Association of mean pericoronary adipose tissue attenuation with different demographic factors in a subgroup of patients without coronary artery disease stratified by sex, body mass index, and age

Introduction

Currently, the study of adipose tissue such as visceral, body surface, and pericardial adipose tissue is attracting increasing attention and has been detected to be associated with the development of numerous diseases (1,2). A prospective study showed that preperitoneal fat thickness or body surface area was related to the development of insulin resistance and diabetes (1). Ishikawa et al. (2) demonstrated a significant correlation between epicardial adipose tissue volume and overall longitudinal strain of the left ventricle assessed using speckle echocardiography in patients with preserved left ventricular ejection fraction and no left ventricular regional ventricular wall motion abnormalities. Recent research has found that pericoronary adipose tissue (PCAT) opens a new window for non-invasive assessment of vascular inflammation (3,4).

PCAT is part of the epicardial adipose tissue adjacent to coronary vessels (5). The mean PCAT attenuation (PCAT_MA) values derived from PCAT can detect biopsy-proven coronary vascular inflammation and correlates with serum levels of atherosclerosis-related inflammatory biomarkers (6,7). In addition, PCAT_MA values have been shown to correlate not only with high-risk plaques (8), the degree of plaque stenosis (9), and plaque progression (10), but also with flow reserve fraction (11) and cardiac function (12). This is because PCAT is an endocrine organ with a key role in the regulation of cardiovascular homeostasis and contains both anti-inflammatory and antioxidant substances as well as inflammatory components, and that it can affect localized microvascular function in the coronary arteries, thus making PCAT a new biomarker of coronary inflammation (13-15).

However, a number of previous studies have evaluated PCAT_MA values in patients with coronary artery disease (CAD) and acute coronary syndrome, whereas PCAT_MA values in populations without CAD are lacking (16,17). Moreover, in patients without CAD, few studies have evaluated the association between PCAT_MA and patient-based demographic factors, for example, age or sex. As such, the aim of this study was to stratify patients without CAD by sex, body mass index (BMI), and age to investigate the association between PCAT_MA and different demographic factors in patients without CAD. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-23-951/rc).

Methods

Study population

This retrospective study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The Ethics Committee of Lanzhou University Second Hospital approved the study (No. 2021A-165), and the requirement for informed consent was waived due to the retrospective nature of the study. In this case-control study, patients who underwent coronary computed tomography angiography (CCTA) for chest pain or discomfort, etc., within 3 days of admission were reviewed, and patients without CAD were retrospectively enrolled between July 2020 and July 2022. Patients without CAD were defined as no coronary plaques visible to the naked eye on CCTA images examined by two radiologists with 10 years of cardiovascular diagnostic experience. If the two radiologists did not agree on the diagnosis, a senior chief radiologist made the final decision. The exclusion criteria were as follows: (I) coronary artery malformation, prosthetic valves, or pacemakers; (II) history of myocardial infarction, myocarditis, or vasculitis; (III) incomplete clinical information; and (IV) poor image quality for imaging assessment. Figure 1 illustrates the specific screening process for the patients.

Figure 1 Flow chart of patient screening. CCTA, coronary computed tomography angiography; CAD, coronary artery disease.

Demographic factors collection

We used the hospital information system to collect clinical information about the patients, including age, gender, BMI, smoking, hypertension, dyslipidemia, and hyperglycaemia. Hypertension was defined as a systolic blood pressure ≥140 mmHg and/or a diastolic blood pressure ≥90 mmHg. Patients with total cholesterol ≥5.2 mmol/L or triglycerides ≥1.7 mmol/L or low-density lipoprotein cholesterol ≥3.4 mmol/L or high-density lipoprotein cholesterol <1.0 mmol/L were diagnosed as dyslipidemia based on fasting venous serum test indices. Patients treated with oral hypoglycemic agents, insulin or with a fasting blood glucose of 7.0 mmol/L were defined as hyperglycaemia.

CCTA examination

The patient examinations were all performed using a 256-row widescope CT scanner (Revolution CT, GE Healthcare; Milwaukee, WI, USA). CCTA acquisition included all levels from 1 cm below the tracheal bifurcation to the bottom of the heart, and was triggered via smart tracking, with the region of interest placed in the ascending aorta. The contrast agent iopromide (370 mg/mL) was injected 0.9 mL/kg into the median cubital vein at a rate of 5.0–5.5 mL/s, followed by a 40 mL normal saline rinse at the same rate. CCTA was acquired with prospective electrocardiographic gating, and set up as follows: tube voltage =100 kvp, tube current =400–700 mA, scanning field of view =36 cm, display field of view =24 cm, matrix =512×512, rotation time =0.28 s, slice thickness =0.625 mm. The reconstruction parameters were smooth kernel (STANDARD) and 60% of adaptive statistical iterative reconstruction Veo.

PCAT attenuation measurement

The PCAT attenuation measurement software (Shukun Technology Co., Ltd., Shanghai, China, version 1.11.5) was employed by a radiologist with more than 10 years of cardiovascular diagnostic experience and without knowledge of the clinical data for quantification. As noted previously (6), the proximal 10–50 mm segment of the right coronary artery (RCA), with a width of radial distance from the outer wall of the vessel equal to the diameter of the coronary artery, was tracked fully automatically by the software. The lumen and internal and external vessel wall boundaries within the aforementioned area were automatically divided. Subsequently, the average CT attenuation values for all voxels between −190 and −30 HU in the foregoing region, which are PCAT attenuation values, were calculated automatically by the software. In this study, we used PCAT_MA of the proximal segment of the RCA for follow-up analysis. Figure 2 shows the measurement of PCAT_MA in the proximal RCA segment.

Figure 2 The measurement of PCAT_MA in the RCA proximal segment. PCAT_MA, mean pericoronary adipose tissue attenuation; RCA, right coronary artery; HU, Hounsfield unit.

Statistical analysis

All data were calculated with SPSS 26.0 (IBM, Armonk, NY, USA) and GraphPad Prism 9.0.0 (GraphPad Software, San Diego, CA, USA). Categorical data were expressed as frequency (percentage) and differences between the two groups were compared using Fisher’s exact test or Chi-squared test. For continuous data normality, tests were administered using the Kolmogorov-Smirnov test, conforming to a normal distribution denoted as mean ± standard deviation and a non-normal distribution denoted as medians (interquartile range). Comparisons of continuous data between the two groups were implemented using independent t-tests or Wilcoxon Mann-Whitney U tests. Between-group correlations were evaluated using Pearson or Spearman correlation analysis, as appropriate. P<0.05 (bilateral) was regarded as statistically significant.

Results

Patient demographics

An overview of patients’ baseline characteristics is presented in Table 1. In total, 806 patients without CAD were recruited, including 374 males and 432 females, with a mean age, BMI, and PCAT_MA of 55.00 (45.00, 66.00) years, 24.91 (22.17, 27.79) kg/m², and −87.00 (−94.00, −80.00) HU, respectively. Of these patients, 264 were smokers, 313 were hypertensive, 253 were hyperglycemic, and 254 were dyslipidemic.

Association of PCAT_MA with different demographic factors in subgroups stratified by sex

In women without CAD, we found that smoking patients had a higher PCAT_MA of −84.00 (−94.00, −78.00) HU than non-smoking patients [−88.00 (−95.00, −81.00) HU, P=0.037], and patients diagnosed with dyslipidemia [−82.00 (−89.00, −77.00) HU] also had a higher PCAT_MA than patients with non-dyslipidemia [−89.00 (−97.00, −82.00) HU, P<0.001]. In addition, men without CAD who were dyslipidemic [−82.00 (−88.00, −77.00) vs. −89.00 (−97.00, −81.75) HU, P<0.001; Table 2, Figure 3] had higher PCAT_MA. Correlation analysis demonstrated that PCAT_MA levels were positively correlated with smoking (rs=0.101, P=0.036) and dyslipidemia (rs=0.328, P<0.001; Table S1) in women without CAD, and PCAT_MA levels were positively correlated with dyslipidemia in men without CAD (rs=0.339, P<0.001; Table S1).

Figure 3 Box plot showing the difference in PCAT_MA values between dyslipidemia and non-dyslipidemia in subgroups stratified by sex, BMI, and age. ***, P<0.001. PCAT_MA, mean pericoronary adipose tissue attenuation; HU, Hounsfield unit; BMI, body mass index.

Association of PCAT_MA with different demographic factors in subgroups stratified by BMI

In patients without CAD having BMI ≥24.91 kg/m², we observed that patients with hypertension [−84.00 (−92.00, −78.00) HU] or dyslipidemia [−81.50 (−88.00, −76.00) HU] had a higher PCAT_MA than those without hypertension [−87.00 (−95.00, −81.00) HU, P=0.004] and dyslipidemia [−89.00 (−96.50, −81.00) HU, P<0.001, Table 3]. However, in the group with BMI <24.91 kg/m², PCAT_MA was only found to be significantly higher in dyslipidemia [−82.00 (−89.00, −78.00) HU] than in those without dyslipidemia [−90.00 (−97.00, −83.00) HU, P<0.001; Table 3, Figure 3]. Furthermore, PCAT_MA values in the BMI ≥24.91 kg/m² group were positively associated with hypertension (rs=0.144, P=0.004) and dyslipidemia (rs=0.342, P<0.001; Table S1), whereas PCAT_MA values in the BMI <24.91 kg/m² group were only positively related to dyslipidemia (rs=0.326, P<0.001; Table S1).

Association of PCAT_MA with different demographic factors in subgroups stratified by age

In patients without CAD aged ≥55 or <55 years, higher PCAT_MA values were present in patients with dyslipidemia [−81.00 (−88.25, −77.00) HU, −83.50 (−88.00, −78.00) HU] than in those without dyslipidemia [−90.00 (−97.00, −82.00) HU, −88.00 (−96.00, −81.00) HU; all P<0.001, Table 4, Figure 3]. The results of the correlation analysis showed a positive relationship between PCAT_MA values and dyslipidemia in either the age ≥55 years group or the <55 years group, with rs 0.367 (P<0.001) and 0.298 (P<0.001), respectively (Table S1).

Discussion

The significant strength of this study is the inclusion of a large number of patients without CAD and stratification by sex, BMI, and age to explore the relationship between different demographic factors and PCAT_MA. The major findings of this study are as follows: PCAT_MA values were higher in patients with dyslipidemia than in those without dyslipidemia and showed a positive relationship with dyslipidemia, which was not influenced by sex, BMI, or age stratification. Furthermore, smoking women and hypertensive patients with BMI ≥24.91 kg/m² had higher PCAT_MA values and a positive correlation with PCAT_MA.

Vascular inflammation inhibits local fat formation in PCAT, which can be detected using CCTA, as PCAT_MA levels are elevated (6). It was determined that PCAT_MA proximal to the RCA measured by conventional CCTA is the most standardized method for PCAT analysis (18). Goeller et al. (7) revealed that an increase in PCAT_MA values proximal to the RCA, as measured using conventional CCTA, was an independent predictor of major adverse cardiac events. Relevance studies have also shown that PCAT_MA values in the proximal segment of the RCA are associated with the progression of noncalcified plaques and total plaque burden (10). In addition, Bittner et al. (19) demonstrated that higher levels of eicosapentaenoic acid, which reduces cardiovascular mortality, were related to lower PCAT_MA values proximal to the RCA, as acquired through CCTA. Therefore, the present study was performed to explore the information on PCAT_MA in the coronary arteries of patients without CAD, which is lacking in current studies, by measuring PCAT_MA values in the proximal segment of the RCA.

In the exploration of different demographic factors in PCAT_MA values, different studies have reported different results (20-23). Yu et al. (20) found that in patients with stable CAD, PCAT_MA values in the proximal segment of the RCA were higher in the type 2 diabetic group than in the non-diabetic group, whereas no differences were observed between the two groups in the left anterior descending and left circumflex. van Rosendael et al. (21) observed that PCAT_MA values in individuals without CAD measured in the proximal segment of the left anterior descending artery, left circumflex artery, and RCA were higher in men than in women. A retrospective study showed that PCAT_MA values in the proximal segment of the RCA were significantly correlated with sex in patients with CAD and that men were independently determined (22). Moreover, other studies have reported that in patients without plaque on CCTA images, PCAT_MA values were significantly related to age and sex but not to BMI (23). In contrast to the results of previous findings (20-22), in patients without CAD, no significant differences in PCAT_MA values by age, sex, BMI, or history of diabetes were found in our study. This may be because the population, type of disease, scanning equipment, scanning parameters, and PCAT analysis differed among studies (18,24). In the future, the group type should be further expanded and PCAT scan parameters and analyses should be standardized to better explore the correlation between PCAT_MA values and different demographic factors.

It is well known that dyslipidemia is one of the most important risk factors for atherosclerosis and cardiovascular disease (25). Several studies have also shown a correlation between indicators associated with dyslipidemia and PCAT in patients with CAD (26-28). Ichikawa et al. (26) observed that highly oxidized high-density lipoprotein levels were related to higher PCAT_MA values proximal to the RCA in CAD patients. A prospective study revealed that PCAT_MA values were significantly higher around plaques with cholesterol crystals versus plaques without cholesterol crystals and that patients with cholesterol crystals had higher PCAT_MA values proximal to the RCA (27). Furthermore, by exploring the relationship between PPAR-γ gene expression in peri-plaque PCAT of CAD patients and patient obesity, Marketou et al. (28) demonstrated that PCAT had a unique phenotype in obese individuals. Unlike previous studies (26-28), we found that in patients without CAD, those with dyslipidemia had higher PCAT_MA values than those without dyslipidemia, indicating that dyslipidemic patients are more likely to present with coronary artery inflammation.

Additionally, we found that smoking in women was correlated with higher PCAT_MA values. Smoking is also a leading cause of cardiovascular disease and death (29). Smoking not only causes direct physical damage to endothelial cells through multiple pathways but also induces tissue remodeling and thrombosis while activating systemic inflammatory signals (30,31). Furthermore, in female smokers, cardiovascular risk is complicated by hormonal factors, which may lead to higher relative risks (32). Consequently, smoking is associated with the risk of coronary inflammation in women without CAD, thereby detecting that PCAT_MA values are elevated in CCTA.

Hypertension is a known cardiovascular risk factor that causes thickening of the fibromuscular lining of the intima and middle layers of blood vessels and narrowing of the lumen of small arteries and arterioles (33,34). Regarding the relationship between hypertension and PCAT, Chang et al. (35) found that PCAT thickness was significantly increased in hypertensive patients compared to the normotensive group and was an independent factor. Further, in the present study we found that hypertensive patients with BMI ≥24.91 kg/m² in patients without CAD were associated with PCAT_MA values, whereas hypertensive patients with BMI <24.91 kg/m² were not correlated with PCAT_MA values. This may be due to the fact that obesity not only elevates blood pressure but also increases left ventricular volume load and exacerbates inflammation (36). Moreover, there are overlapping and potentially synergistic mechanisms by which obesity and hypertension promote inflammation and M1 macrophage polarization, resulting in the release of proinflammatory cytokines that impair cardiac function (37). Thus, it is recommended that weight and blood pressure should be controlled clinically.

The present study had several limitations. First, further replication and validation in a multicenter study are needed despite the large sample size. Second, the optimal threshold for elevated PCAT_MA is unknown, and the normal reference range for PCAT_MA should be determined in more extensive studies. Finally, although the results of the current study are promising, these patients should be followed up in the future to improve the clinical significance of PCAT_MA further.

Conclusions

In patients without CAD, PCAT_MA values were higher in dyslipidemia patients than in non-dyslipidemia patients and were also higher in smoking women and in hypertensive patients with BMI ≥24.91 kg/m². This suggests that PCAT_MA values may be useful in detecting patients at high risk for CAD with future events, even though they do not currently exhibit atherosclerosis.

Acknowledgments

Funding: This work was supported by the National Natural Science Foundation of China (grant No. 82071872 to J.Z.) and Medical Innovation and Development Project of Lanzhou University (grant 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-23-951/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-951/coif). J.Z. reports that this work was supported by the National Natural Science Foundation of China (grant No. 82071872), and Medical Innovation and Development Project of Lanzhou University (grant No. lzuyxcx-2022-139). W.R. is a current employee of GE Healthcare Co. Ltd. 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 Lanzhou University Second Hospital approved the study (No. 2021A-165), and the requirement for informed consent 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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