Pericoronary adipose tissue attenuation on coronary computed tomography angiography associates with male sex and Indigenous Australian status.

To evaluate if Indigenous Australians have higher coronary inflammation demonstrated non-invasively using pericoronary adipose tissue attenuation on coronary computed tomography angiography (CCTA). We retrospectively obtained a cohort 54 Indigenous patients age- and sex-matched to 54 non-Indigenous controls (age: 46.5 ± 13.1 years; male: n = 66) undergoing CCTA at the Royal Darwin Hospital and Monash Medical Centre. Patient groups were defined to investigate the interaction of ethnicity and sex: Indigenous + male, Indigenous + female, control + male, control + female. Semi-automated software was used to assess pericoronary adipose tissue attenuation (PCAT-a) and volume (PCAT-v). Males had significantly higher PCAT-a (- 86.7 ± 7.8 HU vs. - 91.3 ± 7.1 HU, p = 0.003) than females. Indigenous patients had significantly higher PCAT-v (1.5 ± 0.5cm3 vs. 1.3 ± 0.4cm3, p = 0.032), but only numerically higher PCAT-a (p = 0.133) than controls. There was a significant difference in PCAT-a and PCAT-v across groups defined by Indigenous status and sex (p = 0.010 and p = 0.030, respectively). Among patients with matching CCTA contrast density, multivariable linear regression analysis showed an independent association between Indigenous status and PCAT-a. Indigenous men have increased PCAT-a in an age- and sex-matched cohort. Male sex is strongly associated with increased PCAT-a. Coronary inflammation may contribute to adverse cardiovascular outcomes in Indigenous Australians, but larger studies are required to validate these findings.

Atherogenic index of plasma is associated with epicardial adipose tissue volume assessed on coronary computed tomography angiography.

The atherogenic index of plasma (AIP) is a novel biomarker of atherogenic dyslipidaemia (AD), but its relationship with cardiac adipose tissue depots is unknown. We aimed to assess the association of AD with cardiac adipose tissue parameters on coronary computed tomography angiography (CCTA). We studied 161 patients who underwent CCTA between 2008 and 2011 (age 59.0 ± 14.0 years). AD was defined as triglyceride (TG) > 1.7 mmol/L and HDL < 1.0 mmol/L (n = 34). AIP was defined as the base 10 logarithmic ratio of TG to HDL. Plaque burden was assessed using the CT-Leaman score (CT-LeSc). We studied volume and attenuation of epicardial adipose tissue (EAT-v and EAT-a) and pericoronary adipose tissue (PCAT-v and PCAT-a) on CCTA using semi-automated software. Patients with AD had higher PCAT-v (p = 0.042) and EAT-v (p = 0.041). AIP was associated with EAT-v (p = 0.006), type II diabetes (p = 0.009) and male sex (p < 0.001) and correlated with CT-LeSc (p = 0.040). On multivariable analysis, AIP was associated with EAT-v ≥ 52.3 cm3, age, male sex and type II diabetes when corrected for traditional risk factors and plaque burden. AIP is associated with increased EAT volume, but not PCAT-a, after multivariable adjustment. These findings indicate AIP is associated with adverse adipose tissue changes which may increase coronary risk.

Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography.

OBJECTIVES: The aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis. BACKGROUND: It remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events. METHODS: A total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD. For all coronary lesions, high-risk plaque (HRP) characteristics were qualitatively assessed, followed by semiautomated plaque quantification and extraction of 1,103 radiomic features. Machine learning models were built to examine the additive value of radiomic features for discriminating culprit lesions over and above HRP and plaque volumes. RESULTS: Culprit lesions had higher mean volumes of noncalcified plaque (NCP) and low-density noncalcified plaque (LDNCP) compared with the highest-grade stenosis nonculprits and highest-grade stenosis stable CAD lesions (NCP: 138.1 mm3 vs 110.7 mm3 vs 102.7 mm3; LDNCP: 14.2 mm3 vs 9.8 mm3 vs 8.4 mm3; both Ptrend < 0.01). In multivariable linear regression adjusted for NCP and LDNCP volumes, 14.9% (164 of 1,103) of radiomic features were associated with culprits and 9.7% (107 of 1,103) were associated with the highest-grade stenosis nonculprits (critical P < 0.0007) when compared with highest-grade stenosis stable CAD lesions as reference. Hierarchical clustering of significant radiomic features identified 9 unique data clusters (latent phenotypes): 5 contained radiomic features specific to culprits, 1 contained features specific to highest-grade stenosis nonculprits, and 3 contained features associated with either lesion type. Radiomic features provided incremental value for discriminating culprit lesions when added to a machine learning model containing HRP and plaque volumes (area under the receiver-operating characteristic curve 0.86 vs 0.76; P = 0.004). CONCLUSIONS: Culprit lesions and highest-grade stenosis nonculprit lesions in MI have distinct radiomic signatures compared with lesions in stable CAD. Within the vulnerable patient may exist individual vulnerable plaques identifiable by coronary CTA-based precision phenotyping.

Coronary computed tomography angiography-based assessment of vascular inflammation in patients with obstructive sleep apnoea and coronary artery disease.

Background: Obstructive sleep apnoea (OSA) is associated with increased coronary artery disease (CAD) plaque burden, but the role of vascular inflammation in this relationship is unclear. Coronary computed tomography angiography (CTA) enables surrogate assessment of systemic inflammation via subcutaneous adipose tissue attenuation (SCAT-a), and of coronary inflammation via epicardial adipose tissue volume and attenuation (EAT-v and EAT-a) and pericoronary adipose tissue attenuation (PCAT-a). We investigated whether patients with severe OSA and high plaque burden have increased vascular inflammation. Methods: Patients with overnight polysomnography within ≤12 months of coronary CTA were included. Severe OSA was classified as apnoea/hypopnoea index (AHI) >30. High plaque burden was defined as a CT-adapted Leaman score (CT-LeSc) ≥8.3. Patients with both severe OSA and high plaque burden were defined as 'Group 1', all other patients were classified as 'Group 2'. ScAT, PCAT and EAT attenuation and volume were assessed on semi-automated software. Results: A total of 91 patients were studied (59.3±11.1 years). Severe OSA was associated with high plaque burden (P=0.02). AHI correlated with CT-LeSc (r=0.24, P=0.023). Group 1 had lower EAT-a and PCAT-a compared to Group 2 (EAT-a: -87.6 vs. -84.0 HU, P=0.011; PCAT-a: -90.4 vs. -83.4 HU, P<0.01). However, among patients with low plaque burden, EAT-a was higher in the presence of severe OSA versus mild-moderate OSA (-80.3 vs. -84.0 HU, P=0.020). On multivariable analysis, severe OSA and high plaque burden associated with EAT-a (P<0.02), and severe OSA and high plaque burden (P<0.01) and hypertension (P<0.01) associated with PCAT-a. Conclusions: EAT and PCAT attenuation are decreased in patients with severe OSA and high plaque burden, but EAT attenuation was increased in patients with severe OSA and low plaque burden. These divergent results suggest vascular inflammation may be increased in OSA independent of CAD, but larger studies are required to validate these findings.

The Emerging Role of CT-Based Imaging in Adipose Tissue and Coronary Inflammation.

A large body of evidence arising from recent randomized clinical trials demonstrate the association of vascular inflammatory mediators with coronary artery disease (CAD). Vascular inflammation localized in the coronary arteries leads to an increased risk of CAD-related events, and produces unique biological alterations to local cardiac adipose tissue depots. Coronary computed tomography angiography (CTA) provides a means of mapping inflammatory changes to both epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) as independent markers of coronary risk. Radiodensity or attenuation of PCAT on coronary CTA, notably, provides indirect quantification of coronary inflammation and is emerging as a promising non-invasive imaging implement. An increasing number of observational studies have shown robust associations between PCAT attenuation and major coronary events, including acute coronary syndrome, and 'vulnerable' atherosclerotic plaque phenotypes that are associated with an increased risk of the said events. This review outlines the biological characteristics of both EAT and PCAT and provides an overview of the current literature on PCAT attenuation as a surrogate marker of coronary inflammation.

Pericoronary Adipose Tissue Attenuation Is Associated with High-Risk Plaque and Subsequent Acute Coronary Syndrome in Patients with Stable Coronary Artery Disease.

BACKGROUND: High-risk plaques (HRP) detected on coronary computed tomography angiography (CTA) confer an increased risk of acute coronary syndrome (ACS). Pericoronary adipose tissue attenuation (PCAT) is a novel biomarker of coronary inflammation. This study aimed to evaluate the association of PCAT with HRP and subsequent ACS development in patients with stable coronary artery disease (CAD). METHODS: Patients with stable CAD who underwent coronary CTA from 2011 to 2016 and had available outcome data were included. We studied 41 patients with HRP propensity matched to 41 controls without HRP (60 ± 10 years, 67% males). PCAT was assessed using semi-automated software on a per-patient basis in the proximal right coronary artery (PCATRCA) and a per-lesion basis (PCATLesion) around HRP in cases and the highest-grade stenosis lesions in controls. RESULTS: PCATRCA and PCATLesion were higher in HRP patients than controls (PCATRCA: -80.7 ± 6.50 HU vs. -84.2 ± 8.09 HU, p = 0.03; PCATLesion: -79.6 ± 7.86 HU vs. -84.2 ± 10.3 HU, p = 0.04), and were also higher in men (PCATRCA: -80.5 ± 7.03 HU vs. -86.1 ± 7.08 HU, p < 0.001; PCATLesion: -79.6 ± 9.06 HU vs. -85.2 ± 7.96 HU, p = 0.02). Median time to ACS was 1.9 years, within a median follow-up of 5.3 years. PCATRCA alone was higher in HRP patients who subsequently presented with ACS (-76.8 ± 5.69 HU vs. -82.0 ± 6.32 HU, p = 0.03). In time-dependent analysis, ACS was associated with HRP and PCATRCA. CONCLUSIONS: PCAT attenuation is increased in stable CAD patients with HRP and is associated with subsequent ACS development. Further investigation is required to determine the clinical implications of these findings.

Cardiac Computed Tomography Radiomics for the Non-Invasive Assessment of Coronary Inflammation.

Radiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.

Pericoronary adipose tissue computed tomography attenuation distinguishes different stages of coronary artery disease: a cross-sectional study.

AIMS: Vascular inflammation inhibits local adipogenesis in pericoronary adipose tissue (PCAT) and this can be detected on coronary computed tomography angiography (CCTA) as an increase in CT attenuation of PCAT surrounding the proximal right coronary artery (RCA). In this cross-sectional study, we assessed the utility of PCAT CT attenuation as an imaging biomarker of coronary inflammation in distinguishing different stages of coronary artery disease (CAD). METHODS AND RESULTS: Sixty patients with acute myocardial infarction (MI) were prospectively recruited to undergo CCTA within 48 h of admission, prior to invasive angiography. These participants were matched to patients with stable CAD (n = 60) and controls with no CAD (n = 60) by age, gender, BMI, risk factors, medications, and CT tube voltage. PCAT attenuation around the proximal RCA was quantified per-patient using semi-automated software. Patients with MI had a higher PCAT attenuation (-82.3 ± 5.5 HU) compared with patients with stable CAD (-90.6 ± 5.7 HU, P < 0.001) and controls (-95.8 ± 6.2 HU, P < 0.001). PCAT attenuation was significantly increased in stable CAD patients over controls (P = 0.01). The association of PCAT attenuation with stage of CAD was independent of age, gender, cardiovascular risk factors, epicardial adipose tissue volume, and CCTA-derived quantitative plaque burden. No interaction was observed for clinical presentation (MI vs. stable CAD) and plaque burden on PCAT attenuation. CONCLUSION: PCAT CT attenuation as a quantitative measure of global coronary inflammation independently distinguishes patients with MI vs. stable CAD vs. no CAD. Future studies should assess whether this imaging biomarker can track patient responses to therapies in different stages of CAD.

Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study.

OBJECTIVES: This study sought to determine whether coronary computed tomography angiography (CCTA)-based radiomic analysis of pericoronary adipose tissue (PCAT) could distinguish patients with acute myocardial infarction (MI) from patients with stable or no coronary artery disease (CAD). BACKGROUND: Imaging of PCAT with CCTA enables detection of coronary inflammation. Radiomics involves extracting quantitative features from medical images to create big data and identify novel imaging biomarkers. METHODS: In a prospective case-control study, 60 patients with acute MI underwent CCTA within 48 h of admission, before invasive angiography. These subjects were matched to patients with stable CAD (n = 60) and controls with no CAD (n = 60) by age, sex, risk factors, medications, and CT tube voltage. PCAT was segmented around the proximal right coronary artery (RCA) in all patients and around culprit and nonculprit lesions in patients with MI. PCAT segmentations were analyzed using Radiomics Image Analysis software. RESULTS: Of 1,103 calculated radiomic parameters, 20.3% differed significantly between MI patients and controls, and 16.5% differed between patients with MI and stable CAD (critical p < 0.0006); whereas none differed between patients with stable CAD and controls. On cluster analysis, the most significant radiomic parameters were texture or geometry based. At 6 months post-MI, there was no significant change in the PCAT radiomic profile around the proximal RCA or nonculprit lesions. Using machine learning (XGBoost), a model integrating clinical features (risk factors, serum lipids, high-sensitivity C-reactive protein), PCAT attenuation, and radiomic parameters provided superior discrimination of acute MI (area under the receiver operator characteristic curve [AUC]: 0.87) compared with a model with clinical features and PCAT attenuation (AUC: 0.77; p = 0.001) or clinical features alone (AUC: 0.76; p < 0.001). CONCLUSIONS: Patients with acute MI have a distinct PCAT radiomic phenotype compared with patients with stable or no CAD. Using machine learning, a radiomics-based model outperforms a PCAT attenuation-based model in accurately identifying patients with MI.

Remnant cholesterol and coronary atherosclerotic plaque burden assessed by computed tomography coronary angiography.

BACKGROUND AND AIMS: There remains a substantial residual risk of ischaemic heart disease (IHD) despite optimal low-density lipoprotein cholesterol (LDLC) reduction. Part of this risk may be attributable to remnant cholesterol, which is carried in triglyceride-rich lipoproteins. We evaluated the relationship between remnant cholesterol and coronary atherosclerotic plaque burden assessed non-invasively by computed tomography coronary angiography (CTCA) in patients with suspected coronary artery disease (CAD). METHODS AND RESULTS: This was a multicentre study of 587 patients who had a CTCA and fasting lipid profile within 3 months. Calculated remnant cholesterol was total cholesterol minus LDLC minus high-density lipoprotein cholesterol (HDLC). Significant coronary atherosclerotic burden was defined as CT-Leaman score >5 (CT-LeSc), an established predictor of cardiac events. Mean age was 61 ±â€¯12 years and mean pretest probability of CAD was 23.2 ±â€¯19.8%. LDLC levels were <1.8 mmol/L in 134 patients (23%), of whom 82% were statin-treated. Patients with CT-LeSc >5 had higher mean remnant cholesterol than those with CT-LeSc ≤5 (0.76 ±â€¯0.36 mmol/L vs. 0.58 ±â€¯0.33 mmol/L, p = 0.01). On univariable analysis, remnant cholesterol (p = 0.01), LDLC (p = 0.002) and HDLC (p < 0.001) levels predicted CT-LeSc >5, whilst triglycerides (p = 0.79) had no association with CT-LeSc >5. On multivariable analysis in the subset of patients with optimal LDLC levels, remnant cholesterol levels remained predictive of CT-LeSc >5 (OR 3.87, 95% confidence interval 1.34-7.55, p = 0.004), adjusted for HDLC and traditional risk factors. CONCLUSIONS: Remnant cholesterol levels are associated with significant coronary atherosclerotic burden as assessed by CTCA, even in patients with optimal LDLC levels. Future studies examining whether lowering of remnant cholesterol can reduce residual IHD risk are warranted.