To validate echocardiographic epicardial adipose tissue (EAT) thickness as a non-invasive marker of cardiometabolic risk in obesity and compare Eco-Obesity ultrasound-derived measurements of epicardial, preperitoneal, omental, and peri-renal fat depots with standard body composition analysis by dual-energy X-ray absorptiometry (DEXA).
Methods
In a cross-sectional study of 402 adults with obesity attending two Barcelona centers, Eco-Obesity ultrasound was performed in 114 subjects, alongside DEXA scans. Thickness of epicardial and abdominal fat depots, metabolic markers, and anthropometric indices were measured using standardized protocols. Relationships among ultrasound fat measurements, DEXA indices, anthropometric variables, and biochemical markers were analyzed by Pearson correlation coefficients; subgroup comparisons used ANOVA.
Results
Moderate (>7mm) and severe (>10mm) EAT thickness were found in 35.6% and 53.1% of patients. EAT measured in long-axis, short-axis, and posterior cardiac recess (PPR) projections correlated significantly with BMI, waist circumference, and waist-to-height ratio (all p<0.02), and was associated with omental and peri-renal fat compartments. The EAT long-axis projection showed the strongest and most consistent correlations with anthropometric and DEXA visceral fat indices. Omental and peri-renal fat layers correlated significantly with anthropometric and DEXA visceral fat, while pre-peritoneal fat did not. EAT PPR correlated positively with glycemia, HOMA-IR, triglycerides, and negatively with HDL cholesterol; EAT long- and short-axis also correlated with glycemia and HOMA-IR. Omental fat exhibited the strongest associations with glycemia, HOMA index, and HbA1c, and peri-renal fat showed significant positive association with fasting glycemia and inverse association with HDL.
Conclusions
EAT is a metabolically active visceral depot associated with adiposity and metabolic dysfunction in obesity. Routine ultrasonographic assessment of EAT should be integrated into clinical protocols for body composition analysis in the obese population (Eco-Obesity approach).
body composition analysiscardiovascular riskDEXA-scanechocardiographyEco-Obesityepicardial adipose tissueobesityultrasoundThe author(s) declared that financial support was not received for this work and/or its publication.section-at-acceptanceObesityIntroduction
Obesity is a chronic, complex metabolic disease (1) characterized by increased total and ectopic fat depots, some of them (omental and peri-renal fat) with a strong and well-established association with heightened cardiovascular morbidity and mortality (2, 3). Epicardial adipose tissue (EAT), an anatomically contiguous fat depot, is primarily distributed in atrioventricular and interventricular grooves and along the coronary arteries, enveloping the surface of the heart. EAT is precisely defined as the adipose space between the external myocardial wall and the visceral layer of the pericardium. Its unique anatomic, metabolic, and paracrine features are implicated in the progression of coronary artery disease (CAD), atrial fibrillation, and heart failure. Notably, pathological EAT expansion represents an independent predictor of incident metabolic syndrome in men (4), and increased EAT thickness off >5 mm provides clinically relevant discrimination for significant asymptomatic and established CAD (5).
Mechanistically, the pathogenicity of epicardial fat extends beyond passive lipid storage, as this depot demonstrates pronounced metabolic activity and secretes a range of adipocytokines, hormones, and vasoactive peptides known to modulate vascular and myocardial function (6). Increases in EAT and lipomatous hypertrophy (massive deposits of fat within the atrial septum) are associated with arrhythmogenic risk, enhanced coronary calcification, and adverse post-infarction outcomes (7, 8). Together with other visceral fat compartments, epicardial adipose tissue’s endocrine functions contribute acutely to the development of a proatherogenic, inflammatory microenvironment within the adjacent myocardium and coronary vasculature (7, 8).
Current cardiovascular imaging modalities such as computed tomography (CT) and magnetic resonance imaging have been used for volumetric EAT assessment (5, 7, 8), but high cost and repeated radiation limits its accessibility. In contrast, ultrasonography allows reliable measurement of EAT thickness, in a non-radiative, widely and cost-effective way (5, 9). Echo-identified EAT appears as the echo-free space between the outer myocardium and visceral pericardium, with thickness measurements typically ranging from 5 mm up to 23 mm in severely affected individuals (9). Importantly, echocardiographic EAT thickness correlates more closely with markers of visceral adiposity and strongly associates with metabolic syndrome (4), insulin resistance, subclinical atherosclerosis, and risk for future cardiovascular events (9). Its measurement also dynamically responds to weight loss interventions, paralleling reductions in metabolic risk (3, 10).
Studies have shown that, among all visceral depots, omental and peri-renal fat measurements, are associated with metabolic syndrome and fatty liver risk, and specific thickness cut-offs may enhance risk classification (5, 11, 12). Echographic EAT assessment can be done as a routine procedure within the global ultrasound body composition assessment in patients with obesity (Eco-Obesity) (11), enabling non-invasive, simultaneous quantification of epicardial and multiple abdominal fat compartments, including superficial and profound subcutaneous, pre-peritoneal, omental, and peri-renal depots, which are not reliably distinguished by Dual-energy X-ray absorptiometry (DEXA), CT or even MNR.
Recent clinical guidelines now explicitly categorize obesity via both anthropometric and clinical criteria. We therefore need new tools for advanced body composition assessment (such as sonography) to phenotype obesity and redefine cardiovascular risk stratification (1, 3). These guidelines advocate moving beyond traditional body mass index (BMI) dependence, highlighting the diagnostic importance of regional and ectopic adipose depots, and supporting the integration of non-invasive markers into risk prediction, patient staging, and individualized management paradigms (1, 3).
Despite increasing recognition of the role of EAT in cardiovascular and metabolic risk, significant knowledge gaps persist regarding its quantitative and functional relationships with overall and other ectopic adipose depots. This study aims to characterize EAT fat compartments measured in three regions (long-axis, short-axis and posterior cardiac (pericardial) recess [PPR]), in a large cohort of patients with obesity, as part of a standardized Eco-Obesity procedure. Secondary objectives are correlations between Eco-Obesity ultrasound-measured ectopic fat compartments (including EAT and other cardiovascular risk-associated fat compartments [pre-peritoneal, omental and peri-renal layers]) and body fat composition obtained using gold-standard DEXA, anthropometric and biochemical metabolic parameters.
MethodsStudy design and subjects
Cross-sectional study evaluating n=402 consecutive patients with obesity (BMI ≥30 kg/m²) attending the Unidad de Obesidad Cardio-Metabolica, Centre Mèdic Vic (Barcelona, Spain) between January and September 2025. All participants underwent a clinical evaluation, which included detailed anthropometric measurements such as weight, height, BMI, waist circumference, and waist-to-height ratio measurements. Clinically relevant thresholds were applied for cardiovascular risk assessment: abnormal waist-to-height ratio >0.5 indicating increased central adiposity and cardiovascular risk, with values ≥0.58 associated with significantly elevated mortality risk from cardiovascular disease, diabetes, and other obesity-related conditions (13). BMI classifications followed standard criteria (normal: 18.5-24.9 kg/m², overweight: 25-29.9 kg/m², obese: ≥30 kg/m²). Waist circumference thresholds for abdominal obesity were defined as >102 cm in men and >88 cm in women.
Comorbidities were assessed as follows: type 2 diabetes was diagnosed according to ADA guidelines (14). Abnormal blood pressure was defined as a 3-times measured value >130 mm Hg for systolic and/or >85 mm Hg for diastolic blood pressure and dyslipidemia was defined as serum triglycerides (TGs) >150 mg/dL in women and >200 mg/dL in men, or high-density lipoprotein (HDL) cholesterol <40 mg/dL in men and <50 mg/dL in women (15). Dyslipidemic patients with known carotid atheromatosis (CA) were also recorded. As part of routine screening, all patients received a standard electrocardiogram (ECG) to document baseline cardiac electrical activity and detect any previously undiagnosed cardiac conditions. Patients with ECG abnormalities and known CAD were not included in the analysis.
Echocardiographic assessment of EAT and other cardiovascular risk-associated fat compartments
Transthoracic echocardiography was performed using a Vivid 8 ultrasound system (GE Healthcare®) with a 2.5–5 MHz phased-array transducer. All cardiac examinations (including cardiac ultrasound assessment of EAT) were conducted by an experienced cardiologist following a standardized protocol to minimize inter-observer variability. EAT is non-homogeneously distributed around the heart, being predominantly located at the cardiac base, apex, in the cardiac grooves, and around the coronary arteries, with greatest thickness around the right ventricle compared to the left (16). For this reason, and in accordance with current recommendations, EAT thickness was assessed in three regions: the parasternal long-axis, parasternal short-axis, and posterior cardiac (pericardial) recess (PPR), two of which were previously described by Iacobellis et al. (5), and a third innovative echocardiographic plane corresponding to the PPR. The long-axis and short-axis views in 2D enabled more accurate measurement of epicardial fat thickness over the right ventricle. Thickness was measured perpendicularly over the free wall of the right ventricle at end-systole across three consecutive cardiac cycles. The aortic annulus served as an anatomical reference point for consistent localization of the ventricular wall. Reproducibility of the measurements obtained in end-diastole on the free wall of the right ventricle (parasternal long-axis view) showed excellent intraobserver (ICC = 0.98; CV = 3.0–3.5%) and interobserver agreement (ICC = 0.96; CV = 5.0–5.5%). Measurement of epicardial fat within the PPR by transthoracic echocardiography was implemented to enable quantification of the adipose tissue located between the epicardium and pericardium in this specific anatomical space, representing a novel approach for clinical application. The PPR view is distinguished from conventional projections, providing a complementary assessment of EAT distribution. This projection is typically obtained via a subxiphoid echocardiographic approach. Briefly, in the four-chamber echocardiographic view, the PPR appears as an anechoic zone posterior to the right atrium, between the free wall of the right atrium and the pericardium (Figure 1). Measurement was performed at the point of maximal EAT thickness, in millimeters at end-systole, averaging three cardiac cycles to reduce variability. All measurements were recorded, and additional findings such as pericardial effusion or ventricular anomalies were noted. The measured values were compared to reference cut-offs for cardiovascular risk stratification (17).
Ultrasound images corresponding to the three EAT measurements (a) long-axis, (b) short-axis, and (c) PPR. EAT, Epicardial adipose tissue; LV, left ventricle; PPR, posterior cardiac (pericardic) recess; RV, right ventricle. .
Three ultrasound images show different cardiac views. Image a) indicates “LONG-AXIS EAT” near the right ventricle (RV). Image b) highlights “SHORT-AXIS EAT” adjacent to RV. Image c) displays “PPR (posterior pericardic recess)” with both the right ventricle (RV) and left ventricle (LV) labeled.
Epicardial fat thickness severity was categorized according to established criteria: normal: <5 mm, mild: 5–7 mm, moderate: 7–10 mm, and severe: >10 mm (17). Measurements were performed during suspended respiration to minimize respiratory artifacts.
Body composition assessment
Body composition analysis was performed using dual-energy X-ray absorptiometry with a Lunar Prodigy DEXA scanner (GE Healthcare, Software version 18). DEXA measurements included total body fat mass, regional fat distribution, and visceral fat estimation using the CoreVat algorithm, which serves as the gold-standard for visceral adiposity quantification (which corresponds to the sum of pre-peritoneal, omental, and peri-renal). Body fat percentages above 25% in men and 32% in women may indicate obesity-related cardiometabolic risk (1).
Standardized anthropometric assessments included body weight (kg), height (cm), BMI (kg/m²), waist circumference (cm), and waist-to-height ratio. All measurements were performed by trained personnel following international standardized protocols.
A subgroup of patients (n=114) were submitted (Obesity Unit of the CPEN Group for Endocrinology and Nutrition (Barcelona, Spain) to a standardized Eco-Obesity ultrasound evaluation to routinely assess abdominal fat layers with metabolic and cardiovascular relevance: superficial subcutaneous, deep subcutaneous, pre-peritoneal, omental and peri-renal fat thickness, following the protocol defined by Cuatrecasas et al. (10). Briefly, when comparing DEXA with Eco-Obesity ultrasound, the measurement should be performed at the L4 level, rather than at any level referenced to the umbilicus (18), which corresponds ultrasonographically to the bifurcation of the iliac arteries. These measurements were correlated with epicardial fat thickness.
Statistical analyses
Statistical analyses were performed using SPSS version 23. Continuous variables were expressed as mean ± standard deviation, and categorical variables as frequencies and percentages. Analysis of variance (ANOVA) was applied to compare group means and to identify differences across subgroups. When applicable, post hoc comparisons were conducted to determine pairwise differences. Pearson correlation coefficients (R) were calculated to assess relationships between EAT, DEXA-derived visceral fat measurements, and biochemical parameters. A p-value <0.05 was considered statistically significant. DEXA measurements were performed according to manufacturer specifications with regular calibration protocols.
Ethics statement
The study was conducted in accordance with the principles expressed in the Declaration of Helsinki, and the protocol followed the Good Clinical Practice guidelines and was approved by the Ethical Committee of Hospital Teknon-Grupo Quiron Salud, Barcelona. Given the retrospective nature of this study using data from routine clinical practice, general informed consent for research use of anonymized clinical data was obtained from all participants.
ResultsDemographic characteristics
The study included a total of 402 patients in the overall cohort, with a dedicated subgroup of 114 participants undergoing detailed Eco-Obesity ultrasound body composition evaluation. In the overall cohort, 68.8% were female (n=277) and 31.1% male (n=125). The mean age was 55.3 years (SD = 11.8) for women and 53.3 years (SD = 13.1) for men, and 57.2% of female participants were postmenopausal. In the subgroup, 58.8% were female (n=67) and 41.2% male (n=47). The mean age was 57.6 years (SD = 11.5) for women and 54.7 years (SD = 10.5) for men, and 77.6% of female participants were postmenopausal (Table 1).
Demographic and anthropometric characteristics of participants.
Results are expressed as mean (standard deviation) unless specified.
BMI, Body mass index; CA, Carotid atheromatosis.
Anthropometric characteristics in the overall cohort are presented in Table 1. Overall, females showed a mean BMI of 34.4 kg/m², mean body fat percentage of 47.9%, mean waist circumference of 101.5 cm, and mean waist-to-height ratio of 0.64, and males showed values of 34.7 kg/m², 39.4%, 103.7 cm, and 0.65, respectively. Characteristics were similar in the Eco-Obesity subgroup.
Prevalence of all comorbidities analyzed (diabetes, hypertension, dyslipidemia, and carotid atheromatosis) was higher in men than in women both in the overall cohort and in the Eco-Obesity subgroup (Table 1).
Characterization of EAT and other cardiovascular risk-associated fat compartments
Among the 143 patients assessed, only one individual (0.9%) exhibited normal EAT thickness (<5 mm), while 15 participants (10,4%) presented with low-thickness (5–7 mm), 51 (35,6%) with moderate thickness (7–10 mm), and the majority, 76 (53,1%), with severe EAT thickness (>10 mm) (Table 2).
Characterization of EAT layers measured by Eco-Obesity.
Females
Males
Severity of EAT fat thickness
Normal (< 5 mm), n (%)
1 (0.5)
0 (0.0)
Low (5–7 mm), n (%)
15 (8.2)
1 (2.1)
Moderate (7–10 mm), n (%)
51 (27.7)
14 (29.8)
Severe (>10 mm), n (%)
76 (41.3)
32 (68.1)
EAT Long-axis
10.0 (2.4)
11.5 (2.4)
EAT Short-axis
9.6 (2.4)
10.7 (2.9)
EAT PPR
9.6 (2.6)
10.9 (2.8)
Results are expressed as mean (standard deviation) unless specified.
Sex-stratified analysis revealed consistently higher EAT measurements in males across all anatomical projections. In the parasternal long-axis view, mean EAT thickness was 11.5 mm (SD 2.4) in males versus 10.0 mm (SD 2.4) in females. Similar trends were observed in the short-axis (10.7 mm [SD 2.9] in males vs. 9.6 mm [SD 2.4] in females) and posterior cardiac recess (PPR) projections (10.9 mm [SD 2.8] in males vs. 9.6 mm [SD 2.6] in females) (Table 2).
Correlation between EAT and other cardiovascular risk-associated fat compartments, DEXA and anthropometric measurements
The EAT long-axis projection exhibited the most consistent associations across weight, BMI, waist circumference, waist-to-height ratio, and total body fat (R values 0.18–0.37, all p<0.01), while the PPR projection showed the highest individual correlation only with weight (R = 0.39, p<0.001). Short-axis EAT was significantly related to most variables but did not correlate with waist circumference in females or with total body fat. Correlations between EAT thickness in these projections and DEXA-derived visceral adipose tissue (VAT) were all statistically significant but modest in strength (long-axis R = 0.37, short-axis R = 0.32, PPR R = 0.36; all p<0.001; Tables 3, 4).
Correlation between EAT, other cardiovascular risk-associated fat compartments, DEXA and anthropometric measurements.
Eco-Obesity
Weight
BMI
Waist (F)
Waist (M)
Waist/Height index
Total body fat (kg)
EAT
EAT Long-axis
0.321 (<0.001)
0.305 (<0.001)
0.176 (0.02)
0.370 (<0.001)
0.284 (<0.001)
0.280 (<0.001)
EAT Short-axis
0.182 (<0.001)
0.199 (<0.001)
NS
0.415 (<0.001)
0.258 (<0.001)
NS
EAT PPR
0.389 (<0.001)
0.294 (<0.001)
0.212 (<0.001)
0.310 (<0.001)
0.240 (<0.001)
0.292 (<0.001)
Other cardiovascular risk-associated fat compartments
Omental layer
0.488 (<0.001)
0.417 (<0.001)
0.466 (<0.001)
0.404 (<0.001)
0.443 (<0.001)
0.302 (0.05)
Peri-renal layer
0.387 (<0.001)
0.238 (0.05)
NS
NS
0.423 (<0.001)
0.176 (0.05)
Pre-peritoneal layer
NS
NS
NS
NS
NS
NS
DEXA
VAT
0.553 (<0.001)
0.384 (<0.001)
0.656 (<0.001)
0.591 (<0.001)
0.561 (<0.001)
0.358 (<0.001)
Results are presented as R (p-value). Statistically significant correlations are shown in bold.
BMI, Body mass index; DEXA, Dual-energy X-ray absorptiometry; EAT, Epicardial adipose tissue; F, Females; PPR, posterior cardiac (pericardic) recess; M, males; NS, Non-significant; R, Pearson coefficients; VAT, Visceral abdominal fat.
Correlation between EAT and other cardiovascular risk-associated fat compartments and body fat composition with DEXA.
Visceral Fat Mass measured by DEXA -VAT-
Omental Layer measured by Eco-Obesity
EAT
EAT Long-axis
0.370 (<0.001)
0.313 (0.002)
EAT Short-axis
0.323 (<0.001)
0.336 (<0.001)
EAT PPR
0.364 (<0.001)
0.459 (<0.001)
Other cardiovascular risk-associated fat compartments
Assessment of other cardiovascular risk-associated fat compartments showed that omental fat layer was most strongly correlated with body weight, BMI, waist circumference, and waist-to-height ratio for both sexes (all p<0.001), with the highest correlations observed for waist circumference (R = 0.466 in females; R = 0.404 in males) and waist-to-height ratio (R = 0.443, p<0.001). Peri-renal fat also correlated significantly with weight (R = 0.387, p<0.001), BMI (R = 0.238, p<0.05), waist-to-height ratio (R = 0.423, p<0.001), and total body fat (R = 0.176, p<0.05). In contrast, the pre-peritoneal layer showed no significant associations with anthropometric or body composition parameters.
DEXA-derived VAT also presented strong positive correlations with all anthropometric measurements and with omental fat layer (R = 0.64, p<0.001) and peri-renal fat (R = 0.66, p<0.001) (Tables 3, 4).
Correlation between EAT and other cardiovascular risk-associated fat compartments
Correlation analyses demonstrated significant associations between EAT thickness and other cardiovascular risk-associated abdominal fat compartments, all evaluated by Eco-Obesity ultrasound. EAT thickness measured in long-axis, short-axis, and PPR views presented moderate correlations with omental (Figure 2) and peri-renal (Figure 3) fat thickness, with R values ranging from 0.31 to 0.58 (all p<0.002). Specifically, strongest association was observed for PPR views and omental layer (R = 0.46, p<0.001) and peri-renal layer (R = 0.58, p<0.001) (Table 4, Figure 4).
Correlation between EAT measurements (a) long-axis, (b) short-axis, and (c) PPR, and omental layer, all measured by Eco-Obesity. EAT, Epicardial adipose tissue; PPR, posterior cardiac (pericardic) recess.
Three scatter plots show relationships between “Eco-Obesity omental Layer” and different measurements. (a) Correlates with “EAT Long Axis,” showing a positive linear trend, R2=0.114. (b) Examines “EAT Short Axis,” also with a positive trend, R2=0.108. (c) Analyzes “PPR,” displaying a positive trend, R2=0.162. Each graph plots data points with best fit lines.
Correlation between EAT measurements (a) long-axis, (b) short-axis, and (c) PPR, and peri-renal layer, all measured by Eco-Obesity. EAT, Epicardial adipose tissue; PPR, posterior cardiac (pericardic) recess.
Set of three scatter plots showing the relationship between the Eco-Obesity peri-renal layer and three different measurements. Chart a) plots EAT Long Axis in millimeters against the peri-renal layer, showing a positive trend with R² = 0.050. Chart b) displays EAT Short Axis versus the peri-renal layer, with R² = 0.094. Chart c) illustrates PPR in millimeters related to the peri-renal layer, with R² = 0.123. Each plot features a line of best fit indicating a slightly positive correlation.
Correlations between EAT PPR, omental layer and peri-renal layer, all measured by Eco-Obesity, and VAT measured by DEXA. DEXA, Dual-energy X-ray absorptiometry; EAT, Epicardial adipose tissue; PPR, posterior cardiac (pericardic) recess; VAT, Visceral abdominal fat. .
A matrix of scatter plots comparing visceral fat mass (kg), Eco-Obesity omental layer (mm), Eco-Obesity peri-renal layer (mm), and EAT PPR (mm). Each plot includes scattered data points with a line indicating a trend. The data shows relationships among these variables.Association Between EAT and other cardiovascular risk-associated fat compartments and biochemical metabolic parameters
Regarding EAT, both long and short-axis showed weak but significant correlations with glycemia and HOMA index, though no associations were found with HbA1c or lipid variables. The PPR view of EAT was positively correlated with glycemia (R = 0.128, p=0.021) and HOMA index (R = 0.134, p=0.033), and also showed a significant negative association with HDL cholesterol (R=-0.116, p=0.042) and a positive association with triglycerides (R = 0.127, p=0.024) (Table 5), but no association with LDL.
Association between EAT, other cardiovascular risk-associated fat compartments, DEXA and biochemical metabolic parameters.
Eco-Obesity
Glycemia (mg/dl)
HOMA-IR index
HbA1c (%)
HDL (mg/dl)
TG (mg/dl)
EAT
EAT long-axis
0.117 (0.034)
0.184 (0.003)
0.056 (0.342)
-0.064 (0.259)
0.063 (0.264)
EAT short-axis
0.139 (0.012)
0.131 (0.036)
0.104 (0.075)
-0.015 (0.797)
0.054 (0.340)
EAT PPR
0.128 (0.021)
0.134 (0.033)
0.106 (0.073)
-0.116 (0.042)
0.127 (0.024)
Other cardiovascular risk-associated fat compartments
Omental layer
0.380 (<0.001)
0.279 (0.012)
0.232 (0.021)
-0.187 (0.060)
0.133 (0.183)
Peri-renal layer
0.218 (0.010)
0.021 (0.832)
0.073 (0.402)
-0.221 (0.010)
0.021 (0.678)
Pre-peritoneal layer
NS
NS
NS
NS
NS
DEXA
VAT
0.302 (<0.001)
0.316 (<0.001)
0.132 (0.018)
0.210 (<0.001)
0.220 (<0.001)
Statistically significant correlations are shown in bold.
The omental fat layer exhibited the strongest metabolic associations among the abdominal ectopic fat depots, demonstrating significant positive correlations with glycemia (R = 0.380, p<0.001), HOMA index (R = 0.279, p=0.012), and HbA1c (R = 0.232, p=0.021). The only trend with HDL cholesterol was a weak and non-significant inverse relationship (R=-0.187, p=0.060), while correlations with triglycerides were not significant. Peri-renal fat demonstrated a modest but significant positive association with fasting glycemia (R = 0.218, p=0.010) and an inverse association with HDL cholesterol (R=-0.221, p=0.010). No significant associations were observed between pre-peritoneal fat and metabolic parameters (Table 5).
Discussion
Our main findings reinforce the concept that EAT is a major metabolically active visceral fat depot, closely related to markers of adiposity and metabolic dysfunction in patients with obesity. CT and MRN data did not show the striking prevalence of moderate and severe EAT thickness observed in our study. This comparison with DEXA results provides the first direct, head-to-head validation of a global Eco-Obesity ultrasound (with EAT measurement as part of the protocol) measurements against a recognized gold-standard for body composition assessment. EAT and all other ectopic depots correlated significantly with VAT obtained from DEXA. Another important finding is the significant association between EAT thickness and the waist-to-height ratio, an emerging anthropometric index increasingly recognized as a simple marker of visceral adiposity and cardiovascular risk.
All three echocardiographic projections of EAT showed consistent correlations with parameters of glucose metabolism, lipid profile, and insulin resistance, with PPR showing the strongest association. Also, the correlations identified between omental and peri-renal fat are consistent with their recognized roles as the principal visceral adipose compartments linked to cardiovascular risk; these compartments should thus be prioritized in ultrasound-based phenotyping of obesity due to their established metabolic and prognostic significance for cardiovascular health.
The correlations seen in this study are consistent with previous reports linking intra-abdominal fat layers to cardiometabolic risk but further highlights the role of the waist-to-height ratio, a variable frequently used in primary care screening. The waist-to-height threshold of 0.5, widely adopted in preventive medicine, identifies individuals with greater cardiometabolic risk and higher EAT values, and highlights the potential of combining echocardiographic and anthropometric assessments to refine phenotyping of obesity beyond BMI, as recommended by recent clinical guidelines (1, 3). The positive association between ultrasound-measured omental fat and total body fat quantified by DEXA (R = 0.302, p=0.05) also supports the reliability of Eco-Obesity ultrasonography as a practical method for assessing visceral adiposity in clinical research. Additionally, it is essential to mention that Eco-Obesity ultrasonography is the only technique capable of stratifying the different fat layers, highlighting its clinical usefulness.
When analyzing ectopic fat compartments, the study confirms that omental and peri-renal fat, but not pre-peritoneal fat, correlate with both EAT thickness and the metabolic parameters defining insulin resistance and dyslipidemia. The absence of significant associations involving pre-peritoneal fat, neither with anthropometric variables, DEXA-derived VAT, nor EAT, demonstrates that pre-peritoneal adiposity is metabolically irrelevant despite its technical accessibility. This observation challenges the accepted assumption that pre-peritoneal fat correlates with visceral adiposity and could be an easy-to-do assessment of cardiovascular risk estimation and points out to the need to prioritize omental and peri-renal targets for accurate visceral fat quantification. These findings confirm the usefulness of ultrasound-guided visceral fat analysis and emphasize the greater metabolic relevance of omental and peri-renal depots compared to the pre-peritoneal fat compartment. To our opinion, when addressing cardiovascular and cardiometabolic risk phenotyping in patients with obesity, we should focus on these 3 ultrasound measurements: epicardial, omental and peri-renal, rather than more superficial depots (subcutaneous and pre-peritoneal). These findings confirm and expand earlier observations that ultrasonography, when standardized, can provide a practical and reproducible clinical tool for simultaneous quantification of cardiac and abdominal visceral fat (11, 19, 20).
Furthermore, the relationship with glycemia, HbA1c, HDL, triglycerides, and HOMA index, but not LDL suggests that EAT accumulation is closely linked to insulin resistance, as reflected by analogous behavior of key abdominal visceral fat depots. Our results demonstrate that the omental layer shows the strongest associations with glycemic parameters and indices of insulin resistance, while peri-renal fat shows significant but weaker metabolic correlations. These findings align with previous data (11) but reveal stronger associations, which demonstrates a metabolic interdependence between cardiac and abdominal ectopic fat compartments.
Recent data further support a tight interplay between epicardial fat, whole-body composition, and muscle function (21, 22). Epicardial adipose tissue behaves as a metabolically active visceral depot that correlates with anthropometric indices, DEXA-derived visceral fat and cardiometabolic traits, and is now considered a relevant imaging biomarker within ultrasound-based body composition phenotyping in obesity. In parallel, CT- and echocardiography-based studies consistently show that low muscle quantity and quality (e.g. reduced psoas area or handgrip strength as a proxy of global muscle function) are independently associated with higher epicardial fat burden, adverse cardiovascular events, and mortality across cardiac and critical-care populations (23). Moreover, the inverse, age-independent relationship between muscle strength and epicardial fat thickness in healthy adults suggests that simple functional tests such as handgrip strength may help to identify subjects with excessive epicardial fat accumulation and increased cardiovascular risk, bridging the gap between structural (fat depots) and functional (sarcopenia/dynapenia) aspects of body composition (21). We did not include DEXA-derived indices of muscle mass or direct measures of muscle strength, and therefore the correlation between epicardial adipose tissue and skeletal muscle could not be evaluated in this cohort.
Finally, the dynamic nature of EAT as a modifiable parameter must be emphasized. Previous studies have shown that EAT thickness decreases significantly with weight loss and improvement in insulin sensitivity. These results acquire particular relevance in the context of current pharmacologic approaches to obesity, including GLP-1 receptor agonists and dual incretin therapies, which achieve major reductions in visceral fat. Future longitudinal studies should assess whether decreased EAT, induced by these treatments, translates into measurable cardiovascular benefit and improved metabolic health.
This study has several limitations. First, the cross-sectional design precludes causal inferences regarding the relationship between epicardial adipose tissue thickness and metabolic or cardiovascular outcomes that should be validated by longitudinal studies to establish temporal associations and assess the predictive value of EAT for incident cardiometabolic and cardiovascular events. Second, the study population was restricted to patients with obesity from a single center in one geographic region, which may limit generalizability to other populations with different ethnic backgrounds, body composition distributions, or lower BMI ranges. Third, while dual-energy X-ray absorptiometry is an appropriate comparator for abdominal fat layers within the Eco-Obesity protocol, the absence of volumetric EAT assessment using computed tomography or magnetic resonance imaging to directly compare with sonographic EAT measurements represents a methodological limitation. Fourth, the subgroup undergoing comprehensive Eco-Obesity ultrasound evaluation was smaller (n=114) than the overall cohort, potentially reducing statistical power for certain subgroup and correlation analyses. Finally, muscle mass, muscle quality, and functional parameters related to sarcopenia and cardiac frailty (e.g. DEXA-derived lean mass, CT-based muscle indices, handgrip strength) were not systematically evaluated, precluding assessment of the emerging interplay between epicardial adipose tissue, skeletal muscle wasting, and adverse cardiovascular prognosis. Despite these limitations, the study presents important strengths, including the use of DEXA as a gold-standard comparator for body composition, the integration of multiple echocardiographic views of EAT with abdominal fat compartments, and the novel validation of ultrasound-based visceral fat quantification against established metabolic and anthropometric markers.
Conclusions
Given the high prevalence of pathological EAT thickness observed among individuals with obesity, routine ultrasonographic assessment of EAT should be integrated in a routinary ultrasound guided body composition analysis of ectopic fat depots (Eco-Obesity approach). EAT showed marked associations with metabolic parameters, particularly circulating triglycerides and glucose, supporting its relevance as a marker of metabolic dysfunction and insulin resistance. Hypertension and dyslipidemia were most prevalent among males demonstrating higher EAT values, reinforcing the intertwining cardiovascular and metabolic risk profiles. Additionally, the strong correlation between EAT and visceral adiposity measured by DEXA underscores the shared pathophysiological mechanisms underpinning ectopic fat deposition. However, not all visceral fat depots correlate with EAT. Omental and peri-renal fat showed nice correlations with EAT, whereas pre-peritoneal (included in DEXA visceral adiposity component) does not show any significant correlation.
Contemporary therapeutic strategies, including GLP-1 receptor agonists and dual analogs, have demonstrated efficacy in reducing visceral fat (omental and peri-renal fat in particular) (19, 20, 24), suggesting a potential for modifying epicardial fat as well. The integration of EAT assessment may therefore provide a novel means of monitoring therapeutic response and optimizing individualized management decisions. Future research should clarify whether increased epicardial fat mass could serve as a determining factor in selecting obesity pharmacotherapy and in predicting clinical outcomes, emphasizing the need for its evaluation within the broader context of obesity-related cardiovascular risk.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by Ethical Committee of Hospital Teknon-Grupo Quiron Salud, Barcelona. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
The authors sincerely thank all the patients who participated in this study for their valuable time, cooperation, and contribution, which was essential to the successful completion of this research. The authors would like to express their sincere gratitude Carla Lluís-Ganella, PhD, medical writer, for editorial and medical writing services. Their expertise in medical writing and editorial support significantly contributed to the clarity and quality of this article.
Conflict of interest
The authors have received honoraria for lectures from Lilly, Novo Nordisk, and Boeringer.
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Edited by: Andrea P. Rossi, Ca’ Foncello Hospital, Italy
Reviewed by: Germano Guerra, University of Molise, Italy
Angelina Borizanova, Medical University Sofia, Bulgaria