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. Author manuscript; available in PMC: 2023 Mar 1.
Published in final edited form as: Heart. 2023 Feb 23;109(6):485–493. doi: 10.1136/heartjnl-2022-321158

Pericoronary Adipose Tissue Computed Tomography Attenuation in Coronary Artery Plaque Inflammation

Xinming Yu 1,a, Simona Botezatu 1,2,a, Evangelos Tzolos 1, Damini Dey 3,b,†, Jacek Kwiecinski 4,b
PMCID: PMC9974857  NIHMSID: NIHMS1863220  PMID: 36627185

INTRODUCTION

Coronary artery disease (CAD) is associated with significant morbidity and mortality.[1] Despite the improving trend of mortality in a number of European countries, cardiovascular disease remains the leading cause of death in Europe, accounting for over 4 million deaths each year.[1] Therefore, there is a great need for new tools that are able to predict adverse outcomes and stratify risk in CAD.

Novel non-invasive imaging methods to quantify plaque inflammation, which plays an important role in coronary atherosclerosis, may prove of value in the identification of early disease as well as high-risk patients that may benefit from targeted therapy.[2] This is of particular importance given the recent promise demonstrated by anti-inflammatory therapy in randomised controlled trials.[3] The ability to reliably measure inflammatory activity in CAD may well prove crucial in targeting the powerful treatments to the right patient at the right time. In current clinical practice, inflammation can either be depicted with advanced imaging or characterised with blood biomarkers. While the latter lacks specificity for CAD, novel advanced cardiac imaging approaches showed promise in this regard.

Measurement of Pericoronary Adipose Tissue (PCAT) attenuation on computed tomography (CT) is a recently developed imaging technique that evaluates coronary artery inflammatory activity on routinely acquired coronary CT angiograms (CCTAs). In this review, we will first describe the pathophysiological mechanisms underlying coronary vascular inflammation and how these might modify the surrounding coronary perivascular fat. We will then present the current PCAT CT attenuation literature before finally describing potential future clinical perspectives.

PATHOPHYSIOLOGY

Vascular inflammation plays a key role at almost every stage of the atherosclerotic process, from early disease development to plaque rupture and the development of clinical events.[4,5] Vascular inflammation is initiated by mechanical stress to the endothelium and is facilitated by the presence of elevated blood low-density lipoprotein levels. In response to these stresses, the vascular endothelium expresses Vascular Cell Adhesion Molecule-1 to recruit monocytes and T-cells. These immune cells are then attracted to the tunica media layer of the vessel by chemoattractants and release pro-inflammatory cytokines such as Tumour Necrosis Factor-α, Interleukin-1, Interleukin-6 and Interferon-γ. Monocytes differentiate into macrophages, which in turn take up oxidised low-density lipoproteins to form foam cells. This process, in conjunction with smooth muscle proliferation, contributes to the development of the atheromatous plaque.[4]

In addition to plaque formation, inflammation is also involved in plaque rupture [5] and subsequent myocardial infarction. According to pathological studies vulnerable coronary plaque, that are at risk of rupture, have large lipid cores filled with thrombogenic material and have thin overlying fibrous caps.[4] Inflammation disturbs the integrity of the fibrous cap by inhibiting the synthesis of interstitial collagen and by eroding the cap through collagenases that are produced by activated inflammatory cells. When the fibrous cap ruptures, the thrombogenic material in the lipid-rich core becomes exposed, providing a substrate for thrombus formation which leads to subsequent acute coronary syndrome.[4]

At each stage of plaque progression and rupture, there appears to be an important bidirectional relationship between coronary artery inflammation and the surrounding adipose tissue (Figure 1).[2] Adipose tissue surrounding the coronary arteries can induce atherosclerotic changes in the adjacent coronary plaque, through local paracrine signalling. Additionally, adipose tissue can also influence more remote atherosclerotic plaque via endocrine signalling and circulating adipocytokines such as leptin, chemerin and resistin.[6] Perivascular adipose tissue promotes the initiation and progression of atherosclerosis, in what has been referred to as an ‘outside-to-inside’ signalling process.[7] Recent studies have also demonstrated ‘inside-to-outside’ signalling, where coronary artery inflammation induced changes in the phenotype of the adjacent fat.[2] Antonopoulos et al showed that the adipocyte phenotype, in perivascular adipose tissue of aortic explants, changed in response to an inflammatory stimulus that was mediated by interleukin-6, tumour necrosis factor-α and interferon-γ. The shift in adipocyte tissue lipid content leads to a detectable change in CT attenuation which can be used as an imaging biomarker for PCAT inflammation.[2]

Figure 1. The bi-directional interaction between atherosclerosis and perivascular adipose tissue.

Figure 1.

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The ‘outside-to-inside’ signalling process refers to perivascular adipose tissue inducing atherosclerosis in nearby and remote arteries. The process occurs through local paracrine and endocrine signalling and involves adipokines adiponectin, leptin and resistin.[6] These induce a number of changes in the vessel wall interstitium which ultimately contribute to atherosclerosis. In ‘inside-to-outside’ signalling, atherosclerotic coronary plaque release pro-inflammatory cytokines (Tumour Necrosis Factor-α, interleukin-6 and interferon-γ) are released from plaque.[2] These cytokines exert paracrine effects on the perivascular adipose tissue and cause phenotypic changes (lipolysis and suppression of adipogenesis) in the perivascular adipose tissue which alters the balance of the aqueous and lipid phases of the adipose tissue. This change can be detected on routine CCTA imaging. Artist credit: Kiran Slomka.

CURRENT ASSESSMENTS OF ATHEROSCLEROTIC INFLAMMATION

Blood biomarkers, including C-reactive protein (CRP), total white cell count and fibrinogen, are widely used to assess inflammatory activity. CRP is an acute-phase protein that is produced in the liver, in response to inflammation in conditions such as atherosclerosis.[8] Previous studies have demonstrated that CRP levels are associated with coronary inflammation and poor cardiovascular outcomes.[8] Other inflammatory blood biomarkers, such as total white cell count and Interleukin-6, have been associated with cardiac risk.[9] However, other conditions, such as infections, can also cause an increase in CRP and blood biomarkers, meaning that these tests lack specificity for CAD.[8] Specific assessment of coronary inflammation, however, can be achieved with advanced cardiac imaging.

Although advanced cardiovascular imaging primarily focuses on coronary arterial anatomy, myocardial infarction, myocardial perfusion, viability and function, beyond these capabilities imaging techniques such as positron emission tomography can also detect underlying coronary inflammation in CAD.[9] Molecular imaging techniques such as 18F-fluorodeoxyglucose positron emission tomography CT (18F-FDG PET CT), measure inflammatory activity through the uptake of radiotracers in cells with high metabolic activity. Macrophages have been observed to take up 18F-FDG, in the inflamed plaque, with the degree of uptake affected by factors such as hypoxia and the circulation time of the radiotracer.[10] However, 18F-FDG remains a non-specific marker of inflammation and the inflammatory signal in coronary vessels can be obscured by the intense uptake signal from the myocardium.[10] Even with complex dietary preparations, myocardial uptake can still occur, resulting in patchy uptake which obscures the coronary vessels.[11] Moreover, multiple studies have demonstrated 18F-FDG uptake in CAD, however, no studies have yet to report on its prognostic capabilities. Similar to 18F-FDG CT, PET CTs performed using the radiotracer 18F-Sodium Fluoride (18F-NaF) have shown promise in locating plaque and identifying features of vulnerable and culprit plaque, such as microcalcification and low attenuation plaque.[12] In patients with advanced CAD, 18F-NaF PET CT has also demonstrated value in predicting myocardial infarction and is therefore the only radiotracer with outcome implications.[13,14] In addition, the uptake signal of this radiotracer is not affected by myocardial uptake. The radiotracer itself, however, does not directly depict the inflammatory activity but instead binds with areas of developing calcifications which are abundant in inflamed, active and potentially unstable lesions. An alternative PET tracer targeting inflammation is Gallium-68-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid octreotate (68Ga-DOTATATE). It binds to Somatostatin Receptor Subtype 2, which is expressed on the surface of activated macrophages,[15] and has been shown to localise to inflamed coronary plaque. Like 18F-NaF PET CT, 68Ga-DOTATATE is not affected by myocardial uptake.[15] Unfortunately, the availability of PET CT scanners limits the widespread usage of molecular imaging in the clinical setting and, therefore, if coronary PET CT will become a clinical tool it will likely be only employed in carefully selected populations.[16]

ADIPOSE TISSUE ATTENUATION IN CORONARY ARTERY DISEASE

Based on the observed effects of coronary vascular wall inflammation on the lipid and aqueous content of the surrounding adipose tissue, CCTA has been repurposed for measuring PCAT CT attenuation. The density, or attenuation, of perivascular fat is measured by calculating the average attenuation of adipose tissue, measured in Hounsfield units (HU), within the circumferential volume of tissue surrounding the vessel of interest (Figure 2).[17] PCAT CT attenuation measurements take into account voxels that are within the normal HU reference ranges for adipose tissue, which is reported to be −190 to −30 HU per voxel.[17] Higher PCAT CT attenuation values indicate increased water content in the adipose tissue and consequently increased coronary artery inflammatory activity.[2] For each vessel, several measurements can also be made at increasing radial distance from the target vessel, providing information on the spatial distribution of the inflammatory process. There are several different methods of measuring PCAT CT attenuation. Simple methods include a crude measurement within the defined volume of interest [17] whilst more advanced approaches, such as the Fat Attenuation Index (FAI), adjust PCAT CT attenuation values for clinical parameters.[2] The Fat Radiomic Profile has also incorporated PCAT CT attenuation values along with radiomic features and machine learning.[18]

Figure 2. PCAT CT attenuation analysis for the right coronary artery (A-C), left anterior descending artery (D-F) and the left circumflex artery (G-I).

Figure 2.

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In this figure, PCAT CT attenuation measurements are taken from perivascular adipose tissue surrounding the right coronary artery, left anterior descending artery and left circumflex artery. Areas of higher CT attenuation are represented in red and areas of lower CT attenuation are represented in yellow. PCAT CT attenuation is calculated as the average attenuation, between −190 HU and −30 HU, within a radial distance from the vessel wall equal to the vessel diameter. The attenuation of perivascular adipose tissue provides information on the level of inflammation within the adjacent coronary artery segment.

Observational Studies

This novel non-invasive imaging biomarker has been characterised in CAD and has shown to be associated with CAD activity.[2,17,19,20] Initial studies have shown that coronary arteries with plaque had higher PCAT CT attenuation than healthy coronary arteries.[2] Antonopoulos et al measured the FAI, an imaging biomarker that uses PCAT CT attenuation in the proximal right coronary artery (RCA), in 273 patients and found that there were significant differences in the FAI of patients with and without obstructive disease and between patients with unstable and stable coronary plaque.[2] In patients, who recently had a myocardial infarction and subsequent stent insertion, the FAI was 8.76 ± 2.87 HU higher around the culprit plaque, compared to remote non-culprit plaques in the same patients. The FAI of culprit plaque was also significantly higher than obstructive lesions that were previously stented.[2] Similarly, Goeller et al have found that PCAT CT attenuation was significantly higher around culprit compared to stable plaque (Figure 3).[17] The group has also demonstrated that PCAT CT attenuation is associated with the progression of non-calcified and total plaque burden but not with the calcified plaque burden.[19] This suggests that by measuring PCAT it is feasible to differentiate disease progression, in the form of non-calcified plaque build-up, from a healing process such as calcification of the plaque. In an analysis of 111 stable patients, it was found that RCA PCAT CT attenuation was significantly increased in patients who subsequently demonstrated an increase in their non-calcific plaque burden (4.4 [95% confidence interval (CI) 2.6 to 6.2]) and was decreased in patients who demonstrated non-calcific plaque regression (−2.78 [95% CI −4.6 to −1.0]).[19] The above studies have shown that the PCAT CT attenuation was associated with the coronary inflammatory activity, disease burden and recent plaque rupture.

Figure 3. Visualisation of right coronary artery PCAT CT attenuation in a patient who had a non-ST elevation myocardial infarction during follow-up (A-C) and a patient with an uneventful follow-up (D-F).

Figure 3.

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The above case (A-C) was obtained from a patient in his 50’s who developed a non-ST elevation myocardial infarction 5 years after the baseline scan date. The second case (D-F) was obtained from a patient in his 60’s with an uneventful follow-up. In both cases, PCAT CT attenuation analysis was performed for the RCA. Areas of higher CT attenuation are represented in colour scale (higher attenuation in red and lower CT attenuation in yellow). In the above case of myocardial infarction, there were more areas of inflammation (red) visually than in the case below. The PCAT CT attenuation value of the case with myocardial infarction was higher than in the stable patient (−66.5 HU vs −79.4 HU) which suggested there was evidence of coronary inflammation at baseline. This was in line with previous studies which correlated higher PCAT CT attenuation with an increased risk of myocardial infarction.[2,17] HU: Hounsfield Units; MI: myocardial infarction; PCAT: pericoronary adipose tissue; RCA: right coronary artery.

PCAT CT attenuation has also been shown to correlate with other imaging parameters that are associated with plaque vulnerability and vascular dysfunction. Indeed, PCAT CT attenuation correlates with 18F-NaF uptake- a marker of coronary atherosclerotic disease activity.[20] In a multimodality imaging study (Figure 4), PCAT CT attenuation was higher in the patient group with high 18F-NaF uptake compared to the low uptake group (−73 HU [IQR −79 to −68 HU] vs −86 HU [IQR −94 to −80], p< 0.001). In addition to 18F-NaF, in a recent study PCAT CT attenuation was also associated with 68Ga-DOTATATE uptake. It was shown that for each unit increase in 68Ga-DOTATATE maximum target-to-background ratio, PCAT CT attenuation increased by 2.35 HU (SE ± 0.77 HU, p=0.003).[15] Interestingly, there is also evidence to suggest that PCAT CT attenuation is associated with coronary microvascular dysfunction. Studies have shown that PCAT CT attenuation is associated with markers of microvascular dysfunction such as fractional flow reserve.[21] These studies, therefore, demonstrate that PCAT CT attenuation correlates with other imaging measures of plaque vulnerability, inflammatory activity and vascular dysfunction.

Figure 4. Case illustrations of CCTA, 18F-NaF uptake and PCAT CT attenuation analysis in high-risk coronary plaques.

Figure 4.

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Patient 1: (A) A male in his early 50’s with a RCA plaque with positive remodelling (green arrow), (B) focal 18F-NaF uptake with an increased TBR of 1.73, and (C) increased PCAT CT attenuation (−76.7 HU). Patient 2: (D) A male in his 60’s with a LAD lesion with low attenuation plaque (green arrow), (E) focal 18F-NaF uptake with an increased TBR of 1.87, and (F) increased PCAT CT attenuation (−74.8 HU). Patient 3: (G) A male in his 50’s with a LAD lesion with low attenuation plaque (green arrow), (H) focal 18F-NaF uptake with an increased TBR of 2.28, and (I) increased PCAT CT attenuation (−73.6 HU). Figure 4 is reproduced with permission from Kwiecinski et al 2019, figure 2.[20]1 18F-NaF: 18F-sodium fluoride; CT: computed tomography; HU: Hounsfield Units; LAD: left anterior descending artery; PCAT: pericoronary adipose tissue; RCA: right coronary artery; TBR: target-to-background ratio.

Prognostic Data

PCAT CT attenuation has been correlated to cardiac mortality and has prognostic value across the spectrum of CAD manifestations.[2,17,19,20,22] In the Cardiovascular Risk Prediction using Computed Tomography (CRISP-CT) study, RCA FAI on CCTA was found to be predictive of cardiac mortality (hazard ratio (HR) 2.06 [95% CI 1.50 to 2.83], p<0.0001).[22] This study also found that the FAI for the RCA, left anterior descending artery (LAD) and left circumflex artery (LCx) was associated with all-cause mortality and that, interestingly, only the RCA and LAD FAI was significantly associated with cardiac mortality. The authors report that this discrepancy could be due to culprit lesions being located mostly in the RCA and LAD. The study also found a significant increase in cardiac mortality when the RCA FAI attenuation increases beyond a threshold of −70.1 HU (HR 5.62 [95% CI 2.90 to 10.88], p< 0.0001).

More recently, our group has confirmed that the RCA PCAT CT attenuation can identify patients who are at an increased risk of myocardial infarction.[23] In a post-hoc analysis of the multicenter SCOT-HEART trial (n=1697), we investigated the relationships between the future risk of fatal or non-fatal myocardial infarction and PCAT CT attenuation. On univariable analysis, RCA PCAT CT attenuation was a predictor of myocardial infarction (HR 1.55, 95% CI 1.08 to 2.22, p=0.017), with an optimum threshold of −70.5 HU (HR 2.45, p=0.01), however, LAD and LCx PCAT CT attenuation did not have similar prognostic implications. Using the same cohort, we have previously reported that low-attenuation plaque burden is the strongest independent predictor of myocardial infarction, outperforming risk scores and other measures of CAD severity including stenosis severity and coronary calcium score.[23] In our study, we established the complementary predictive value of low-attenuation plaque burden and PCAT CT attenuation for 5-year risk of fatal or non-fatal myocardial infarction (Figure 5). Multivariable analysis found PCAT CT attenuation and low-attenuation plaque to be the only independent variables that were predictive for future myocardial infarction. Analysis based on the Youden index of the receiver operating characteristic curves found that adding RCA PCAT ≥−70.5 HU to low-attenuation plaque burden >4% (a previous published optimum threshold for future events; HR 4.87, p<0.0001) led to improved prediction of myocardial infarction (HR 11.7, p<0.0001).[23]

Figure 5. Complementary predictive value of PCAT CT attenuation and low-attenuation plaque burden.

Figure 5.

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(A) Out of the analysed variables, low-attenuation plaque burden and PCAT were the only independent predictors of fatal or nonfatal myocardial infarction in multivariable analysis. (B) Receiver operating curve analysis, combining PCAT CT attenuation and low-attenuation plaque burden (area under the curve 0.75 [IQR 0.65 - 0.85]) had superior myocardial infarction risk prediction than either PCAT CT attenuation (0.64 [IQR 0.54 - 0.74], p<0.0001) or low-attenuation plaque burden alone (area under the curve 0.71 [IQR 0.62 - 0.80], p=0.037). (C) Patients with a low-attenuation plaque burden >4% and (D) RCA PCAT CT attenuation ≥–70.5 HU have a significantly increased risk of future myocardial infarction. Figure 5 is reproduced with permission from Tzolos et al 2022, central illustration.[23]2 CAD: coronary artery disease; HU: Hounsfield units; LAP: low attenuation plaque; PCAT: pericoronary adipose tissue; RCA: right coronary artery.

Besides measuring PCAT attenuation in Hounsfield units, the fat surrounding coronary vessels can be also phenotyped using radiomics analysis. Lin et al demonstrated that by leveraging machine learning to combine information provided by radiomic analysis, it is feasible to further enhance the predictive value of PCAT (Figure 6).[5] In the study, large amounts of quantitative imaging parameters, or radiomic phenotypes, were extracted from the PCAT CT attenuation of patients with myocardial infarction. The authors showed that the radiomic phenotypes of patients with myocardial infarction differed significantly from stable CAD and control patients, with a 20.3% and 16.5% difference respectively (p<0.0006). When analysed with machine learning, radiomic phenotypes, PCAT CT attenuation and clinical features demonstrated superior risk prediction to PCAT CT attenuation alone. The receiver operating characteristic area under the curve, a measure of a machine learning model’s performance, for the combined machine learning model and PCAT CT attenuation alone was 0.87 and 0.77 respectively (p=0.001).

Figure 6. (A) Radiomic phenotypes of PCAT in acute myocardial infarction, (B) PCAT analysis and (C) PCAT based machine learning model performance.

Figure 6.

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(A) Manhattan plots of p-values for pairwise comparisons of all radiomic parameters among the three cohorts: acute myocardial infarction against controls, acute myocardial infarction and stable CAD and stable CAD and controls. The negative logarithm of p-values are plotted on the y axis for each radiomic parameter (colour-coded by category) lined up on the x axis. The blue horizontal line indicates the Bonferroni-corrected p-value of 0.0006, and the parameters above the line were considered statistically significant. (B) PCAT CT attenuation analysis for the right coronary artery. (C) Receiver operator characteristic curves of the three machine learning models for identifying patients with myocardial infarction. The area under the curve represents the performance of machine models and compares machine learning models trained using: clinical features, PCAT CT attenuation and radiomic patterns (blue line), clinical features and PCAT CT attenuation (red line) and clinical features alone (black line). Figure 6 is reproduced with permission from Lin et al 2020, central illustration. [5]3 CCTA: coronary computed tomography angiography PCAT: pericoronary adipose tissue; RCA: right coronary artery.

Oikonomou et al utilised 1391 PCAT radiomic features to train a machine learning model, the Fat Radiomic Profile (FRP), to identify major adverse cardiac events from standard control CCTA images and further enhance the predictive capabilities of the FAI.[18] The Fat Radiomic Profile was able to identify patients who experienced major adverse cardiac events with a C-statistic of 0.77 (95% CI 0.62 to 0.93) in 101 patients. The predictive capabilities of the Fat Radiomic Profile were then assessed using 1585 patients from the SCOT-HEART trial. The Fat Radiomic Profile was found to be positively associated with major adverse cardiac events, with an incremental hazard ratio increase of 1.12 (95% CI 1.08 to 1.15) per 0.01 increase in the Fat Radiomic Profile. The Fat Radiomic Profile was also more effective at predicting major adverse cardiac events than traditional risk factors such as coronary calcium score, coronary stenosis and presence of high-risk plaque, with a difference in C-statistic of 0.126 (p<0.001). Furthermore, there was a 10.8 higher risk of major adverse cardiac events in patients with a high Fat Radiomic Profile compared to those with a low Fat Radiomic Profile, as defined by a threshold of 0.63. The above studies suggest that machine learning models, based on PCAT CT attenuation analysis, can potentially further augment risk prediction beyond that of PCAT CT attenuation and traditional risk factors.

Response to Therapy

Recent studies have shown that PCAT CT attenuation was able to track CAD response to anti-inflammatory therapeutic agents such as biologic therapy and statins. In a prospective cohort study, Elnabawi et al examined the FAI in the proximal RCA of 134 patients with moderate to severe psoriasis. In this study, the group has shown that FAI decreased in psoriatic patients that were treated with anti-inflammatory biologic medication.[24] Importantly, these favourable changes in the FAI were independent of coronary plaque and the type of biologic therapy received. A study that investigated the effect of statin treatment on the FAI revealed that the FAI of non-calcified plaque and mixed plaque was also significantly reduced following statin treatment.[25] In 180 patients with chest pain and intermediate risk of CAD, it was found that the mean FAI for non-calcified and mixed plaque was reduced from −68.0 ± 8.5 HU to −71.5 ± 8.1 HU (p<0.001) and −70.5 ± 8.9 HU to −72.8 ± 9.0 HU (p=0.014) respectively. The FAI for calcified plaque was not significantly changed on follow-up (−70.6 ± 9.7 HU to −71.7 ± 9.9 HU, p=0.258). The above studies suggest that the FAI is able to capture information about coronary inflammation and CAD activity, which can help with risk stratification in early CAD and identify patients that have active disease that would potentially benefit from targeted treatment.

ADVANTAGES AND DISADVANTAGES OF PCAT CT ATTENUATION

Perivascular adipose tissue CT attenuation analysis is a flexible image analysis technique and is applicable to conditions other than CAD that involve vascular inflammation. Studies have investigated the association of perivascular adipose tissue CT attenuation with the progression of abdominal aortic aneurysms, thoracic aneurysms and stroke.[26] These studies suggest that PCAT analysis can complement existing biomarkers and provide added prognostic value in different conditions. In addition to being applicable to different conditions, perivascular adipose tissue CT attenuation has a number of other advantages that makes it an attractive imaging target.

PCAT CT attenuation is a non-invasive imaging biomarker that can provide insight into the inflammatory activity of coronary atherosclerosis and offer several advantages over other imaging modalities. PCAT CT attenuation analysis can be performed on standard CCTAs without extra costs or radiation. It is therefore more readily available compared to other imaging modalities, such as PET CT which is limited by the availability of scanners.[16,17] Between observers, PCAT CT attenuation has been shown to have excellent inter-observer and intra-observer reproducibility.[27] PCAT CT attenuation is, therefore, a readily available, non-invasive, and reproducible image analysis technique that can offer information on coronary artery inflammatory activity.

While undoubtfully promising, PCAT CT attenuation analysis requires further validation before clinical application.[2] Despite the fact that perivascular adipose tissue CT attenuation analysis can be measured on CCTAs, the time-consuming nature of analysis should be considered when applying this technique to clinical practice.[16] PCAT CT attenuation is derived from the attenuation of adipose tissue on CCTA images. The measured adipose tissue attenuation has been shown to be affected by the tube voltage setting of CT scanners and so PCAT CT attenuation values may require adjustment according to tube voltage.[28] Furthermore, PCAT CT attenuation has been shown to vary significantly between individuals, even in the absence of CAD.[2] To date, PCAT CT attenuation’s efficacy for risk stratification was shown primarily in patients with suspected CAD. It remains unclear whether PCAT CT attenuation analysis would perform equally well in patients with established CAD. In particular among subjects who underwent percutaneous revascularisation the ability to derive clinically meaningful results might be hampered. In patients who underwent percutaneous revascularisation, other imaging modalities may prove more suitable.[29] Finally, the relatively small difference in PCAT CT attenuation between control patients and those who experienced adverse events can translate into difficulties in decision making at the point of care.

FUTURE PERSPECTIVES

PCAT CT attenuation analysis can detect culprit plaques and is a marker of coronary inflammation. Studies published to date suggest that it can be used to identify patients at high risk of coronary events and so identify those who would benefit from primary or secondary prevention therapies. However, this imaging technique has yet to be validated in the clinical setting.[2,22] The number of studies that investigated PCAT CT attenuation’s ability to track response to anti-inflammatory treatments is limited and therefore in the future, further efforts should be made to address this important clinical application.

To date, most studies have focussed on analysing the adipose tissue attenuation around the coronary arteries. There is a paucity of studies on perivascular adipose tissue attenuation in other conditions. A recent systematic review has only identified four studies that investigated perivascular adipose tissue attenuation beyond the coronary vessels.[26] These studies explored the correlation between perivascular adipose tissue attenuation and the progression of aortic aneurysms and stroke.[26] Given the potential for perivascular adipose tissue attenuation to detect vascular inflammation in CAD, it would be interesting to explore other conditions where inflammation plays a key role in the disease pathophysiology.

CONCLUSION

PCAT CT attenuation has been shown to correlate with CAD activity, plaque burden and plaque rupture. Previous studies have demonstrated that PCAT CT attenuation is able to measure inflammatory activity in CAD by analysing the perivascular adipose tissue phenotype. High PCAT CT attenuation has demonstrated a significant association with all-cause and cardiac mortality. There is promising evidence to suggest that PCAT CT attenuation can be used to identify candidates for target primary prevention, risk stratification and aid in the development of anti-inflammatory treatments for CAD. Further studies are required, to validate PCAT CT as a reliable tool for monitoring inflammation in response to anti-inflammatory treatments and ultimately to facilitate widespread use in the clinical setting.

Supplementary Material

Supp1

LEARNING OBJECTIVES.

The bidirectional relationship between coronary vessel inflammation and the pericoronary adipose tissue.

The use of pericoronary adipose tissue computed tomography attenuation to measure coronary artery inflammation.

The current literature on the use of pericoronary adipose tissue computed tomography attenuation in coronary artery disease.

The strengths and limitations of this image analysis technique and future perspectives.

KEY MESSAGES.

Inflammation plays a key role in the pathogenesis of coronary artery disease.

There is bi-directional local signalling between the inflamed coronary plaque and pericoronary adipose tissue.

Measuring inflammatory activity in coronary artery disease can provide important prognostic information and aid in the development of anti-inflammatory therapeutics.

Pericoronary adipose tissue analysis is performed on computed tomography images and is a non-invasive marker of coronary artery inflammation.

Pericoronary adipose tissue computed tomography attenuation is associated with coronary artery disease burden and is a predictor of disease progression and outcomes.

Perivascular adipose tissue analysis can also be applied to other conditions involving vascular inflammation.

Funding:

Simona Botezatu is supported by a Romanian Society of Cardiology Research Grant (contract no 266/8.06.2021). Damini Dey is supported by National Institutes of Health/National Heart, Lung, and Blood Institute grants 1R01HL148787-01A1 and 1R01HL151266.

Footnotes

Competing interests:

None declared.

Patient and public involvement:

Patients and the public were not involved in the design, conduct or reporting of this study.

1

This article was published in JACC: Cardiovascular Imaging, Vol 12, Kwiecinski et al, Peri-Coronary Adipose Tissue Density Is Associated With 18F-Sodium Fluoride Coronary Uptake in Stable Patients With High-Risk Plaques, Page Nos 2000 - 2010, Copyright Elsevier (2019).

2

This article was published in JACC: Cardiovascular Imaging, Vol 15, Tzolos et al, Pericoronary Adipose Tissue Attenuation, Low-Attenuation Plaque Burden, and 5-Year Risk of Myocardial Infarction, Page Nos 1078 - 1088, Copyright Elsevier (2022).

3

This article was published in JACC: Cardiovascular Imaging, Vol 13, Lin et al, Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study, Page Nos 2371 - 2383, Copyright Elsevier (2020).

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