Association of CT-derived body composition with coronary plaque components and vascular inflammation

Abstract

Objective

To investigate the relationship of body composition with coronary plaque components and vascular inflammation.

Materials and methods

This retrospective study included 412 individuals who underwent computed tomography angiography scan between August 2024 and July 2025. Body composition including both muscle and adipose components was analyzed. Plaque characteristics and pericoronary fat attenuation index (FAI) were quantified. The associations between body composition, plaque burden, and FAI were examined using correlation and multivariable linear regression analyses. Furthermore, we explored the mediating roles of specific body composition metrics in the relationship between body mass index (BMI) and plaque burden, as well as the potential mediation by FAI in the association linking body composition to plaque burden.

Results

Of all-subjects, 269 (65.29%) were male. The average age was 62.85 ± 13.81 years old. Correlation analyses revealed significant moderate to strong positive associations of BMI, visceral adipose tissue (VAT), intermuscular adipose tissue (IMAT), and fatty muscle fraction (FMF) with lipid plaque burden. Additionally, BMI was moderately positively correlated with calcified plaque burden, while skeletal muscle (SM) was weakly negatively correlated with it. Specifically, in a multivariable-adjusted model, higher BMI (β= 0.053) were independently associated with greater lipid plaque burden. Higher BMI (β = 0.909) and lower SM (β = −0.004) were associated with an increased calcified plaque burden. Moreover, higher BMI (β = −0.547), subcutaneous adipose tissue (SAT) (β = −0.004), and SM (β = −0.001), as well as lower VAT (β = 0.005), epicardial adipose tissue (EAT) (β = 0.041), IMAT (β = 0.061), and FMF (β = 0.075) remained independently associated with decreased FAI. Mediation analysis revealed that SM and VAT mediated −29.555% and 56.489% of the association between BMI and calcified plaque burden and lipid plaque burden, respectively. Furthermore, FAI mediated 40.426% of the effect of VAT and 30.909% of the effect of IMAT on lipid plaque burden.

Conclusion

Different body composition metrics exert divergent effects on various components of coronary plaque. Our study suggests that VAT and IMAT may contribute to lipid plaque formation, potentially mediated by a state of increased vascular inflammation as captured by FAI, while a higher muscle mass may protect against the progression of calcified plaque. These findings highlights the necessity of precise body composition analysis in cardiovascular risk assessment.

1. Introduction

The high prevalence of overweight and obesity has become a significant global public health issue, posing a formidable challenge to healthcare systems worldwide [1]. Nowadays, BMI is the predominant metric used to define and quantify obesity. However, some studies have not found an association between elevated BMI and the development of cardiovascular disease (CVD) [2,3].The inconsistency between BMI and CVD risk might stem from its failure to distinguish fat from muscle mass or to assess the distribution and quality of body fat—all factors vital to cardiovascular health [4]. Excess adiposity accelerates atherosclerosis and promotes adverse cardiac remodeling [5]. Observation also shown that a higher level of muscle mass confers a protective effect against the risk of composite cardiovascular events [6]. Therefore, accurately distinguishing between different body composition components is essential for promoting better health.

It is commonly accepted that inflammation plays a critical role throughout the initiation and progression of the coronary artery disease (CAD) [7].Therefore, reducing inflammation levels helps slow the progression of atherosclerosis. In inflamed vessels, paracrine effects influence the pericoronary adipose tissue (PCAT), leading to smaller adipocytes, reduced lipid storage, macrophage infiltration, and an altered tissue lipid-to-water ratio [8]. Since CT attenuation is sensitive to this ratio, this shift can be quantified on coronary computed tomography angiography (CTA) as the fat attenuation index (FAI) [8]. Higher FAI reflects this pro-inflammatory remodeling of PCAT, serving as a non-invasive, imaging-based biomarker of localized coronary inflammatory activity [8]. Studies demonstrate that FAI can effectively assess plaque characteristics and monitor progression [9]. It is reported that fat mass was significant correlated with inflammatory markers [10]. Additionally, visceral adipose tissue (VAT) exhibits a stronger positive association with systemic inflammation than subcutaneous adipose tissue (SAT) or intermuscular adipose tissue (IMAT) [11]. What’s more, increasing muscle mass can reduce the levels of inflammatory markers in the body [12]. To the best of our knowledge, no study has investigated the relationship between body composition and plaque characteristics. Hence, this study seeks to fill this gap by precisely examining the relationship of muscle and fat mass with quantitative plaque components. Advances in imaging technology allow computed tomography (CT) scans to provide simultaneous data on both body composition and coronary artery. Additionally, CT’s 3D reconstruction feature enables repeated and precise measurements.

2. Materials and methods

2.1. Participants

This retrospective study was approved by the ethics committee of The First Affiliated Hospital of Xi’an Jiaotong University (No.XJTU1AF2025LSY Y-445). The requirement for written informed consent was formally waived due to the study’s observational, retrospective design, the use of fully anonymized patient data, and the absence of risk to participants. This study consecutively enrolled patients presenting with chest pain who underwent combined coronary and thoracoabdominal aorta CT scanning between August 2024 and July 2025. The exclusion criteria for the study were as follows: (1) a history of coronary artery revascularization, such as percutaneous coronary intervention with stent placement or coronary artery bypass graft surgery; (2) cardiac or coronary artery malformation; (3) infectious disease; (4) malignancy; (5) liver or kidney dysfunction; (6) immune system disorder; (7) incomplete clinical information; and (8) images with artifacts (Fig.1).

Fig. 1.

Flowchart illustrating the recruitment of study individuals.

2.2. CT protocol

Combined coronary and thoracoabdominal aorta CT scans were performed on two scanner systems: Revolution CT (GE Healthcare, Milwaukee, WI, USA) and uCT960+ (United Imaging Healthcare, Shanghai, China). As this was a retrospective study, CT scanner assignment for CTA examinations depended on clinical scheduling and availability at the time of each patient’s scan. Nevertheless, all scanners adhered to standardized imaging protocols, ensuring consistent comparability between examinations. Each scanner underwent routine daily calibration and quality assurance procedures to ensure stability and comparability of HU measurements. The imaging range extends from above the aortic arch to the groin. The scanning parameters as follows: tube voltage, 100–120 KV; slice thickness, 0.625 mm; slice interval, 0.625 mm; matrix size of 512 × 512; field of view, 420 mm. Intravenous bolus administration of iohexol (350 mg I/mL; GE Healthcare) utilized weight-based dosing at 3−5 mL/s flow rate, succeeded by a 30-mL saline chaser.

2.3. Body composition, coronary plaque and FAI analysis

2.3.1. Body composition measurement

The 3D-Slicer software (Version 5.0.3, https: //www. slicer. org/) was used to analyze CT image data. Measurements were performed at the T12 vertebral level [13] (Supplementary Fig. S1). Skeletal muscle (SM) and adipose tissue were delineated based on established Hounsfield Units (HU) thresholds: −29 to 150 HU for SM and −190 to −30 HU for adipose tissue [14,15]. SM, SAT, VAT, epicardial adipose tissue (EAT), and IMAT were manually delineated. EAT was defined as adipose tissue located between the myocardial outer wall and pericardial visceral layer, measured by manually tracing the pericardial boundary on images covering the area from the pulmonary artery trunk down to left ventricular apex [16]. Fatty muscle fraction (FMF, %) was defined as IMAT/ (SM + IMAT) × 100 [13].

2.3.2. Coronary artery FAI and plaque analysis

The Perivascular Adipose Tissue FAI intelligent analysis software (Beijing Shukun Network Technology Co., Ltd., China) was used to analyzed FAI. FAI was defined as the mean CT attenuation (in Hounsfield units, HU) of the pericoronary adipose tissue (PCAT), with values typically ranging from −190 to −30 HU [17]. For each major coronary vessel, FAI was quantified in the PCAT. The region of interest was defined as the area within a radial distance from the outer vessel wall equal to the diameter of the respective vessel. The software measured FAI along a standardized segment of each coronary artery: a 4 cm segment located 1–5 cm proximal to the ostium of the right coronary artery (RCA), and at a point 4 cm proximal to the ostium of the left anterior descending (LAD) and left circumflex (LCX) arteries. For consistent analysis across all vessels and in accordance with guidelines, the FAI value from the 4 cm proximal location was used, as this measurement has been shown to sensitively reflect coronary inflammation [18,19]. (Supplementary Fig. S2).

Quantitative characteristics of the coronary plaques was analyzed using professional intelligence analysis software (Beijing Shukun Network Technology Co., Ltd., China). The software defined the vessel and lumen contours. Plaque burden was defined as total plaque volume divided by vessel volume [20]. Plaque components were classified based on HU values as follows: −30 to 30 for lipid, 31–130 HU for fibro-lipid, 131–350 HU for fiber, and >350 HU for calcium (Supplementary Fig. S2) [21].

2.4. Reproducibility

The intra- and interobserver variability of body composition metrics was assessed in a group of 30 randomly selected patients. To evaluate intraobserver reproducibility, the same observer (L.G.) reassessed the images at least 1 month later. Interobserver reproducibility was assessed by another observer (Z.J.J), who analyzed the same images. Both observers (L.G. and Z.J.J) were blinded to the first analysis results and clinical data.

2.5. Statistical analysis

Continuous data are reported as mean ± standard deviation, and categorical data are presented as number (percentage). Group comparisons for continuous variables were performed with unpaired t-tests (for normally distributed data) and Mann-Whitney U tests (for non-normally distributed data). Categorical variables were assessed with chi-square tests. Either Pearson or Spearman correlation analysis was applied to quantify associations of body composition metrics with both FAI and plaque characterization. Univariate and multivariate linear regression models were used to estimate the association of body composition metrics with both FAI and plaque characterization. Furthermore, we assessed the mediation effects of body composition metrics on the association between BMI and plaque burden. The fully adjusted model included age, sex, and BMI as foundational risk factors. To assess the independent association of body composition by accounting for the influence of related metabolic and inflammatory factors, we additionally adjusted for glycosylated hemoglobin, type A1c (HbA1c), triglycerides, white blood cell count, and C-reactive protein. Variance inflation factor (VIF) was also obtained. Multiple comparisons were addressed by applying the False Discovery Rate (FDR) correction to all relevant tests. Associations with an FDR-adjusted P-value (q-value) < 0.05 were considered statistically significant. Additionally, the mediation effects of FAI on the association between body composition metrics and plaque burden were also evaluated, using the PROCESS macro (v3.5) for SPSS (version 23.0 for Windows, IBM Corp, Armonk, NY, USA). Percentage mediated (Pm) was obtained. Confidence intervals (CIs) were reported for regression and mediation analyses. Intraclass correlation coefficients and Bland-Altman plots were employed to evaluate intraobserver and interobserver reliability. The statistical analyses were performed using SPSS 23.0 (IBM Corp, Armonk, NY, USA) and MedCalc 13.0 (Mariakerke, Belgium). Statistical significance was defined as p < 0.05.

3. Results

3.1. Clinical baseline characteristics

412 patients were enrolled in the study. The baseline demographic and clinical characteristics of the participants are summarized in Table 1. The average age in this study was 62.85 ± 13.81 years old with 269 men (65.29%) and 143 women (34.71%). Approximately one-third of the participants were overweight or obese. Compared with non-overweight/obese, overweight/obese participants exhibited higher HbA1c, triglycerides and white blood cell count. Other variables were similar between groups.

Table 1.

Demographics and baseline characteristics.

Variables	Total (n = 412)	Overweight/Obese		P value
		Yes (n = 139)	No (n = 273)
Age, year	62.85 ± 13.81 (18.00−94.00)	62.71 ± 11.65 (18.00−92.00)	62.92 ± 14.81 (19.00−94.00)	0.144
Male, %	269 (65.29)	97 (69.78)	172 (63.00)	0.261
BMI, kg/m2	23.69 ± 3.34 (18.03−32.37)	27.45 ± 1.76 (25.00−32.37)	21.77 ± 2.08 (18.03−24.98)	＜0.001
Systolic pressure, mmHg	123.10 ± 20.16 (79.00−170.00)	125.55 ± 19.10 (79.00−157.00)	121.85 ± 20.59 (79.00−170.00)	0.052
Diastolic pressure, mmHg	70.44 ± 12.17 (51.00-108.00)	71.35 ± 12.46 (53.00-108.00)	69.97 ± 12.02 (51.00−108.00)	0.650
Fasting glucose, mmol/L	6.63 ± 2.41 (5.65−7.11)	6.79 ± 2.45 (5.65−7.02)	6.55 ± 2.39 (5.45−7.11)	0.211
HbA1c, %	6.09 ± 0.85 (4.04−7.39)	6.28 ± 1.07 (4.80−7.30)	6.00 ± 0.70 (4.04−7.39)	0.025
TC, mmol/L	3.71 ± 1.02 (1.55−6.45)	3.68 ± 1.03 (1.55−6.12)	3.73 ± 1.02 (1.61−6.45)	0.477
Triglycerides, mg/dL	1.25 ± 0.79 (0.36−4.02)	1.48 ± 0.92 (0.36−4.02)	1.12 ± 0.53 (0.36−3.69)	＜0.001
LDL-cholesterol, mmol/L	2.02 ± 0.76 (0.76−3.60)	2.09 ± 0.81 (0.78−3.57)	1.99 ± 0.74 (0.76−3.60)	0.360
HDL-cholesterol, mmol/L	1.13 ± 0.30 (0.57−1.63)	1.12 ± 0.30 (0.57−1.58)	1.14 ± 0.30 (0.57−1.63)	0.670
Aspartate transaminase,U/L	28.12 ± 9.04 (13.00−54.00)	26.623 ± 16.41 (13.00−53.00)	28.89 ± 3.70 (14.00−54.00)	0.129
Alanine aminotransferase, U/L	24.21 ± 9.01 (11.00−48.00)	22.03 ± 12.08 (11.00−46.00)	25.32 ± 4.55 (12.00−48.00)	0.514
Creatinine, μmol/L	76.94 ± 65.63 (41.00−114.00)	75.44 ± 66.97 (41.00−114.00)	77.71 ± 65.04 (42.00−113.00)	0.670
White blood cell count,109/L	6.65 ± 1.42 (4.80−8.90)	7.50 ± 1.32 (4.80−8.90)	6.21 ± 1.26 (5.00−8.00)	＜0.001
CRP, mg/L	14.40 ± 19.62 (8.00−44.50)	16.16 ± 24.52 (8.00−44.50)	13.49 ± 16.53 (8.00−43.20)	0.773

Continuous data are presented as mean ± standard deviation (range).

BMI: body mass index, HbA1c: glycosylated hemoglobin, type A1c, TC: total cholesterol, LDL: low density lipoprotein, HDL: high density lipoprotein, CRP: C-reactive protein.

3.2. Body composition, FAI and plaque characterization

Compared with non-overweight/obese individuals, those who were overweight or obese had greater muscle and fat mass, along with significantly higher FAI values. Moreover, when assessing coronary plaque characteristics, significant differences were observed between the groups in the burden of lipid, fibro-lipid, fibrous, and calcified plaques (Table 2).

Table 2.

A comparison of body composition metrics, FAI, and plaque characterization between patients with and without overweight/obesity.

Variables	Total (n = 412)	Overweight/Obese		P value
		Yes (n = 139)	No (n = 273)
SM, cm2	135.38 ± 52.69	149.66 ± 57.17	128.10 ± 48.77	＜0.001
SAT, cm2	114.19 ± 78.69	136.25 ± 83.27	102.95 ± 73.90	＜0.001
VAT, cm2	142.81 ± 112.67	181.46 ± 137.80	123.14 ± 91.64	＜0.001
EAT, cm3	94.87 ± 51.94	100.38 ± 37.76	92.07 ± 57.71	0.001
IMAT, cm2	12.41 ± 10.17	14.57 ± 13.89	11.31 ± 7.41	0.002
FMF, %	12.41 ± 10.17	14.94 ± 12.03	6.69 ± 4.99	＜0.001
FAI, HU	−78.77 ± 9.03	−76.53 ± 10.28	−79.92 ± 8.11	0.005
Lipid plaque burden, %	0.35 ± 1.03	0.53 ± 1.67	0.25 ± 0.39	0.005
Fibro-lipid plaque burden, %	0.94 ± 1.45	1.29 ± 1.83	0.76 ± 1.18	0.003
Fibrous plaque burden, %	1.24 ± 1.63	1.56 ± 1.84	1.08 ± 1.49	0.002
Calcified plaque burden, %	4.11 ± 7.18	4.84 ± 7.48	3.74 ± 7.02	0.012

FAI, fat attenuation index; SM, skeletal muscle; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; EAT, epicardial adipose tissue; IMAT, intermuscular adipose tissue; FMF, fatty muscle fraction.

3.3. Association between body composition and FAI and plaque characterization

A heatmap was generated to visualize the associations among body composition metrics, FAI, and plaque characteristics (Fig. 2). The detailed correlation coefficients (r) and corresponding p-values can be found in Supplementary Table S1. FAI was weakly negatively associated with BMI and moderately negatively associated with SM and SAT. In contrast, it showed a strong positive association with VAT and moderate positive associations with other ectopic fat depots (EAT and IMAT).

Fig. 2.

Correlation heatmap of body composition, FAI, and plaque burden. FAI: fat attenuation index.

Regarding the association between body composition and plaque characterization, our results indicated a moderate to strong positive correlation of lipid plaque burden with BMI, VAT, IMAT, and FMF (r_BMI = 0.355, r_VAT = 0.500, r_IMAT = 0.375, r_FMF = 0.358, all p ＜ 0.001). Meanwhile, calcified plaque burden was moderately positively associated with BMI yet inversely associated with SM (r_BMI = 0.472, r_SM = −0.138, all p＜0.001). Furthermore, a significant relationship was also found between BMI and measures of SM, SAT, and VAT (Supplementary Fig. S3).

These associations were further examined by univariable and multivariable linear regression analysis (Table 3). Following FDR correction, the associations reported in Table 3 remained statistically significant (q < 0.05). In univariable analysis (model 1), a one-unit increase in BMI, SM or SAT was associated with a decrease in FAI ranging from 0.006 to 0.382 HU. Conversely, a one-unit increase in VAT, EAT, IMAT or FMF was associated with a increase in FAI ranging from 0.047 to 0.588 HU. A one-unit increase in BMI, VAT, IMAT and FMF was associated a increase in lipid plauqe burden ranging from 0.004 to 0.069%. A one-unit increase in BMI was associated with a 0.766% increase in calcified plaque burden, whereas an equivalent increase in SM was linked to a 0.019% decrease.

Table 3.

Linear regression of body composition metrics with average FAI and plaque characteristics.

Statistics	FAI (HU)		Lipid plaque burden%		Fibro-lipid plaque burden%		Fibrous plaque burden%		Calcified plaque burden%
Model 1	β and p value	q value	β and p value	q value	β and p value	q value	β and p value	q value	β and p value	q value
BMI, kg/m2	−0.382 (−0.635, −0.129) 0.003	0.005	0.040 (0.010, 0.069) 0.009	0.009	0.038 (−0.004, 0.080) 0.078	0.058	−0.025 (−0.072, 0.022)0.300	0.107	0.766 (0.570, 0.962)＜0.001	0.004
SM,cm2	−0.053 (−0.069, −0.038＜0.001	0.004	−4.254 (−9.237, 0.748) 0.095	0.054	−0.002 (−0.005, 0.001) 0.131	0.056	−0.002 (−0.005, 0.001)0.170	0.053	−0.019 (−0.032, −0.006)0.004	0.005
SAT, cm2	−0.006 (−0.018, −0.005) 0.025	0.020	−0.002 (−0.004, 0.001) 0.054	0.050	−0.001 (−0.003, 0.001) 0.344	0.115	0.001 (−0.001, 0.003)0.203	0.066	0.005 (−0.004, 0.014)0.294	0.105
VAT, cm2	0.051 (0.045, 0.057)＜0.001	0.004	0.005 (0.004, 0.005)＜0.001	0.004	0.001 (0.000, 0.002)0.196	0.061	−0.001 (−0.002, 0.001)0.336	0.112	−0.007 (−0.013, −0.001)0.061	0.034
EAT, cm3	0.047 (0.031, 0.063) 0.031	0.023	0.001 (−0.001, 0.003) 0.224	0.069	0.002 (0.000, 0.005)0.073	0.058	0.001 (−0.002, 0.004)0.344	0.115	−0.006 (−0.019, 0.007)0.380	0.123
IMAT, cm2	0.471 (0.398, 0.544)＜0.001	0.004	0.055 (0.047, 0.064)＜0.001	0.004	0.010 (−0.004, 0.0024)0.166	0.053	−0.002 (−0.018, 0.013)0.762	0.191	−0.043 (−0.112, 0.025)0.213	0.066
FMF, %	0.588 (0.477, 0.640)＜0.001	0.004	0.069 (0.060, 0.078)＜0.001	0.004	0.012 (−0.004, 0.028)0.138	0.046	−0.002 (−0.020, 0.016)0.816	0.191	−0.040 (−0.118, 0.038)0.312	0.109
Model 2
BMI, kg/m2	−0.391 (−0.644, −0.138)0.003	0.005	0.040(0.010, 0.070)0.008	0.007	0.037 (−0.005, 0.08)0.082	0.050	−0.026 (−0.073, 0.022)0.287	0.101	0.769 (0.573, 0.964)＜0.001	0.004
SM,cm2	−0.053 (−0.069, −0.038)＜0.001	0.004	−0.002 (−0.003, 0.00)0.098	0.050	−0.002 (−0.005, 0.001)0.131	0.046	−0.002 (−0.005, 0.001)0.167	0.054	−0.002 (−0.015, −0.011)0.020	0.015
SAT, cm2	−0.007 (−0.018, −0.004)0.002	0.004	−0.002 (−0.004, −0.001)0.052	0.031	−0.001 (−0.003, 0.001)0.314	0.099	0.001 (−0.001, 0.003)0.226	0.079	0.004 (−0.004, 0.013)0.325	0.106
VAT,cm2	0.051 (0.045, 0.057)＜0.001	0.004	0.005 (0.004, 0.005)＜0.001	0.004	0.001 (0.000, 0.002)0.213	0.075	−0.001 (−0.002, 0.001)0.310	0.099	−0.007 (−0.014, −0.001)0.068	0.033
EAT,cm3	0.048 (0.031, 0.064)＜0.001	0.004	0.001 (−0.001,0.003)0.236	0.0826	0.003 (0.000, 0.005)0.069	0.033	0.002 (−0.002, 0.005)0.325	0.106	−0.006 (−0.019, 0.008)0.399	0.121
IMAT,cm2	0.470 (0.397, 0.544)＜0.001	0.004	0.055 (0.047, 0.064)＜0.001	0.004	0.010 (−0.004, 0.023)0.177	0.061	−0.003 (−0.018, 0.013)0.738	0.172	−0.045 (−0.114, 0.023)0.196	0.069
FMF, %	0.558 (0.476, 0.640)＜0.001	0.004	0.069 (0.060, 0.078)＜0.001	0.004	0.012 (−0.004, 0.028)0.139	0.046	−0.002 (−0.020, 0.016)0.822	0.172	−0.041 (−0.119, 0.037)0.306	0.099
Model 3
BMI, kg/m2	−0.547 (−0.883, −0.211)0.001	0.005	0.053 (0.014, 0.092)0.009	0.023	0.058 (0.003, 0.114)0.051	0.060	−0.028 (−0.091, 0.035)0.387	0.272	0.909 (0.647, 1.171)＜0.001	0.004
SM, cm2	−0.001 (−0.004, −0.002)0.042	0.062	−0.001 (−0.003, 0.001)0.599	0.382	−0.001 (−0.004, 0.002)0.361	0.253	−0.003 (−0.006, 0.000)0.087	0.076	−0.004 (−0.017, −0.010)0.048	0.062
SAT, cm2	−0.004 (−0.003, −0.006)＜0.001	0.004	−0.002 (−0.004, −0.001)0.051	0.060	−0.001 (−0.003, 0.001)0.472	0.297	0.001 (−0.001, 0.003)0.286	0.200	0.002 (−0.007, 0.012)0.601	0.382
VAT, cm2	0.005 (0.004, 0.007)＜0.001	0.004	0.004 (0.003, 0.005)＜0.001	0.004	0.001 (0.000, 0.002)0.257	0.180	−0.001 (−0.002, 0.001)0.501	0.323	−0.002 (−0.010, −0.001)0.060	0.062
EAT, cm3	0.041 (0.028, 0.053)＜0.001	0.004	0.001 (−0.001, 0.003)0.276	0.193	0.002 (−0.001, 0.005)0.208	0.150	0.002 (−0.001, 0.005)0.171	0.150	−0.005 (−0.019, 0.008)0.427	0.272
IMAT, cm2	0.061 (0.046, 0.077)＜0.001	0.004	0.047 (0.037, 0.057)＜0.001	0.004	0.007 (−0.007, 0.022)0.306	0.213	−0.004 (−0.020, 0.012)0.656	0.383	−0.057 (−0.135, 0.021)0.151	0.132
FMF, %	0.075 (0.058, 0.092)＜0.001	0.004	0.063 (0.052, 0.074)＜0.001	0.004	0.001 (−0.018, 0.020)0.918	0.461	−0.003 (−0.021, 0.015)0.727	0.382	−0.024 (−0.114, 0.066)0.605	0.382

Model 1 unadjusted; model 2 was adjusted for age and sex; model 3 was adjusted for age, sex, HbA1c, triglycerides, white blood cell count, and CRP (or BMI).

FAI, fat attenuation index; BMI, body mass index; SM, skeletal muscle; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue; EAT, epicardial adipose tissue; IMAT, intermuscular adipose tissue; FMF, fatty muscle fraction; HbA1c, glycated Haemoglobin A1c, CRP, C-reactive protein.

In the fully adjusted model, we found that all variance inflation factors (VIFs) were below 2, indicating no significant multicollinearity (Supplementary Table S2). In the fully adjusted model (model 3), multivariable analysis revealed distinct associations of body composition metrics with both FAI and plaque characterization. A one-unit increase in BMI, SM or SAT was associated with a decrease in FAI ranging from 0.001 to 0.547 HU. In addition, a one-unit increase in VAT, EAT, IMAT or FMF was associated with a increase in FAI ranging from 0.005 to 0.075 HU. A one-unit increase in BMI, VAT, IMAT and FMF was associated a increase in lipid plauqe burden ranging from 0.004% to 0.063%. A one-unit increase in BMI was associated with a 0.909% increase in calcified plaque burden, whereas an equivalent increase in SM was linked to a 0.004% decrease. In the mediation analysis for BMI and plaque burden, SM showed significant mediation effects on the relationships between BMI and calcified plaque burden (Pm = −29.555%, total effect = 0.247, p = 0.021; direct effect = 0.320, p = 0.002; indirect effect = −0.073, 95% [CI] = −0.147 to −0.012), VAT showed significant mediation effects on the relationships between BMI and lipid plaque burden (Pm = 56.489%, total effect = 0.131, p＜0.001; direct effect = 0.057, p ＜ 0.001; indirect effect = 0.074, 95% [CI] = 0.021 to 0.168). The FAI also showed significant mediation effects on the relationship between VAT and lipid plaque burden (Pm = 40.426%, total effect = 0.005, p ＜ 0.001; direct effect = 0.003, p ＜ 0.001; indirect effect = 0.002, 95% [CI] = 0.001 to 0.003) as well as between IMAT and lipid plaque burden (Pm = 30.909%, total effect = 0.055, p ＜ 0.001; direct effect = 0.039, p ＜ 0.001; indirect effect = 0.017, 95% [CI] = 0.012 to 0.036) (Fig. 3).

Fig. 3.

Mediation analysis between body composition and plaque burden.

3.4. Intra- and interobserver reproducibility of body composition metrics

Excellent intraobserver reliability was observed for body composition metrics, with values ranging from 0.983 to 0.998. Similarly, interobserver reliability was also excellent, with values ranging from 0.974 to 0.997. The Bland-Altman analysis demonstrated consistency between the two repeated measurements, with all differences lying within the agreement limits (Supplementary Fig. S4).

4. Discussion

Atherosclerosis, which is strongly driven by inflammation, is consequently associated with poor clinical outcomes [22]. Moreover, the value of BMI as a reliable marker of cardiovascular prognosis is contentious and has been questioned. A growing body of evidence supports a link between body composition and adverse cardiovascular outcomes [13,23]; however, its association with specific plaque components remains elusive. In this study, we found a moderate to strong positive correlation between lipid plaque burden and BMI, VAT, IMAT, and FMF. Meanwhile, a moderate positive correlation was observed between calcified plaque burden and BMI, whereas an inverse correlation was found with SM. The association persisted after adjustment for various confounding factors. In our exploratory mediation analysis, the associations of BMI with both calcified and lipid plaque burdens appeared to be partly explained by SM and VAT. Furthermore, the association of VAT and IMAT with lipid plaque burden were also suggestive of a potential mediating role for FAI. This study highlights the importance of considering body composition in clinical practice for the prevention of atherosclerosis. To the best of our knowledge, this study provides the first investigation into the associations of muscle and fat mass with quantitative plaque components.

Obesity, characterized by abnormal or excessive fat accumulation, is closely linked to CVD including atherosclerosis, hypertension, and hyperlipidemia, and is primarily diagnosed based on BMI [24,25]. A BMI of 25 indicates overweight, whereas a BMI of 30 defines obesity [25]. However, BMI cannot distinguish between muscle and fat mass, or determine fat distribution, both of which are critical for predicting CVD outcomes. Previous study indicated that lower muscle mass was independently correlated with adverse events [23]. In addition, evidence from multiple studies points to a negative correlation between SM mass and atherosclerosis across different age groups, from young adults to the elderly [26,27]. Higher fat mass was associated with an elevated risk for CAD. In contrast, a higher fat-free mass showed a protective association [28]. These findings collectively suggest that SM and adipose tissue make divergent contributions to vascular pathophysiology. We also found that BMI, VAT, IMAT, and FMF were moderately to strongly positively correlated with lipid plaque burden. Moreover, BMI was moderately positively correlated with calcified plaque burden, whereas SM was negatively correlated. All these associations remained significant even after adjusting for confounding factors. Hence, our study suggests that for cardiovascular health, it is beneficial to reduce region-specific body fat and increase muscle mass, and that these nutritional factors should be considered in cardiovascular risk stratification.

Previous studies have primarily explored the relationship between body composition and the coronary artery calcium score [29,30]. To date, however, no study has precisely quantified the associations of various body compositions with different plaque types—specifically with non-calcified plaque, a critical determinant of future cardiovascular risk. Therefore, to comprehensively understand atherosclerosis, we systematically quantified the burden of lipid plaque, fibro-lipid plaque, fibrous plaque and calcium plaque. We noted that a higher BMI was associated with a higher burden of both calcified and lipid plaque. Higher VAT, IMAT and FMF were associated with higher burden of lipid plaque burden. Moreover, our mediation analysis indicated that SM and VAT potentially accounted for part of the association between BMI and both calcified and lipid plaque burdens. FAI was also identified as a potential mediator in the associations of VAT and IMAT with lipid plaque burden These findings suggest that the impact of obesity on coronary plaque is complex, as muscle and adipose tissue might influence plaques through distinct pathophysiological mechanisms, with effects that vary by plaque type. Consequently, this work provides new mechanistic insights for understanding the “obesity paradox” and advancing the precision assessment of cardiovascular risk. Our research demonstrate that over half of BMI’s effect on lipid plaque burden is mediate by VAT. VAT is not merely an energy storage depot but a highly active endocrine system [31]. It drives systemic inflammation and insulin resistance by releasing pro-inflammatory cytokines and free fatty acids, thereby fostering an environment conducive to the formation of high-risk, prone-to-rupture lipid plaques [32,33]. In our study, overweight/obese participants exhibited higher FAI values. What’s more, FAI emerged as a significant mediator linking both VAT and IMAT to lipid plaque burden, explaining 40.426% and 30.909% of their respective effects. Systemic low-grade inflammation and more aggressive adipose tissue inflammation in obesity contribute to increased inflammation around the coronary arteries, thereby promoting the formation of vulnerable lipid plaque [17,18]. In addition, our analysis indicated a potential protective association for SM, which mediated the relationship between BMI and calcified plaque burden. This observed mediation, accounting for 29.555% of the total effect, suggests the possibility that greater muscle mass may be linked to a lower burden of calcified plaque in individuals with high BMI. As a key metabolic organ, SM contributes to metabolic homeostasis by enhancing glucose uptake and secreting beneficial myokines [34]. Thus, higher muscle mass may serve as a metabolic reserve, counteracting some of the adverse effects of obesity and thereby delaying the progression of coronary artery calcification.

Jing et al. [35]. demonstrated that higher lipid plaque proportion was associated with elevated periplaque FAI, whereas a greater calcified proportion was linked to lower FAI. Other studies also found positive correlations between lipid plaque and FAI [36,37]. Consistent with previous studies, we found that lipid plaque burden correlated moderately to strongly with FAI, whereas calcified plaque burden was moderately negatively correlated with and FAI. Furthermore, to our surprise, BMI was inversely associated with FAI. Lipid plaque are unstable and likely to rupture, which can trigger dangerous blood clots [38]. Studies have demonstrated a strong association between lipid plaques and cardiovascular events in patients with acute coronary syndromes [39,40]. Lipid plaques have more inflammatory cells related to heightened inflammatory activity at the fibrous cap. Pro-inflammatory mediators from released by the diseased coronary arteries induce phenotypic switching of PCAT, which leads to increased FAI values [41]. However, the proportion of calcified components is associated with plaque stability and risk reduction. Pathological studies have demonstrated an inverse correlation between calcification extent and cap inflammation; furthermore, the spatial density of vasa vasorum is significantly lower in coronary segments with calcified plaques compared to non-calcified ones [42,43]. Collectively, these findings suggest that vascular calcification is associated with lesion stabilization. We found a weak negative association between BMI and FAI, indicating that adipose tissue distribution and phenotype, rather than BMI alone, play a more critical role in driving pericoronary inflammation. Our data show that FAI is strongly and positively associated with VAT but negatively associated with SAT and BMI. This suggests that individuals with “metabolically healthy obesity”-characterized by high BMI yet a favorable fat distribution (low VAT, high SAT)-may exhibit lower perivascular inflammation [44]. Additionally, despite having a high BMI, individuals with high muscle mass can be linked to lower levels of pericoronary inflammation. Nevertheless, no significant correlation was observed between FAI and fibrous plaque burden or fibro-lipid plaque burden. We supposed that both fibrous plaques (stable lesions) and fibro-lipid plaques (with thicker fibrous caps) exhibit low inflammatory activity, thus having minimal influence on PCAT and resulting in no significant change in FAI [45].

Despite yielding valuable insights, it is imperative to recognize the limitations inherent in this study. First, as a retrospective study utilizing two different CT scanner models, some variability in acquisition parameters may have existed. Nevertheless, the core scanning principles were kept consistent across all examinations. Moreover, the use of multiple protocols reflects real-world clinical imaging conditions, which improves the generalizability of our findings. Second, the study cohort comprised predominantly overweight individuals, with a low rate of obesity, aligning with typical Asian population characteristics. It should be noted that the conventional BMI cutoff used for descriptive group comparisons may not optimally reflect obesity-associated risk gradients specific to Asian populations, where metabolic risk increases at lower BMI thresholds. As the research primarily investigates body composition, our core findings rely on direct, continuous measures of adiposity and lean mass, which are robust to this concern. Furthermore, BMI was addressed as a confounding factor in the analyses. This approach underscores the value of moving beyond broad BMI categories to more precise, continuous body composition metrics in metabolic and cardiovascular research. Third, the single-center, retrospective nature of our study precludes definitive conclusions regarding the effect of anti-inflammatory therapy on FAI and coronary plaque, highlighting a need for future exploration. Fourth, as this is a cross-sectional study, caution is warranted in interpreting the results. The findings demonstrate an association between body composition, FAI, and coronary plaque but not causation. Moreover, the analysis lacked data on clinical outcomes. Future studies should involve follow-up of patients with elevated VAT, IMAT, and FMF to further investigate the causal relationship between ectopic fat deposits and coronary plaque. Finally, although body composition, FAI, and coronary plaque were all accurately derived from CT imaging, their underlying biological mechanisms-such as the specific inflammatory factors involved-require further elucidation through biomarkers or basic research.

5. Conclusion

In conclusion, this study demonstrates a close relationship between body composition and CAD. Our findings indicate that VAT and IMAT may exacerbate lipid plaque development via a state of increased vascular inflammation as captured by FAI, while increased muscle mass exerts a protective effect against calcified plaque advancement. These findings highlight the importance of precise body composition phenotyping for assessing cardiovascular risk and formulating personalized prevention and treatment strategies.

CRediT authorship contribution statement

Lu Gao: Conceptualization, Methodology, Software, Data curation, Writing-Original draft preparation, Funding acquisition and Supervision; Chenbo Xu: Methology, Data curation, Investigation, Visualization and Funding acquisition; Jing Li: Data curation and Resources; Zhijie Jian: Writing - Review & Editing; Yue Wu: Supervision.

Declaration of Generative AI and AI-assisted technologies in the writing process

No artificial intelligence-assisted technologies were used during the writing process of this manuscript.

Funding

This study was supported by the Natural Science Basic Research Program of Shaanxi (Program No. 2024JC-YBQN-0844), the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University, China (No. XJTU1AF-CRF-2023-011).

Institutional review board statement

This study was approved by the institutional review board of The First Affiliated Hospital of Xi’an Jiaotong University (No. XJTU1AF2025LSYY-445), and implemented in accordance with the Declaration of Helsinki.

Availability of data and materials

The data underlying this article will be provided by the corresponding author on reasonable request.

Declaration of competing interest

The authors declare no conflict of interest.

Appendix A. Supplementary data

The following are Supplementary data to this article: