Relationship Between Clinical Parameters and Histological Features of Epicardial Adipose Tissue and Aortic Valve Calcification Assessed on Computed Tomography

Abstract

Background: The relationships of the clinical and biological attributes of epicardial adipose tissue (EAT) with aortic valve calcification (AVC) have not been characterized. We evaluated the relationships of the clinical and histological features of EAT with AVC assessed using computed tomography (CT).

Methods and Results: We enrolled 43 patients undergoing cardiac CT examination prior to elective cardiac surgery in whom AVC was identified on CT. EAT volume and density, coronary calcium score (CCS), AVC score (AVCS), and coronary atherosclerosis on CT angiography were evaluated in each patient. During cardiac surgery, 2 EAT samples were obtained for immunohistochemistry. The number of CD68- and CD11c-positive macrophages and osteocalcin-positive cells was counted in 6 random high-power fields of EAT sections. EAT density, but not EAT volume normalized to body surface area, was positively correlated with the number of macrophages and osteocalcin-positive cells in EAT. There was a positive correlation between ln(AVCS), but not ln(CCS+1), and the number of macrophages and osteocalcin-positive cells in EAT. Multivariate analysis revealed significant positive correlations for ln(AVCS) with EAT density (β=0.42; P=0.0072) and the number of CD68-positive macrophages (β=0.57; P=0.0022), CD11c-positive macrophages (β=0.62; P=0.0003), and osteocalcin-positive cells (β=0.52; P=0.0021) in EAT.

Conclusions: Inflammation and osteogenesis in EAT, reflected by high CT density, are associated with the severity of AVC representing aortic valve degeneration.

The clinical and pathological significance of epicardial adipose tissue (EAT) for cardiovascular disease has become a subject of considerable interest.¹ Because there is no fibrous fascial layer between the EAT and underlying vessels on the myocardium, cellular proliferation in the EAT and the cytokines it secretes may have direct effects on coronary arteries.^2–4 We previously showed that the histological features (macrophage infiltration and neoangiogenesis) and elevated cytokine expression (tumor necrosis factor-α and interleukin-1β) in EAT are associated with the formation and characteristics of coronary atherosclerotic plaque assessed using computed tomography (CT).^5–8 The association of local inflammation in EAT with coronary plaque characteristics has been also demonstrated in fresh cadaveric hearts.^9,10 In addition, it has been hypothesized that inflammatory in EAT can influence the structure and function of the heart, thereby contributing to the pathogenesis of several cardiac diseases, including calcific aortic stenosis (AS).¹¹ It is widely recognized that biological mechanisms, including chronic inflammation, lipoprotein deposition and activation of specific osteogenic and apoptotic signaling pathways, are involved in the development of calcific AS.¹² Coronary artery disease and AS share many similarities, including common risk factors, and frequently coexist.¹³ Thus, the involvement of EAT in aortic valve inflammation and atherogenesis, as well as its degeneration and calcification, is worthy of further investigation.

A large EAT volume, identified using cardiac CT, has been shown to be associated with the presence, severity, and potentially the vulnerability of coronary atherosclerosis.^14,15 Signals from inflamed coronary arteries affect the composition of perivascular fat, shifting its attenuation on CT from the lipid (more negative Hounsfield unit [HU] values) to aqueous (less negative HU values) phase.¹⁶ On positron emission tomography, ¹⁸F-sodium fluoride uptake is indicative of high-risk atherosclerotic lesions with active calcification, and we have shown that high CT density in the EAT surrounding coronary atherosclerotic lesions is associated with greater ¹⁸F-sodium fluoride uptake.¹⁷ Consequently, the clinical assessment of EAT on CT images (volume and density) may improve our understanding of the pathogenesis of coronary artery disease.

As for the relationship between EAT and aortic valve disease, the thickness of EAT, assessed using echocardiography, is reportedly greater in patients with calcific AS than in healthy individuals, and is correlated with the expression of EAT-derived proinflammatory and proatherogenic cytokines.¹⁸ However, it remains unclear how the clinical and biological attributes of EAT are related to aortic valve degeneration and calcification. An analysis of this relationship would contribute to the elucidation of the pathogenic significance of EAT in the progression of aortic valve disease, which may lead to the identification of novel therapeutic targets for this disease. Therefore, in the present study we evaluated the relationships of CT-derived clinical parameters and the histological features of EAT with aortic valve calcification (AVC) assessed using CT.

Methods

The study complied with the Declaration of Helsinki. The Ethics Committee for Clinical Research of Hiroshima University approved the study protocol (Approval no. C2020-0314), and written informed consent was obtained from all participants. The protocol has been published in the Japan University Hospital Medical Information Network (UMIN) Clinical Trials Registry (ID: UMIN000043455).

Participants

Between March 2021 and December 2023, we enrolled 46 patients undergoing cardiac CT examination prior to elective cardiac surgery (coronary artery bypass graft [CABG] and/or cardiac valve surgery). All patients had been referred for cardiac CT to diagnose and characterize their coronary artery disease and/or cardiac valve disease and had AVC identified on CT. Patients with a history of percutaneous coronary intervention and/or CABG, and those receiving dialysis, were excluded from the study because these factors could potentially confound analysis of coronary atherosclerosis and AVC on CT images.

The patients’ clinical information, including their coronary risk factors and the use of statins and sodium-glucose cotransporter 2 (SGLT2) inhibitors, was recorded. Laboratory data (serum lipid concentrations, HbA1c level, and C-reactive protein concentration) obtained immediately prior to surgery were also recorded. We obtained samples of EAT that were adjacent to the proximal portions of left anterior descending and right coronary arteries (left and right EAT, respectively) during cardiac surgery (two EAT samples per participant), as described in our previous reports.^5–8 In addition, subcutaneous adipose tissue (one sample per participant) was obtained from the subcutaneous fat on the sternum of each patient as control adipose tissue. The size of each sample was generalized (longest diameter 5 mm). These samples were used in later immunohistochemical staining analyses.

Cardiac CT Protocol

Cardiac CT imaging was performed using a 320-slice CT scanner (Aquilion One; Canon Medical Systems, Otawara, Japan) within the month preceding cardiac surgery, as described previously.^5–8 All images were acquired with retrospective electrocardiographic gating. Briefly, we performed a non-contrast scan (maximum tube current, 270 mA; tube voltage, 120 kV) to analyze the EAT (slice thickness, 0.5 mm) and measure the coronary calcium score (CCS) and AVC score (AVCS) according to the standard Agatston method (slice thickness, 3.0 mm). Data sets for coronary computed tomography angiography (CCTA) were then acquired using the HeartNAVI^® system (collimation, 320×0.5 mm; tube current, 350–580 mA; tube voltage, 120 kV; Canon Medical Systems). The mean effective radiation dose, calculated based on the dose–length product, was 8 mSv per participant, as described in our previous study with the same scanner.⁵ All reconstructed CT image data were transferred to an offline workstation (Advantage Workstation version 4.2; GE Healthcare, Waukesha, WI, USA), and 2 blinded independent observers performed the post-processing and image analysis.

Analysis of Adipose Tissue, AVC, and Coronary Atherosclerosis Using CT

The abdominal visceral fat (AVF) area and the volume and density of EAT depots were measured on plain CT images using dedicated software (Virtual Place; AZE Inc., Tokyo, Japan). The AVF area was defined as the intraperitoneal adipose tissue area in an image at the level of the umbilicus.¹⁹ As in our previous studies,^5–8,15,17 we defined EAT as the adipose tissue surrounding the myocardium and limited by the epicardium with a density range between −250 and −30 HU on non-contrast CT images. EAT density was defined according to an earlier study of EAT.²⁰ We measured the EAT volume of each participant by calculating the total sum of the EAT areas from 1 cm above the left main coronary artery to the left ventricular apex on images obtained at 1-cm intervals (Figure 1A). The EAT volume was normalized to the body surface area of each participant (epicardial adipose tissue volume index [EATI]: EAT volume/body surface area, mL/m²). We also assessed the density of EAT by placing regions of interest in the EAT surrounding the left main trunk, proximal to the mid-portion of the left anterior descending and right coronary arteries. We visually placed regions of interest alongside the coronary arteries from the vessel wall to adjacent cardiac structures avoiding partial volume and undershooting artifacts derived from coronary calcium and calculated the mean densities (Figure 1B). We previously showed good interobserver agreement with respect to the density of the EAT surrounding the coronary arteries (mean difference 1; 95% confidence interval −1, 2; 95% limits of agreement −16, 17).¹⁷

Figure 1.

Analysis of epicardial adipose tissue (EAT) on computed tomography images. (A) Axial images of the total EAT area (blue) for the calculation of the depot volume. (B) Axial and volume rendering images of the pericoronary EAT area (blue) for the placement of regions of interests and the measurement of density. LAD, left anterior descending artery; LM, left main trunk.

AVC was defined as a calcified lesion (structure with a CT density ≥130 HU) just inferior to the origin of the right coronary artery and located at the aortic leaflets, including the valvular point of attachment.²¹ In addition to the measurement of AVCS, the extent of the AVC in each participant was evaluated quantitatively on non-contrast CT images, as in our previous study.²² AVC density (AVCD) was determined by placing 5 regions of interest in the AVC and calculating the mean value for all the regions of interest in each participant. The total AVC volume (AVCV) was measured using dedicated software (Smartscore version 4.2; GE Healthcare).

On CCTA images, the coronary lumen for stenosis and atherosclerotic lesions in all coronary segments >2 mm in diameter were evaluated, as in our previous study.²² Lumen stenosis ≥70% in any vessel or ≥50% in the left main coronary artery was considered to be clinically obstructive. The presence or absence of high-risk plaque, which was defined as coronary plaques with a low density (<30 HU) and a high remodeling index (>1.1), was determined for each participant. Low density and a high remodeling index have been reported to be high-risk characteristics of coronary plaques resulting in acute coronary syndrome.^23,24 If the results of the initial assessments of coronary stenosis and high-risk plaque performed independently by 2 researchers differed, a consensus was obtained through discussion.

Immunohistochemical Staining and Image Analysis

Immunohistochemical staining of adipose tissue samples (left EAT, right EAT, and subcutaneous adipose tissue from each participant) was performed as described previously.⁵ Samples were fixed in 10% buffered formalin and embedded in paraffin, then 5-μm sections were cut. Antigen retrieval was performed in citrate buffer (pH 6.0) by heating in a 500-W microwave oven for 15 min. After endogenous peroxidase activity was blocked with 3% H₂O₂-methanol for 10 min, the sections were incubated with normal goal serum (Dako, Carpinteria, CA, USA) for 20 min to block non-specific antibody binding. Dako Envision Kits (Dako) were then used for immunohistochemical analysis. Sections were incubated with primary antibodies against CD68 (1 : 100, clone KP-1; Dako, Glostrup, Denmark), CD11c (1 : 200, clone ITGAX/1,242; Abcam, Cambridge, UK), or osteocalcin (1 : 100, polyclonal; Abcam) for 60 min at room temperature, then with peroxidase-linked anti-mouse or anti-rabbit IgG for 60 min. Staining was completed with a 10-min incubation with the substrate-chromogen solution. Sections were counterstained with 0.1% hematoxylin. Appropriate positive and negative control samples were included.

Histological images were analyzed at a magnification of ×400. We counted CD68- and CD11c-positive cells to quantify the levels of infiltration of pan- and proinflammatory macrophages, respectively, into the adipose tissue depots. In addition, we counted osteocalcin-positive cells to quantify the level of calcification in these depots. Cells were counted in 3 random high-power fields (with each field corresponding to a circle with a radius of 250 μm) in each of the left and right EAT samples, and the total number of cells was recorded for each participant. For subcutaneous adipose tissue, the cells in each sample were counted in 6 random high-power fields and the total number was reported for each participant. This method was used in our previous studies.^5–8

Statistical Analysis

The CCS, AVCS, AVCD (HU), and AVCV (mm³) obtained from CT images are expressed as the median with interquartile range (IQR); other continuous variables are expressed as the mean±SD. The Wilcoxon signed-rank test was used to compare immunohistochemical data for paired EAT and subcutaneous adipose tissue samples, and Student’s t-test or the Mann-Whitney U test were used to compare groups with respect to the other continuous variables. Potential correlations between histological data for EAT, CT-derived EAT parameters, and AVC parameters were assessed using Pearson’s correlation coefficient. For quantitative analysis, CCS and AVC-related parameters were logarithmically transformed to normalize their distribution: ln(CCS+1), ln(AVCS), ln(AVCD), and ln(AVCV). Linear regression was used to evaluate the relationships of ln(AVCS) with CT-derived EAT parameters and EAT histological data. Basically, two-tailed P values <0.05 were considered statistically significant. Analyses were performed using JMP Pro 17 statistical software (SAS Institute, Cary, NC, USA).

Results

Baseline Participant Characteristics

In 43 of 46 participants, EAT and subcutaneous adipose tissue samples were obtained safely and in sufficient quantity for subsequent immunohistochemical staining. The baseline clinical characteristics of these 43 participants are presented in Table 1. The peak aortic valve velocity, measured on Doppler echocardiography, was 3.3±1.5 m/s (range 1.0–6.6 m/s). Twenty-six (60%) and 19 (44%) participants were diagnosed as having clinical (peak aortic valve velocity ≥2.5 m/s) and severe (peak aortic valve velocity ≥4.0 m/s) AS, respectively. CCTA demonstrated that 15 (35%) and 5 (12%) participants had at least 1 obstructive coronary stenosis and high-risk plaque, respectively. Consequently, 31 participants underwent cardiac valve surgery alone (aortic valve repair and/or mitral valve repair, and/or tricuspid valve repair), 8 underwent CABG alone, and 4 underwent both cardiac valve surgery and CABG.

Table 1.

Baseline Participant Characteristics (n=43)

Age (years)	74±7
Male sex	31 (72)
Body mass index (kg/m2)	24±4
Hypertension	31 (72)
Hyperlipidemia	27 (63)
Diabetes	18 (42)
Current smoking	10 (23)
Use of statins	24 (56)
Use of SGLT2 inhibitors	11 (26)
Laboratory results
HDL-C (mg/dL)	65±18
LDL-C (mg/dL)	102±30
Triglyceride (mg/dL)	125±66
HbA1c (%)	6.2±1.0
C-reactive protein (mg/L)	1.5±2.0
Peak aortic valve velocity (m/s)	3.3±1.5
Clinical AS	26 (60)
Severe AS	19 (44)
Bicuspid aortic valve	2 (5)
AVF area (cm2)	98±41
CCS	603 [236–1,493]
CCTA findings
Obstructive coronary stenosis	15 (35)
High-risk plaque	5 (12)
Cardiac surgery
Cardiac valve surgery	31 (72)
CABG	8 (19)
Cardiac valve surgery and CABG	4 (9)

CCS is expressed as the median [interquartile range]; other data are expressed as either the mean±SD or n (%). AS, aortic stenosis; AVF, abdominal visceral fat; CABG, coronary artery bypass graft surgery; CCS, coronary calcium score; CCTA, coronary computed tomography angiography; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SGLT2, sodium-glucose cotransporter 2.

CT-Derived EAT Parameters and Histological Findings

The EAT volume, EATI, and EAT density obtained using CT were 108±40 mL, 66±22 mL/m², and −82±8 HU, respectively, and there was a negative correlation between EATI and EAT density (r=−0.42, P=0.0056). The mean number of CD68-positive macrophages, CD11c-positive macrophages, and osteocalcin-positive cells in EAT samples was 34±18, 19±10, and 28±21, respectively, which was significantly higher than the number in paired subcutaneous adipose tissue samples (9±5, 5±4, and 3±3, respectively; P<0.0001 for all). The histological data for the EAT did not show correlations with the coronary risk factors, use of statins and SGLT2 inhibitors, laboratory data, or AVF area (Supplementary Table). The number of osteocalcin-positive cells was positively correlated with the number of CD68-positive (r=0.44, P=0.0029) and CD11c-positive (r=0.54, P=0.0002) macrophages.

There were no correlations between EATI and the histological data for EAT (number of CD68-positive macrophages, r=−0.039, P=0.80; number of CD11c-positive macrophages, r=0.15, P=0.33; and number of osteocalcin-positive cells, r=0.14, P=0.38). EAT density was positively correlated with the histological data for EAT (number of CD68-positive macrophages, r=0.36, P=0.017; number of CD11c-posotive macrophages, r=0.33, P=0.029; and number of osteocalcin-positive cells, r=0.40, P=0.0077; Figure 2).

Figure 2.

Correlations between epicardial adipose tissue (EAT) density and histological data for EAT. Correlation coefficients (r) and P values were acquired using Pearson’s correlation test. HU, Hounsfield units.

Relationships Between CT-Derived EAT Parameters and AVC

The median AVCS, AVCD, and AVCV obtained using CT was 550 (IQR 169–1,215), 686 HU (IQR 443–866 HU), and 162 mm³ (IQR 71–339 mm³), respectively. The correlations between EATI and ln(AVCS) (r=0.28, P=0.066), ln(AVCD) (r=0.25, P=0.099), and ln(AVCV) (r=0.29 P=0.058) did not reach statistical significance. EAT density was positively correlated with ln(AVCS) (r=0.38, P=0.012), ln(AVCD) (r=0.34, P=0.028), and ln(AVCV) (r=0.30, P=0.048). In univariate linear regression analysis to identify clinical factors associated with EAT density, the presence of diabetes, current smoking, use of SGLT2 inhibitors, and AVF area and EATI were significantly negatively correlated with EAT density, whereas high-density lipoprotein cholesterol and ln(AVCS) were significantly positively correlated with EAT density. After adjustment for age and these factors, multivariate analysis revealed a significant positive correlation between ln(AVCS) and EAT density (β=0.42; P=0.0072; Table 2).

Table 2.

Results of Linear Regression Analysis to Identify Clinical Factors Associated With EAT Density

	Univariate β	P value	Multivariate β	P value
Age (years)	0.25	0.10	0.10	0.48
Male sex	0.15	0.34
Hypertension	0.020	0.90
Hyperlipidemia	−0.029	0.85
Diabetes	−0.30	0.050	0.021	0.88
Current smoking	−0.34	0.027	−0.022	0.88
Use of statins	−0.094	0.55
Use of SGLT2 inhibitors	−0.39	0.0094	0.00080	0.99
HDL-C	0.34	0.024	0.10	0.42
LDL-C	0.018	0.91
Triglyceride	−0.21	0.18
HbA1c	−0.029	0.85
C-reactive protein	0.076	0.63
AVF area	−0.60	<0.0001	−0.30	0.068
ln(CCS+1)	−0.14	0.38
EATI	−0.42	0.0056	−0.39	0.034
Coronary obstructive stenosis	−0.11	0.48
Coronary high-risk plaque	−0.041	0.79
ln(AVCS)	0.38	0.012	0.42	0.0072

AVCS, aortic valve calcification score; EAT, epicardial adipose tissue; EATI, EAT volume index. Other abbreviations as in Table 1.

Relationship Between EAT Histology and AVC

There was a positive correlation between ln(AVCS) and histological data for EAT (number of CD68-positive macrophages, r=0.42, P=0.0046; number of CD11c-posotive macrophages, r=0.55, P=0.0001; and number of osteocalcin-positive cells, r=0.57, P<0.0001; Figure 3). There was no such correlation between ln(CCS+1) and the number of CD68-positive macrophages (r=−0.059, P=0.71), the number of CD11c-posotive macrophages (r=−0.084, P=0.59) or the number of osteocalcin-positive cells (r=0.028, P=0.86). We then performed linear regression analysis to identify CT findings for the coronary artery, AVC, and EAT associated with the EAT histology. Multivariate analysis after adjusting for age, sex, ln(CCS+1), EATI, and the presence of coronary obstructive stenosis and high-risk plaque on CCTA revealed significant positive correlations of ln(AVCS) with the number of CD68-positive macrophages (β=0.57; P=0.0022), CD11c-positive macrophages (β=0.62; P=0.0003), and osteocalcin-positive cells (β=0.52; P=0.0021) in EAT (Table 3).

Figure 3.

Correlations between ln-transformed aortic valve calcification score (AVCS) and histological data for epicardial adipose tissue (EAT). Correlation coefficients (r) and P values were acquired by Pearson’s correlation test.

Table 3.

Results of the Linear Regression Analysis to Identify Cardiac CT Findings Associated With EAT Histology

	Univariate β	P value	Multivariate β	P value
For CD68-positive macrophages
Age	0.070	0.66
Male sex	0.0051	0.97
ln(CCS+1)	−0.059	0.71
EATI	−0.039	0.80
Coronary obstructive stenosis	−0.019	0.90
Coronary high-risk plaque	−0.19	0.23
ln(AVCS)	0.42	0.0046	0.57	0.0022
For CD11c-positive macrophages
Age	0.18	0.24
Male sex	0.097	0.54
ln(CCS+1)	−0.084	0.59
EATI	0.15	0.33
Coronary obstructive stenosis	−0.23	0.13	−0.0060	0.97
Coronary high-risk plaque	−0.24	0.12	−0.11	0.47
ln(AVCS)	0.55	0.0001	0.62	0.0003
For osteocalcin-positive cells
Age	0.40	0.0082	0.17	0.27
Male sex	0.062	0.69
ln(CCS+1)	0.028	0.86
EATI	0.14	0.38
Coronary obstructive stenosis	−0.24	0.11	−0.059	0.73
Coronary high-risk plaque	−0.22	0.15	−0.017	0.91
ln(AVCS)	0.57	<0.0001	0.52	0.0021

CT, computed tomography. Other abbreviations as in Tables 1,2.

Figure 4 shows representative images from 2 patients with different levels of AVC severity and EAT histology.

Figure 4.

Images obtained for 2 representative patients. Patient 1 was an 81-year-old man who was scheduled for aortic valve surgery and had severe aortic valve calcification (AVC), with an AVC score (AVCS) of 2,238 and a coronary calcification score (CCS) of 928 on computed tomography (CT), as well as extensive infiltration of pan- and proinflammatory macrophages, and a high proliferation of osteocalcin-positive cells in the epicardial adipose tissue (EAT). Patient 2 was a 76-year-old man who was scheduled for coronary artery bypass grafting and had mild AVC (AVCS 119) and very severe coronary calcification (CCS 1,754) on CT, along with a lower level of infiltration of macrophages and fewer osteocalcin-positive cells in the EAT. CCTA, coronary computed tomography angiography; HU, Hounsfield units. Scale bars, 100 µm.

Discussion

To investigate the clinical and biological significance of EAT for the pathogenesis of AVC, we sought to explore the relationships between CT-derived EAT parameters and the histological features of EAT with AVC assessed using CT. We made the following findings:

1. EAT had much higher inflammatory and osteogenic activities than subcutaneous adipose tissue in patients with AVC.

2. There was a relationship between the inflammatory and osteogenic activities of EAT in patients with AVC.

3. Unlike EAT volume, EAT density on CT was positively correlated with the inflammatory and osteogenic activities of EAT in patients with AVC.

4. The severity of AVC on CT was positively correlated with the density of EAT on CT, and with the inflammatory and osteogenic activities of EAT assessed histologically.

To the best of our knowledge, this is the first study to have directly evaluated the relationships of the clinical and histological features of EAT with clinically assessed AVC on CT. The results suggest that the inflammatory and osteogenic activities of EAT, reflected by high CT density, are associated with the severity of AVC, which may imply the possibility of a specific contribution of the EAT to aortic valve degeneration through biological activities.

EAT is the cardiac form of visceral adipose tissue and is considered to be involved in the adverse effects of systemic inflammation and metabolic disorders on the heart.²⁵ It is known that EAT represents an important source of proinflammatory and proatherogenic cytokines and may negatively affect the myocardium and epicardial coronary arteries through paracrine and vasocrine mechanisms. Interestingly, another study showed that EAT expresses genes that are associated with the process of calcification.²⁶ In the present study, we demonstrated that EAT contains larger numbers of pan- and proinflammatory macrophages and osteocalcin-expressing cells than subcutaneous adipose tissue from patients with AVC. This suggests that EAT, the cardiac form of visceral adipose tissue, may play a role in the etiology and pathogenesis of AVC.

The complex mechanisms involved in the calcific degeneration of the aortic valve include lipid deposition, oxidative stress, and inflammation. Inflammatory cells, including monocytes and T lymphocytes, adhere to and infiltrate the valvular subendothelium, where they differentiate into macrophages and activated T cells, release proinflammatory cytokines, and promote fibrosis and calcification.^27,28 Bone extracellular matrix proteins (osteocalcin and osteopontin) have been shown to be present in the calcified aortic valve, where they are thought to regulate the mineralization of the tissue.²⁹ Thus, inflammation and bone extracellular matrix may play key roles in aortic valve degeneration. The role of visceral adipose tissue in the development of AS has been extensively studied, and the presence of metabolic syndrome is associated with a high risk of AVC.³⁰ In the present study we focused on EAT, which may directly influence aortic valve degeneration, and found a close relationship between the severity of AVC and the infiltration of pan- and proinflammatory macrophages and the proliferation of osteocalcin expressing-cells in EAT. Notably, the number of proinflammatory (CD11c-positive) macrophages showed a closer relationship with the number of osteocalcin-expressing cells in the EAT, implying that inflammation may promote osteogenesis in the EAT. The present results suggest that such biological activities of EAT may have synergistic effects to progress aortic valve degeneration, and that they have the potential to be novel predictors of aortic valve disease. In the present study, there was no relationship between the severity of coronary calcification and either the inflammatory or osteogenic activities of EAT assessed histologically. We previously showed that inflammation in EAT is correlated with moderate, rather than severe, coronary calcification.⁵ It appears that the severity of coronary calcification, unlike that of AVC, is not linearly related to the biological activities of EAT, but the reason for this difference remains unclear and requires further investigation.

Although assessment of the biological characteristics of EAT is challenging in a clinical setting, the analysis of EAT density on CT images may be clinically useful. EAT density has been reported to be higher around culprit lesions than around non-culprit lesions in patients with acute coronary syndrome and around lesions of matched controls.³¹ In addition, the results of a large cohort study demonstrated that higher pericoronary EAT density predicts all-cause and cardiac mortality, independent of clinical risk factors and the CCTA-derived features of high-risk plaque.³² In these studies, CCTA data were used to assess EAT density, whereas we used the densities of the depots in non-contrast CT images to determine EAT density, as in our and others’ previous studies.^17,33 The density of pericoronary EAT density, determined using CCTA, reportedly decreases with growing distance from the contrast-enhanced coronary lumen, which can be explained by partial volume effects and image interpolation.³⁴ It is thought that the use of non-contrast CT images is a preferable means of acquiring accurate data for EAT density. We found that high EAT density on non-contrast CT reflects high inflammatory and osteogenic activities, assessed histologically, in this tissue, and that it is associated with more severe AVC. It is difficult to determine the mechanism by which inflammation and osteogenesis in EAT result in high CT density; however, we hypothesize that, as suggested partially in a previous study,¹⁶ the aqueous phase derived from inflammation and osteogenesis contributes to the increased CT density in EAT. The parameter reflecting EAT volume (EATI) was not correlated with the histological parameters of EAT or the severity of AVC; therefore, the analysis of EAT density, rather than volume, would be a useful means of predicting the biological characteristics of EAT and further exploring the pathology of aortic valve degeneration.

Study Limitations

This study has certain limitations. First, the sample size was relatively small and there may have been bias because of the method used to select patients. Second, despite the good level of interobserver agreement, we assessed EAT density by placing regions of interest manually, which may have caused sampling bias with respect to the CT images obtained. In addition, it is challenging to completely exclude image artifacts derived from severe coronary calcium from the regions of interest. Third, we cannot draw conclusions regarding the mechanisms of the calcification of EAT on the basis of the results of the present study. In a preliminary immunohistochemical study of EAT, we found high expression of fibroblast markers, in addition to great proliferation of osteocalcin-positive cells (Figure 5). The presence of these cells may indicate that osteoblast-like cells differentiate from fibroblasts in EAT. However, this hypothesis warrants further investigation. Finally, although in the present study we found close relationships between the inflammatory and osteogenic activities of EAT and the severity of AVC, we cannot explain how these characteristics of EAT influence the etiology and pathogenesis of AVC. Currently, there are no pharmacological means of preventing the progression of aortic valve degeneration and AVC, but, based on the findings of the present study, EAT may be a novel target for the prevention of aortic valve disease.

Figure 5.

Immunohistochemically stained serial sections of epicardial adipose tissue (EAT) from a single patient. Immunohistochemical staining was performed as described in the Methods using primary antibodies against osteocalcin and fibroblast marker (α-smooth muscle actin [1 : 200, clone E184; Abcam, Cambridge, UK], vimentin [1 : 200, clone EPR3776; Abcam], and S100A4 [1 : 1,000, clone EPR14639; Abcam]). High expression of the fibroblast marker is present in the area showing the proliferation of osteocalcin-positive cells. Scale bars, 100 µm.

Conclusions

Inflammation and osteogenesis, assessed histologically, in EAT are reflected by higher CT density and are associated with the severity of AVC, assessed using CT. The findings of the present study indicate that EAT may make a specific contribution to aortic valve degeneration through biological activities. Its potential use as a predictor of, or potential therapeutic target for, AVC and aortic valve disease warrants further investigation.

Acknowledgment

The authors thank Mark Cleasby, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

Sources of Funding

This study was supported, in part, by the Suzuki Memorial Foundation and a KAKENHI Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (Grant no. 21K08127).

Disclosures

The authors declare that there are no conflicts of interest.

IRB Information

The study was approved by the Ethics Committee for Clinical Research of Hiroshima University (Reference no. C2020-0314).

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-24-0226

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