Relationship between different clinical characteristics and pericoronary adipose tissue attenuation values quantified from coronary computed tomographic angiography (CCTA) in patients without coronary heart disease (CHD)

Introduction

Pericoronary inflammation is closely associated with the development and progression of coronary heart disease (CHD) and the occurrence of acute coronary syndromes (ACS) (1-3). The adipose tissue surrounds the cardiac, especially epicardial adipose tissue (EAT), which is anatomically and physiologically critical for CHD disease (4). The pericoronary adipose tissue (PCAT) is not only part of the EAT but also a metabolically active endocrine organ directly surrounding the coronary arteries, which has been found to have an essential role in regulating cardiovascular homeostasis and be associated with CHD (5-7). In addition, previous studies have also identified a significant association between EAT density, but not its volume, and mean PCAT attenuation values in patients with CAD and without CAD (8). The cardiovascular system has a reciprocal interaction between PCAT and vascular inflammation. Not only can PCAT release pro-inflammatory mediators, but inflamed vessels can also release inflammatory signals to induce PCAT breakdown and inhibit its formation, leading to pericoronary edema (9-11).

Recent landmark studies of PCAT attenuation have demonstrated that PCAT attenuation can not only detect biopsy-proven vascular inflammation (9) but can also be used as a noninvasive biomarker of vascular inflammation to assess CHD (12), adverse cardiac events (13), left ventricular function (14), and microvascular complications in patients with diabetes (15). In addition, high PCAT attenuation values detected using coronary computed tomographic angiography (CCTA) are related to endothelial dysfunction (16), oxidized HDL levels (17), cardiovascular events in patients with nonalcoholic fatty liver disease (18), and the recurrence of atrial fibrillation after ablation (19).

However, it is unclear which clinical characteristics are associated with increased PCAT attenuation values in patients without CHD, which is likely to contribute to the development of subsequent CHD. Moreover, few studies have used CCTA to determine the relationship between PCAT and clinical features in patients without CHD. Therefore, we intended to use CCTA to measure PCAT attenuation values to identify high-risk clinical features early and explore the relationship between increased PCAT attenuation values and different clinical features in patients without CHD. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1814/rc).

Methods

Population collection

The Ethics Committee of the Lanzhou University Second Hospital approved this study (ethical approval No. 2021A-165), and did not require the informed consent due to the nature of retrospective research. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Patients who met the following criteria were included:

• Patients without CHD who underwent CCTA examination from July 2020 to July 2022.
• Plaques that were not visible on CCTA images, evaluated by two radiologists with 10 years of cardiovascular diagnostic experience.
• No history of myocardial infarction, coronary artery malformation, prosthetic valves or pacemakers, myocarditis, or vasculitis.
• Complete clinical and imaging records.
• Excellent-quality CCTA images for image evaluation and PCAT calculation.

The detailed screening process for the patients is shown in Figure 1. Subsequently, we collected the clinical data of the eligible patients, including age, sex, smoking, and drinking. Smoking history was defined as having smoked for more than 1 year and not quitting for more than 6 months. Drinking history was defined as greater than one alcoholic drink per week of >50 mL.

Figure 1 Flowchart of the included study population. CHD, coronary artery disease; CCTA, coronary computed tomography angiography; PCAT, pericoronary adipose tissue.

CCTA examination

The CCTA images were acquired with revolution computed tomography (CT) (General Electric Company, USA) in our institution, which includes all levels from 1 cm below tracheal bifurcation to the bottom of the heart. Before CCTA, blood pressure and heart rate were monitored, respiratory training was performed, and compliance was ensured. For imaging parameters, the following preset values were applied: prospective electrocardiographic gating, tube voltage, 100 KV; tube current, 400–700 mA; field of view, 36 cm; display field of view, 24 cm; matrix, 512×512; ASIR-V, 60%; rotation time, 0.28 s; and slice thickness, 0.625 mm. Each patient was administered 0.9 mL/kg of iopromide (370 mg/mL) at a flow rate of 5.0–5.5 mL/s via the median cubital vein using a high-pressure double-barrel injector (Bayer Health Care, CA, USA), followed by 40 mL normal saline at the same rate.

Fully automated measurement of PCAT attenuation values

Quantitative computation of PCAT attenuation values based on CCTA images was conducted on the Pericoronary Adipose Tissue Analysis software (Shukun Technology Co., Ltd., Shanghai, China) by a radiologist with vast training and 10 years of cardiovascular imaging experience. PCAT attenuation values were measured at 10–40 mm from the beginning of the left anterior descending branch (LAD), 10–40 mm from the beginning of the left circumflex branch (LCX), and 10–50 mm from the right coronary artery (RCA), with the cross-sectional area of the three locations set at three times the diameter of the respective vessel lumen (20). Within this region, a material with CT values of −190 to −30 Hounsfield units (HUs) is considered adipose tissue. Subsequently, the PCAT attenuation values for LAD (LAD_PCAT), LCX (LCX_PCAT), and RCA (RCA_PCAT) were recorded, respectively. Figure 2 shows the measurement of PCAT attenuation values for the three coronary arteries.

Figure 2 Measurement of PCAT attenuation values on three coronary arteries. HU, Hounsfield unit; LAD, left anterior descending branch; LCX, left circumflex branch; RCA, right coronary artery; LM, left main; PCAT, pericoronary adipose tissue.

Statistical analysis

Statistical analyses were conducted using SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 9.0.0 (GraphPad Software Inc., San Diego, CA, USA). Categorical variables are presented as frequency (percentage) and were compared using Fisher’s exact or chi-squared test. Continuous variables are presented as mean ± standard deviation or median (interquartile range) according to the results of the Kolmogorov-Smirnov test and were compared using the Wilcoxon Mann-Whitney U test or independent t-test. Correlations between the groups were assessed using Pearson’s or Spearman’s correlation analysis. In addition, linear regression was used to assess the relationship between PCAT and different clinical characteristics. P values of <0.05 (bilateral) were deemed statistically significant.

Results

Clinical characteristics of all patients

A total of 785 consecutive patients [361 men, 424 women; mean age, 55.00 (45.00, 66.00) years] were retrospectively recruited for our study. LAD_PCAT, LCX_PCAT, and RCA_PCAT values were −87.00 (−96.00, −80.00) HU, −83.00 (−91.00, −77.00) HU, and −87.00 (−94.00, −80.00) HU, respectively. The clinical information of the patients is presented in Table 1.

Comparison of PCAT between different clinical characteristics

Among patients without CHD, LAD_PCAT, and LCX_PCAT values were significantly higher in patients with body mass index (BMI) of ≥24.89 kg/m² than in those with BMI of <24.89 kg/m² [−87.00 (−95.00, −78.50) vs. −89.00 (−97.00, −81.25) HU, P=0.001; −83.00 (−90.00, −76.00) vs. −84.00 (−92.00, −78.00) HU, P=0.014], whereas RCA_PCAT values showed no such difference between the two groups [−86.00 (−94.00, −79.00) vs. −88.00 (−94.00, −80.00) HU, P=0.181] (Table 2). Compared with patients without CHD who smoked (<15 years) or did not drink alcohol, higher LAD_PCAT values were observed in those who smoked (≥15 years) or drank alcohol [−85.00 (−94.00, −77.00) vs. −88.00 (−96.00, −81.00) HU, P=0.006; −84.00 (−91.00, −79.00) vs. −89.00 (−97.00, −81.00) HU, P<0.001], while no significant difference was observed in LCX_PCAT and RCA_PCAT values between the two groups (Table 2). In patients without CHD, LAD_PCAT and RCA_PCAT values were consistently higher in those with hyperlipidemia [−85.00 (−95.00, −78.00) and −82.00 (−88.00, −77.00) HU, respectively] than in those without hyperlipidemia [−89.00 (−97.00, −81.00) and −89.00 (−97.00, −82.00) HU; all P<0.05]. Regarding the different medications used, RCA_PCAT values were remarkably lower in patients without CHD taking statins [−92.00 (−98.00, −83.00) HU] than in those not taking this drug [−86.00 (−93.00, −79.00) HU, P<0.001; Table 2].

Correlation of different clinical characteristics with PCAT

The results showed that the LAD_PCAT values were positively correlated with BMI (rs=0.109, P=0.002), smoking duration (rs=0.099, P=0.006), drinking (rs=0.165, P<0.001), and hyperlipidemia (rs=0.089, P=0.012). LCX_PCAT was positively associated with BMI (r=0.076, P=0.032; Table 3, Figure 3). In addition, RCA_PCAT values were positively correlated with BMI (rs=0.083, P=0.019) and hyperlipidemia (rs=0.334, P<0.001) but negatively correlated with statin use (rs=−0.145, P<0.001; Table 3, Figure 3).

Figure 3 Correlation heat map of different clinical characteristics with (A) LAD_^PCAT, (B) RCA_^PCAT, and (C) LCX_^PCAT values. LAD, left anterior descending branch; PCAT, pericoronary adipose tissue; BMI, body mass index; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; CCB, calcium channel blockers; RCA, right coronary artery; LCX, left circumflex branch.

Linear regression model for different clinical characteristics with PCAT

The significant clinical parameters were included in the linear regression model, and the results revealed that the significant determinants of LAD_PCAT were BMI [β=0.359 (0.147–0.572), P=0.001], duration of smoking [β=2.612 (0.964–4.260), P=0.002], drinking [β=4.106 (2.472–5.740), P<0.001], and hyperlipidemia [β=1.664 (0.190–3.137), P=0.027; Table 4, Figure 4]. The linear regression model of LCX_PCAT showed that the most significant determinant of LCX_PCAT was BMI [β=0.218 (0.029–0.406), P=0.024]. In addition, the critical determinants of RCA_PCAT were hyperlipidemia [β=6.110 (4.901–7.319), P<0.001] and statins [β=−3.338 (−4.991 to −1.686), P<0.001]. On the other hand, BMI was not significant in the linear regression model of RCA_PCAT (P=0.100; Table 4, Figure 4).

Figure 4 Linear regression model for different clinical characteristics with PCAT attenuation values. (A) LAD_^PCAT; (B) RCA_^PCAT; (C) LCX_^PCAT; (D) QQ plot for LAD_^PCAT; (E) QQ plot for RCA_^PCAT. LAD, left anterior descending branch; PCAT, pericoronary adipose tissue; HU, Hounsfield unit; RCA, right coronary artery; LCX, left circumflex branch; BMI, body mass index; QQ, quantile-quantile.

Discussion

Based on this large-scale clinical study of 785 patients without CHD, we concluded that LAD_PCAT was related to BMI, duration of smoking, drinking, and hyperlipidemia. In contrast, LCX_PCAT is only related to BMI, while RCA_PCAT is associated with hyperlipidemia and statin use.

Among the studies conducted on the relationship between PCAT and the clinic, most studies focused on the correlation between PCAT and ACS or CHD (2,21,22). Clinical risk factors combined with CT-based imaging parameters and PCAT radiomic features in patients with type 2 diabetes showed that this model effectively diagnosed CHD in patients with type 2 diabetes (21). A cross-sectional study showed that the mean PCAT attenuation values were correlated with plaque parameters and the degree of stenosis in patients with ACS (22). In addition, Araki et al. (2) demonstrated that individuals diagnosed with ACS exhibited elevated PCAT attenuation values compared to those with stable CHD. Specifically, patients with ACS had higher PCAT values in the most stenotic lesions (culprit plaques) identified on coronary angiography or in the most stenotic plaques (culprit plaques) identified in patients undergoing percutaneous coronary intervention. Additionally, elevated values were noted in the vessels where these culprit plaques were located. However, the literature does not provide many studies that used CCTA to determine an association between PCAT and clinical characteristics in patients without CHD. A retrospective study showed that women had lower mean PCAT attenuation values than men, and LAD had significantly lower mean PCAT attenuation values than LCX and RCA (23). Contrary to the above research (23), we did not observe differences in PCAT attenuation values across sexes. It should be noted that some clinical characteristics can be found to be more dangerous after quantification of pericoronary inflammation based on PCAT attenuation values.

Obesity is an independent risk factor for cardiovascular disease, and the relationship between obesity-related indicators and fatty tissue around the heart is constantly being explored (24,25). Yuvaraj et al. (26) demonstrated that the plasma atherogenic index (the base 10 logarithmic ratio of triglyceride to high-density lipoprotein) was related to increased EAT volume after correcting for coronary plaque load assessed by the CT-Leaman score and traditional cardiovascular risks factors such as obesity, smoking, hypertension, and hyperglycemia. As part of the EAT, PCAT was found to have a significantly negative relationship with density and BMI (27). In contrast, Maurovich-Horvat et al. (28) discovered that coronary atherosclerosis is related to the PCAT volume but not to the volume of epicardial, periaortic, and extracardiac adipose tissue. A retrospective study showed that the PCAT attenuation values were not associated with BMI in patients without plaques on CCTA images (29). However, we observed a significant positive correlation between PCAT attenuation values (LAD_PCAT, LCX_PCAT, and RCA_PCAT) and BMI in patients without CHD. The difference between the results of the present study and the abovementioned studies may be the different methods of PCAT measurement and the differences related to the population. In the present study, hyperlipidemia was associated with increased attenuation values (LAD_PCAT and RCA_PCAT). Hence, our findings suggest that overweight or obesity causes or even aggravates inflammation in the coronary arteries.

Because PCAT attenuation values are a risk indicator for vascular inflammation that can be quantified by CCTA, they can be used clinically to monitor disease onset and progression in patients. PCAT attenuation values are therefore considered an modifiable risk factor for measurement using CCTA. Moreover, these can be used to track responses to coronary artery disease interventions. Prospective research has revealed that the biological treatment of moderate-to-severe psoriasis is related to reduced coronary artery inflammation, as shown by PCAT attenuation values (30). Statins significantly reduce mortality associated with cardiovascular diseases (31). Our research also showed that statins inhibited inflammation (32,33), lowering PCAT attenuation values on CCTA images. Therefore, statin use in patients without CHD is associated with lower PCAT attenuation values. As such, PCAT attenuation may be a potential follow-up marker of drugs with cardiovascular benefits.

Most studies have demonstrated that smoking and drinking are recognized risk factors for cardiovascular disease (34,35). The present study found no significant difference in PCAT attenuation values between smokers and nonsmokers in patients without CHD. However, after more than 15 years of smoking, the PCAT attenuation values were higher than those of patients who had smoked for <15 years. This is related to the coronary inflammation caused by long-term smoking (36,37), suggesting that smoking cessation will reduce the incidence of cardiovascular risk events. Additionally, we showed that drinking was associated with high PCAT attenuation in patients without CHD, corroborating the harmful effects of drinking on the cardiovascular system. However, an overview summarized that moderate alcohol intake has anti-inflammatory effects, reducing the risk of CHD (38). In future studies, we need to further explore the relationship among the duration of drinking, alcohol intake, and PCAT attenuation values to specifically determine the inflammatory effects of alcohol on the coronary arteries.

This study has some limitations. First, the retrospective nature of the study did not allow the inclusion of additional and more detailed clinical features, such indicators as hematology, liver function, and kidney function tests. In addition, the primary limitation of this research is its single-center design. Despite the positive findings, there is a need to expand the sample size across different centers for further popularization. Finally, imaging parameters such as tube voltage and tube current may affect PCAT attenuation values, warranting further exploration in the future.

Conclusions

In conclusion, the PCAT attenuation values measured using CCTA were related to different clinical characteristics in patients without CHD. LAD_PCAT was found to be associated with BMI, smoking duration, drinking, and hyperlipidemia, whereas LCX_PCAT was associated with BMI. Furthermore, RCA_PCAT was associated with hyperlipidemia and statin use.

Acknowledgments

Funding: This work was supported by the National Natural Science Foundation of China (No. 82071872, to J.Z.), and Medical Innovation and Development Project of Lanzhou University (No. lzuyxcx-2022-139, to J.Z.).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-23-1814/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1814/coif). W.R. is an employee of GE Healthcare Co. Ltd. J.Z. reports that this work was supported by the National Natural Science Foundation of China (No. 82071872) and Medical Innovation and Development Project of Lanzhou University (No. lzuyxcx-2022-139). The other 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 (as revised in 2013). The Ethics Committee of the Lanzhou University Second Hospital approved this study (ethical approval No. 2021A-165), and did not require the informed consent due to the nature of retrospective research.

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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