Diagnostic potential of pericoronary adipose tissue mean attenuation for coronary atherosclerotic heart disease: a comparative analysis with the fat attenuation index

Introduction

Coronary artery disease (CAD) and its complications are the leading cause of cardiovascular disease-related death worldwide (1). Pericoronary adipose tissue (PCAT) constitutes a distinct subset of epicardial adipose tissue (2), and is defined as adipose tissue that is located at a radial distance from the outer vessel wall equal to the diameter of the adjacent coronary artery (3). Research has shown that there is an intricate bidirectional interaction between the coronary arteries and peripheral PCAT (4,5), which contributes to vascular inflammation and plays a pivotal role in the development and progression of atherosclerosis (6,7). Marwan et al. (8) used intravascular ultrasound to confirm the presence of atherosclerotic plaque, and found that the mean computed tomography (CT) value of PCAT in plaque segments exceeded that of normal segments. Subsequently, Antonopoulos et al. (3) introduced the fat attenuation index (FAI), based on coronary computed tomography angiography (CCTA), as an imaging indicator of PCAT. Recent studies have corroborated the close association between the FAI and inflammatory changes in PCAT as well as the presence of CAD, highlighting FAI’s potential to enhance cardiac risk stratification through its quantitative assessment of coronary inflammation (9,10).

However, previous studies have used different threshold definitions [from a minimum of −200 to −149 Hounsfield units (HU) to a maximum of −45 to −30 HU] for adipose tissue in the evaluation of PCAT, and this variation in threshold selection might have affected the average CT attenuation values of PCAT (11,12). Further, several external factors could affect the CT attenuation of PCAT, including enhanced coronary arteries on PCAT vascularization and partial volume effects (13,14). Hell et al. (15) suggested that variations in the CT density of PCAT are primarily attributed to partial volume effects and image interpolation rather than solely arising from differences in tissue composition or metabolic activity. These findings underscore the importance of accounting for confounding factors when assessing PCAT attenuation to ensure accurate and reliable measurements.

Given the above, the present study aimed to examine the effect of the distance from the coronary artery lumen on PCAT density. This study also sought to comprehensively analyze the relationship between pericoronary adipose tissue mean attenuation (PCAT_MA) and the FAI, considering pertinent factors such as plaque presence, plaque type, and the severity of major coronary artery stenosis. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-828/rc).

Methods

Study population

This was a single-center observational study (ClinicalTrials ID: ChiCTR2400079760). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study protocol was reviewed and approved by the Ethics Committee of The First People’s Hospital of Yunnan Province (No. KHLL2023-KY087). Informed consent was obtained from all the patients. Consecutive patients with suspected or known CAD, were included in the study from May 2021 to October 2022. To be eligible for inclusion in the study, the patients had to meet the following inclusion criteria: (I) have a heart rate <75 bpm; (II) have successfully undergone CCTA; and (III) have complete clinical data available. Patients were excluded from the study if they met any of the following exclusion criteria: (I) had stents, bypass grafts, pacemakers, or prosthetic valves; (II) had poor image quality CCTA scans (13,16); (III) had an anomalous coronary artery origin from the aortic sinus, leading to inaccurate measurements (13); (IV) had segments with a lumen diameter <2 mm and myocardial bridges; and/or (V) had segments with a distance from the pericardium or myocardium less than the diameter of the corresponding coronary artery.

CCTA scans and post-processing protocol

The CCTA scans were performed using a 320-row detector scanner (Aquilion Vision Edition, Canon Medical Systems Corporation, Tokyo, Japan). The scans were acquired during breath holds with prospective electrocardiogram triggering. The acquisition parameters of CCTA were as follows: detector collimation: 320 mm × 0.5 mm, gantry rotation time: 275 ms; and tube voltage: 120 kV (17). The tube current was set using the automatic exposure control depending on body mass index. The noise level was set at a standard deviation (SD) of 26. Iodinated contrast media (35–45 mL, 350 mg iodine/mL, Omnipaque, GE Healthcare, Waukesha, WI, USA) was injected at a rate of 3.5–4 mL/s using a dual injector technique, followed by a saline flush at the same rate. Image reconstruction was mediated by an adaptive iterative dose reduction three-dimensional algorithm (AIDR3D, Canon Medical Systems Corporation, Tokyo, Japan) using a filter convolution of 43 to generate a 512×512 matrix with a 0.5-mm slice thickness and a 0.25-mm interval.

Two experienced radiologists (reader A and reader B, with 5 and 10 years of experience in cardiovascular CT, respectively) independently evaluated all the CCTA scans, conducted coronary anatomical segmentation, and performed the PCAT analysis. When uncertainties arose regarding the identification of coronary anatomical segments, the two radiologists engaged in a consensus-based second reading to ensure consistency and accuracy.

Plaque analysis

For the assessment of plaque composition and diameter stenosis (DS), the most severe plaque per coronary artery was carefully evaluated. Post-processing software (SurePlaque, Vitrea, Canon Medical System, version 4.0.693) was used to enable the quantitative analysis of the identified plaques. The patients were grouped into the non-plaque and plaque groups. Patients were allocated to the non-plaque group if they had both a calcium score of zero and no non-calcified plaques (NCPs). Plaques detected on CCTA were classified into different categories using established criteria. Specifically, calcified plaque (CP) was defined as plaque with a CT value ≥350 HU, while NCP was defined as plaque with a CT value <350 HU. Mixed plaque (MP) was also identified using post-processing software. NCPs have various components, including the necrotic core (CT value ranging from −30 to 30 HU), fibrous fat (CT value ranging from 31 to 130 HU), and fibrous components (CT value ranging from 131 to 350 HU) (18,19). To assess the severity of coronary artery stenosis, the DS grading scale from the Coronary Artery Disease Reporting and Data System was employed; DS was classified into the following four stenosis categories: minimal (DS 1–24%), mild (DS 25–49%), moderate (DS 50–69%), and severe (DS 70–99%) (20).

PCAT density measurement

The PCAT density measurements were conducted in the main segments of the coronary arteries, including the proximal right coronary artery (RCA), mid-RCA, distal RCA, proximal left anterior descending artery (LAD), mid-LAD artery, distal LAD artery, proximal left circumflex artery (LCx), and mid and distal LCx artery (21). The workstation (Vitrea, Canon Medical System, version 4.0.693) automatically generated three-dimensional volume-rendered and curved multi-planar reformat images, which were meticulously reviewed and manually corrected by the radiologist to rectify any identification errors. The following steps were performed for the PCAT density measurements: (I) selection of the start and end points for PCAT density measurements based on coronary anatomical segmentation; (II) introduction of gaps of varying sizes (0.50, 0.75, 1.00, 1.25, 1.50, 1.75, and 2.00 mm) around the vascular wall to assess the effect of the measuring point’s distance from the coronary vascular wall on the PCAT density measurements (only in the non-plaque group). Density differences between various diameter sizes were calculated by subtracting PCAT density within the smaller diameter (density _small) from PCAT density in the area between the larger and smaller diameter circle [d_large-small = (d_large × v_large) − (d_small × v_small)/(v_large-small − v_small)], where d represents the density and v represents the voxels in the defined area (15) (Figure 1A). This step aimed to examine the relationship between PCAT density and the distance from the vessel wall in normal segments, and to confirm the distance at which the PCAT density tended to stabilize; (III) calculation of PCAT_MA by subtracting PCAT density within a distance of 0.75 mm from the area located within a radial distance from the outer vessel wall equal to the diameter of the respective vessel, using the same formula (15) (Figure 1B). This step aimed to reduce the effect of the HU values in the voxels adjacent to the luminal border; and (IV) re-measurement of PCAT density by the same reader after a minimum interval of 2 weeks to ensure image recognition consistency and assess intra-observer agreement. For inter-observer agreement, a second independent reader, who underwent adequate training, conducted the PCAT density measurements.

Figure 1 Measurement methods for PCAT_MA and the FAI. (A) PCAT density was measured from the vessel wall at distances of 0.50, 0.75, 1.00, 1.25, 1.50, 1.75, and 2.00 mm. (B,C) CCTA images from the same patient with NCP. (B) Localized NCP in the proximal segment of the left anterior descending artery, resulting in minimal DS, and the PCAT_MA measurement of the segment in which the plaques are located. (C) The FAI measurement of the proximal segment of the left anterior descending artery. CCTA, coronary computed tomography; DS, diameter stenosis; FAI, fat attenuation index; NCP, non-calcified plaque; PCAT_MA, pericoronary adipose tissue mean attenuation.

FAI measurement

The original CCTA data of all the enrolled patients were imported into the Shukun Angiography Image Analysis Software (1150.1150.1142, Shukun Technology, Shanghai, China). The FAI was defined as the mean CT attenuation of the PCAT (−190 to −30 HU), which was located within a radial distance from the outer vessel wall equal to the diameter of the respective vessel. The measurement length was defined as the same as the start and end points for PCAT_MA, and the software automatically calculated the FAI based on the coronary anatomy segmentation (Figure 1C).

Statistical analysis

The sample size required for the study was calculated using PASS 15.0 software. All the statistical analyses were conducted using SPSS 26.0 (IBM, Armonk, NY, USA). The normality testing for the continuous variables was performed using the Shapiro-Wilk test. The continuous variables were expressed as the mean ± standard deviation (SD), while the categorical variables were presented as the number (percentage). The parametric data were compared using the Student’s t-test, while the non-parametric data were analyzed using the Mann-Whitney U test for the continuous variables. The intra- and inter-group consistency of PCAT density measurements by the two radiologists at different distances from the coronary artery lumen were assessed by a Bland-Altman analysis. Intraclass correlation coefficients and their respective 95% confidence intervals were calculated using a mean-rating (k =2), agreement, two-way mixed-effects model. Repeated-measure analysis of variance was used to evaluate differences in the PCAT_MA values among the different branches and segments of the coronary artery in the non-plaque group, as well as differences in the PCAT_MA values between the non-plaque group and the plaque group based on the plaque type and lumen stenosis. All the statistical tests were two-sided. A P value <0.05 was considered statistically significant.

Results

Study population

In total, 75 patients with plaque and 63 patients without plaque were enrolled in the study. The patients with plaque exhibited a significantly higher prevalence of hypertension (P<0.001), smoking history (P<0.001), and drinking history (P=0.006) compared to those without plaque (Table 1).

Repeatability analysis of PCAT density measurement in the non-plaque group

For each coronary artery segment in the non-plaque group, the CT values were measured by two radiologists at distances of 0.50, 0.75, 1.00, 1.25, 1.50, 1.75, and 2.00 mm around the assessable coronary artery segments, and the PCAT density values were calculated. The difference between the two measurements fell overwhelmingly within the 95% limits of agreement. Fixed errors were assessed using the t-test. There were no statistically significant differences in terms of the intragroup agreement (P=0.972 and P=0.295) and intergroup agreement (P=0.560). Consequently, the two measurements demonstrated good agreement (Table 2 and Figure 2).

Figure 2 Bland-Altman analysis of pericoronary adipose tissue density measurements within a 0.75-mm circular distance to the outer vessel lumen, and intra-group agreement (A, P=0.972; B, P=0.295), and inter-group agreement (C, P=0.560).

PCAT density in relation to the distance from the vessel lumen in the non-plaque group

In total, 98 coronary segments from the non-plaque group were evaluated, including 34 proximal RCAs, 33 mid-RCAs, four distal RCAs, 12 proximal LAD arteries, nine mid-LAD arteries, and five proximal LCx arteries. In the pairwise comparisons analysis of each segment, the PCAT density was higher in the region closest to the lumen (within a circular distance of 0.5 mm from the outer boundary of the lumen, P<0.001). As the distance from the lumen increased, the PCAT density gradually decreased; however, after a distance of 0.75 mm from the lumen, there was no statistically significant difference in the PCAT density (P=0.907) (Figure 3).

Figure 3 The density of PCAT at different distances from the lumen. HU, Hounsfield unit; PCAT, pericoronary adipose tissue.

PCAT_MA and the FAI in the non-plaque group

A consistency analysis was conducted to provide a methodological basis for the feasibility of PCAT_MA. No significant density changes were observed per vessel in the non-plaque group (PCAT_MA of RCA vs. LAD artery: −83.22±14.72 vs. −84.80±11.98 HU, P=0.648; FAI of RCA vs. LAD artery: −88.15±9.25 vs. −86.55±8.05 HU, P=0.465). When comparing the proximal RCA to the mid-RCA, the proximal segment had a significantly higher FAI (−84.50±8.13 vs. −90.70±8.92 HU, P=0.028). In the other per-segment analyses, PCAT_MA and the FAI in the proximal segment were higher than those in the distal segment, but the differences were not statistically significant (PCAT_MA of proximal RCA vs. mid-RCA: −79.57±13.60 vs. −85.51±14.86 HU, P=0.375; PCAT_MA of proximal LAD artery vs. mid-LAD artery: −81.52±10.76 vs. −87.40±12.70 HU, P=0.813; FAI of proximal LAD artery vs. mid-LAD artery: −84.17±7.96 vs. −88.56±7.49 HU, P=0.702) (Figure 4 and Tables S1,S2). In the consistency comparison between PCAT_MA and the FAI, except for the poor reliability of the proximal LAD artery, each segment (including the proximal RCA, mid-RCA, and mid-LAD artery) displayed moderate reliability (Table 3).

Figure 4 Detailed segment and vessel analysis of PCAT_MA (A) and the FAI (B). FAI, fat attenuation index; HU, Hounsfield unit; LAD, left anterior descending artery; mLAD, mid left anterior descending artery; mRCA, mid right coronary artery; PCAT_MA, pericoronary adipose tissue mean attenuation; pLAD, proximal left anterior descending artery; pRCA, proximal right coronary artery; RCA, right coronary artery.

PCAT_MA and the FAI in the plaque group

A total of 95 coronary artery segments were evaluated in the plaque group, including 49 segments with CP, 20 segments with NCP, and 26 segments with MP. Compared with the non-plaque group, significant differences in PCAT_MA were observed in the coronary artery segments with NCP (P<0.001) and MP (P=0.047), while no such significant differences were found in relation to the FAI (P>0.05). Moreover, there were no significant differences in PCAT_MA and the FAI between the different types of plaque (all P>0.05) (Figure 5 and Table 4).

Figure 5 PCAT_MA (A) and the FAI (B) of different types of plaque. FAI, fat attenuation index; HU, Hounsfield units; PCAT_MA, pericoronary adipose tissue mean attenuation.

Discussion

This study used a non-fat threshold setting method to investigate the effect of the distance from the coronary artery lumen on PCAT density measurements. In addition, it explored the relationship between PCAT_MA and the FAI in relation to plaque presence, plaque type, and the severity of major coronary artery stenosis in symptomatic patients undergoing CCTA. The findings indicated that after a distance of 0.75 mm from the vascular wall, PCAT density tended to stabilize, and was not affected by partial volume effects, the iodine contrast enhancement of coronary artery surrounding fat, or image interpolation. Further, in the coronary artery segments with small, mild, and moderate stenosis lesions, PCAT_MA showed greater sensitivity than the FAI in assessing changes in CT adipose tissue characteristics resulting from early coronary artery inflammation, but it was not associated with plaque type.

PCAT has dual roles as both a structural support for blood vessels and a metabolically active endocrine organ, capable of secreting various pro-inflammatory and anti-inflammatory factors (22). It has a critical role in maintaining cardiovascular homeostasis, and its involvement in the pathogenesis of cardiovascular diseases is increasingly recognized. PCAT can sense and respond to coronary artery inflammation through a “from-inside-to-outside” signaling pathway, which involves the release of inflammatory mediators from the vascular wall that alter the morphology and secretory profile of PCAT (23). Functioning as a sensor of coronary inflammation, PCAT is affected by vascular inflammation (24,25). This inflammatory response inhibits the differentiation of adjacent adipose cells, leading to detectable changes in tissue composition that can be visualized on CCTA images (26). Specifically, PCAT attenuation, measured as changes in CT HU, provides valuable insights into plaque metabolic activity and inflammation. Elevated PCAT attenuation is associated with an increased risk of cardiovascular events (3). By quantifying PCAT attenuation, clinicians can non-invasively assess coronary artery inflammation, monitor changes in perivascular fat composition, and predict individual cardiovascular risk (27).

In previous studies, PCAT has typically been evaluated in a range of approximately 3 mm from the coronary artery wall, which is equivalent to the vessel diameter (3,9). However, there is still no consensus as to the effects of partial volume effects, the iodinated contrast enhancement of PCAT, or image interpolation on PCAT values. Hell et al. (15) suggested that the CT attenuation of adipose tissue surrounding the coronary artery may be influenced by the attenuation within the coronary artery lumen, and the distance between the measurement point and the lumen. Almeida et al. (14) conducted a study of 12 cases of mid-RCA without plaque and measured PCAT using two different semi-automatic software platforms to assess the effect of volumetric efficiency on PCAT measurements. Their findings indicated that including the first millimeter surrounding the RCA did not result in significantly different attenuation, suggesting a minimal effect of partial volume averaging. Ma et al. (13) noted that the contrast enhancement of the lumen has been found to influence the HU values in the voxels adjacent to the luminal border; thus, PCAT_MA is generally measured by setting a 1-mm gap around the vascular wall. However, the rationale behind selecting this specific 1-mm gap was not adequately examined in their studies (13,21,28). Further, these studies used different thresholds for adipose tissue during the FAI and PCAT measurements, ranging from −200 to −149 HU to −45 to −30 HU (typically −190 to −30 HU) (13,21,28). Unfortunately, such variabilities in thresholds prevent the determination of reference values and direct comparisons among studies. In addition, the effect of the distance from the vessel wall on the measurements of PCAT_MA was not considered.

This study first addressed the existing knowledge gap regarding the distance from the vessel wall. The findings revealed that after a distance of 0.75 mm from the lumen, the PCAT density values were susceptible to influence by partial volume effects, the iodinated contrast enhancement of PCAT, or image interpolation. These results provide valuable insights into the accurate measurement of PCAT_MA and enhance our understanding of its relationship with the coronary artery wall. Thus, this study provides a rationale and support for previous studies in which other scholars have set 1-mm gap to avoid the influence of enhanced blood vessels.

In the non-plaque group, the present study found no significant differences in PCAT_MA and the FAI when the analysis was based on each vessel; however, there was one exception—when comparing the proximal RCA to the mid-RCA, the proximal segment exhibited a significantly higher FAI. In other per-segment analyses, the PCAT_MA and FAI values were higher in the proximal segment than the distal segment, but the differences were not statistically significant. The higher lipid content in the adipose tissue surrounding the proximal RCA, leading to lower FAI values, may be attributed to unique anatomical or hemodynamic characteristics. Additional studies are necessary to determine the exact clinical value of these differences and whether performing measurements on different parts of specific arteries could be more predictive of future cardiac events. Nevertheless, the study showed the good repeatability and feasibility of PCAT_MA in evaluating each vessel and segment.

In recent years, numerous studies have provided compelling evidence supporting the use of the FAI as a novel, non-invasive method for assessing coronary inflammation on a per-patient basis (10,21,29). Changes in CT adipose tissue characteristics due to coronary inflammation may occur independently of changes in plaque morphology, including those associated with a greater risk of coronary events (30). Dai et al. (28) demonstrated that low-attenuation plaque (LAP) and the napkin-ring sign (NRS) are significantly more prevalent in patients with CAD who exhibit elevated serum levels of high-sensitivity C-reactive protein (hs-CRP), a well-established biomarker of systemic inflammation. In contrast, perivascular FAI failed to demonstrate a significant correlation with hs-CRP levels. These findings suggest that FAI may primarily reflect localized coronary inflammatory activity. CRP levels in relation to local FAI and PCAT_MA measurements should be used with caution. Indeed, CRP is a marker of systemic inflammation but reveals nothing about the inflammatory state of very small areas like the PCAT surrounding a specific segment of a coronary artery.

This was the first study to simultaneously use PCAT_MA and the FAI to evaluate the adipose tissue around the same coronary artery segments. This study found that compared with the non-plaque group, significant differences in PCAT_MA were observed in coronary artery segments with NCP and MP, while no such significant differences were found in relation to the FAI. These results are supported by previous studies that have shown that the relationship between coronary artery inflammation and PCAT density may be more pronounced in NCP and MP than in CP, as CP is relatively stable and has only a minimal inflammatory component (31,32). Thus, mild and moderate DS may have more inflammation than severe DS (33). This is consistent with the hypothesis that the corresponding increase in edema and inflammatory cells may lead to an additional increase in PCAT attenuation (34). Similar results were reported by Goeller et al. (35), who showed that attenuation was more increased in NCP culprit lesions than non-culprit lesions.

This study had several limitations. It was a single-center study of CCTA patients, which led to a small sample size, especially in relation to patients with severe stenosis and MP. Some follow-up studies examining the correlation between PCAT_MA and diabetes, CAD inflammatory status, gender differences, and the clinical characteristics of the patients are underway. This study found a relationship between the presence of plaques and PCAT_MA in moderate and below stenosis lesions, as well as differences between PCAT_MA and the FAI; however, it did not provide more convincing evidence that PCAT_MA and the FAI are related to plaque type. An important point that the study did not examine is the predictive value of PCAT_MA for vascular events and prognosis. Finally, a multivariable regression analysis was initially planned in the study design, but when subgrouping the plaque group, the sample sizes for the NCP and MP groups were small, and the multivariable analyses could thus not be performed.

Conclusions

The novel PCAT_MA measurement method based on non-fat threshold limitations has good feasibility and repeatability. PCAT_MA may be more sensitive than the FAI in assessing changes in PCAT density resulting from early coronary artery inflammation, but it is not associated with plaque type.

Acknowledgments

The authors would like to thank Ge Bing, a Support Senior Specialist at Canon Medical Systems Clinical Scientific Department Technical, for his assistance in the development of the PCAT density and PCAT_MA measurements during the image post-processing.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-828/rc

Funding: This study was supported by funding from the Technology Program of Yunnan Province Science and Technology Department (grant No. 202001AY070001-125) and Yunnan First People’s Hospital Provincial Clinical Key Specialty Platform Open Project of Medical Imaging Department (grant No. 2024YXKFKT-02). The funders had no role in the study design, data collection and analysis, decision to publish, nor preparation of the article. The work was not funded by any industry sponsors.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-828/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 any questions related to the accuracy or integrity of any part of the work have been appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study protocol was reviewed and approved by the Ethics Committee of The First People’s Hospital of Yunnan Province (No. KHLL2023-KY087). Informed consent was obtained from all the patients.

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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