Combined lesion-specific pericoronary adipose tissue attenuation and triglyceride-glucose body mass index for improved risk stratification of major adverse cardiovascular events in patients with stable angina pectoris

Introduction

Coronary artery disease (CAD) remains the leading cause of morbidity and mortality worldwide, with a rising prevalence and an increasing trend toward younger populations (1). Stable angina pectoris (SAP), the most common clinical manifestation of CAD, poses significant clinical challenges due to its complex pathophysiology and varied prognosis (2). Despite patients with SAP are generally classified as a lower risk compared to those with acute coronary syndromes (ACS), they still face a considerable likelihood of developing major adverse cardiovascular events (MACEs). Consequently, precise risk stratification and early identification of SAP patients at higher risk for MACEs are essential for optimizing disease management and improving clinical outcomes.

Conventional risk models, which predominantly depend on the extent of luminal stenosis and clinical variables, often fail to capture the complex interplay between metabolic dysfunction and vascular inflammation underlying plaque progression and instability. Inflammation is a critical factor in the progression of atherosclerosis and the subsequent rupture of plaques, which are significant contributors to MACEs (3-5).

Traditional inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6) help assess systemic inflammation but lack specificity. Neutrophils and their derived indicators can serve as independent risk factors for patients with CAD, but they may be influenced by non-cardiovascular factors and may not accurately reflect local coronary artery inflammation (6).

The lesion-specific pericoronary adipose tissue attenuation (PCATa-lesion), quantified through coronary computed tomography angiography (CCTA), has emerged as a promising imaging biomarker for detecting coronary inflammation, with potential to distinguish between stable and unstable plaques, as well as providing prognostic information regarding future MACEs (7,8). Several studies have demonstrated its significant value in quantifying coronary inflammation and predicting MACEs (9-12). Furthermore, recent studies have demonstrated that PCATa-lesion provided incremental prognostic value beyond conventional CCTA findings for prediction of MACEs in patients with CAD (5,13). Additionally, insulin resistance (IR) represents another crucial mechanism in the development and progression of CAD. Triglyceride-glucose-body mass index (TyG-BMI) is a new surrogate marker for IR, which has been shown to be independently associated with cardiovascular risk and heart disease incidence. Among all visceral fat indices and TyG-related values, TyG-BMI shows the highest correlation and is significantly positively correlated with the increased risk of CAD severity (14,15). Thus, integrating both the TyG-BMI index and PCATa-lesion may provide a more comprehensive approach to risk assessment in patients with SAP. However, the combined predictive value of the TyG-BMI index and PCATa-lesion for MACEs in patients with SAP has not been thoroughly investigated.

Therefore, the primary aim of this study was to develop a comprehensive model that integrates clinical risk factors, conventional CCTA features, as well as PCATa-lesion and TyG-BMI, to improve the prediction of MACEs in patients with SAP. Moreover, we further investigated the incremental value of PCATa-lesion for risk stratification. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1536/rc).

Methods

Study design

This retrospective study enrolled patients with SAP who underwent CCTA at The First Affiliated Hospital of Chengdu Medical College between January 2017 and December 2020 due to chest pain or discomfort. The “SAP” was defined as follows: effort-induced chest pain that remained stable in terms of frequency, intensity, or duration over the preceding four weeks, supported by positive stress test results (16). Exclusion criteria were as follows: history of acute or chronic myocardial infarction, prior coronary artery bypass grafting (CABG) or coronary stent implantation, incomplete baseline clinical data, history of various heart diseases; or poor image quality (Figure 1). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Chengdu Medical College (No. 2025CYFYIRB-BA-057). Individual consent for this analysis was waived due to the retrospective nature of the study. Follow-up data were obtained through medical record review and telephone interviews.

Figure 1 The flowchart of the research. Bypass, coronary artery bypass grafting; MACEs, major adverse cardiovascular events; SAP, stable angina pectoris.

Data collection

Blood samples were collected within 24 hours of the patients’ admission to the hospital or outpatient visit. Demographic characteristics and laboratory parameters were collected from the medical record system. Demographic characteristics include age, sex, hypertension status, smoking history, medication history, and body mass index (BMI). Laboratory parameters comprised total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides (TG), lipoprotein(a), homocysteine, fasting glucose, and the TyG-BMI, which defined as ln[Triglycerides (mg/dL) × Glucose (mg/dL) /2] × BMI.

CCTA acquisition

All CT examinations were performed with a 128-row spiral CT system (SOMATOM Definition; Siemens AS). The CCTA protocol comprised an initial non-contrast scan for coronary artery calcium scoring (CACS), followed by a contrast-enhanced CCTA scan. Prospective electrocardiographic (ECG) gating was utilized for all acquisitions. Oral β-blockers were administered to achieve a target heart rate ≤70 bpm. All patients underwent breath-holding training and received sublingual nitroglycerin spray (0.5 mg) for coronary vasodilation immediately before image acquisition. Iodinated contrast medium (iopamidol, 370 mg I/mL; total dose 60–80 mL, body weight-adjusted) was intravenously administered. Scanning parameters: 120 kV tube voltage, 64×0.6 mm; matrix size: layer thickness 0.75 mm; interlayer clearance, 0 mm; on the pitch, 0.18; rotation time 0.3 s/time. The degree of coronary artery lesions was assessed using the Gensini scoring system, based on the degree of stenosis on CCTA images, as previously described (17). Moreover, the specific measurement methodology for epicardial adipose tissue volume (EATV) is referenced from previous study (Figure S1) (18).

Plaque analysis

In SAP patients with MACEs, culprit lesions were analyzed; in those without MACEs, the most stenotic plaques underwent examination. The location of culprit lesions was determined through multimodal integration of ECG, echocardiography, and invasive coronary angiography (ICA) (10). Coronary plaques were assessed using uAI-coronary software (version 20231030_release) for automated segmentation and quantification. Plaques were classified by attenuation as calcified [>350 Hounsfield units (HU)], fibrous (131–350 HU), fibrofatty (31–130 HU), and low-attenuation (−30 to 30 HU). High-risk plaques (HRPs) were defined by the presence of ≥2 features: positive remodeling (PR), low-attenuation plaque (LAP), napkin-ring sign (NRS), and spotty calcification (SC), as previously detailed in the literature (19). Quantitative parameters included CACS, plaque length (PL), remodeling index (RI), total plaque volume (TPV), calcified plaque volume (CPV), non-calcified plaque volume (NCPV), fibrous volume (FV), fibrofatty volume (FFV), low-attenuation plaque volume (LAPV), minimal lumen area (MLA), and diameter stenosis (DS), categorized as mild (1–49%) or moderate-to-severe (50–99%).

PCATa-lesion

Analysis was performed using the PCATa-lesion software (United Imaging Healthcare). The adipose tissue was defined as all the voxels within the HU range of −190 to −30, located at a radial distance from the outer vessel wall equal to the average diameter of the target vessel (20). PCATa-lesion was defined as the longitudinal measurement from the proximal to the distal region of a specific stenosis, to account for eccentric plaques and the cylindrical volume of the measurement, a 1 mm gap was left between the vessel outer wall to avoid artifacts, as detailed in previous studies (21-23) (Figure 2). All measurements were performed by two radiologists with 3 and 5 years of imaging diagnostic experience, respectively, who were blinded to clinical data. Each measurement was repeated three times for each patient, with the final parameter being the average value from both observers.

Figure 2 CCTA of a 71-year-old female STEMI patient reveals PCATa-lesion. MPR shows severe stenosis of a long proximal LAD segment with a mixed plaque (orange-yellow area) and a PCATa-lesion value of −68.79 HU. Invasive coronary angiography confirms severe stenosis in the proximal LAD segment (as indicated by the arrow). CCTA, coronary computed tomography angiography; HU, Hounsfield units; LAD, left anterior descending artery; MPR, multi-planar reconstruction; PCATa-lesion, lesion-specific pericoronary adipose tissue attenuation; STEMI, ST-elevation myocardial infarction.

Follow-up and endpoint definition

Physicians conducted follow-up via telephone interviews and hospital information system record reviews at 6-month intervals, blinded to CCTA findings. MACEs included second admission to the hospital for chest pain or congestive heart failure, unplanned revascularization, ACS, stroke, cardiac death.

Cardiac death was defined as mortality attributable to acute myocardial infarction, ventricular arrhythmia, or refractory heart failure. ACS encompassed ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), and unstable angina (UA), with events recorded only when supported by confirmed culprit lesions and emergency revascularization (24).

Statistical methods

Normality of continuous variables was assessed using the Kolmogorov-Smirnov test. Normally distributed data are presented as (mean ± standard deviation), with group comparisons performed using independent samples t-tests. Non-normally distributed data are expressed as median (interquartile range), with two-group comparisons conducted using Mann-Whitney U tests and multi-group comparisons analyzed with Kruskal-Wallis H tests. Categorical variables are reported as percentages and compared using Chi-squared tests or Fisher’s exact tests as appropriate. Univariable and multivariable Cox regression analysis identified potential predictors of MACEs, with results presented as hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). Based on these results, risk factors were categorized and cumulative survival probabilities calculated using Kaplan-Meier analysis, compared via log-rank tests. Time-dependent receiver operating characteristic (ROC) curve was used to evaluate model predictive performance. Model goodness-of-fit was assessed using likelihood ratio tests for nested models, with decision curve analysis (DCA) evaluating clinical net benefit. Intraclass correlation coefficient (ICC) was employed to assess intra-observer and inter-observer agreement in data measurements. All analyses were performed using SPSS version 26.0 (SPSS Inc., Armonk, NY, USA) and R software version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Study population and baseline characteristics

Finally, a cohort of 212 patients with SAP were included in this study. During a median follow-up of 36 months, 43 patients (20.1%) experienced MACEs, including second admission to the hospital for chest pain or congestive heart failure (n=13), unplanned revascularization (n=15), ACS (n=7), stroke (n=3), or all-cause mortality (n=5). Clinical characteristics and CCTA features appear in Tables 1,2 respectively. Patients in the MACEs group exhibited significantly higher age, BMI, TyG-BMI (all P<0.001), and prevalence of diabetes mellitus (DM) compared to the non-MACEs group (P=0.016). The proportion of HRP was significantly greater in the MACEs group than in the non-MACEs (all P<0.001). Additionally, the MACEs group demonstrated significantly greater DS, LAPV, TPV and PCATa-lesion compared to the non-MACEs group (all P<0.001). In addition, the subgroup analysis results show that compared to non-DM patients, the likelihood of experiencing endpoint events increases with higher levels of PCATa-lesion and TyG-BMI in DM patients, as detailed in the supplementary materials (Figure S2). ICC results (Table S1) confirmed excellent consistency in PCATa-lesion and EATV measurements.

Comparison of model performance and incremental value

As shown in Table 3, univariable Cox regression analysis demonstrated that age, DS, TyG-BMI, TPV, and PCATa-lesion were significantly associated with MACEs (all P<0.001). Subsequent multivariable Cox regression analysis identified age (HR =1.052, P=0.049), DS (HR =1.079, P=0.031), TyG-BMI (HR =2.198, P=0.027), PCATa-lesion (HR =1.117, P<0.001) as independent risk factors for MACEs. Accordingly, we constructed four multivariable Cox regression models: Model 1 incorporated traditional risk factors (age + DS); Model 2 added TyG-BMI; Model 3 added PCATa-lesion; and Model 4 included both TyG-BMI and PCATa-lesion. With the sequential addition of variables, model performance improved progressively. Compared to Model 1, Model 4 demonstrated superior goodness-of-fit and discrimination (Figure 3A,3B). Specifically, compared to Model 1 (without PCATa-lesion), Model 3 increased the Chi-squared value by 28.2 and improved AUC from 0.829 to 0.978. Compared to Model 2 (without PCATa-lesion), the combined Model 4 (PCATa-lesion + TyG-BMI) increased the Chi-squared value by 25.2 and improved AUC from 0.934 to 0.994. All differences were statistically significant (all P<0.001). DCA evaluated the clinical utility of all models (Figure 3C), revealing that Model 4 provided superior net clinical benefit across threshold probability ranges. Time-dependent receiver operating characteristic (ROC) curves (Figure 4) further confirmed that Model 4 maintained robust discriminative performance over time, with area under the curve (AUC) values of 0.898 at 2 years, 0.871 at 3 years, and 0.878 at 4 years. These results collectively indicate that Model 4 is the optimal predictive model, and both TyG-BMI and PCATa-lesion provide significant incremental prognostic value for MACEs in patients with SAP.

Figure 3 Performance comparison of different clinical models. Model 1 incorporated traditional risk factors (age + DS); Model 2 added TyG-BMI; Model 3 added PCATa-lesion; and Model 4 included both TyG-BMI and PCATa-lesion. With the sequential addition of variables, model performance improved progressively (A,B). Compared to Model 1 (without PCATa-lesion), Model 3 increased the Chi-squared value by 28.2 and improved AUC from 0.829 to 0.978. Compared to Model 2 (without PCATa-lesion), the combined Model 4 (PCATa-lesion + TyG-BMI) increased the Chi-squared value by 25.2 and improved AUC from 0.934 to 0.994. All differences were statistically significant (all P<0.001). DCA evaluated the clinical utility of all models (C), revealing that Model 4 provided superior net clinical benefit across threshold probability ranges. AUC, area under the curve; DCA, decision curve analysis; DS, degree of stenosis; PCATa-lesion, lesion-specific pericoronary adipose tissue attenuation; ROC, receiver operating characteristic; TyG-BMI, triglyceride-glucose-body mass index.

Figure 4 Time-dependent ROC curves (A) further confirmed that Model 4 maintained robust discriminative performance over time (B), with AUC values of 0.898 at 2 years, 0.871 at 3 years, and 0.878 at 4 years. Model 4: age + DS + TyG-BMI + PCATa-lesion. AUC, area under the curve; DS, degree of stenosisc; PCATa-lesion, lesion-specific pericoronary adipose tissue attenuation; ROC, receiver operating characteristic; TyG-BMI, triglyceride-glucose-body mass index.

Risk stratification based on PCATa-lesion

Patients were stratified into tertiles according to PCATa-lesion levels at admission: Tertile 1 (n=70, PCATa-lesion <−76.77 HU), Tertile 2 (n=70, −76.77≤ PCATa-lesion <−71.11 HU), and Tertile 3 (n=72, PCATa-lesion ≥−71.11 HU). As shown in Table 4, the incidence of HRP (P=0.014), the characteristics of HRP (LAP, P=0.011; NRS, P=0.038), and the proportion of MACEs (P<0.001) all increased with elevated PCATa-lesion values. However, no significant correlations were observed between PCATa-lesion and CPV, CACS, DS, or other factors (all P>0.05). Kaplan-Meier curve analysis (Figure 5) revealed that individuals in the highest tertile of both PCATa-lesion and TyG-BMI had significantly shorter MACEs-free survival compared to those in the middle and low tertiles (all P<0.001).

Figure 5 Kaplan-Meier curves for MACEs-free survival based on imaging and metabolic risk stratification. Kaplan-Meier curve analysis revealed that individuals in the highest tertile of both PCATa-lesion and TyG-BMI had significantly shorter MACEs-free survival compared to those in the middle and low tertiles. MACEs, major adverse cardiovascular event; PCATa-lesion, lesion-specific pericoronary adipose tissue attenuation; TyG-BMI, triglyceride-glucose-body mass index.

Discussion

During a median follow-up of 36 months, 43 patients (20.1%) experienced MACEs. Both the PCATa-lesion and TyG-BMI serve as independent predictors of MACEs in patients with SAP, and further demonstrated that the integrated model combining both TyG-BMI and PCATa-lesion provided superior goodness-of-fit and discrimination compared to models based on traditional risk factors (age and DS) alone or those incorporating only TyG-BMI or PCATa-lesion individually. Importantly, this comprehensive model showed the greatest predictive performance for MACEs events. Additionally, survival analysis revealed that individuals in the highest tertile for both TyG-BMI and PCATa-lesion had significantly shorter MACEs-free survival compared to those with low-to-mid tertile values for both markers. These findings indicate that the combined use of a metabolic marker (TyG-BMI) and an imaging biomarker of coronary inflammation (PCATa-lesion) provides significant incremental prognostic value beyond traditional risk factors and CCTA-derived stenosis severity, contributing to improve risk stratification and guide personalized management in patients with SAP, particularly within diabetic populations.

As an indicator integrating visceral adiposity and metabolic dysregulation, TyG-BMI is more effective in reflecting IR, as obesity has been widely proven to be a major contributor to IR (25). The TyG-BMI index systemically captures glucolipid metabolic disturbances, which constitute fundamental drivers of endothelial dysfunction and systemic inflammation-key pathological processes directly linked to atherosclerosis progression and incident cardiovascular events (26). In our study, subgroup analysis revealed a significant difference in the attenuation of PCATa-lesion between SAP patients with and without comorbid DM, and as PCATa-lesion levels increased, DM patients demonstrated a substantially greater likelihood of endpoint events compared to non-DM individuals. PCATa-lesion is a biomarker of localized inflammation within atherosclerotic plaques. The increased attenuation of PCATa in DM patients reflects an exacerbation of local inflammatory processes within the vessels, which serves as a marker of increased plaque vulnerability and rupture risk, and constitutes a central determinant of MACEs (27,28).

Consistent with previous studies, our research found that patients in the MACEs group exhibited higher average PCAT attenuation compared to those in the SAP group (29,30). A recent large prospective study showed that high PCAT values can predict all-cause mortality and cardiac death (31). Additionally, a prospective study involving 134 psoriasis patients found that after one year of biologic treatment, PCAT attenuation significantly decreased, further validating its role in monitoring vascular inflammation and treatment response (32,33). Previous studies primarily focused on populations with ACS, demonstrating a strong association between elevated PCATa-lesion at the culprit lesion site and plaque vulnerability as well as inflammatory activity (16,34). This study extends the application of PCATa-lesion to SAP population. Despite the relatively stable clinical condition of SAP patients, increased PCATa-lesion remains an important indicator of potential cardiovascular risk.

Moreover, earlier studies have indicated that inflammation may mediate the relationship between IR and the severity of CAD (35,36). IR may activate a pro-inflammatory phenotype in pericoronary adipose tissue (PCAT), leading to the sustained release of inflammatory cytokines such as IL-6 and MCP-1 (37). These cytokines further stimulate local inflammatory responses around the coronary arteries, which exacerbate vascular inflammation and amplify IR, jointly accelerating the progression of coronary atherosclerosis (38).

Our findings are consistent with previous studies, highlighting the significant potential of PCAT in cardiovascular risk assessment and treatment monitoring. This study is the first to demonstrate that the combined application of PCATa-lesion and the TyG-BMI offers significant incremental value and complementary advantages in cardiovascular risk prediction. As an imaging biomarker, PCATa-lesion enables direct quantification of local coronary inflammation, thereby reflecting the inflammatory status at the target organ level. In contrast, TyG-BMI captures systemic dysregulation of glucose and lipid metabolism, serving as an indicator of overall metabolic-inflammatory burden. Previous studies have shown that the combined use of the TyG index and RCA-PCATa-lesion may help identify individuals at increased risk of multivessel CAD (36). The translation of PCATa-lesion and TyG-BMI into routine practice warrants consideration. While challenges exist, automated software solutions for PCAT analysis are emerging, and TyG-BMI components (glucose, triglycerides, BMI) are routinely measured. Alternative systemic markers (e.g., hs-CRP, NLR) are more accessible but do not capture coronary-specific inflammation. Clinically, the combined use of these biomarkers could enable personalized treatment pathways. However, using RCA-PCATa-lesion as a surrogate for overall PCATa-lesion remains controversial, as it does not assess PCATa-lesion values directly adjacent to coronary plaques, which may be more clinically relevant. Our study, by focusing on lesion-specific plaques, may more accurately identify localized inflammatory risk around vulnerable plaques. Moreover, we found that higher PCATa-lesion values were associated with an increased prevalence of HRP, as well as elevated proportions of LAP and NRS—all of which are well-established predictors of MACEs (39). However, in contrast to previous studies, we did not observe a rising trend in the incidence of obstructive stenosis across higher PCATa-lesion tertiles (8,40). This discrepancy may be attributable to the characteristics of our study population, which exclusively included patients with SAP. The relatively low levels of local coronary inflammation in SAP patients may have reduced the sensitivity for detecting significant associations between PCATa-lesion and obstructive stenosis, potentially obscuring the true relationship between PCATa-lesion and degree of stenosis. In addition, homocysteine is a recognized biomarker for cardiovascular disease risk and is closely associated with increased cardiovascular damage and morbidity (41). In contrast to previous reports, this study did not observe a significant association between homocysteine levels and MACEs. This lack of association may be attributable to the relatively limited number of events in our cohort, and the inclusion of homocysteine in the multivariate model could have increased the risk of multicollinearity and model overfitting.

However, there are some limitations in this study. Firstly, this study was conducted at a single center with a relatively small sample size, which may affect the external validity of the findings, particularly in terms of generalizability to populations with different ethnic backgrounds and healthcare systems. Future studies should consider multi-center designs with more diverse populations to validate our findings and assess their applicability across various clinical contexts. Secondly, although diabetic status was considered, our analysis did not account for important confounders such as diabetes duration, glycemic control [hemoglobin A1c (HbA1c)], and antidiabetic medications. These factors are known to significantly impact inflammation, plaque instability, and MACEs risk, and their omission is a limitation of this study. Thirdly, this study did not consider the long-term effects of lifestyle factors during follow-up (such as smoking intensity, alcohol consumption, and weight fluctuations) on TyG-BMI and residual inflammatory risk. Fourthly, this study did not include other lipid-lowering treatments beyond statins. Some lipid-lowering medications have well-established anti-inflammatory and plaque-stabilizing effects, and overlooking their impact may lead to confounding bias. Besides, previous studies have confirmed that pre-β HDL particles play a significant role in anti-inflammatory and antioxidant properties. The focus of this study was on inflammation and plaque vulnerability, but the exclusion of pre-β HDL particles as a variable during data collection may be a limitation of the study. Lastly, the observational design constrains our ability to establish causality between the imaging biomarkers and adverse cardiovascular events. Therefore, future multicenter, prospective studies are necessary to validate these results.

Conclusions

This study demonstrates that PCATa-lesion serves as an independent prognostic factor for MACEs in patients with SAP. The combination of PCATa-lesion and TyG-BMI provides significant incremental prognostic value for assessing MACEs risk in SAP patients. This study contributes to enhancing risk stratification in SAP patients, facilitating early intervention and personalized management.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1536/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1536/dss

Funding: This research was supported by the project International Clinical Research Center of Chengdu Medical College (grant No. 25LHGJ2-03) and Research Projects of the Sichuan Medical Association (grant No. S2024005).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1536/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Chengdu Medical College (No. 2025CYFYIRB-BA-057). Individual consent for this analysis was waived due to the retrospective nature of the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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