Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis

Kroll, Lennard; Naßenstein, Kai; Jochims, Markus; Koitka, Sven; Nensa, Felix

doi:10.3390/jcm10020356

Cited by 23 publications

(25 citation statements)

References 35 publications

(65 reference statements)

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“… 10 Similar to our results that showed that an increase in M2 macrophage density was correlated with an increase in plaque size and calcification, other imaging studies found an association between PvAT volume and coronary artery calcification. 25 , 26 , 27 Moreover, macrophage infiltration and neoangiogenesis in the epicardial adipose tissue were associated with mild coronary calcification; however, macrophage polarization was not assessed. 28 In atherosclerotic plaques, M2 macrophages seem to play a role in inflammation resolution through macrocalcification.…”

Section: Discussionmentioning

confidence: 99%

Macrophage Polarization in the Perivascular Fat Was Associated With Coronary Atherosclerosis

Farias‐Itao

Pasqualucci

Andrade

et al. 2022

JAHA

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Background Inflammation of the perivascular adipose tissue (PvAT) may be related to atherosclerosis; however, the association of polarized macrophages in the pericoronary PvAT with measurements of atherosclerosis components in humans has not been fully investigated. Methods and Results Coronary arteries were dissected with surrounding PvAT. We evaluated the percentage of arterial obstruction, intima‐media thickness, fibrous cap thickness, plaque components, and the number of vasa vasorum. The number of proinflammatory (M1) and anti‐inflammatory (M2) macrophages in the periplaque and control PvAT were evaluated using immunohistochemistry. Regression models adjusted for sociodemographic and clinical variables were used. In 319 segments from 82 individuals, we found a correlation of the M1/M2 macrophage density ratio with an increase in arterial obstruction ( P =0.02) and lipid content ( P =0.01), and a decrease in smooth muscle cells ( P =0.02). M1 and the ratio of M1/M2 macrophages were associated with an increased risk of thrombosis ( P =0.03). In plaques with thrombosis, M1 macrophages were correlated with a decrease in fibrous cap thickness ( P =0.006), an increase in lipid content ( P =0.008), and the number of vasa vasorum in the adventitia layer ( P =0.001). M2 macrophages were correlated with increased arterial obstruction ( P =0.01), calcification ( P =0.02), necrosis ( P =0.03) only in plaques without thrombosis, and decrease of the number of vasa vasorum in plaques with thrombosis ( P =0.003). Conclusions M1 macrophages in the periplaque PvAT were associated with a higher risk of coronary thrombosis and were correlated with histological components of plaque progression and destabilization. M2 macrophages were correlated with plaque size, calcification, necrotic content, and a decrease in the number of vasa vasorum in the adventitia layer.

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Section: Discussionmentioning

confidence: 99%

Macrophage Polarization in the Perivascular Fat Was Associated With Coronary Atherosclerosis

Farias‐Itao

Pasqualucci

Andrade

et al. 2022

JAHA

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“…Additional determination of PAT might, therefore, be a useful tool to improve risk stratification in particular, as PAT volume can be assessed easily from data sets acquired for calcium screening. It has already been shown before that PAT combined with CAC might improve risk stratification for future cardiovascular events [ 20 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

Increased Pericardial Adipose Tissue in Smokers

Zimmermann

Ruether

Ziegler

et al. 2021

JCM

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Background: Pericardial adipose tissue (PAT), a visceral fat depot directly located to the heart, is associated with atherosclerotic and inflammatory processes. The extent of PAT is related to the prevalence of coronary heart disease and might be used for cardiovascular risk prediction. This study aimed to determine the effect of smoking on the extent of PAT. Methods: We retrospectively examined 1217 asymptomatic patients (490 females, age 58.3 ± 8.3 years, smoker n = 573, non-smoker n = 644) with a multislice CT scanner and determined the PAT volume. Coronary risk factors were determined at inclusion, and a multivariate analysis was performed to evaluate the influence of smoking on PAT independent from accompanying risk factors. Results: The mean PAT volume was 215 ± 107 mL in all patients. The PAT volume in smokers was significantly higher compared to PAT volume in non-smokers (231 ± 104 mL vs. 201 ± 99 mL, p = 0.03). Patients without cardiovascular risk factors showed a significantly lower PAT volume (153 ± 155 mL, p < 0.05) compared to patients with more than 1 risk factor. Odds ratio was 2.92 [2.31, 3.61; p < 0.001] for elevated PAT in smokers. Conclusion: PAT as an individual marker of atherosclerotic activity and inflammatory burden was elevated in smokers. The finding was independent from metabolic risk factors and might therefore illustrate the increased inflammatory activity in smokers in comparison to non-smokers.

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“…No cut-off scores were identified in EAT and pericardial adipose tissue features for high-risk coronary artery disease (46).…”

Section: Ai: Epicardial and Pericardial Adipose Tissuesmentioning

confidence: 99%

“…Kroll et al examined a method for fully automatic quantification of epicardial and paracardial adipose tissues for possible use in cardiovascular risk assessment (46). A deep learning network was used for quantification of adipose tissue from coronary CT in 966 patients with intermediate Framingham risk scores.…”

Section: Ai: Epicardial and Pericardial Adipose Tissuesmentioning

confidence: 99%

Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review

Greco¹,

Salgado²,

Hecke³

et al. 2022

Quant Imaging Med Surg

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The present review summarizes the available evidence on artificial intelligence (AI) algorithms aimed to the segmentation of epicardial and pericardial adipose tissues on computed tomography (CT) images. Body composition imaging is a novel concept based on quantitative analysis of body tissues. Manual segmentation of medical images allows to obtain quantitative and qualitative data on several tissues including epicardial and pericardial fat. However, since manual segmentation requires a considerable amount of time, the analysis of adipose tissue compartments based on AI has been proposed as an automatic, reliable, accurate and fast tool. The literature research was performed on March 2021 using MEDLINE PubMed Central and "adipose tissue artificial intelligence", "adipose tissue deep learning" or "adipose tissue machine learning" as keywords for articles search. Relevant articles concerning epicardial adipose tissue, pericardial adipose tissue and AI were selected. The evaluation of adipose tissue compartments can provide additional information on the pathogenesis and prognosis of several diseases, including cardiovascular. AI can assist physicians to obtain important information, possibly improving the patient's quality of life and identifying patients at risk of developing variable disorders.

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Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis

Cited by 23 publications

References 35 publications

Macrophage Polarization in the Perivascular Fat Was Associated With Coronary Atherosclerosis

Macrophage Polarization in the Perivascular Fat Was Associated With Coronary Atherosclerosis

Increased Pericardial Adipose Tissue in Smokers

Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review

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