Association of semaglutide treatment with coronary artery inflammation in type 2 diabetes mellitus patients: a retrospective study based on pericoronary adipose tissue attenuation.

BACKGROUND: The pericoronary fat attenuation index (FAI) has emerged as a novel and sensitive biomarker reflecting the degree of coronary artery inflammation. Semaglutide has been demonstrated to exert a cardiovascular protective effect independent of hypoglycemia; however, its impact on coronary artery inflammation remains elusive. This study aimed to investigate the association between semaglutide treatment and coronary artery inflammation based on FAI in patients with type 2 diabetes mellitus (T2DM). METHODS: This study enrolled 497 T2DM patients who underwent coronary computed tomography angiography (CCTA) at Hebei General Hospital, of whom 93 treated with semaglutide (Sema+) and 404 did not (Sema-). Clinical data, laboratory indicators, and CCTA parameters were collected and compared between the two groups at baseline. Propensity score matching (PSM) was used to adjust for confounders, and pericoronary FAI was compared. Multivariate linear regression models were used to analyze the association between semaglutide treatment and pericoronary FAI. RESULTS: Before PSM, pericoronary FAI of the LAD and LCX was lower in patients treated with semaglutide than those without semaglutide treatment. The results of the PSM analysis revealed a lower FAI in all three major coronary arteries in the Sema + group compared to the Sema- group. Multivariate linear regression analyses revealed an independent association between semaglutide treatment and reduced FAI in all three major coronary arteries. This association varied across T2DM patients of differing profiles. CONCLUSION: Semaglutide treatment may be associated with lower coronary artery inflammation in patients with T2DM, which might partially explain its cardiovascular protective mechanism.

Relationship Between Calcified Plaque Burden, Vascular Inflammation, and Plaque Vulnerability in Patients With Coronary Atherosclerosis.

BACKGROUND: Coronary artery calcification is an integral part of atherosclerosis. It has been suggested that early coronary artery calcification is associated with active inflammation, and advanced calcification forms as inflammation subsides. Inflammation is also an important factor in plaque vulnerability. However, the relationship between coronary artery calcium burden, vascular inflammation, and plaque vulnerability has not been fully investigated. OBJECTIVES: This study aimed to correlate calcified plaque burden (CPB) at the culprit lesion with vascular inflammation and plaque vulnerability. METHODS: Patients with coronary artery disease who had both computed tomography angiography and optical coherence tomography were included. The authors divided the patients into 4 groups: 1 group without calcification at the culprit lesion; and 3 groups based on the CPB tertiles. CPB was calculated as calcified plaque volume divided by vessel volume in the culprit lesion. The authors compared pericoronary adipose tissue (PCAT) attenuation for vascular inflammation and optical coherence tomography-derived vulnerable features among the 4 groups. RESULTS: Among 578 patients, the highest CPB tertile showed significantly lower PCAT attenuation of culprit vessel compared with the other groups. The prevalence of features of plaque vulnerability (including lipid-rich plaque, macrophage, and microvessel) was also lowest in the highest CPB tertile. In the patients with calcification, higher age, statin use, and lower PCAT attenuation were independently associated with CPB. CONCLUSIONS: Greater calcium burden is associated with a lower level of vascular inflammation and plaque vulnerability. A greater calcium burden may represent advanced stable plaque without significant inflammatory activity. (Massachusetts General Hospital and Tsuchiura Kyodo General Hospital Coronary Imaging Collaboration; NCT04523194).

Predictive value of CCTA-based pericoronary adipose tissue imaging for major adverse cardiovascular events.

RATIONALE AND OBJECTIVE: To evaluate the ability of the radiomic characteristics of pericoronary adipose tissue (PCAT) as determined by coronary computed tomography angiography (CCTA) to predict the likelihood of major adverse cardiovascular events (MACEs) within the next five years. MATERIALS AND METHODS: In this retrospective casecontrol study, the case group consisted of 210 patients with coronary artery disease (CAD) who developed MACEs within five years, and the control group consisted of 210 CAD patients without MACEs who were matched with the case group patients according to baseline characteristics. Both groups were divided into training and testing cohorts at an 8:2 ratio. After data standardization and the exclusion of features with Pearson correlation coefficients of |r| ≥ 0.9, independent logistic regression models were constructed using selected radiomics features of the proximal PCAT of the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA) via least absolute shrinkage and selection operator (LASSO) techniques. An integrated PCAT radiomics model including all three coronary arteries was also developed. Five models, including individual PCAT radiomics models for the LAD artery, LCX artery, and RCA; an integrated radiomics model; and a fat attenuation index (FAI) model, were assessed for diagnostic accuracy via receiver operating characteristic (ROC) curves, calibration curves, and decision curves. RESULTS: Compared with the FAI model (AUC=0.564 in training, 0.518 in testing), the integrated radiomics model demonstrated superior diagnostic performance (area under the curve [AUC]=0.923 in training, 0.871 in testing). The AUC values of the integrated model were greater than those of the individual coronary radiomics models, with all the models showing goodness of fit (P > 0.05). The decision curves indicated greater clinical utility of the radiomics models than the FAI model. CONCLUSION: PCAT radiomics models derived from CCTA data are highly valuable for predicting future MACE risk and significantly outperform the FAI model.

Diagnosis of perimenopausal coronary heart disease patients using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography.

To assess whether the radiomics signature of pericoronary adipose tissue (PCAT) from coronary computed tomography angiography (CCTA) can distinguish between perimenopausal women with coronary heart disease (CHD) and those without coronary artery disease (CAD). This single-center retrospective case-control study comprised 140 perimenopausal women with CHD presenting with chest pain who underwent CCTA within 48 h of admission. They were matched with 140 control patients presenting with chest pain but without CAD, based on age, risk factors, radiation dose and CT tube voltage. For all participants, PCAT around the proximal right coronary artery was segmented, from which radiomics features and the fat attenuation index (FAI) were extracted and analyzed. Subsequently, corresponding models were developed and internally validated using Bootstrap methods. Model performance was assessed through measures of identification, calibration, and clinical utility. Using logistic regression analysis, an integrated model that combines clinical features, fat attenuation index and radiomics parameters demonstrated enhanced discrimination ability for perimenopausal CHD (area under the curve [AUC]: 0.80, 95% confidence interval [CI]:0.740-0.845). This model outperformed both the combination of clinical features and PCAT attenuation (AUC 0.67, 95% CI 0.602-0.727) and the use of clinical features alone (AUC 0.66, 95% CI 0.603-0.732). Calibration curves for the three predictive models indicated satisfactory fit (all p > 0.05). Moreover, decision curve analysis demonstrated that the integrated model offered greater clinical benefit compared to the other two models. The CCTA-based radiomics signature derived from the PCAT model outperforms the FAI model in differentiating perimenopausal CHD patients from non-CAD individuals. Integrating PCAT radiomics with the FAI could enhance the diagnostic accuracy for perimenopausal CHD.

Perivascular Fat: A Novel Risk Factor for Coronary Artery Disease.

Perivascular adipose tissue (PVAT) interacts with the vascular wall and secretes bioactive factors which regulate vascular wall physiology. Vice versa, vascular wall inflammation affects the adjacent PVAT via paracrine signals, which induce cachexia-type morphological changes in perivascular fat. These changes can be quantified in pericoronary adipose tissue (PCAT), as an increase in PCAT attenuation in coronary computed tomography angiography images. Fat attenuation index (FAI), a novel imaging biomarker, measures PCAT attenuation around coronary artery segments and is associated with coronary artery disease presence, progression, and plaque instability. Beyond its diagnostic capacity, PCAT attenuation can also ameliorate cardiac risk stratification, thus representing an innovative prognostic biomarker of cardiovascular disease (CVD). However, technical, biological, and anatomical factors are weakly related to PCAT attenuation and cause variation in its measurement. Thus, to integrate FAI, a research tool, into clinical practice, a medical device has been designed to provide FAI values standardized for these factors. In this review, we discuss the interplay of PVAT with the vascular wall, the diagnostic and prognostic value of PCAT attenuation, and its integration as a CVD risk marker in clinical practice.

Pericoronary adipose tissue for predicting long-term outcomes.

AIMS: Pericoronary adipose tissue (PCAT) attenuation obtained by coronary computed tomography angiography (CCTA) has been associated with coronary inflammation and outcomes. Whether PCAT attenuation is predictive of major adverse cardiac events (MACE) during long-term follow-up is unknown. METHODS AND RESULTS: Symptomatic patients with coronary artery disease (CAD) who underwent CCTA were included, and clinical outcomes were evaluated. PCAT was measured at all lesions for all three major coronary arteries using semi-automated software. A comparison between patients with and without MACE was made on both a per-lesion and a per-patient level. The predictive value of PCAT attenuation for MACE was assessed in Cox regression models. In 483 patients (63.3 ± 8.5 years, 54.9% men), 1561 lesions were analysed over a median follow-up duration of 9.5 years. The mean PCAT attenuation was not significantly different between patients with and without MACE. At a per-patient level, the adjusted hazard ratio (HR) and 95% confidence interval (CI) for MACE were 0.970 (95% CI: 0.933-1.008, P = 0.121) when the average of all lesions per patient was analysed, 0.992 (95% CI: 0.961-1.024, P = 0.622) when only the most obstructive lesion was evaluated, and 0.981 (95% CI: 0.946-1.016, P = 0.285) when only the lesion with the highest PCAT attenuation per individual was evaluated. Adjusted HRs for vessel-specific PCAT attenuation in the right coronary artery, left anterior descending artery, and left circumflex artery were 0.957 (95% CI: 0.830-1.104, P = 0.548), 0.989 (95% CI: 0.954-1.025, P = 0.550), and 0.739 (95% CI: 0.293-1.865, P = 0.522), respectively, in predicting long-term MACE. CONCLUSION: In patients referred to CCTA for clinically suspected CAD, PCAT attenuation did not predict MACE during long-term follow-up.

Longitudinal Evaluation of Coronary Arteries and Myocardium in Breast Cancer Using Coronary Computed Tomographic Angiography.

BACKGROUND: The association of coronary computed tomography angiography (CTA) and left ventricular (LV) myocardium measurements with cancer therapy-related cardiac dysfunction (CTRCD) is limited. OBJECTIVES: In this study, the authors sought to evaluate the changes in coronary arteries and LV myocardium in patients with left breast cancer (BC) receiving anthracycline with or without radiotherapy, with the use of coronary CTA. METHODS: Participants with left BC receiving anthracycline with or without radiotherapy were prospectively included. All participants underwent coronary CTA before and after treatment, including nonenhanced calcium-scoring scan, computed tomography angiography, and dual-energy late enhancement scan. Computed tomographic fractional flow reserve (CT-FFR), pericoronary adipose tissue (PCAT) CT attenuation, and LV segments' extracellular volume (ECV) before and after treatment were compared. Logistic regression analysis was used to assess the association between baseline coronary CTA parameters and CTRCD. RESULTS: Eighty participants receiving anthracycline and 59 participants receiving anthracycline with radiotherapy were included. CT-FFR decreased and PCAT CT attenuation and LV global and segments' ECV increased after treatment (all P < 0.05). After chemoradiotherapy, CT-FFR was lower and PCAT CT attenuation and LV myocardial ECV were higher than after chemotherapy. Twenty-four participants developed CTRCD. After adjustment by Heart Failure Association-International Cardio-Oncology Society risk in multivariable logistic regression analysis, baseline stenosis of the left anterior descending artery (LAD) (OR: 1.987 [95% CI: 1.322-2.768]; P = 0.021), left circumflex artery (LCX) (OR: 1.895 [95% CI: 1.281-2.802]; P = 0.031), and right coronary artery (RCA) (OR: 1.920 [95% CI: 1.405-2.811]; P = 0.028), and baseline CT-FFR of the LAD (OR: 3.425 [95% CI: 1.621-9.434]; P < 0.001), LCX (OR: 2.058 [95% CI: 1.030-5.076]; P = 0.006), and RCA (OR: 2.469 [95% CI: 1.232-6.944]; P = 0.004) were associated with CTRCD. CONCLUSIONS: Multiparameter coronary CTA contributes to comprehensive assessment of the coronary arteries and myocardium in patients with left BC receiving anthracycline with or without radiotherapy. Baseline coronary artery stenosis and CT-FFR might be imaging markers for predicting CTRCD in these patients.

Predicting major adverse cardiac events using radiomics nomogram of pericoronary adipose tissue based on CCTA: A multi-center study.

BACKGROUND: The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE). PURPOSE: To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE. METHODS: We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE was delineated as cardiovascular mortality, newly diagnosed myocardial infarction, hospitalization (or re-hospitalization) for heart failure, and coronary target vessel revascularization occurring more than 30 days post-CCTA examination. All enrolled patients underwent CCTA scanning. Radiomic features were meticulously extracted from the optimal diastolic phase axial slices of CCTA images. Feature reduction was achieved through a composite feature selection algorithm, laying the groundwork for the radiomic signature model. Both univariate and multivariate analyses were employed to assess clinical variables. A multifaceted logistic regression analysis facilitated the crafting of a clinical-radiological-radiomic combined model (or nomogram). Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were delineated, with the area under the ROC curve (AUCs) computed to gauge the predictive prowess of the clinical model, radiomic model, and the synthesized ensemble. RESULTS: A total of 12 radiomic features closely associated with MACE were identified to establish the radiomic model. Multivariate logistic regression results demonstrated that smoking, age, hypertension, and dyslipidemia were significantly correlated with MACE. In the integrated nomogram, which amalgamated clinical, imaging, and radiomic parameters, the diagnostic performance was as follows: 0.970 AUC, 0.949 accuracy (ACC), 0.833 sensitivity (SEN), 0.981 specificity (SPE), 0.926 positive predictive value (PPV), and 0.955 negative predictive value (NPV). The calibration curve indicated a commendable concordance of the nomogram, and the decision curve analysis underscored its superior clinical utility. CONCLUSIONS: The integration of radiomic signatures from PCAT based on CCTA, clinical indices, and imaging parameters into a nomogram stands as a promising instrument for prognosticating MACE events.

Protocol and rationale of the Australian multicentre registry for serial cardiac computed tomography angiography (ARISTOCRAT): a prospective observational study of the natural history of pericoronary adipose tissue attenuation and radiomics.

Background: Vascular inflammation plays a crucial role in the development of atherosclerosis and atherosclerotic plaque rupture resulting in acute coronary syndrome (ACS). Pericoronary adipose tissue (PCAT) attenuation quantified from routine coronary computed tomography angiography (CCTA) has emerged as a promising non-invasive imaging biomarker of coronary inflammation. However, a detailed understanding of the natural history of PCAT attenuation is required before it can be used as a surrogate endpoint in trials of novel therapies targeting coronary inflammation. This article aims to explore the natural history of PCAT attenuation and its association with changes in plaque characteristics. Methods: The Australian natuRal hISTOry of periCoronary adipose tissue attenuation, RAdiomics and plaque by computed Tomographic angiography (ARISTOCRAT) registry is a multi-centre observational registry enrolling patients undergoing clinically indicated serial CCTA in 9 centres across Australia. CCTA scan parameters will be matched across serial scans. Quantitative analysis of plaque and PCAT will be performed using semiautomated software. Discussion: The primary endpoint is to explore temporal changes in patient-level and lesion-level PCAT attenuation by CCTA and their associations with changes in plaque characteristics. Secondary endpoints include evaluating: (I) impact of statin therapy on PCAT attenuation and plaque characteristics; and (II) changes in PCAT attenuation and plaque characteristics in specific subgroups according to sex and risk factors. ARISTOCRAT will further our understanding of the natural history of PCAT attenuation and its association with changes in plaque characteristics. Trial Registration: This study has been prospectively registered with the Australia and New Zealand Clinical Trials Registry (ACTRN12621001018808).

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.

OBJECTIVE: To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD). MATERIALS AND METHODS: The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic. RESULTS: Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model. CONCLUSION: Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

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).

Background: Pericoronary adipose tissue (PCAT) is a sensor of vascular inflammation. Elevated PCAT attenuation values indicate the presence of coronary inflammation in patients. However, it is unclear which clinical characteristics are associated with increased PCAT attenuation values in patients without coronary heart disease (CHD). The study aims to investigate the relationship between increased PCAT attenuation values and clinical characteristics of patients without CHD. Methods: We recruited 785 eligible patients without CHD who underwent coronary computed tomographic angiography (CCTA). Clinical data were recorded for each patient, and PCAT attenuation values for the left anterior descending branch (LADPCAT), left circumflex branch (LCXPCAT), and right coronary artery (RCAPCAT) were quantified by CCTA using fully automated software. Univariate and multivariate analyses were performed to identify the associations between different clinical characteristics and elevated LADPCAT, LCXPCAT, and RCAPCAT. Results: Univariate analysis showed body mass index (BMI) to be positively associated with LADPCAT (rs=0.109), LCXPCAT (rs=0.076), and RCAPCAT (rs=0.083). Moreover, the duration of smoking, and drinking was positively associated with LADPCAT (rs=0.099, 0.165). Hyperlipidemia was positively associated with LADPCAT (rs=0.089) and RCAPCAT (rs=0.334), while statin use was negatively associated with RCAPCAT (rs=-0.145). Multivariate analysis showed that the significant determinants of LADPCAT were BMI (ß=0.359, P=0.001), duration of smoking (ß=2.612, P=0.002), drinking (ß=4.106, P<0.001), and hyperlipidemia (ß=1.664, P=0.027). LCXPCAT was associated with BMI (ß=0.218, P=0.024), while RCAPCAT was associated with hyperlipidemia (ß=6.110, P<0.001) and statin use (ß=-3.338, P<0.001). Conclusions: In patients without CHD, the PCAT attenuation values measured using CCTA were associated with various clinical characteristics. LADPCAT was associated with BMI, smoking duration, drinking, and hyperlipidemia. On the other hand, LCXPCAT was associated with BMI, while RCAPCAT was associated with hyperlipidemia and statin use.

Coronary CTA-based radiomic signature of pericoronary adipose tissue predict rapid plaque progression.

OBJECTIVES: To explore the value of radiomic features derived from pericoronary adipose tissue (PCAT) obtained by coronary computed tomography angiography for prediction of coronary rapid plaque progression (RPP). METHODS: A total of 1233 patients from two centers were included in this multicenter retrospective study. The participants were divided into training, internal validation, and external validation cohorts. Conventional plaque characteristics and radiomic features of PCAT were extracted and analyzed. Random Forest was used to construct five models. Model 1: clinical model. Model 2: plaque characteristics model. Model 3: PCAT radiomics model. Model 4: clinical + radiomics model. Model 5: plaque characteristics + radiomics model. The evaluation of the models encompassed identification accuracy, calibration precision, and clinical applicability. Delong' test was employed to compare the area under the curve (AUC) of different models. RESULTS: Seven radiomic features, including two shape features, three first-order features, and two textural features, were selected to build the PCAT radiomics model. In contrast to the clinical model and plaque characteristics model, the PCAT radiomics model (AUC 0.85 for training, 0.84 for internal validation, and 0.81 for external validation; p < 0.05) achieved significantly higher diagnostic performance in predicting RPP. The separate combination of radiomics with clinical and plaque characteristics model did not further improve diagnostic efficacy statistically (p > 0.05). CONCLUSION: Radiomic feature analysis derived from PCAT significantly improves the prediction of RPP as compared to clinical and plaque characteristics. Radiomic analysis of PCAT may improve monitoring RPP over time. CRITICAL RELEVANCE STATEMENT: Our findings demonstrate PCAT radiomics model exhibited good performance in the prediction of RPP, with potential clinical value. KEY POINTS: Rapid plaque progression may be predictable with radiomics from pericoronary adipose tissue. Fibrous plaque volume, diameter stenosis, and fat attenuation index were identified as risk factors for predicting rapid plaque progression. Radiomics features of pericoronary adipose tissue can improve the predictive ability of rapid plaque progression.

Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study.

BACKGROUND: Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS: This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS: The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION: The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

Cardiovascular Disease from Pathophysiology to Risk Estimation: Is Inflammation Estimated through Perivascular Attenuation on Computed Tomography the Key?

(1) Background: Systemic inflammation stands as a well-established risk factor for ischemic cardiovascular disease, as well as a contributing factor in the development of cardiac arrhythmias, notably atrial fibrillation. Furthermore, scientific studies have brought to light the pivotal role of localized vascular inflammation in the initiation, progression, and destabilization of coronary atherosclerotic disease. (2) Methods: We comprehensively review recent, yet robust, scientific evidence elucidating the use of perivascular adipose tissue attenuation measurement on computed tomography applied to key anatomical sites. Specifically, the investigation extends to the internal carotid artery, aorta, left atrium, and coronary arteries. (3) Conclusions: The examination of perivascular adipose tissue attenuation emerges as a non-invasive and indirect means of estimating localized perivascular inflammation. This measure is quantified in Hounsfield units, indicative of the inflammatory response elicited by dense adipose tissue near the vessel or the atrium. Particularly noteworthy is its potential utility in assessing inflammatory processes within the coronary arteries, evaluating coronary microvascular dysfunction, appraising conditions within the aorta and carotid arteries, and discerning inflammatory states within the atria, especially in patients with atrial fibrillation. The widespread applicability of perivascular adipose tissue attenuation measurement underscores its significance as a diagnostic tool with considerable potential for enhancing our understanding and management of cardiovascular diseases.

Evaluation of peri-plaque pericoronary adipose tissue attenuation in coronary atherosclerosis using a dual-layer spectral detector CT.

Purpose: This study aimed to evaluate the differences between pericoronary adipose tissue (PCAT) attenuation at different measured locations in evaluating coronary atherosclerosis using spectral computed tomography (CT) and to explore valuable imaging indicators. Methods: A total of 330 patients with suspicious coronary atherosclerosis were enrolled and underwent coronary CT angiography with dual-layer spectral detector CT (SDCT). Proximal and peri-plaque fat attenuation index (FAI) of stenosis coronary arteries were measured using both conventional images (CIs) and virtual monoenergetic images (VMIs) ranging from 40 keV to 100 keV. The slopes of the spectral attenuation curve (λ) of proximal and peri-plaque PCAT at three different monoenergetic intervals were calculated. Additionally, peri-plaque FAI on CI and virtual non-contrast images, and effective atomic number were measured manually. Results: A total of 231 coronary arteries with plaques and lumen stenosis were finally enrolled. Peri-plaque FAICI and FAIVMI were significantly higher in severe stenosis than in mild and moderate stenosis (p < 0.05), while peri-plaque λ, proximal FAI, and proximal λ were not statistically different. Proximal FAI, peri-plaque FAI, and peri-plaque λ were significantly higher in low-density non-calcified plaque (LD-NCP) and non-calcified plaque (NCP) than in calcified plaque (p < 0.01). Peri-plaque FAI was the highest in the LD-NCP group, while proximal FAI was the highest in the NCP group. In severe stenosis and in LD-NCP, peri-plaque FAI was significantly higher than proximal FAI (p < 0.05). The manually measured parameters related to peri-plaque PCAT attenuation had a positive correlation with the results of peri-plaque FAI measured automatically. Conclusion: Peri-plaque PCAT has more value in assessing coronary atherosclerosis than proximal PCAT. Peri-plaque PCAT attenuation is expected to be used as a standard biomarker for evaluating plaque vulnerability and hemodynamic characteristics.

Association between metformin treatment and coronary artery inflammation based on pericoronary adipose tissue attenuation in type 2 diabetes mellitus patients.

Cardiovascular disease (CVD) is the leading cause of mortality in type 2 diabetes mellitus (T2DM) patients. The role of metformin in reducing cardiovascular events is well-established, but its effect on coronary artery inflammation in T2DM patients is still unclear. In this study, we evaluated 547 T2DM patients who underwent coronary computed tomography angiography (CCTA) at Wuhan Central Hospital. Using propensity score matching, we compared the attenuation of pericoronary adipose tissue (PCAT), an imaging marker of coronary artery inflammation, between patients treated with and without metformin. Multiple linear regression models were used to analyze the influence of metformin on PCAT attenuation. The results of the propensity-matched analysis showed that patients on metformin therapy had significantly lower PCAT attenuation, indicating reduced coronary inflammation. Specifically, the PCAT attenuation in the left anterior descending artery (LAD) and right coronary artery (RCA) was lower in the metformin group compared to the non-metformin group. Metformin use was independently associated with decreased LAD-PCAT attenuation in the multivariate regression analysis. The association of metformin with PCAT attenuation differed significantly in populations analyzed in subgroups of patients with obesity and chronic kidney disease. In conclusion, our study shows a preliminary signal that metformin therapy may be associated with decreased coronary artery inflammation in T2DM patients, as indicated by PCAT attenuation on CCTA. And this correlation may vary depending on the patient population. This initial finding suggests that PCAT attenuation could be potentially used as an imaging biomarker to monitor the anti-inflammatory effects of medication.

Computed tomography pericoronary adipose tissue density predicts coronary allograft vasculopathy and adverse clinical outcomes after cardiac transplantation.

AIMS: To assess pericoronary adipose tissue (PCAT) density on coronary computed tomography angiography (CCTA) as a marker of inflammatory disease activity in coronary allograft vasculopathy (CAV). METHODS AND RESULTS: PCAT density, lesion volumes, and total vessel volume-to-myocardial mass ratio (V/M) were retrospectively measured in 126 CCTAs from 94 heart transplant patients [mean age 49 (SD 14.5) years, 40% female] who underwent imaging between 2010 and 2021; age- and sex-matched controls; and patients with atherosclerosis. PCAT density was higher in transplant patients with CAV [n = 40; -73.0 HU (SD 9.3)] than without CAV [n = 86; -77.9 HU (SD 8.2)], and controls [n = 12; -86.2 HU (SD 5.4)], P < 0.01 for both. Unlike patients with atherosclerotic coronary artery disease (n = 32), CAV lesions were predominantly non-calcified and comprised of mostly fibrous or fibrofatty tissue. V/M was lower in patients with CAV than without [32.4 mm3/g (SD 9.7) vs. 41.4 mm3/g (SD 12.3), P < 0.0001]. PCAT density and V/M improved the ability to predict CAV from area under the receiver operating characteristic curve (AUC) 0.75-0.85 when added to donor age and donor hypertension status (P < 0.0001). PCAT density above -66 HU was associated with a greater incidence of all-cause mortality {odds ratio [OR] 18.0 [95% confidence interval (CI) 3.25-99.6], P < 0.01} and the composite endpoint of death, CAV progression, acute rejection, and coronary revascularization [OR 7.47 (95% CI 1.8-31.6), P = 0.01] over 5.3 (SD 2.1) years. CONCLUSION: Heart transplant patients with CAV have higher PCAT density and lower V/M than those without. Increased PCAT density is associated with adverse clinical outcomes. These CCTA metrics could be useful for the diagnosis and monitoring of CAV severity.

Coronary inflammation based on pericoronary adipose tissue attenuation in type 2 diabetic mellitus: effect of diabetes management.

BACKGROUND: Coronary inflammation plays crucial role in type 2 diabetes mellitus (T2DM) induced cardiovascular complications. Both glucose-lowering drug interventions (GLDIS) and glycemic control (GC) status potentially correlate coronary inflammation, as indicated by changes in pericoronary adipose tissue (PCAT) attenuation, and thus influence cardiovascular risk. This study evaluated the impact of GLDIS and GC status on PCAT attenuation in T2DM patients. METHODS: This retrospective study collected clinical data and coronary computed tomography angiography (CCTA) images of 1,342 patients, including 547 T2DM patients and 795 non-T2DM patients in two tertiary hospitals. T2DM patients were subgroup based on two criteria: (1) GC status: well: HbA1c < 7%, moderate: 7 ≤ HbA1c ≤ 9%, and poor: HbA1c > 9%; (2) GLDIS and non-GLDIS. PCAT attenuations of the left anterior descending artery (LAD-PCAT), left circumflex artery (LCX-PCAT), and right coronary artery (RCA-PCAT) were measured. Propensity matching (PSM) was used to cross compare PCAT attenuation of non-T2DM and all subgroups of T2DM patients. Linear regressions were conducted to evaluate the impact of GC status and GLDIS on PCAT attenuation in T2DM patients. RESULTS: Significant differences were observed in RCA-PCAT and LCX-PCAT between poor GC-T2DM and non-T2DM patients (LCX: - 68.75 ± 7.59 HU vs. - 71.93 ± 7.25 HU, p = 0.008; RCA: - 74.37 ± 8.44 HU vs. - 77.2 ± 7.42 HU, p = 0.026). Higher PCAT attenuation was observed in LAD-PCAT, LCX-PCAT, and RCA-PCAT in non-GLDIS T2DM patients compared with GLDIS T2DM patients (LAD: - 78.11 ± 8.01 HU vs. - 75.04 ± 8.26 HU, p = 0.022; LCX: - 71.10 ± 8.13 HU vs. - 68.31 ± 7.90 HU, p = 0.037; RCA: - 78.17 ± 8.64 HU vs. - 73.35 ± 9.32 HU, p = 0.001). In the linear regression, other than sex and duration of diabetes, both metformin and acarbose were found to be significantly associated with lower LAD-PCAT (metformin: ß coefficient = - 2.476, p=0.021; acarbose: ß coefficient = - 1.841, p = 0.031). CONCLUSION: Inadequate diabetes management, including poor GC and lack of GLDIS, may be associated with increased coronary artery inflammation in T2DM patients, as indicated by PCAT attenuation on CCTA, leading to increased cardiovascular risk. This finding could help healthcare providers identify T2DM patients with increased cardiovascular risk, develop improved cardiovascular management programs, and reduce subsequent cardiovascular related mortality.

Differentiation of acute coronary syndrome with radiomics of pericoronary adipose tissue.

OBJECTIVE: To assess the potential values of radiomics signatures of pericoronary adipose tissue (PCAT) in identifying patients with acute coronary syndrome (ACS). METHODS: In total, 149, 227, and 244 patients were clinically diagnosed with ACS, chronic coronary syndrome (CCS), and without coronary artery disease (CAD), respectively, and were retrospectively analysed and randomly divided into training and testing cohorts at a 2:1 ratio. From the PCATs of the proximal left anterior descending branch, left circumflex branch, and right coronary artery (RCA), the pericoronary fat attenuation index (FAI) value and radiomics signatures were calculated, among which features closely related to ACS were screened out. The ACS differentiation models AC1, AC2, AC3, AN1, AN2, and AN3 were constructed based on the FAI value of RCA and the final screened out first-order and texture features, respectively. RESULTS: The FAI values were all higher in patients with ACS than in those with CCS and no CAD (all P < .05). For the identification of ACS and CCS, the area-under-the-curve (AUC) values of AC1, AC2, and AC3 were 0.92, 0.94, and 0.91 and 0.91, 0.86, and 0.88 in the training and testing cohorts, respectively. For the identification of ACS and no CAD, the AUC values of AN1, AN2, and AN3 were 0.95, 0.94, and 0.94 and 0.93, 0.87, and 0.89 in the training and testing cohorts, respectively. CONCLUSIONS: Identification models constructed based on the radiomics signatures of PCAT are expected to be an effective tool for identifying patients with ACS. ADVANCES IN KNOWLEDGE: The radiomics signatures of PCAT and FAI values are expected to differentiate between patients with ACS, CCS and those without CAD on imaging.

Assessing the Impact of Long-Term High-Dose Statin Treatment on Pericoronary Inflammation and Plaque Distribution-A Comprehensive Coronary CTA Follow-Up Study.

Computed tomography angiography (CTA) has validated the use of pericoronary adipose tissue (PCAT) attenuation as a credible indicator of coronary inflammation, playing a crucial role in coronary artery disease (CAD). This study aimed to evaluate the long-term effects of high-dose statins on PCAT attenuation at coronary lesion sites and changes in plaque distribution. Our prospective observational study included 52 patients (mean age 60.43) with chest pain, a low-to-intermediate likelihood of CAD, who had documented atheromatous plaque through CTA, performed approximately 1 year and 3 years after inclusion. We utilized the advanced features of the CaRi-Heart® and syngo.via Frontier® systems to assess coronary plaques and changes in PCAT attenuation. The investigation of changes in plaque morphology revealed significant alterations. Notably, in mixed plaques, calcified portions increased (p < 0.0001), while non-calcified plaque volume (NCPV) decreased (p = 0.0209). PCAT attenuation generally decreased after one year and remained low, indicating reduced inflammation in the following arteries: left anterior descending artery (LAD) (p = 0.0142), left circumflex artery (LCX) (p = 0.0513), and right coronary artery (RCA) (p = 0.1249). The CaRi-Heart® risk also decreased significantly (p = 0.0041). Linear regression analysis demonstrated a correlation between increased PCAT attenuation and higher volumes of NCPV (p < 0.0001, r = 0.3032) and lipid-rich plaque volume (p < 0.0001, r = 0.3281). Our study provides evidence that high-dose statin therapy significantly reduces CAD risk factors, inflammation, and plaque vulnerability, as evidenced by the notable decrease in PCAT attenuation, a critical indicator of plaque progression.