411 to 1.165) 21.274 (24.826 to 2.278)

.036 .480

21.161 (26.300 to 23.978) 1.771 (26.118 to 9.660)

.656 .658

0.047 (22.730 to 22.825) 0.348 (20.197 to 0.893)

.973 .206

CAD, coronary artery disease; DM, diabetes mellitus; EAT, epicardial fat; HTN, hypertension; LGE, late gadolinium enhancement; LVEDVi, left ventricular end diastolic volume index; LVEF, left ventricular ejection fraction; LVMi, left ventricular mass index.

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Journal of Clinical Lipidology, Vol -, No -, - 2017

Figure 3 Scatter plot of (A) extracellular volume (ECV) vs body mass index (BMI) and (B) indexed epicardial fat vs BMI in the total population, systolic heart failure (HF), heart failure with preserved ejection fraction (HFpEF), and non–heart failure (HF) groups.

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Eat and myocardial diffuse fibrosis

prognosis of HF. On the other hand, in the previous study,4 which enrolled a population with a much lower ejection fraction (mean LV ejection fraction around 27%), EAT reflected the severity of cardiac cachexia and could no longer present its normal physiological function. Also, we included non-HF subjects rather than healthy ones as controls. The imbalanced prevalence of metabolic risk factors between HF and controls might confound the comparison of EAT between the 2 groups. Finally, within HF group analysis in that study, the authors demonstrated a significant positive relationship between the EAT amount and the severity of HF (LVM, LV volumes, and LVEF), which are similar to our findings.4

Potential mechanisms linking increased EAT to myocardial fibrosis In the present study, we also showed that EAT had a significant correlation with global ECV in our cohorts. There are some possible explanations. First, EAT was noted to be associated with the presence of atrial or ventricular arrhythmia in recent years.6 According to previous studies, systemic circulating inflammatory markers and microvasculopathy have been linked to obesity and lethal arrhythmia.23 EAT is directly contiguous with atrial and ventricular myocardium and served as a critical regulator of lipid fluxes with great flexibility to fulfill the energy needs of arterial walls and heart muscle.24 However, in metabolic cardiovascular disease states, these fat tissues expand, becoming hypoxic and dysfunctional, which would lead to reducing the production of protective cytokines, increasing detrimental adipocytokines and leading to progressive cardiac fibrosis eventually.25 Kankaanpaa et al.5 had quantified myocardial fat, EAT, and free fatty acid level in obese population and concluded that excessive free fatty acid exposure could lead to significant fat accumulation in and around the myocardium. EAT could be a predictor of myocardial fat amount, which could activate fibroblasts, together with extracellular matrix deposition and fibrosis.26 We calculated cardiac fibrosis by ECV, which might also include myocardial fat and lead to the concurrent augmentation of EAT and ECV. Finally, EAT is highly associated with various atrial or ventricular arrhythmia.27,28 In addition to biomedical interaction from paracrine factors, there are electrical and mechanical interactions between cardiomyocytes and fibroblasts/myofibroblasts.29 Cardiomyocytes and fibroblasts are also able to directly interact via gap junctions.30 Findings from 2-dimensional and 3-dimensional co-cultures have identified connexin 43 plaques and expression between ventricular cardiomyocytes and fibroblasts.31 These electrical connections can have a significant role in cardiac conduction. Fibroblasts themselves are unable to generate action potentials; however, they can exhibit conductive properties and could be influenced by the stimulation of increased amounts of ventricular arrhythmia, thus are activated and produced greater amounts of myocardial fibrosis.

9

EAT and ECV in different subgroup After adjustment for possible confounding factors, indexed EAT volume was only associated with global ECV in elder population, male sex, patients with HTN, those who have LGE on MRI-LGE images. The results were consistent to previous observational study for CAD patients. Male, sex, and HTN were the most significant factors for CAD or cardiac fibrosis. The relationship between adiposity and cardiovascular disease is well established.32 EAT could present the true adiposity that had the strongest influence over myocardium. Therefore, in patients with higher risk for cardiovascular disease, cardiac fibrosis might be more evident and the relationship between EAT and ECV could then be more apparent. Furthermore, LGE is a useful diagnostic tool to detect scar formation, differentiate prognostic characteristics, and may help guide risk stratification and management in patients with cardiomyopathy.33 In our subgroup analysis (Table 4), EAT correlated to ECV most significantly (P , .002) especially for patients with LGE. In fact, our previous retrospective study also showed that EAT correlated with the presence of ventricular tachyarrhythmia or long-term mortality in a group of severe HF patients.28 Therefore, we can conclude that higher EAT predicts higher fibrosis amounts especially in patients with more morbidities, impaired heart function or those with enlarged fibrosis myocardium. Our finding concurred with some literature that higher indexed EAT was noted in patients with SHF. However, obviously the EAT volume did not increase proportionally as LV mass in patients with SHF. There are several possible explanations. First, EAT has been proved to be affected by many metabolic factors including medications, infection, or even menopause.34,35 Patients with SHF usually have many comorbidities, which might affect the absolute EAT amount. EAT could serve as metabolically active organ and a source for inflammatory.4 Nevertheless, EAT also possesses a high lipolytic activity and could be associated with cardiac energy especially for patients with cardiac cachexia. Poor heart function and higher heart rate might consume the reservoir.4 Doesch et al.4 recruited 66 HF patients with average LV ejection fraction around 27% and they concluded that patients with SHF have less indexed EAT value. Our SHF patients were included in a relative stable status from out-patient clinic and possess an average LV ejection fraction around 37%. These reasons could explain the discrepancy for indexed EAT. However, the fat-muscle ratio is significantly lower in patients with SHF compared with HFpEF and controls (EAT volume/ LV mass). These results are in concordance with previous postmortem studies.36 This could be the effect of an insufficient EAT increase in the failing heart. In patients with CHF and reduced LV-EF, the chamber size and cardiac muscle volume increase in response of HTN, valvular heart disease, and greater systemic vascular resistance. Insufficient EAT increase may indicate a decreased buffering

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Journal of Clinical Lipidology, Vol -, No -, - 2017

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capacity for cardiac energy as well as a diminished responsiveness to adjust to special energy demands of the heart.

risk factors. The amount of EAT also increases as heart function declines in HF patients.

Clinical relevance

Uncited table

The precursors of cardiac fibrosis are apparent in patients with falling hearts. In contrast to traditional risk factors, myocardial fibrosis markers, ECV, is positively correlated with EAT content and high EAT content can be arrhythmogenic, both of which could be associated with development of cardiac fibrosis. CMR imaging with ECV analysis provides an automated and objective means to assess the whole-heart fibrosis amounts and the true EAT volume. Larger prospective studies are needed to extend this observation to other populations and clarify the nature of the association.

Study limitations This study was not designed to address whether increased EAT is a cause or consequence of myocardial fibrosis. Although the most likely mechanism involves inflammation or fat infiltration, this needs to be proved in future studies. This study defined EAT as all adipose tissue within the epicardial sac, according to standard methodology. In addition, if the logistic regression model is over fitted, it may not replicate well with larger datasets. However, this study is the first to demonstrate an association between EAT and myocardial fibrosis in a database of different stages of HF. CMR allowed us to differentiate the extent of fibrosis and also the amount of EAT concurrently. Larger prospective studies are needed to extend this observation to other populations and clarify the nature of the association. An elevation of T1 and ECV occur in various myocardial diseases and conditions (eg, myocarditis, amyloidosis and MI). An ECV elevation might also been seen in remote myocardium after MI; furthermore, the remote ECV predicts adverse remodeling.37 However, because our main topic is patients with HF and these patients have high incidence of multiple risk factors. In normal population or in patients without comorbidities, ECV varies in a limited range and it might not be easy to find a correlation between ECV and EAT. Actually, we think that increased EAT volume alone could not result in elevation of ECV value. Those comorbidities could turn on several proinflammatory cytokines of the EAT tissue and result in elevation of myocardial fibrosis. Besides, theoretically, endomyocardial biopsy could not be performed as gold standard because of ethnic issue. Nevertheless, CMR-ECV was proved to be associated with both cardiac hemodynamic changes and actual fibrosis contents and could thus be an alternative choice in our study.38

Conclusion EAT is positively associated with ECV in SHF or DHF patients independently of traditional myocardial fibrosis

Table 3.

Acknowledgments The authors wish to thank all the participants in the study. Availability of data and materials: The datasets used and/or analyzed during the present study available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This work was supported in part by the National Science Council of the Republic of China (NSC 102-2314-B-002-058- and 102-2314-B-002 -087- MY2), and the TVGH-NTUH Joint Research Program (VN10401). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Authors’ contributions: C.K.-W., H.-Y.T., and L.-Y.L. designed the whole study and analyzed and interpreted the data. C.-K.W. wrote the article. M.-Y.S., J.-J.H., Y.-F.W., and J.-J. L. performed the laboratory work and critically reviewed the article for important intellectual content. L.Y.L. was also in charge of the whole program. All authors have read and approved the final version of the article.

References 1. Deswal A, Petersen NJ, Feldman AM, Young JB, White BG, Mann DL. Cytokines and cytokine receptors in advanced heart failure: an analysis of the cytokine database from the Vesnarinone trial (VEST). Circulation. 2001;103(16):2055–2059. 2. Iacobellis G, Willens HJ. Echocardiographic epicardial fat: a review of research and clinical applications. J Am Soc Echocardiogr. 2009; 22(12):1311–1319. 3. Sacks HS, Fain JN. Human epicardial adipose tissue: a review. Am Heart J. 2007;153(6):907–917. 4. Doesch C, Haghi D, Fluchter S, et al. Epicardial adipose tissue in patients with heart failure. J Cardiovasc Magn Reson. 2010;12:40. 5. Kankaanpaa M, Lehto HR, Parkka JP, et al. Myocardial triglyceride content and epicardial fat mass in human obesity: relationship to left ventricular function and serum free fatty acid levels. J Clin Endocrinol Metab. 2006;91(11):4689–4695. 6. Wong CX, Abed HS, Molaee P, et al. Pericardial fat is associated with atrial fibrillation severity and ablation outcome. J Am Coll Cardiol. 2011;57(17):1745–1751. 7. Mascherbauer J, Marzluf BA, Tufaro C, et al. Cardiac magnetic resonance postcontrast T1 time is associated with outcome in patients with heart failure and preserved ejection fraction. Circ Cardiovasc Imaging. 2013;6(6):1056–1065. 8. Lee JJ, Liu S, Nacif MS, et al. Myocardial T1 and extracellular volume fraction mapping at 3 tesla. J Cardiovasc Magn Reson. 2011;13:75.

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1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110

Chen et al 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166

Eat and myocardial diffuse fibrosis

9. Mottram PM, Haluska B, Leano R, Cowley D, Stowasser M, Marwick TH. Effect of aldosterone antagonism on myocardial dysfunction in hypertensive patients with diastolic heart failure. Circulation. 2004;110(5):558–565. 10. McMurray JJ, Adamopoulos S, Anker SD, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: the Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2012;14(8):803–869. 11. Wu CK, Lee JK, Chiang FT, et al. Plasma levels of tumor necrosis factor-alpha and interleukin-6 are associated with diastolic heart failure through downregulation of sarcoplasmic reticulum Ca21 ATPase. Crit Care Med. 2011;39(5):984–992. 12. Wu CK, Tsai CT, Chang YC, et al. Genetic polymorphisms of the angiotensin II type 1 receptor gene and diastolic heart failure. J Hypertens. 2009;27(3):502–507. 13. Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;62(16):e147–e239. 14. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986; 1(8476):307–310. 15. Kellman P, Arai AE, McVeigh ER, Aletras AH. Phase-sensitive inversion recovery for detecting myocardial infarction using gadolinium-delayed hyperenhancement. Magn Reson Med. 2002;47(2):372–383. 16. Wong TC, Piehler K, Meier CG, et al. Association between extracellular matrix expansion quantified by cardiovascular magnetic resonance and short-term mortality. Circulation. 2012;126(10): 1206–1216. 17. Tseng WY, Liao TY, Wang JL. Normal systolic and diastolic functions of the left ventricle and left atrium by cine magnetic resonance imaging. J Cardiovasc Magn Reson. 2002;4(4):443–457. 18. Nelson AJ, Worthley MI, Psaltis PJ, et al. Validation of cardiovascular magnetic resonance assessment of pericardial adipose tissue volume. J Cardiovasc Magn Reson. 2009;11:15. 19. Rosito GA, Massaro JM, Hoffmann U, et al. Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the Framingham Heart Study. Circulation. 2008;117(5):605–613. 20. Fluchter S, Haghi D, Dinter D, et al. Volumetric assessment of epicardial adipose tissue with cardiovascular magnetic resonance imaging. Obesity (Silver Spring). 2007;15(4):870–878. 21. Gronemeyer SA, Steen RG, Kauffman WM, Reddick WE, Glass JO. Fast adipose tissue (FAT) assessment by MRI. Magn Reson Imaging. 2000;18(7):815–818. 22. Mazurek T, Zhang L, Zalewski A, et al. Human epicardial adipose tissue is a source of inflammatory mediators. Circulation. 2003; 108(20):2460–2466.

11 23. Issac TT, Dokainish H, Lakkis NM. Role of inflammation in initiation and perpetuation of atrial fibrillation: a systematic review of the published data. J Am Coll Cardiol. 2007;50(21):2021–2028. 24. Iozzo P. Myocardial, perivascular, and epicardial fat. Diabetes Care. 2011;34(Suppl 2):S371–S379. 25. Greenstein AS, Khavandi K, Withers SB, et al. Local inflammation and hypoxia abolish the protective anticontractile properties of perivascular fat in obese patients. Circulation. 2009;119(12):1661–1670. 26. Nguyen BL, Fishbein MC, Chen LS, Chen PS, Masroor S. Histopathological substrate for chronic atrial fibrillation in humans. Heart Rhythm. 2009;6(4):454–460. 27. Al Chekakie MO, Welles CC, Metoyer R, et al. Pericardial fat is independently associated with human atrial fibrillation. J Am Coll Cardiol. 2010;56(10):784–788. 28. Wu CK, Tsai HY, Su MY, et al. Pericardial fat is associated with ventricular tachyarrhythmia and mortality in patients with systolic heart failure. Atherosclerosis. 2015;241(2):607–614. 29. Pellman J, Zhang J, Sheikh F. Myocyte-fibroblast communication in cardiac fibrosis and arrhythmias: mechanisms and model systems. J Mol Cell Cardiol. 2016;94:22–31. 30. Rook MB, Jongsma HJ, de Jonge B. Single channel currents of homo- and heterologous gap junctions between cardiac fibroblasts and myocytes. Pflugers Arch. 1989;414(1):95–98. 31. Oyamada M, Kimura H, Oyamada Y, Miyamoto A, Ohshika H, Mori M. The expression, phosphorylation, and localization of connexin 43 and gap-junctional intercellular communication during the establishment of a synchronized contraction of cultured neonatal rat cardiac myocytes. Exp Cell Res. 1994;212(2):351–358. 32. Lloyd-Jones DM, Wang TJ, Leip EP, et al. Lifetime risk for development of atrial fibrillation: the Framingham Heart Study. Circulation. 2004;110(9):1042–1046. 33. Kuruvilla S, Adenaw N, Katwal AB, Lipinski MJ, Kramer CM, Salerno M. Late gadolinium enhancement on cardiac magnetic resonance predicts adverse cardiovascular outcomes in nonischemic cardiomyopathy: a systematic review and meta-analysis. Circ Cardiovasc Imaging. 2014;7(2):250–258. 34. Iozzo P, Lautamaki R, Borra R, et al. Contribution of glucose tolerance and gender to cardiac adiposity. J Clin Endocrinol Metab. 2009; 94(11):4472–4482. 35. Whayne TF Jr. Epicardial fat thickness in heart failure and other clinical conditions. Angiology. 2013;64(3):169–172. 36. Schejbal V. Epicardial fatty tissue of the right ventricle–morphology, morphometry and functional significance. Pneumologie. 1989;43: 490–499. 37. Bulluck H, White SK, Rosmini S, et al. T1 mapping and T2 mapping at 3T for quantifying the area-at-risk in reperfused STEMI patients. J Cardiovasc Magn Reson. 2015;17:73. 38. Rommel KP, von Roeder M, Latuscynski K, et al. Extracellular volume fraction for characterization of patients with heart failure and preserved ejection fraction. J Am Coll Cardiol. 2016;67(15):1815–1825.

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