Predictive value of the perivascular fat attenuation index and delta-CT-FFR for the formation of atherosclerotic plaques proximal to left anterior descending myocardial bridges

Introduction

Myocardial bridges (MBs) represent a prevalent anatomical variant characterized by an intramyocardial trajectory of a major epicardial coronary artery. The muscular band overlying the coronary artery is termed the MB, whereas the segment of the artery coursing within the myocardium is designated as the mural coronary artery (MCA). MBs can occur in any coronary artery, but are most common in the left anterior descending artery (LAD) (1). MBs can compress the coronary arteries during systole, resulting in impaired diastolic coronary flow. Several studies have confirmed the association between MBs and cardiovascular diseases, such as myocardial ischemia, myocardial infarction (MI), arrhythmia, or sudden death (2-4). A disturbed blood flow pattern is a central factor in the spatial distribution of atherosclerosis, making the proximal end of an MB more susceptible to atherosclerotic plaques than the distal end (5).

An important factor in the formation of atherosclerotic plaques is vascular inflammation (6-8). Coronary artery inflammation triggers significant lipid deposition in perivascular adipose tissue, with concurrently elevated fat attenuation index (FAI) values. An elevated FAI typically signifies vascular endothelial dysfunction and serves as a robust biomarker with high sensitivity and specificity for localized tissue inflammation (9). The perivascular FAI, mapped by coronary computed tomography angiography (CCTA), can be used as a novel noninvasive imaging index to detect edema in pericoronary adipose tissue and the inflammatory state of coronary vessels (10,11). Research indicates that perivascular FAI is closely associated with the formation of coronary atherosclerotic plaques, the degree of stenosis in the coronary lumen, and acute aortic dissection (AAD) (12-14).

Meanwhile, it has been confirmed that the thickness and length of MBs can have an impact on bridge hemodynamics (15). In addition, the hemodynamic significance of MBs can be assessed using advanced noninvasive techniques, including the CCTA-derived fractional flow reserve (CT-FFR), which can yield highly consistent results by mimicking the principles and procedures of an invasive FFR examination (16,17). However, whether perivascular FAI and CT-FFR are clinically relevant to the formation of atherosclerotic plaques proximal to a left anterior descending myocardial bridge (LAD-MB) is unclear. Therefore, herein, we aimed to investigate the diagnostic value of perivascular FAI and the change in CT-FFR (∆CT-FFR) to predict the formation of proximal atherosclerotic plaques in the LAD-MB. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2752/rc).

Methods

Study population

We enrolled patients with LAD-MB who received CCTA at least twice as emergency outpatients or inpatients from January 2014 to December 2023. The inclusion criteria were as follows: (I) patients who underwent multiple CCTA examinations and had the presence of MB lesions confirmed by CCTA post-processing; and (II) patients with or without plaques formed proximal to the LAD-MB during 10 years of follow-up. The exclusion criteria were as follows: (I) patients with concomitant cardiac comorbidities, including myocardial disorders and valvular diseases; (II) patients with missing clinical data; (III) patients with combined atherosclerosis in any vessel segment according to baseline CCTA; (IV) a previous history of coronary artery bypass grafting and percutaneous coronary intervention; and (V) insufficient image quality of CCTA images affecting the measurement of CT-FFR or coronary imaging parameters. Figure 1 shows a flowchart of the participants and the study design.

Figure 1 Research design flow chart. CCTA, coronary computed tomography angiography; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; LAD-MB, left anterior descending myocardial bridge.

Ethics approval

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Qilu Hospital of Shandong University Dezhou Hospital (Ethics Approval No. 2022106) and the requirement for individual consent for this retrospective analysis was waived.

Scanning process for CCTA

A Toshiba Aquilion ONE 640 CT scanner (Toshiba, Tokyo, Japan) was employed to acquire CCTA images. During the examination, the patient’s heart rhythm was monitored in real time, and when the heart rate was detected as being more than 70 beats/minute, sublingual Betaloc (25–100 mg) was given to calm the heart rate in a quiet environment. The patient was given 0.5 mg of nitroglycerin sublingually for 5 minutes before the examination. Different scanning modes were used depending on the patient’s rhythm, such as retrospective electrocardiogram (ECG)-gated scanning to obtain images when the patient’s heart rate was ≥70 beats/min, rather than prospective ECG-gated scanning protocols. The following scanning parameters were used: reconstruction layer thickness of 0.5 mm, tube current of 300–550 mA, and tube voltage of 100–120 kv [body mass index (BMI)-dependent], and 0.27 seconds of rotation time.

Baseline data collection

Traditional clinical risk factors for coronary heart disease were collected from hospital charts: total blood cholesterol, fasting blood glucose, BMI, blood pressure, smoking status, sex, and age. Hypertension was defined as systolic blood pressure greater than or equal to 140 mmHg/diastolic blood pressure greater than 90 mmHg, or the use of antihypertensive medications. Diabetes mellitus was diagnosed by the presence of typical diabetes mellitus symptoms and any time plasma glucose level ≥11.1 mmol/L or fasting plasma glucose ≥7.0 mmol/L, or a 2-hour glucose tolerance test plasma glucose level of ≥11.1 mmol/L, or a clear history of diabetes mellitus, taking hypoglycemic drugs, or applying insulin. A patient’s BMI was calculated using their height and weight according to their clinical history. Lipid-lowering drug treatment mainly includes statins (e.g., atorvastatin, rosuvastatin), cholesterol absorption inhibitors (e.g., ezetimibe), and PCSK9 inhibitors (e.g., alirocumab). In our retrospective period, the first CCTA examination was designated as the baseline timepoint for each patient. The follow-up cutoff time for the LAD-MB with plaque formation group was defined as the date of the CCTA examination when any plaque was first detected within 30 mm proximal to the MB entry. The follow-up cutoff time for the LAD-MB without plaque formation group was defined as the date of the most recent CCTA examination closest to the end of the review period. The interval time was calculated as: follow-up cutoff time − baseline time, measured in days.

Anatomical characterization of MBs

All scanning data were transferred to a dedicated coronary image analysis platform, AW VolumeShare 4.6 (Advantage Workstation; GE HealthCare Technologies, Inc., Chicago, IL, USA). The location of an MB was defined by the distance from the ostium of the LAD to the entrance of the MB on the diastolic curved planar reformation (CPR) images. The length of an MB comprised the length of the tunneled artery between the inlet and the outlet on diastolic CPR images. The depth of an MB comprised the thickness of the deepest region covering the myocardial tissue surface to the tunneled artery on vascular cross-sectional images. The muscle index of an MB comprised the MB length (mm) × the MB depth (mm) (5). Stenosis of an MB was defined as: (MB proximal coronary artery diameter − MB minimum diameter) / MB proximal coronary artery diameter) ×100% (18). The lumen diameter was determined on the systolic multiplanar reformation (MPR) images. The images were independently reviewed by two experienced radiologists, with any discrepancies resolved through consensus discussion.

Plaque risk

In follow-up CCTA, LAD MB proximal atherosclerotic plaques were defined as any plaque that was within 30 mm proximal to the MB inlet (19). High-risk plaques have features that include: punctate calcification (localized calcification in the coronary artery wall <3 mm in maximum diameter) (20,21), positive remodeling (ratio of the largest vessel diameter of the segment in which the plaque is located to the average diameter of the proximal and distal vessels ≥1.1), low-density plaques (central area of the plaque has a CT value <30 HU) (22,23), and napkin ring sign (24) (comprising a low-density plaque in the central region with high-density at the peripheral edges and a CT value of no more than 130 HU in the high-density region). High-risk plaques were defined as those containing two or more high-risk plaque characteristics (23). The images were independently reviewed by two experienced radiologists, with any discrepancies resolved through consensus discussion.

Measurement of ∆CT-FFR

The methods and software for CT-FFR measurement were mainly based on deep learning noninvasive FFR measurement technology (CoronaryDoc, ShuKun Technology, Beijing, China), which measures the simulated FFR values on noninvasive CCTA images. The CT-FFR values were measured at 10 mm proximal and 20–40 mm distal to the LAD-MB during systole. ∆CT-FFR was defined as the proximal CT-FFR value of the MB − the distal CT-FFR value of the MB (25). A mean value of the ∆CT-FFR of 0.08 was taken as the limit, with ∆CT‑FFR ≥0.08 being considered a high difference, and ∆CT-FFR <0.08 being considered a low difference.

Measurement of perivascular FAI

Coronary CT imaging stenosis-assisted triage software (CoronaryDoc, ShuKun Technology) was used to measure the perivascular FAI in the 20–30 mm segment proximal to the LAD-MB (19). Adipose tissue CT values were defined in the range of −190 to −30 HU, including adipose tissue voxels located within a radius of 4 mm from the vessel’s outer wall. The artificial intelligence (AI) automatically removes non-adipose tissue, such as myocardium and blood vessels, calculated by averaging the attenuation of perivascular adipose tissue around the vessel in which the target vessel is located. The perivascular FAI measurements were not performed for the left main trunk because of its variable length and large variability in adipose distribution (26). When there were differences in measurement data between two investigators, they resolved the differences via negotiation.

Statistical analysis

Statistical processing was performed with SPSS 27.0 software (IBM Corp., Armonk, NY, USA) for statistical analysis, continuous variables were expressed as mean ± standard deviation or median (quartiles), and comparisons between groups were made using either the Student’s t-test (normal distribution) or the Mann-Whitney U test (non-normal distribution). Categorical variables were expressed as numbers of cases and percentages, and between-group comparisons were made using the chi-square test or Fisher’s exact probability. To compare changes from baseline to follow-up, we performed a two-way repeated measures analysis of variance (ANOVA) to examine interaction effects between groups. To search for imaging indicators that could predict proximal atherosclerotic plaque formation in LAD-MB, the association of each indicator with plaque formation was analyzed by using univariate and multivariate logistic regression. To further evaluate the diagnostic efficacy of each index, a subject receiver operating characteristic (ROC) curve was plotted to characterize the efficacy of each index in the diagnosis and prediction of coronary hemodynamic abnormalities. A difference of P<0.05 was considered statistically significant.

Results

Assessment of clinical risk factors

In this study, 249 patients underwent at least two CCTA examinations during the 10-year period. Based on the exclusion criteria, 135 patients were finally enrolled in the study, among whom 90 patients formed plaques proximal to the LAD-MB. The average age of the patients was 60.6±9.4 years and 67.8% were male. Hypertension in the LAD-MB with plaque formation group was significantly higher than that in the LAD-MB without plaque formation group (P=0.027). The patients’ clinical data are shown in Table 1.

Anatomical characterization of LAD-MBs

Compared with those in the LAD-MB without plaque formation group, MB lumen stenosis, MB muscle index, and MB length were significantly higher in the LAD-MB with plaque formation group (P<0.05). Baseline perivascular FAI was higher in the LAD-MB with plaque formation group than it was in the LAD-MB without plaque formation group (P<0.05). Proximal plaque formation was significantly higher in LAD-MBs with a ∆CT-FFR ≥0.08 than in LAD-MBs with a ∆CT-FFR <0.08 (P<0.05, Table 2).

Logistic regression analysis prediction of risk factors for the formation of proximal plaques in LAD-MBs

Univariate analysis showed that MB length [odds ratio (OR): 1.061, 95% confidence interval (CI): 1.023–1.100, P=0.002], MB muscle index, MB luminal stenosis rate, ∆CT-FFR (OR: 0.465, 95% CI: 0.225–0.963, P=0.039), and baseline perivascular FAI (OR: 1.137, 95% CI: 1.067–1.211, P<0.001) were associated with LAD-MB proximal plaque formation. Multifactorial logistic regression analysis showed that MB length (OR: 1.053, 95% CI: 1.002–1.107, P=0.043) and baseline perivascular FAI (OR: 1.127, 95% CI: 1.053–1.205, P=0.001) were independent predictors for the formation of proximal plaques in LAD-MB (Table 3).

Diagnostic efficacy of MB length and perivascular FAI to predict plaque formation proximal to an LAD-MB

According to the ROC curve analysis, perivascular FAI had the highest efficiency to predict the formation of proximal plaque in an LAD-MB. The area under the curve (AUC) value for perivascular FAI was 0.715 (95% CI: 0.625–0.806), the optimal cutoff value was −79.5 HU, and the AUC for MB length was 0.677 (95% CI: 0.576–0.778), the optimal cutoff value was 32.9 mm. Combining MB length and perivascular FAI (AUC =0.755, 95% CI: 0.665–0.845) could better predict the formation of proximal plaques in LAD-MBs and improve predictive efficacy (all P<0.05) (Figure 2).

Figure 2 ROC curve analysis of MB length, FAI, and combined MB length + FAI in predicting plaque formation at the proximal arterial segment of LAD-MB. AUC, area under curve; FAI, fat attenuation index; LAD-MB, left anterior descending myocardial bridge; ROC, receiver operating characteristic.

Perivascular FAI correlates with vulnerability to proximal plaque in the LAD-MB

A total of 90 patients exhibited atherosclerotic plaque formation proximal to the LAD-MB, from which two distinct subgroups were established based on plaque vulnerability: MB with high-risk plaque formation (n=38), MB with non-high-risk plaque formation (n=52). At baseline or follow-up, the high-risk plaque group and non-high-risk plaque group did not show a statistically significant difference for MB length (P>0.05; Figure 3A). Although the baseline perivascular FAI values between the high-risk plaque group and the non-high-risk plaque group were not significantly different (P=0.294; Figure 3B), the perivascular FAI between the follow-up and baseline CCTA showed significant dynamic changes (P=0.016; Figure 3C). Figure 4 shows a typical case of high-risk plaque formation proximal to an LAD-MB.

Figure 3 Post-hoc comparison results of MB anatomical features and perivascular FAI changes between high-risk and non-high-risk plaque patients at baseline and follow-up CCTA, following a two-way repeated measures ANOVA. ANOVA, analysis of variance; CCTA, coronary computed tomography angiography; FAI, fat attenuation index; MB, myocardial bridge.

Figure 4 An example of high-risk plaque formation in the proximal of LAD-MB. (A) The image of CCTA showing MB in the middle LAD without plaque formation at baseline (arrow). (B) The color-coded image shows that the perivascular FAI proximal to LAD-MB at baseline was −82 HU. (C) The image showing high-risk plaque formation proximal to LAD-MB after 6 years of follow-up (arrow). (D) The color-coded image shows that the perivascular FAI proximal to LAD-MB after follow-up was −73 HU. (E,F) The image revealing that the CT-FFR proximal and distal to LAD-MB was 0.97 and 0.88, respectively. CCTA, coronary computed tomography angiography; CT-FFR, CCTA-derived fractional flow reserve; D1/D2, diagonal branches; FAI, fat attenuation index; LAD, left anterior descending artery; LAD-MB, left anterior descending myocardial bridge; LCX, left circumflex artery; OM, obtuse marginal branches; PDA, posterior descending artery; PLB, posterior lateral branch; RCA, right coronary artery.

Discussion

Coronary atherosclerosis is affected by a variety of clinical factors, such as smoking history, blood glucose, blood lipids, blood pressure, gender, and age. In this study, these clinical parameters showed no statistical differences between the LAD-MB plaque forming group and the LAD-MB non-plaque forming group, thus avoiding the interference of these factors on the study results. We found that MB length and perivascular FAI correlated with the formation of proximal atherosclerotic plaques in the LAD-MB, and retained their independent predictive value after adjustment for clinically relevant risk factors. Perivascular FAI was superior to MB length in the prediction of the formation of proximal atherosclerotic plaques in LAD-MB. Moreover, the diagnostic efficacy of combining the two indices had incremental value. The predictive findings of this study can help to identify high-risk MB patients who may require early cardiovascular preventive therapy to mitigate the formation of atherosclerotic coronary plaques. Both MB length and pericoronary FAI can be readily measured via CCTA, demonstrating significant clinical utility due to its accessibility and efficiency.

The FAI could represent a novel parameter reflecting atherosclerosis in proximal MB coronary arteries, which is consistent with previous findings (27,28). On the one hand, vascular inflammation can inhibit the differentiation and maturation of extravascular adipocytes, leading to alterations in perivascular adipose tissue from the fatty phase to the aqueous phase, for which change in tissue density can be detected by the CT value of the CCTA (26). On the other hand, cytokines produced by perivascular adipose tissue cause endothelial damage, which promotes vasoconstriction, immune cell adhesion, and progression of atherosclerosis (29). Previous studies reported that immune activation and vascular inflammation are key factors that influence the formation of the lipid core and the thickness of the fibrous cap, as well as being causal factors of plaque vulnerability (30,31). Sun et al. (32) found that FAI correlated significantly and positively with the necrotic core volume, and correlated negatively with fiber volume. Our research is not completely consistent with the above results. The baseline perivascular FAI values were not significantly different between the non-high-risk plaque group and the high-risk plaque group; however, dynamic alterations in the perivascular FAI showed an association with LAD-MB vulnerability to proximal plaques, which suggested the existence of a certain correlation between perivascular FAI and coronary plaque characteristics. This might have been caused by the small sample size and our exclusion of patients with other intravascular atherosclerotic diseases.

In our study, MB length was shown to have the ability to predict the risk of atherosclerosis in the proximal part of the LAD-MB, probably because MB length affects hemodynamic abnormalities in the proximal part of the MB, thereby exacerbating plaque formation, which agrees with the results of a previous study. Javadzadegan et al. (33) also showed a trend toward decreasing vascular shear in the proximal segment of MBs and increasing vascular shear in the bridge segments as MB length increased. They also observed that low vascular shear led to endothelial cell dysfunction, which resulted in an increased incidence of coronary atherosclerosis in the anterior segment of the bridge. In this study, MB length was not associated with LADMB proximal plaque vulnerability at baseline and follow-up, which might be because MBs do not associate with more advanced atherosclerotic disease or might reflect the small sample size of this study (34).

Myocardial bridging can be accompanied by myocardial ischemia (35). Due to the unique hemodynamic characteristics of myocardial bridging during both systole and diastolic phases, it is challenging for traditional FFR to accurately assess the functional significance of myocardial bridging (36). The CT-FFR algorithm is based on the fluid mechanics of fixed tubular stenosis. Throughout the cardiac cycle, the luminal diameter of an MB changes dynamically, and only at the end-systolic phase does the luminal geometry represent maximal compression. Consequently, several studies (37) have shown that ΔCT-FFR_systolic has higher sensitivity than ΔCT-FFR_diastolic in diagnosing myocardial ischemia. A previous machine learning study (25) found that ΔCT-FFR values could predict the formation of plaques proximal to LAD-MB. Our results are not completely consistent with the results of that study. We found that ∆CT-FFR was associated with the formation of proximal atherosclerotic plaques in the LAD-MB, but had no independent predictive value. Machine learning might have improved the ability of CT-FFR to predict proximal plaques in an LAD-MB, which may widen the clinical application of CT-FFR in the risk stratification of patients with MBs. ∆CT-FFR measurement is solely dependent on the anatomical features of the MB and remains unaffected by other factors such as atherosclerosis. This non-invasive FFR measurement technology, enabled by deep learning algorithms, provides rapid, simplified, and readily obtainable imaging metrics, exhibiting substantial potential for broader clinical applications.

This study has some limitations. First, this single-center study involved a relatively small sample size and lacked a non-MB patient cohort, which may limit the comprehensive assessment of risk factors for atherosclerotic plaque formation at the proximal segment of LAD-MB. Second, only MBs in the LAD were studied, because this is the most common location for MBs; therefore, the findings might not be applicable to MBs in other locations. Therefore, future prospective multicenter studies with expanded cohorts are required to validate our results and assess the effects of confounding factors.

Conclusions

The length of an MB and the perivascular FAI might be independent predictors for the formation of atherosclerotic plaques proximal to an LAD-MB. Although ∆CT-FFR lacked predictive value, it might be associated with the formation of atherosclerotic plaques in the proximal segment of LAD-MB. In addition, dynamic perivascular FAI changes correlated with LAD-MB proximal plaque vulnerability. These findings will help to identify individuals at high risk and allow for more targeted early implementation of proactive primary prevention measures.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2752/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2752/dss

Funding: This work was supported by the Beijing Changzhao Charity Fund and Shandong Province Traditional Chinese Medicine Science and Technology Development Plan Project (No. M-2023114).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2752/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 Qilu Hospital of Shandong University Dezhou Hospital (Ethics Approval No. 2022106) and individual consent for this retrospective analysis was waived.

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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