Epicardial adipose tissue density and volume are related to subclinical atherosclerosis, inflammation and major adverse cardiac events in asymptomatic subjects

Epicardial fat modifies the relationship between coronary calcium score and all-cause mortality: The St. Francis Heart Study

Objective

Epicardial fat is associated with cardiovascular risk factors and adverse outcomes. However, it is not clear if epicardial fat remains to be a mortality risk when coronary calcium score (CAC) is taken into account.

Methods

We studied the 1005 participants from the St. Francis Heart Study who were apparently healthy with CAC scores at 80th percentile or higher for age and gender, randomly assigned to placebo or statin therapy. At baseline, lipid profiles and non-contrast CT images were obtained where the epicardial fat volume was analyzed. Likelihood ratio testing was used to assess the additional prognostic value of epicardial fat to CAC for the risk of all-cause mortality.

Results

Increased epicardial fat volume was associated with higher CAC. For each unit increase in lnCAC, the average epicardial fat volume increased by 3.34 mL/m2. After a mean follow-up period of 17 years, 179 (18%) participants died. Increased epicardial fat volume was associated with an adjusted hazard ratio of 1.11 (95% CI: 1.02 to 1.20) predicting all-cause mortality. In the stratified analysis testing strata of epicardial fat and CAC, those with increased epicardial fat and increased CAC had the highest risk of death. Compared with a model containing lnCAC and traditional risk factors, a model additionally containing epicardial fat volume yielded a better model fit (likelihood ratio test p < 0.001).

Conclusion

Increased epicardial fat volume is associated with increased all-cause mortality risk. In addition, it portends incremental prognostic value to CAC score in mortality prediction.

Epicardial adipose tissue dispersion at CT and recurrent atrial fibrillation after pulmonary vein isolation

OBJECTIVES

Epicardial adipose tissue (EAT) remodeling is associated with atrial fibrillation (AF). Left atrial (LA) EAT dispersion on cardiac CT is a non-invasive imaging biomarker reflecting EAT heterogeneity. We aimed to investigate the association of LA EAT dispersion with AF recurrence after pulmonary vein isolation (PVI).

METHODS

In a prospective registry of consecutive patients undergoing first PVI, mean EAT attenuation values were measured on contrast-enhanced cardiac CT scans in Hounsfield units (HU) within low (- 195 to - 45 HU) and high (- 44 to - 15 HU) threshold EAT compartments around the left atrium (LA). EAT dispersion was defined as the difference between the mean HU values within the two EAT compartments. Continuous variables were compared between groups using the Mann-Whitney U test and cox proportional hazard models were used to calculate hazard ratios of predictors of 1-year AF recurrence.

RESULTS

A total of 208 patients were included, 135 with paroxysmal AF and 73 with persistent AF. LA EAT dispersion was significantly larger in patients with persistent compared to paroxysmal AF (52.6 HU vs. 49.9 HU; p = 0.001). After 1 year of follow-up, LA EAT dispersion above the mean (> 50.8 HU) was associated with a higher risk of AF recurrence (HR 2.3, 95% CI 1.5-3.6; p < 0.001). It retained its predictive value when corrected for age, sex, body mass index, LA volume, and AF type (HR 2.8, 95% CI 1.6-4.6; p < 0.001).

CONCLUSION

A larger LA EAT dispersion on contrast-enhanced cardiac CT scans, reflecting EAT heterogeneity, is independently associated with AF recurrence after PVI.

CLINICAL RELEVANCE STATEMENT

Based on LA EAT dispersion assessment, a more accurate risk stratification and patient selection may be possible based on a pre-procedural cardiac CT when planning PVI.

KEY POINTS

• Epicardial adipose tissue (EAT) remodeling is associated with atrial fibrillation (AF). • A larger left atrial EAT dispersion in a pre-procedural cardiac CT was associated with a higher 1-year AF recurrence risk after pulmonary vein isolation. • A pre-procedural cardiac CT with left atrial EAT dispersion assessment may provide a more accurate risk stratification and patient selection for PVI.

Exploring the relationship between epicardial fat and coronary plaque burden and characteristics: insights from cardiac ct imaging

Epicardial adipose tissue (EAT) may enhance the risk of coronary artery disease (CAD). We investigated the relationship between EAT density (a maker of local inflammation) and coronary plaque characteristics in stable CAD patients. This study included 123 individuals who underwent coronary artery calcium scan and coronary CT angiography to evaluate CAD. Plaque characteristics were analyzed by semi-automated software (QAngio, Leiden, Netherlands). Non-contrast CT scans were used to measure EAT density (HU) and volume (cc) (Philips, Cleveland, OH). Multivariate regression models were used to evaluate the association of EAT density and volume with different plaque types. The mean (SD) age was 59.4±10.1 years, 53% were male, the mean (SD) EAT density was -77.2±4.6 HU and the volume was 118.5±41.2 cc. After adjustment for cardiovascular risk factors, EAT density was associated with fibrous fatty (FF) plaque (p<0.03). A 1 unit increase in HU was associated with a 7% higher FF plaque, and lower EAT density is independently associated to FF plaque. The association between EAT density and fibrous (p=0.08), and total noncalcified (p=0.09) plaque trended toward but did not reach significance. There was no association between EAT volume and any plaque type. These results suggest that inflammatory EAT may promote coronary atherosclerosis. Therefore, non-contrast cardiac CT evaluation of EAT quality can help better assess cardiovascular risk.

Combined conventional factors and the radiomics signature of coronary plaque texture could improve cardiac risk prediction

OBJECTIVES

This study aims to investigate how radiomics analysis can help understand the association between plaque texture, epicardial adipose tissue (EAT), and cardiovascular risk. Working with a Photon-counting CT, which exhibits enhanced feature stability, offers the potential to advance radiomics analysis and enable its integration into clinical routines.

METHODS

Coronary plaques were manually segmented in this retrospective, single-centre study and radiomic features were extracted using pyradiomics. The study population was divided into groups according to the presence of high-risk plaques (HRP), plaques with at least 50% stenosis, plaques with at least 70% stenosis, or triple-vessel disease. A combined group with patients exhibiting at least one of these risk factors was formed. Random forest feature selection identified differentiating features for the groups. EAT thickness and density were measured and compared with feature selection results.

RESULTS

A total number of 306 plaques from 61 patients (mean age 61 years +/- 8.85 [standard deviation], 13 female) were analysed. Plaques of patients with HRP features or relevant stenosis demonstrated a higher presence of texture heterogeneity through various radiomics features compared to patients with only an intermediate stenosis degree. While EAT thickness did not significantly differ, affected patients showed significantly higher mean densities in the 50%, HRP, and combined groups, and insignificantly higher densities in the 70% and triple-vessel groups.

CONCLUSION

The combination of a higher EAT density and a more heterogeneous plaque texture might offer an additional tool in identifying patients with an elevated risk of cardiovascular events.

CLINICAL RELEVANCE STATEMENT

Cardiovascular disease is the leading cause of mortality globally. Plaque composition and changes in the EAT are connected to cardiac risk. A better understanding of the interrelation of these risk indicators can lead to improved cardiac risk prediction.

KEY POINTS

Cardiac plaque composition and changes in the EAT are connected to cardiac risk. Higher EAT density and more heterogeneous plaque texture are related to traditional risk indicators. Radiomics texture analysis conducted on PCCT scans can help identify patients with elevated cardiac risk.

CT radiomic features reproducibility of virtual non-contrast series derived from photon-counting CCTA datasets using a novel calcium-preserving reconstruction algorithm compared with standard non-contrast series: focusing on epicardial adipose tissue

PURPOSE

We aimed to evaluate the reproducibility of computed tomography (CT) radiomic features (RFs) about Epicardial Adipose Tissue (EAT). The features derived from coronary photon-counting computed tomography (PCCT) angiography datasets using the PureCalcium (VNCPC) and conventional virtual non-contrast (VNCConv) algorithm were compared with true non-contrast (TNC) series.

METHODS

RFs of EAT from 52 patients who underwent PCCT were quantified using VNCPC, VNCConv, and TNC series. The agreement of EAT volume (EATV) and EAT density (EATD) was evaluated using Pearson's correlation coefficient and Bland-Altman analysis. A total of 1530 RFs were included. They are divided into 17 feature categories, each containing 90 RFs. The intraclass correlation coefficients (ICCs) and concordance correlation coefficients (CCCs) were calculated to assess the reproducibility of RFs. The cutoff value considered indicative of reproducible features was > 0.75.

RESULTS

the VNCPC and VNCConv tended to underestimate EATVs and overestimate EATDs. Both EATV and EATD of VNCPC series showed higher correlation and agreement with TNC than VNCConv series. All types of RFs from VNCPC series showed greater reproducibility than VNCConv series. Across all image filters, the Square filter exhibited the highest level of reproducibility (ICC = 67/90, 74.4%; CCC = 67/90, 74.4%). GLDM_GrayLevelNonUniformity feature had the highest reproducibility in the original image (ICC = 0.957, CCC = 0.958), exhibiting a high degree of reproducibility across all image filters.

CONCLUSION

The accuracy evaluation of EATV and EATD and the reproducibility of RFs from VNCPC series make it an excellent substitute for TNC series exceeding VNCConv series.

Synergistic Effects of Weight Loss and Catheter Ablation: Can microRNAs Serve as Predictive Biomarkers for the Prevention of Atrial Fibrillation Recurrence?

In atrial fibrillation (AF), multifactorial pathologic atrial alterations are manifested by structural and electrophysiological changes known as atrial remodeling. AF frequently develops in the context of underlying cardiac abnormalities. A critical mechanistic role played by atrial stretch is played by abnormal substrates in a number of conditions that predispose to AF, including obesity, heart failure, hypertension, and sleep apnea. The significant role of overweight and obesity in the development of AF is known; however, the differential effect of overweight, obesity, cardiovascular comorbidities, lifestyle, and other modifiable risk factors on the occurrence and recurrence of AF remains to be determined. Reverse remodeling of the atrial substrate and subsequent reduction in the AF burden by conversion into a typical sinus rhythm has been associated with weight loss through lifestyle changes or surgery. This makes it an essential pillar in the management of AF in obese patients. According to recently published research, microRNAs (miRs) may function as post-transcriptional regulators of genes involved in atrial remodeling, potentially contributing to the pathophysiology of AF. The focus of this review is on their modulation by both weight loss and catheter ablation interventions to counteract atrial remodeling in AF. Our analysis outlines the experimental and clinical evidence supporting the synergistic effects of weight loss and catheter ablation (CA) in reversing atrial electrical and structural remodeling in AF onset and in recurrent post-ablation AF by attenuating pro-thrombotic, pro-inflammatory, pro-fibrotic, arrhythmogenic, and male-sex-associated hypertrophic remodeling pathways. Furthermore, we discuss the promising role of miRs with prognostic potential as predictive biomarkers in guiding approaches to AF recurrence prevention.

Circulating adipose tissue proteins involved in atrial fibrillation: An explorative scoping review

Obesity increases the risk of atrial fibrillation (AF), potentially through proteins secreted by adipose tissue (AT) that affect atrial electrical and structural remodeling. We aim to give a comprehensive overview of circulating AT proteins involved in inflammation and fibrosis, that are associated with prevalent AF (paroxysmal or persistent) and the risk on developing new-onset AF. These include adipokines, defined as proteins enriched in AT as adiponectin, but also proteins less specific to AT. We systematically performed an explorative search for studies reporting associations between proteins secreted from cells residing in the AT and AF, and additionally assessed the effect of obesity on these proteins by a secondary search. The AT proteins involved in inflammation were mostly increased in patients with prevalent and new-onset AF, and with obesity, while the AT enriched adipokines were mostly not associated with AF. This review provides insight into circulating adipose tissue proteins involved in AF substrate formation.

Epicardial Adipose Tissue: a Potential Therapeutic Target for Cardiovascular Diseases

With increased ageing of the population, cardiovascular disease (CVD) has become the most important factor endangering human health worldwide. Although the treatment of CVD has become increasingly advanced, there are still a considerable number of patients with conditions that have not improved. According to the latest clinical guidelines of the European Cardiovascular Association, obesity has become an independent risk factor for CVD. Adipose tissue includes visceral adipose tissue and subcutaneous adipose tissue. Many previous studies have focused on subcutaneous adipose tissue, but visceral adipose tissue has been rarely studied. However, as a type of visceral adipose tissue, epicardial adipose tissue (EAT) has attracted the attention of researchers because of its unique anatomical and physiological characteristics. This review will systematically describe the physiological characteristics and evaluation methods of EAT and emphasize the important role and treatment measures of EAT in CVD.

PCSK9 plasma concentration is associated with epicardial adipose tissue volume and metabolic control in patients with type 1 diabetes

Patients with type 1 diabetes (T1D) have a greater risk of cardiovascular disease. Proconvertase subtilisin-kexin 9 (PCSK9) is involved in the atherosclerosis process. This study aimed to determine the relationship between PCSK9 levels and epicardial adipose tissue (EAT) volume and cardiometabolic variables in patients with T1D. This was an observational cross-sectional study including 73 patients with T1D. Clinical, biochemical and imaging data were collected. We divided the patients into two groups according to their glycemic control and the EAT index (iEAT) percentile. We performed a correlation analysis between the collected variables and PCSK9 levels; subsequently, we performed a multiple regression analysis with the significant parameters. The mean age was 47.6 ± 8.5 years, 58.9% were men, and the BMI was 26.9 ± 4.6 kg/m2. A total of 31.5%, 49.3% and 34.2% of patients had hypertension, dyslipidemia and smoking habit, respectively. The PCSK9 concentration was 0.37 ± 0.12 mg/L, which was greater in patients with worse glycemic control (HbA1c > 7.5%), dyslipidemia and high EAT volume (iEAT > 75th percentile). The PCSK9 concentration was positively correlated with age (r = 0.259; p = 0.027), HbA1c (r = 0.300; p = 0.011), insulin dose (r = 0.275; p = 0.020), VLDL-C level (r = 0.331; p = 0.004), TG level (r = 0.328; p = 0.005), and iEAT (r = 0.438; p < 0.001). Multiple regression analysis revealed that 25% of the PCSK9 variability was explained by iEAT and HbA1c (p < 0.05). The PCSK9 concentration is associated with metabolic syndrome parameters, poor glycemic control and increased EAT volume in patients with T1D.

The Role of Epicardial Adipose Tissue in Acute Coronary Syndromes, Post-Infarct Remodeling and Cardiac Regeneration

Epicardial adipose tissue (EAT) is a fat deposit surrounding the heart and located under the visceral layer of the pericardium. Due to its unique features, the contribution of EAT to the pathogenesis of cardiovascular and metabolic disorders is extensively studied. Especially, EAT can be associated with the onset and development of coronary artery disease, myocardial infarction and post-infarct heart failure which all are significant problems for public health. In this article, we focus on the mechanisms of how EAT impacts acute coronary syndromes. Particular emphasis was placed on the role of inflammation and adipokines secreted by EAT. Moreover, we present how EAT affects the remodeling of the heart following myocardial infarction. We further review the role of EAT as a source of stem cells for cardiac regeneration. In addition, we describe the imaging assessment of EAT, its prognostic value, and its correlation with the clinical characteristics of patients.

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers

BACKGROUND

Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium.

METHODS

We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9-12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke).

RESULTS

Here we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score.

CONCLUSIONS

Our findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.

Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment

Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.

Examining the effects of coronary artery disease- and mitochondrial biogenesis-related genes' and microRNAs' expression levels on metabolic disorders in epicardial adipose tissue

BACKGROUND AND AIMS

Epicardial adipose tissue (EAT) surrounds the heart and coronary arteries and is important for comprehending the pathogenesis of coronary artery disease (CAD). We aimed to evaluate the expressions of mitochondrial biogenesis- and CAD-related genes and miRNAs in EAT by comparing them to visceral adipose tissue (VAT) in CAD, diabetes, and obesity subgroups.

METHODS

In this study, a total of 93 individuals were recruited, and EAT samples (63 CAD; 30 non-CAD) and VAT samples from 65 individuals (46 CAD; 19 non-CAD) were collected. For further analysis, the study population was divided according to obesity and diabetes status. PRKAA1, PPARGC1A, SIRT1, RELA, TNFA, and miR-155-5p, let-7g-5p, miR-1247-5p, miR-326 expression levels were examined.

RESULTS

PRKAA1 and let-7g-5p were differentially expressed in EAT compared to VAT. TNFA expression was upregulated significantly in both tissues of CAD patients. In EAT, PRKAA1, PPARGC1A, and SIRT1 were downregulated with diabetes. Moreover, PPARGC1A expression is decreased under the condition of obesity in both tissues. EAT expressions of miR-1247-5p and miR-326 were downregulated with obesity, while miR-155-5p is decreased only in the VAT of obese. Also, miRNAs and genes were correlated with biochemical parameters and each other in EAT and VAT (p < 0.050).

CONCLUSIONS

The findings demonstrating distinct let-7g-5p and AMPKα1 mRNA expression between EAT and VAT underscores the importance of tissue-specific regulation in different clinical outcomes. In addition, the differential expressions of investigated genes and miRNAs highlight their responsiveness to obesity, DM, and CAD in adipose tissues.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion 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.

CT Images in Follicular Lymphoma: Changes after Treatment Are Predictive of Cardiac Toxicity in Patients Treated with Anthracycline-Based or R-B Regimens

The aim of this study is to evaluate changes in epicardial adipose tissue (EAT) and cardiac extracellular volume (ECV) in patients with follicular lymphoma (FL) treated with R-CHOP-like regimens or R-bendamustine. We included 80 patients with FL between the ages of 60 and 80 and, using computed tomography (CT) performed at onset and at the end of treatment, we assessed changes in EAT by measuring tissue density at the level of the cardiac apex, anterior interventricular sulcus and posterior interventricular sulcus of the heart. EAT is known to be associated with metabolic syndrome, increased calcium in the coronary arteries and therefore increased risk of coronary artery disease. We also evaluated changes in ECV, which can be used as an early imaging marker of cardiac fibrosis and thus myocardial damage. The R-CHOP-like regimen was associated with lower EAT values (p < 0.001), indicative of a less active metabolism and more adipose tissue, and an increase in ECV (p < 0.001). Furthermore, in patients treated with anthracyclines and steroids (R-CHOP-like) there is a greater decrease in ejection fraction (EF p < 0.001) than in the R-B group. EAT and ECV may represent early biomarkers of cardiological damage, and this may be considered, to our knowledge, the first study investigating radiological and cardiological parameters in patients with FL.

AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score

Background

Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE).

Objectives

Create novel, hand-crafted EAT features, "fat-omics", to capture the pathophysiology of EAT and improve MACE prediction.

Methods

We segmented EAT using a previously-validated deep learning method with optional manual correction. We extracted 148 radiomic features (morphological, spatial, and intensity) and used Cox elastic-net for feature reduction and prediction of MACE.

Results

Traditional fat features gave marginal prediction (EAT-volume/EAT-mean-HU/BMI gave C-index 0.53/0.55/0.57, respectively). Significant improvement was obtained with 15 fat-omics features (C-index=0.69, test set). High-risk features included volume-of-voxels-having-elevated-HU-[-50, -30-HU] and HU-negative-skewness, both of which assess high HU, which as been implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, while high-risk group having HR 2.4 times that of the low-risk group (P<0.001).

Conclusion

Preliminary findings indicate an opportunity to use more finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction.

Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images

COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (CT) imaging has remained the first line of diagnosis for individuals infected with SARS-CoV-2. However, differentiating COVID-19 from other types of pneumonia and predicting associated cardiovascular complications from the same chest-CT images have remained challenging. In this study, we have first used transfer learning method to distinguish COVID-19 from other pneumonia and healthy cases with 99.2% accuracy. Next, we have developed another CNN-based deep learning approach to automatically predict the risk of cardiovascular disease (CVD) in COVID-19 patients compared to the normal subjects with 97.97% accuracy. Our model was further validated against cardiac CT-based markers including cardiac thoracic ratio (CTR), pulmonary artery to aorta ratio (PA/A), and presence of calcified plaque. Thus, we successfully demonstrate that CT-based deep learning algorithms can be employed as a dual screening diagnostic tool to diagnose COVID-19 and differentiate it from other pneumonia, and also predicts CVD risk associated with COVID-19 infection.

Relationships of pericoronary and epicardial fat measurements in male and female patients with and without coronary artery disease

INTRODUCTION

Although pericoronary adipose tissue (PCAT) is a component of the epicardial adipose tissue (EAT) depot, they may have different associations to coronary artery disease (CAD). We explored relationships between pericoronary adipose tissue mean attenuation (PCATMA) and EAT measurements in coronary CT angiography (CCTA) in patients with and without CAD.

MATERIAL AND METHODS

CCTA scans of 185 non-CAD and 81 CAD patients (86.4% >50% stenosis) were included and retrospectively analyzed. PCATMA and EAT density/volume were measured and analyzed by sex, including associations with age, risk factors and tube voltage using linear regression models.

RESULTS

In non-CAD and CAD, mean PCATMA and EAT volume were higher in men than in women (non-CAD: -92.5 ± 10.6HU vs -96.2 ± 8.4HU, and 174.4 ± 69.1 cm3 vs 124.1 ± 57.3 cm3; CAD: -92.2 ± 9.0HU vs -97.4 ± 9.7HU, and 193.6 ± 62.5 cm3 vs 148.5 ± 50.5 cm3 (p < 0.05)). EAT density was slightly lower in men than women in non-CAD (-96.4 ± 6.3HU vs -94.4 ± 5.5HU (p < 0.05)), and similar in CAD (-98.2 ± 5.2HU vs 98.2 ± 6.4HU). There was strong correlation between PCATMA and EAT density (non-CAD: r = 0.725, p < 0.001, CAD: r = 0.686, p < 0.001) but no correlation between PCATMA and EAT volume (non-CAD: r = 0.018, p = 0.81, CAD: r = -0.055, p = 0.63). A weak inverse association was found between EAT density and EAT volume (non-CAD: r = -0.244, p < 0.001, CAD: r = -0.263, p = 0.02). In linear regression models, EAT density was significantly associated with PCATMA in both non-CAD and CAD patients independent of risk factors and tube voltage.

CONCLUSION

In CAD and non-CAD patients, EAT density, but not EAT volume, showed significant associations with PCATMA. Compared to women, men had higher PCATMA and EAT volume independently of disease status, but similar or slightly lower EAT density. Differences in trends and relations of PCATMA and EAT by sex could indicate that personalized interpretation and thresholding is needed.

Inter-software and inter-scan variability in measurement of epicardial adipose tissue: a three-way comparison of a research-specific, a freeware and a coronary application software platform

OBJECTIVES

Epicardial adipose tissue (EAT) is a proposed marker of cardiovascular risk; however, clinical application may be limited by variability in post-processing software platforms. We assessed inter-vendor agreement of EAT volume (EATv) and attenuation on both contrast-enhanced (CE) and non-contrast CT (NCT) using a standard coronary CT reporting software (Vitrea), an EAT research-specific software (QFAT) and a freeware imaging software (OsiriX).

METHODS

Seventy-six consecutive patients undergoing simultaneous CE and NCT had complete volumetric EAT measurement. Between-software, within-software NCT vs. CE, and inter- and intra-observer agreement were evaluated with analysis by ANOVA (with post hoc adjustment), Bland-Altman with 95% levels of agreement (LoA) and intraclass correlation coefficient (ICC).

RESULTS

Mean EATv (freeware 53 ± 31 mL vs. research 93 ± 43 mL vs. coronary 157 ± 64 mL) and attenuation (freeware  - 72 ± 25 HU vs. research  - 75 ± 3 HU vs. coronary  - 61 ± 10 HU) were significantly different between all vendors (ANOVA p < 0.001). EATv was consistently higher in NCT vs. CE for all software packages, with most reproducibility found in research software (bias 26 mL, 95% LoA: 2 to 56 mL), compared to freeware (bias 11 mL 95% LoA: - 46 mL to 69 mL) and coronary software (bias 10 mL 95% LoA: - 127 to 147 mL). Research software had more comparable NCT vs. CE attenuation (- 75 vs. - 72 HU) compared to freeware (- 72 vs. - 57 HU) and coronary (- 61 vs.  - 39 HU). Excellent inter-observer agreement was seen with research (ICC 0.98) compared to freeware (ICC 0.73) and coronary software (ICC 0.75) with narrow LoA on Bland-Altman analysis.

CONCLUSION

There are significant inter-vendor differences in EAT assessment. Our study suggests that research-specific software has better agreement and reproducibility compared to freeware or coronary software platforms.

KEY POINTS

• There are significant differences between EAT volume and attenuation values between software platforms, regardless of scan type. • Non-contrast scans routinely have higher mean EAT volume and attenuation; however, this finding is only consistently seen with research-specific software. • Of the three analyzed packages, research-specific software demonstrates the highest reproducibility, agreement, and reliability for both inter-scan and inter-observer agreement.

Artificial intelligence in cardiac computed tomography

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.

Epicardial fat volume, an independent risk factor for major adverse cardiovascular events, had an incremental prognostic value to myocardial perfusion imaging in Chinese populations with suspected or known coronary artery disease with a normal left ventricular ejection fraction

Background

Most coronary artery disease (CAD) patients with a normal left ventricular ejection fraction (LVEF) experience a poor prognosis. Single-photon emission computerized tomography (SPECT)-myocardial perfusion imaging (MPI), a routine examination, is useful in assessing risk and predicting major adverse cardiovascular events (MACEs) in populations with suspected or known CAD. SPECT/CT is a "one-stop shop" examination, which, through non-contrast CT, can produce attenuation correction for MPI and obtain information on coronary artery calcium (CAC) and epicardial fat volume (EFV) simultaneously. This study aims to investigate the predictive and incremental value of EFV to MPI for MACE in Chinese populations with suspected or known CAD with a normal LVEF.

Methods and results

We retrospectively studied 290 suspected or known CAD inpatients with a normal LVEF who underwent SPECT/CT between February 2014 and December 2017. Abnormal MPI was defined as a summed stress score ≥4 or summed difference score ≥2. EFV and CAC were calculated using non-contrast CT. The end date of follow-ups was in February 2022. The follow-up information was obtained from the clinical case notes of the patients or reviews of telephone calls. MACE was defined as cardiac death, late coronary revascularization ≥3 months after MPI, non-fatal myocardial infarction, angina-related rehospitalization, heart failure, and stroke. During the 76-month follow-up, the event rate was 32.0% (93/290). Univariate and multivariate Cox regression analyses concluded that high EFV (>108.3 cm3) [hazard ratio (HR): 3.3, 95% CI: 2.1-5.2, P < 0.000] and abnormal MPI (HR: 1.8, 95% CI: 1.1-2.8, P = 0.010) were independent risk factors for MACE. The event-free survival of patients with high EFV was significantly lower than that of the low EFV group (log-rank test P < 0.001). In the subgroup with normal MPI, high EFV was associated with reduced event-free survival (log-rank P < 0.01), with a higher annualized event rate (8.3% vs. 1.9%). Adding high EFV to MPI could predict MACEs more effectively, with a higher concordance index (0.56-0.69, P < 0.01), higher global chi square (7.2-41.4, P < 0.01), positive integrated discrimination improvement (0.10, P < 0.01), and net reclassification index (0.37, P < 0.01).

Conclusions

In Chinese populations with suspected or known CAD with normal LVEF, high EFV was an independent risk factor for MACE after adjusting for traditional risk factors, CAC and MPI. In subgroups with normal MPI, EFV could also improve risk stratification. Adding EFV to MPI had an incremental value for predicting MACE.

Obesity and atrial fibrillation: a narrative review from arrhythmogenic mechanisms to clinical significance

The prevalence of obesity and atrial fibrillation (AF), which are inextricably linked, is rapidly increasing worldwide. Obesity rates are higher among patients with AF than healthy individuals. Some epidemiological data indicated that obese patients were more likely to develop AF, but others reported no significant correlation. Obesity-related hypertension, diabetes, and obstructive sleep apnea are all associated with AF. Additionally, increased epicardial fat, systemic inflammation, and oxidative stress caused by obesity can induce atrial enlargement, inflammatory activation, local myocardial fibrosis, and electrical conduction abnormalities, all of which led to AF and promoted its persistence. Weight loss reduced the risk and reversed natural progression of AF, which may be due to its anti-fibrosis and inflammation effect. However, fluctuations in weight offset the benefits of weight loss. Therefore, the importance of steady weight loss urges clinicians to incorporate weight management interventions in the treatment of patients with AF. In this review, we discuss the epidemiology of obesity and AF, summarize the mechanisms by which obesity triggers AF, and explain how weight loss improves the prognosis of AF.

Distinct Features of Vascular Diseases in COVID-19

The Coronavirus Disease 2019 (COVID-19) pandemic was declared in early 2020 after several unexplained pneumonia cases were first reported in Wuhan, China, and subsequently in other parts of the world. Commonly, the disease comprises several clinical features, including high temperature, dry cough, shortness of breath, and hypoxia, associated with findings of interstitial pneumonia on chest X-ray and computer tomography. Nevertheless, severe forms of acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) are not limited to the respiratory tract but also may be extended to other systems, including the cardiovascular system. The bi-directional relationship between atherosclerosis and COVID-19 is accompanied by poor prognosis. The immune response hyperactivation due to SARS-CoV-2 infection causes an increased secretion of cytokines, endothelial dysfunction, and arterial stiffness, which promotes the development of atherosclerosis. Also, due to the COVID-19 pandemic, access to healthcare amenities was reduced, resulting in increased morbidity and mortality in patients at risk. Furthermore, as lockdown measures were largely adopted worldwide, the sedentary lifestyle and the increased consumption of processed nutrients or unhealthy food increased, and in the consequence, we might observe even 70% of overweight and obese population. Altogether, with the relatively low ratio of vaccinated people in many countries, and important health debt appeared, which is now and will be for next decade a large healthcare challenge. However, the experience gained in the COVID-19 pandemic and the new methods of patients' approaching have helped the medical system to overcome this crisis and will hopefully help in the case of new possible epidemics.

Non-Contrast and Contrast-Enhanced Cardiac Computed Tomography Imaging in the Diagnostic and Prognostic Evaluation of Coronary Artery Disease

In recent decades, cardiac computed tomography (CT) has emerged as a powerful non-invasive tool for risk stratification, as well as the detection and characterization of coronary artery disease (CAD), which remains the main cause of morbidity and mortality in the world. Advances in technology have favored the increasing use of cardiac CT by allowing better performance with lower radiation doses. Coronary artery calcium, as assessed by non-contrast CT, is considered to be the best marker of subclinical atherosclerosis, and its use is recommended for the refinement of risk assessment in low-to-intermediate risk individuals. In addition, coronary CT angiography (CCTA) has become a gate-keeper to invasive coronary angiography (ICA) and revascularization in patients with acute chest pain by allowing the assessment not only of the extent of lumen stenosis, but also of its hemodynamic significance if combined with the measurement of fractional flow reserve or perfusion imaging. Moreover, CCTA provides a unique incremental value over functional testing and ICA by imaging the vessel wall, thus allowing the assessment of plaque burden, composition, and instability features, in addition to perivascular adipose tissue attenuation, which is a marker of vascular inflammation. There exists the potential to identify the non-obstructive lesions at high risk of progression to plaque rupture by combining all of these measures.

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction

BACKGROUND

Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented.

OBJECTIVES

This study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care.

METHODS

The deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value.

RESULTS

External validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post-cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01).

CONCLUSIONS

Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.

Association between periodontal disease and pericardial adipose tissue in patients with cardiovascular disease

Background

Periodontal disease (PD) is associated with an increased risk of cardiovascular disease (CVD). Pericardial adipose tissue (PAT) is known as a marker of progressive CVD. This study sought to assess the association between PD and PAT in patients with CVD.

Methods

We retrospectively investigated 135 patients admitted for CVD who underwent computed tomography coronary angiography (CTCA) and periodontal examinations. Periodontal assessment using the community periodontal index (CPI) was based on the probing pocket depth around teeth. Patients with CPI ≥3 were categorized as having PD. PAT volume was measured with a quantitative semi-automated procedure using CTCA images. Patients were divided into tertiles according to PAT volume. Baseline characteristics and PD findings were compared among the tertiles.

Results

Eighty-six patients were diagnosed with PD (63.7 %). Mean PAT volume was 181.4 ml, and patients were categorized as small-PAT (PAT <148.9 ml), intermediate-PAT (148.9 ml ≤ PAT ≤204.6 ml), and large-PAT (PAT >204.6 ml). The prevalence of PD was significantly higher in large-PAT (38/46, 82.6 %) than in small-PAT (18/45, 40.0 %) and intermediate-PAT (30/44, 68.2 %) patients. Multivariate logistic regression analysis showed that body weight, history of hypertension, and the presence of PD were independent predictors for large-PAT (odds ratio [OR]: 1.12, P < 0.001, OR: 3.97, P = 0.017, and OR: 4.18, P = 0.0078, respectively).

Conclusion

The presence and severity of PD were significantly correlated with PAT volume, which has been associated with progressive CVD. Further prospective studies are warranted to assess the impact of PD on the onset and outcomes of CVD.

Relationship between epicardial adipose tissue measured by computed tomography and premature ventricular complexes originating from different sites

AIMS

This study aims to explore the association between the features of epicardial adipose tissue (EAT) in different zones and premature ventricular complexes (PVCs) originating from different sites by computed tomography (CT).

METHODS AND RESULTS

A total of 136 patients who underwent radiofrequency ablation for PVCs were incorporated in this study. One hundred and thirty-six matched controls were included in this study using the case-control method (1:1 matching). PVCs were classified into four subgroups: (1) right ventricular outflow tract (RVOT-PVCs), (2) non-RVOT of the right ventricle (RV-PVCs), (3) left ventricular outflow tract (LVOT-PVCs), and (4) non-LVOT of the left ventricle (LV-PVCs). The volume and density of EAT were quantified by CT. Patients with PVCs had a significantly higher volume and lower density of EAT than the controls (P < 0.001). The LVOT-PVCs and LV-PVCs had a higher left ventricle periventricular EAT volume (LV-EATv) proportion (P < 0.05). The right ventricle periventricular EAT volume (RV-EATv) proportion was higher in ROVT-PVCs and LVOT-PVCs (P < 0.05). RVOT-PVC patients had a higher volume ratio and a smaller density differential (P < 0.05). Patients with LVOT-PVCs had a lower volume ratio and the LV-PVCs showed a greater density differential (P < 0.05).

CONCLUSION

Higher volume and lower density of EAT were significantly associated with frequent PVCs. The RVOT-PVC patients had a higher volume ratio and a smaller density differential. The LVOT-PVCs had a lower volume ratio and the LV-PVCs showed a greater density differential. These suggest a link between EAT structural properties and PVCs and a potential role for regional EAT in the development of PVCs.

The role of epicardial adipose tissue dysfunction in cardiovascular diseases: an overview of pathophysiology, evaluation, and management

In recent decades, the epicardial adipose tissue (EAT) has been at the forefront of scientific research because of its diverse role in the pathogenesis of cardiovascular diseases (CVDs). EAT lies between the myocardium and the visceral pericardium. The same microcirculation exists both in the epicardial fat and the myocardium. Under physiological circumstances, EAT serves as cushion and protects coronary arteries and myocardium from violent distortion and impact. In addition, EAT acts as an energy lipid source, thermoregulator, and endocrine organ. Under pathological conditions, EAT dysfunction promotes various CVDs progression in several ways. It seems that various secretions of the epicardial fat are responsible for myocardial metabolic disturbances and, finally, leads to CVDs. Therefore, EAT might be an early predictor of CVDs. Furthermore, different non-invasive imaging techniques have been proposed to identify and assess EAT as an important parameter to stratify the CVD risk. We also present the potential therapeutic possibilities aiming at modifying the function of EAT. This paper aims to provide overview of the potential role of EAT in CVDs, discuss different imaging techniques to assess EAT, and provide potential therapeutic options for EAT. Hence, EAT may represent as a potential predictor and a novel therapeutic target for management of CVDs in the future.

Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT

Introduction

The aim of our study was to evaluate the feasibility of texture analysis of epicardial fat (EF) and thoracic subcutaneous fat (TSF) in patients undergoing cardiac CT (CCT).

Materials and methods

We compared a consecutive population of 30 patients with BMI ≤25 kg/m² (Group A, 60.6 ± 13.7 years) with a control population of 30 patients with BMI >25 kg/m² (Group B, 63.3 ± 11 years). A dedicated computer application for quantification of EF and a texture analysis application for the study of EF and TSF were employed.

Results

The volume of EF was higher in group B (mean 116.1 cm³ vs. 86.3 cm³, p = 0.014), despite no differences were found neither in terms of mean density (-69.5 ± 5 HU vs. -68 ± 5 HU, p = 0.28), nor in terms of quartiles distribution (Q1, p = 0.83; Q2, p = 0.22, Q3, p = 0.83, Q4, p = 0.34). The discriminating parameters of the histogram class were mean (p = 0.02), 0,1st (p = 0.001), 10_th (p = 0.002), and 50_th percentiles (p = 0.02). DifVarnc was the discriminating parameter of the co-occurrence matrix class (p = 0.007).The TSF thickness was 15 ± 6 mm in group A and 19.5 ± 5 mm in group B (p = 0.003). The TSF had a mean density of -97 ± 19 HU in group A and -95.8 ± 19 HU in group B (p = 0.75). The discriminating parameters of texture analysis were 10_th (p = 0.03), 50_th (p = 0.01), 90_th percentiles (p = 0.04), S(0,1)SumAverg (p = 0.02), S(1,-1)SumOfSqs (p = 0.02), S(3,0)Contrast (p = 0.03), S(3,0)SumAverg (p = 0.02), S(4,0)SumAverg (p = 0.04), Horzl_RLNonUni (p = 0.02), and Vertl_LngREmph (p = 0.0005).

Conclusions

Texture analysis provides distinctive radiomic parameters of EF and TSF. EF and TSF had different radiomic features as the BMI varies.

Epicardial Adipose Tissue Assessed by Computed Tomography and Echocardiography Are Associated With Adverse Cardiovascular Outcomes: A Systematic Review and Meta-Analysis

BACKGROUND

Epicardial adipose tissue (EAT) has garnered attention as a prognostic and risk stratification factor for cardiovascular disease. This study, via meta-analyses, evaluates the associations between EAT and cardiovascular outcomes stratified across imaging modalities, ethnic groups, and study protocols.

METHODS

Medline and Embase databases were searched without date restriction on May 2022 for articles that examined EAT and cardiovascular outcomes. The inclusion criteria were (1) studies measuring EAT of adult patients at baseline and (2) reporting follow-up data on study outcomes of interest. The primary study outcome was major adverse cardiovascular events. Secondary study outcomes included cardiac death, myocardial infarction, coronary revascularization, and atrial fibrillation.

RESULTS

Twenty-nine articles published between 2012 and 2022, comprising 19 709 patients, were included in our analysis. Increased EAT thickness and volume were associated with higher risks of cardiac death (odds ratio, 2.53 [95% CI, 1.17-5.44]; P=0.020; n=4), myocardial infarction (odds ratio, 2.63 [95% CI, 1.39-4.96]; P=0.003; n=5), coronary revascularization (odds ratio, 2.99 [95% CI, 1.64-5.44]; P<0.001; n=5), and atrial fibrillation (adjusted odds ratio, 4.04 [95% CI, 3.06-5.32]; P<0.001; n=3). For 1 unit increment in the continuous measure of EAT, computed tomography volumetric quantification (adjusted hazard ratio, 1.74 [95% CI, 1.42-2.13]; P<0.001) and echocardiographic thickness quantification (adjusted hazard ratio, 1.20 [95% CI, 1.09-1.32]; P<0.001) conferred an increased risk of major adverse cardiovascular events.

CONCLUSIONS

The utility of EAT as an imaging biomarker for predicting and prognosticating cardiovascular disease is promising, with increased EAT thickness and volume being identified as independent predictors of major adverse cardiovascular events.

REGISTRATION

URL: https://www.crd.york.ac.uk/prospero; Unique identifier: CRD42022338075.

Coronary Computed Tomography Angiography: Beyond Obstructive Coronary Artery Disease

Nowadays, coronary computed tomography angiography (CCTA) has a role of paramount importance in the diagnostic algorithm of ischemic heart disease (IHD), both in stable coronary artery disease (CAD) and acute chest pain. Alongside the quantification of obstructive coronary artery disease, the recent technologic developments in CCTA provide additional relevant information that can be considered as "novel markers" for risk stratification in different settings, including ischemic heart disease, atrial fibrillation, and myocardial inflammation. These markers include: (i) epicardial adipose tissue (EAT), associated with plaque development and the occurrence of arrhythmias; (ii) late iodine enhancement (LIE), which allows the identification of myocardial fibrosis; and (iii) plaque characterization, which provides data about plaque vulnerability. In the precision medicine era, these emerging markers should be integrated into CCTA evaluation to allow for the bespoke interventional and pharmacological management of each patient.

Assessment of epicardial adipose tissue on virtual non-contrast images derived from photon-counting detector coronary CTA datasets

OBJECTIVES

To assess epicardial adipose tissue (EAT) volume and attenuation of different virtual non-contrast (VNC) reconstructions derived from coronary CTA (CCTA) datasets of a photon-counting detector (PCD) CT-system to replace true non-contrast (TNC) series.

METHODS

Consecutive patients (n = 42) with clinically indicated CCTA and coronary TNC were included. Two VNC series were reconstructed, using a conventional (VNC_Conv) and a novel calcium-preserving (VNC_PC) algorithm. EAT was segmented on TNC, VNC_Conv, VNC_PC, and CCTA (CTA_-30) series using thresholds of -190 to -30 HU and an additional segmentation on the CCTA series with an upper threshold of 0 HU (CTA₀). EAT volumes and their histograms were assessed for each series. Linear regression was used to correlate EAT volumes and the Euclidian distance for histograms. The paired t-test and the Wilcoxon signed-rank test were used to assess differences for parametric and non-parametric data.

RESULTS

EAT volumes from VNC and CCTA series showed significant differences compared to TNC (all p < .05), but excellent correlation (all R² > 0.9). Measurements on the novel VNC_PC series showed the best correlation (R² = 0.99) and only minor absolute differences compared to TNC values. Mean volume differences were -12%, -3%, -13%, and +10% for VNC_Conv, VNC_PC, CTA_-30, and CTA₀ compared to TNC. Distribution of CT values on VNC_PC showed less difference to TNC than on VNC_Conv (mean attenuation difference +7% vs. +2%; Euclidean distance of histograms 0.029 vs. 0.016).

CONCLUSIONS

VNC_PC-reconstructions of PCD-CCTA datasets can be used to reliably assess EAT volume with a high accuracy and only minor differences in CT values compared to TNC. Substitution of TNC would significantly decrease patient's radiation dose.

KEY POINTS

• Measurement of epicardial adipose tissue (EAT) volume and attenuation are feasible on virtual non-contrast (VNC) series with excellent correlation to true non-contrast series (all R²>0.9). • Differences in VNC algorithms have a significant impact on EAT volume and CT attenuation values. • A novel VNC algorithm (VNC_PC) enables reliable assessment of EAT volume and attenuation with superior accuracy compared to measurements on conventional VNC- and CCTA-series.

Relationship between epicardial adipose tissue attenuation and coronary artery disease in type 2 diabetes mellitus patients

BACKGROUND AND AIMS

High epicardial adipose tissue (EAT) attenuation is a key characteristic of adipose tissue dysfunction and associated with coronary artery disease (CAD). As little is known about the modulation of EAT attenuation by metabolic disorders, we investigated the association between EAT attenuation and CAD risk factors, CAD presence and CAD severity in type 2 diabetes mellitus (T2DM) patients.

METHODS

We included 276 inpatients with T2DM and 305 control patients with normal glucose metabolism (NGM), who underwent cardiac computed tomography angiography (CCTA) and coronary artery calcium (CAC) scoring. EAT attenuation and volume were evaluated by contrast-enhanced CCTA image analysis. Furthermore, segment stenosis scores (SSSs) of the left main coronary artery (LMCA), left anterior descending artery (LAD), left circumflex artery (LCX), right coronary artery (RCA), diagonal/intermediate branch (D/I) and obtuse marginal branch (OM) were calculated to assess CAD severity.

RESULTS

T2DM patients showed higher significant CAC scores, coronary plaque prevalence, total SSSs and LMCA-SSSs, LAD-SSSs, LCX-SSSs, RCA-SSSs and D/I-SSSs compared with NGM controls. In contrast to NGM controls, EAT volume was significantly increased in T2DM patients, whereas EAT attenuation was similar. In T2DM patients, EAT attenuation was associated with discrete CAD risk factors, the presence of coronary and triple-vessel plaques, as well as LAD-SSSs, LCX-SSSs, RCA-SSSs and total SSSs. In addition, EAT attenuation was only associated with the total SSS of calcified plaques, but not with noncalcified plaques.

CONCLUSION

In T2DM patients, high EAT attenuation is associated with the presence and severity of CAD in general and with coronary stenosis caused by calcified plaques in particular.

The predictive value of left atrium epicardial adipose tissue on recurrence after catheter ablation in patients with different types of atrial fibrillation

BACKGROUND

A growing body of evidence supports that the left atrium epicardial adipose tissue (LA-EAT) is related to the occurrence and recurrence of atrial fibrillation (AF). The relationship between LA-EAT and the recurrence after radiofrequency catheter ablation (RFCA) in patients with different types of AF is still unclear. This study aims to evaluate the predictive value of LA-EAT on the recurrence of AF after RFCA in patients with different types of AF.

METHODS

301 AF patients who underwent RFCA for the first time were divided into the paroxysmal atrial fibrillation (PAF) group (n = 181) and the persistent atrial fibrillation (PersAF) group(n = 120), which were followed up at 3, 6, and 12 months. All patients underwent left atrial computed tomography angiography (CTA) examination before the operation, and LA-EAT was measured using software (Advantage Workstation4.6, GE, USA).

RESULTS

After a median follow-up of 10.7 months, 73/301 patients (24.25%) had a recurrence of AF, including 43 /120(35.83%) patients with PersAF and 30/181(16.57%) patients with PAF. In multivariable Cox regression analysis, LA-EAT volume (OR = 1.053;95%CI: 1.024-1.083, p < 0.001), attenuation (OR = 0.949;95%CI:0.911-0.988, p = 0.012) and left atrial diameter (LAD) (OR = 1.063;95%CI:1.002-1.127，p = 0.043) were independent risk factors for recurrence in patients with PersAF but not in patients with PAF.

CONCLUSION

LA-EAT volume and attenuation are independent risk factors for recurrence after RFCA in patients with PersAF.

Epicardial adipose tissue density is a better predictor of cardiometabolic risk in HFpEF patients: a prospective cohort study

BACKGROUND

Epicardial adipose tissue (EAT) accumulation is associated with multiple cardiometabolic risk factors and prognosis of heart failure with preserved ejection fraction (HFpEF). The correlation between EAT density and cardiometabolic risk and the effect of EAT density on clinical outcome in HFpEF remain unclear. We evaluated the relationship between EAT density and cardiometabolic risk factors, also the prognostic value of EAT density in patients with HFpEF.

METHODS

We included 154 HFpEF patients who underwent noncontrast cardiac computed tomography (CT) and all patients received follow-up. EAT density and volume were quantified semi-automatically. The associations of EAT density and volume with cardiometabolic risk factors, metabolic syndrome and the prognostic impact of EAT density were analyzed.

RESULTS

Lower EAT density was associated with adverse changes in cardiometabolic risk factors. Each 1 HU increase in fat density, BMI was 0.14 kg/m² lower (95% CI 0.08-0.21), waist circumference was 0.34 cm lower (95% CI 0.12-0.55), non-HDL-cholesterol was 0.02 mmol/L lower (95% CI 0-0.04), triglyceride was 0.03 mmol/L lower (95% CI 0.01-0.04), fasting plasma glucose was 0.05 mmol/L lower (95% CI 0.02-0.08), TyG index was 0.03 lower (95% CI 0.02-0.04), Log₂(TG/HDL-C) was 0.03 lower (95% CI 0.02-0.05), METS-IR was 0.36 lower (95% CI 0.23-0.49), MetS Z-score was 0.04 lower (95% CI 0.02-0.06), and Log₂(CACS + 1) was 0.09 lower (95% CI 0.02-0.15). After adjusting for BMI and EAT volume, the associations of non-HDL-cholesterol, triglyceride, fasting plasma glucose, insulin resistance indexes, MetS Z-score, and CACS with fat density remained significant. The area under the curve (AUC) for the presence and severity of metabolic syndrome was greater in EAT density than volume (AUC: 0.731 vs 0.694, 0.735 vs 0.662, respectively). Over a median follow-up of 16 months, the cumulative incidence of heart failure readmission and composite endpoint increased with lower level of EAT density (both p < 0.05).

CONCLUSIONS

EAT density was an independent impact factor of cardiometabolic risk in HFpEF. EAT density might have better predictive value than EAT volume for metabolic syndrome and it might have prognostic value in patients with HFpEF.

Epicardial fat density, coronary artery disease and inflammation in people living with HIV

Studies have shown an increased risk of coronary artery disease (CAD) in the human immunodeficiency virus (HIV) population. Epicardial fat (EF) quality may be linked to this increased risk. In our study, we evaluated the associations between EF density, a qualitative characteristic of fat, and inflammatory markers, cardiovascular risk factors, HIV-related parameters, and CAD. Our study was cross-sectional, nested in the Canadian HIV and Aging Cohort Study, a large prospective cohort that includes participants living with HIV (PLHIV) and healthy controls. Participants underwent cardiac computed tomography angiography to measure volume and density of EF, coronary artery calcium score, coronary plaque, and low attenuation plaque volume. Association between EF density, cardiovascular risk factors, HIV parameters, and CAD were evaluated using adjusted regression analysis. A total of 177 PLHIV and 83 healthy controls were included in this study. EF density was similar between the two groups (-77.4 ± 5.6 HU for PLHIV and -77.0 ± 5.6 HU for uninfected controls, P = .162). Multivariable models showed positive association between EF density and coronary calcium score (odds ratio, 1.07, P = .023). Among the soluble biomarkers measured in our study, adjusted analyses showed that IL2Rα, tumor necrosis factor alpha and luteizing hormone were significantly associated with EF density. Our study showed that an increase in EF density was associated with a higher coronary calcium score and with inflammatory markers in a population that includes PLHIV.

Incremental Value of Noncontrast Chest Computed Tomography-derived Parameters in Predicting Subclinical Carotid Atherosclerosis: From the PERSUADE Study

PURPOSE

To investigate the incremental value of noncontrast chest computed tomography (CT)-derived parameters, such as coronary artery calcium score (CACS) and epicardial adipose tissue volume (EATv), in predicting subclinical carotid atherosclerosis above traditional risk factors in community-based asymptomatic populations of northern China.

MATERIALS AND METHODS

A total of 2195 community-based asymptomatic individuals were enrolled from Jidong Oilfield in accordance with the PERSUADE study. CACS and EATv were measured on noncontrast chest CT. Demographics and ideal cardiovascular health score (ICHS) were collected through questionnaires. We recalculated the ideal cardiovascular health risk score (ICHRS) (ICHRS=14-ICHS) and standardized the parameters as log-CACS and body mass index adjusted EATv (i-EATv). Subclinical carotid atherosclerosis was assessed by Doppler sonography and defined as any prevalence of average carotid intima-media thickness ≥1.00 mm, appearance of carotid plaque, and carotid arterial stenosis in the areas of extracranial carotid arteries on both sides.

RESULTS

A total of 451 (20.55%) individuals presented subclinical carotid atherosclerosis. CACS and EATv were significantly greater in the subclinical group, while ICHS was lower. In multivariate logistic regression, ICHRS (odds ratio [OR]=1.143, 95% confidence interval [CI]: 1.080-1.210, P <0.001), log-CACS (OR=1.701, 95% CI: 1.480-1.955, P <0.001), and i-EATv (OR=1.254, 95% CI: 1.173-1.341, P <0.001) were found to be independent risk predictors for subclinical carotid atherosclerosis. In receiver-operating characteristic curve analysis, when combined with male sex and age level, the area under the curve of the ICHRS basic model increased from 0.627 (95% CI: 0.599-0.654) to 0.757 (95% CI: 0.732-0.781) ( P <0.0001). Further adding log-CACS and i-EATv, the area under the curve demonstrated a statistically significant improvement (0.788 [95% CI: 0.765-0.812] vs. 0.757 [95% CI: 0.732-0.781], P <0.0001).

CONCLUSION

Noncontrast chest CT-derived parameters, including CACS and EATv, could provide significant incremental improvement for predicting subclinical carotid atherosclerosis beyond the conventional risk assessment model based on ICHRS.

The association of epicardial adipose tissue volume and density with coronary calcium in HIV-positive and HIV-negative patients

AIMS

We sought to assess and compare the association of epicardial adipose tissue (EAT) with cardiovascular disease (CVD) in HIV-positive and HIV-negative groups.

METHODS AND RESULTS

Using existing clinical databases, we analyzed 700 patients (195 HIV-positive, 505 HIV-negative). CVD was quantified by the presence of coronary calcification from both dedicated cardiac computed tomography (CT) and non-dedicated CT of the thorax. Epicardial adipose tissue (EAT) was quantified using dedicated software. The HIV-positive group had lower mean age (49.2 versus 57.8, p < 0.005), higher proportion of male sex (75.9 % versus 48.1 %, p < 0.005), and lower rates of coronary calcification (29.2 % versus 58.2 %, p < 0.005). Mean EAT volume was also lower in the HIV-positive group (68mm3 versus 118.3mm3, p < 0.005). Multiple linear regression demonstrated EAT volume was associated with hepatosteatosis (HS) in the HIV-positive group but not the HIV-negative group after adjustment for BMI (p < 0.005 versus p = 0.066). In the multivariate analysis, after adjustment for CVD risk factors, age, sex, statin use, and body mass index (BMI), EAT volume and hepatosteatosis were significantly associated with coronary calcification (odds ratio [OR] 1.14, p < 0.005 and OR 3.17, p < 0.005 respectively). In the HIV-negative group, the only significant association with EAT volume after adjustment was total cholesterol (OR 0.75, p = 0.012).

CONCLUSIONS

We demonstrated a strong and significant independent association of EAT volume and coronary calcium, after adjustment, in HIV-positive group but not in the HIV-negative group. This result hints at differences in the mechanistic drivers of atherosclerosis between HIV-positive and HIV-negative groups.

CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease

This study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue — around the anterior interventricular artery (IVA) — to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alone. In fact, with the best model (Random Forest + Mutual Information) the AUROC reached $$0.820 \pm 0.076$$ 0.820 ± 0.076 . As a matter of fact, the combined use of both types of features (i.e., radiomic and clinical) allows for improved performance regardless of the feature selection method used. Experimental findings demonstrated that the use of radiomic features alone achieves better performance than the use of clinical features alone, while the combined use of both clinical and radiomic biomarkers further improves the predictive ability of the models. The main contribution of this work concerns: (i) the implementation of multimodal predictive models, based on both clinical and radiomic features, and (ii) a trusted system to support clinical decision-making processes by means of explainable classifiers and interpretable features.

Cardiac Computed Tomography Evaluation of Association of Left Ventricle Disfunction and Epicardial Adipose Tissue Density in Patients with Low to Intermediate Cardiovascular Risk

Background and objectives: Epicardial adipose tissue density (EAD) has been associated with coronary arteries calcium score, a higher load of coronary artery disease (CAD) and plaque vulnerability. This effect can be related to endocrine and paracrine effect of molecules produced by epicardial adipose tissue (EAT), that may influence myocardial contractility. Using coronary computed tomography angiography (CCT) the evaluation of EAD is possible in basal scans. The aim of the study is to investigate possible associations between EAD and cardiac function. Material and Methods: 93 consecutive patients undergoing CCT without and with contrast medium for known or suspected coronary CAD were evaluated. EAD was measured on basal scans, at the level of the coronary ostia, the lateral free wall of the left ventricle, at the level of the cardiac apex, and at the origin of the posterior interventricular artery. Cardiac function was evaluated in post-contrast CT scans in order to calculate ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), and stroke volume (SV). Results: A statistically significant positive correlation between EAD and ejection fraction (r = 0.29, p-value < 0.01) was found. Additionally, a statistically significant negative correlation between EAD and ESV (r = -0.25, p-value < 0.01) was present. Conclusion: EAD could be considered a new risk factor associated with reduced cardiac function. The evaluation of this parameter with cardiac CT in patients with low to intermediate cardiovascular risk is possible.

Computed tomography measured epicardial adipose tissue and psoas muscle attenuation: new biomarkers to predict major adverse cardiac events (MACE) and mortality in patients with heart disease and critically ill patients. Part I: Epicardial adipose tissue

Over the last two decades, the potential role of epicardial adipocyte tissue (EAT) as a marker for major adverse cardiovascular events has been extensively studied. Unlike other visceral adipocyte tissues (VAT), EAT is not separated from the adjacent myocardium by a fascial layer and shares the same microcirculation with the myocardium. Adipocytokines, secreted by EAT, interact directly with the myocardium through paracrine and vasocrine pathways. The role of the Randle cycle, linking VAT accumulation to insulin resistance, and the relevance of blood flow and mitochondrial function of VAT, are briefly discussed. The three available imaging modalities for the assessment of EAT are discussed. The advantages of echocardiography, cardiac CT, and cardiac magnetic resonance (CMR) are compared. The last section summarises the current stage of knowledge on EAT as a clinical marker for major adverse cardiovascular events (MACE). The association between EAT volume and coronary artery disease (CAD) has robustly been validated. There is growing evidence that EAT volume is associated with computed tomography coronary angiography (CTCA) assessed high-risk plaque features. The EAT CT attenuation coefficient predicts coronary events. Many studies have established EAT volume as a predictor of atrial fibrillation after cardiac surgery. Moreover, EAT thickness has been independently associated with severe aortic stenosis and mitral annular calcification. Studies have demonstrated that EAT volume is associated with heart failure. Finally, we discuss the potential role of EAT in critically ill patients admitted to the intensive care unit. In conclusion, EAT seems to be a promising new biomarker to predict MACE.

Computed Tomography-derived Characterization of Pericoronary, Epicardial, and Paracardial Adipose Tissue and Its Association With Myocardial Ischemia as Assessed by Computed Fractional Flow Reserve

BACKGROUND

Increased pericoronary adipose tissue (PCAT) attenuation derived from coronary computed tomography (CT) angiography (CTA) relates to coronary inflammation and cardiac mortality. We aimed to investigate the association between CT-derived characterization of different cardiac fat compartments and myocardial ischemia as assessed by computed fractional flow reserve (FFRCT).

METHODS

In all, 133 patients (median 64 y, 74% male) with coronary artery disease (CAD) underwent CTA including FFRCT measurement followed by invasive FFR assessment (FFRINVASIVE). CT attenuation and volume of PCAT were quantified around the proximal right coronary artery (RCA), left anterior descending artery (LAD), and left circumflex artery (LCX). Epicardial adipose tissue (EAT) and paracardial adipose tissue (PAT; all intrathoracic adipose tissue outside the pericardium) were quantified in noncontrast cardiac CT datasets.

RESULTS

Median FFRCT was 0.86 [0.79, 0.91] and median FFRINVASIVE was 0.87 [0.81, 0.93]. Subjects with the presence of myocardial ischemia (n=26) defined by an FFRCT-threshold of ≤0.75 showed significantly higher RCA PCAT attenuation than individuals without myocardial ischemia (n=107) (-75.1±10.8 vs. -81.1±10.6 HU, P=0.011). In multivariable analysis adjusted for age, body mass index, sex and risk factors, increased RCA PCAT attenuation remained a significant predictor of myocardial ischemia. Between individuals with myocardial ischemia compared with individuals without myocardial ischemia, there was no significant difference in the volume and CT attenuation of EAT and PAT or in the PCAT volume of RCA, LAD, and LCX.

CONCLUSIONS

Increased RCA PCAT attenuation is associated with the presence of myocardial ischemia as assessed by FFR, while PCAT volume, EAT, and PAT are not.

Comparison of epicardial adipose tissue volume quantification between ECG-gated cardiac and non-ECG-gated chest computed tomography scans

BACKGROUND

This study investigated accuracy and consistency of epicardial adipose tissue (EAT) quantification in non-ECG-gated chest computed tomography (CT) scans.

METHODS

EAT volume was semi-automatically quantified using a standard Hounsfield unit threshold (- 190, - 30) in three independent cohorts: (1) Cohort 1 (N = 49): paired 120 kVp ECG-gated cardiac non-contrast CT (NCCT) and 120 kVp non-ECG-gated chest NCCT; (2) Cohort 2 (N = 34): paired 120 kVp cardiac NCCT and 100 kVp non-ECG-gated chest NCCT; (3) Cohort 3 (N = 32): paired non-ECG-gated chest NCCT and chest contrast-enhanced CT (CECT) datasets (including arterial phase and venous phase). Images were reconstructed with the slice thicknesses of 1.25 mm and 5 mm in the chest CT datasets, and 3 mm in the cardiac NCCT datasets.

RESULTS

In Cohort 1, the chest NCCT-1.25 mm EAT volume was similar to the cardiac NCCT EAT volume, while chest NCCT-5 mm underestimated the EAT volume by 7.5%. In Cohort 2, 100 kVp chest NCCT-1.25 mm were 13.2% larger than 120 kVp cardiac NCCT EAT volumes. In Cohort 3, the chest arterial CECT and venous CECT dataset underestimated EAT volumes by ~ 28% and ~ 18%, relative to chest NCCT datasets. All chest CT-derived EAT volumes were similarly associated with significant coronary atherosclerosis with cardiac CT counterparts.

CONCLUSION

The 120 kVp non-ECG-gated chest NCCT-1.25 mm images produced EAT volumes comparable to cardiac NCCT. Chest CT EAT volumes derived from consistent imaging settings are excellent alternatives to the cardiac NCCT to investigate their association with coronary artery disease.

Association of epicardial adipose tissue with coronary CT angiography plaque parameters on cardiovascular outcome in patients with and without diabetes mellitus

BACKGROUND AND AIMS

We aimed to evaluate the association of epicardial adipose tissue (EAT) with coronary CT angiography (CCTA) plaque parameters on cardiovascular outcome in patients with and without diabetes mellitus.

METHODS

Data of 353 patients (62.9 ± 10.4 years, 62% male), who underwent CCTA as part of their clinical workup for the evaluation of suspected or known CAD, were retrospectively analyzed. EAT volume and plaque parameters from CCTA were compared in patients with diabetes (n = 63) and without diabetes (n = 290). Follow-up was performed to record adverse cardiovascular events. The predictive value to detect adverse cardiovascular events was assessed using concordance indices (CIs) and multivariable Cox proportional hazards analysis.

RESULTS

In total, 33 events occurred after a median follow-up of 5.1 years. In patients with diabetes, EAT volume and plaque parameters were significantly higher than in patients without diabetes (all p < 0.05). A multivariable model demonstrated an incrementally improved C-index of 0.84 (95%CI 0.80-0.88) over the Framingham risk score and single measures alone. In multivariable Cox regression analysis EAT volume (Hazard ratio[HR] 1.21, p = 0.022), obstructive CAD (HR 1.18, p = 0.042), and ≥2 high-risk plaque features (HR 2.13, p = 0.031) were associated with events in patients with diabetes and obstructive CAD (HR 1.88, p = 0.017), and Agatston calcium score (HR 1.009, p = 0.039) in patients without diabetes.

CONCLUSIONS

EAT, as a biomarker of inflammation, and plaque parameters, as an extent of atherosclerotic CAD, are higher in patients with diabetes and are associated with increased adverse cardiovascular outcomes. These parameters may help identify patients at high risk with need for more aggressive therapeutic and preventive care.

Association of COVID-19 patient’s condition with fasting blood glucose and body mass index: A retrospective study

BACKGROUND: The COVID-19 pandemic broke out in 2019 and rapidly spread across the globe. Most of the severe and dead cases are middle-aged and elderly patients with chronic systemic diseases. OBJECTIVE: This study aimed to assess the association between fasting blood glucose (FPG) and body mass index (BMI) levels in patients with coronavirus disease 2019 (COVID-19) under different conditions. METHODS: Experimental-related information (age, gender, BMI, and FPG on the second day of admission) from 86 COVID-19 cases (47 males and 39 females) with an average age of (39 ± 17) years was collected in April and November 2020. These cases were divided into three groups according to the most severe classification of each case determined by the clinical early warning indicators of severe-critically illness, the degree of progression, and the treatment plan shown in the diagnosis and treatment plan of COVID-19 pneumonia. Statistical models were used to analyze the differences in the levels of FPG and BMI, age, and gender among the three groups. RESULTS: 1. Experimental group: 21 patients with asymptomatic or and mild symptoms (group A), 45 patients with common non-progression (group B), and 20 patients with common progression and severe symptoms (group C). 2. The age differences among the three groups were statistically significant and elderly patients had a higher risk of severe disease (t= 4.1404, 3.3933, 9.2123, P= 0.0001, 0.0012, 0.0000). There was a higher proportion of females than males in the normal progression and severe disease cases (χ2= 5.512, P= 0.019). 3. The level of FPG was significantly higher in group C than in group A (t= 3.1655, P= 0.0030) and B (t= 2.0212, P= 0.0475). The number of diabetes or IFG in group C was significantly higher than in group A (χ2= 5.979, P= 0.014) and group B (χ2= 6.088, P= 0.014). 4. BMI was significantly higher in group C than in groups A (t= 3.8839, P= 0.0004) and B (t= 3.8188, P= 0.0003). The number of overweight or obese patients in group C was significantly higher than in groups A (χ2= 8.838, P= 0.003) and B (χ2= 10.794, P= 0.001). 5. Patients’ age, gender, and FPG were independent risk factors for COVID-19 disease progression (β= 0.380, 0.191, 0.186; P= 0.000, 0.034, 0.045). CONCLUSION: The levels of FPG and BMI were significantly increased in the population with common progressive and severe COVID-19. FPG and age are independent risk factors for the progression of COVID-19.

Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity

The complex process of manual biomarker extraction from body composition analysis (BCA) has far restricted the analysis of SARS-CoV-2 outcomes to small patient cohorts and a limited number of tissue types. We investigate the association of two BCA-based biomarkers with the development of severe SARS-CoV-2 infections for 918 patients (354 female, 564 male) regarding disease severity and mortality (186 deceased). Multiple tissues, such as muscle, bone, or adipose tissue are used and acquired with a deep-learning-based, fully-automated BCA from computed tomography images of the chest. The BCA features and markers were univariately analyzed with a Shapiro–Wilk and two-sided Mann–Whitney-U test. In a multivariate approach, obtained markers were adjusted by a defined set of laboratory parameters promoted by other studies. Subsequently, the relationship between the markers and two endpoints, namely severity and mortality, was investigated with regard to statistical significance. The univariate approach showed that the muscle volume was significant for female (p_severity ≤ 0.001, p_mortality ≤ 0.0001) and male patients (p_severity = 0.018, p_mortality ≤ 0.0001) regarding the severity and mortality endpoints. For male patients, the intra- and intermuscular adipose tissue (IMAT) (p ≤ 0.0001), epicardial adipose tissue (EAT) (p ≤ 0.001) and pericardial adipose tissue (PAT) (p ≤ 0.0001) were significant regarding the severity outcome. With the mortality outcome, muscle (p ≤ 0.0001), IMAT (p ≤ 0.001), EAT (p = 0.011) and PAT (p = 0.003) remained significant. For female patients, bone (p ≤ 0.001), IMAT (p = 0.032) and PAT (p = 0.047) were significant in univariate analyses regarding the severity and bone (p = 0.005) regarding the mortality. Furthermore, the defined sarcopenia marker (p ≤ 0.0001, for female and male) was significant for both endpoints. The cardiac marker was significant for severity (p_female = 0.014, p_male ≤ 0.0001) and for mortality (p_female ≤ 0.0001, p_male ≤ 0.0001) endpoint for both genders. The multivariate logistic regression showed that the sarcopenia marker was significant (p_severity = 0.006, p_mortality = 0.002) for both endpoints (OR_severity = 0.42, 95% CI_severity: 0.23–0.78, OR_mortality = 0.34, 95% CI_mortality: 0.17–0.67). The cardiac marker showed significance (p = 0.018) only for the severity endpoint (OR = 1.42, 95% CI 1.06–1.90). The association between BCA-based sarcopenia and cardiac biomarkers and disease severity and mortality suggests that these biomarkers can contribute to the risk stratification of SARS-CoV-2 patients. Patients with a higher cardiac marker and a lower sarcopenia marker are at risk for a severe course or death. Whether those biomarkers hold similar importance for other pneumonia-related diseases requires further investigation.

The Importance of the Assessment of Epicardial Adipose Tissue in Scientific Research

Epicardial adipose tissue (EAT) exhibits morphological similarities with pericardial adipose tissue, however, it has different embryological origin and vascularization. EAT is a metabolically active organ and a major source of anti-inflammatory and proinflammatory adipokines, which have a significant impact on cardiac function and morphology. Moreover, it can regulate vascular tone by releasing various molecules. The relationship between EAT and cardiovascular disease and diseases of other organ systems is now considered a common discussion subject. The present clinical review article summarizes the epidemiological findings based on imaging techniques in studies conducted so far. In conclusion, evaluation of the epicardial adipose tissue constitutes a helpful scientific parameter, which can be assessed by means of different diagnostic imaging examinations.

Segmentation and volume quantification of epicardial adipose tissue in computed tomography images

BACKGROUND

Many cardiovascular diseases are closely related to the composition of epicardial adipose tissue (EAT). Accurate segmentation of EAT can provide a reliable reference for doctors to diagnose the disease. The distribution and composition of EAT often have significant individual differences, and the traditional segmentation methods are not effective. In recent years, deep learning method has been gradually introduced into EAT segmentation task.

PURPOSE

The existing EAT segmentation methods based on deep learning have a large amount of computation and the segmentation accuracy needs to be improved. Therefore, the purpose of this paper is to develop a lightweight EAT segmentation network, which can obtain higher segmentation accuracy with less computation and further alleviate the problem of false positive segmentation.

METHODS

Firstly, the obtained Computed Tomography (CT) was preprocessed. That is, the threshold range of EAT was determined to be (-190, -30) HU according to prior knowledge, and the non-adipose pixels were excluded by threshold segmentation to reduce the difficulty of training. Secondly, the image obtained after thresholding was input into the lightweight RDU-Net network to perform the training, validating, and testing process. RDU-Net uses a residual multi-scale dilated convolution block in order to extract a wider range of information without changing the current resolution. At the same time, the form of residual connection is adopted to avoid the problem of gradient expansion or gradient explosion caused by too deep network, which also makes the learning easier. In order to optimize the training process, this paper proposes PNDiceLoss, which takes both positive and negative pixels as learning targets, fully considers the class imbalance problem and appropriately highlights the status of positive pixels.

RESULTS

In this paper, 50 CCTA images were randomly selected from the hospital, and the commonly used Dice similarity coefficient (DSC), Jaccard similarity (JS), Accuracy (ACC), Specificity (SP), Precision (PC), and Pearson correlation coefficient are used as evaluation metrics. Bland-Altman analysis results show that the extracted EAT volume is consistent with the actual volume. Compared with the existing methods, the segmentation results show that the proposed method achieves better performance on these metrics, achieving the DSC of 0.9262. The number of false positive pixels has been reduced by more than half. Pearson correlation coefficient reached 0.992, and linear regression coefficient reached 0.977 when measuring the volume of EAT obtained. In order to verify the effectiveness of the proposed method, experiments are carried out in the cardiac fat database of VisualLab. On this database, the proposed method also achieved good results, and the DSC value reached 0.927 in the case of only 878 slices.

CONCLUSIONS

A new method to segment and quantify EAT is proposed. Comprehensive experiments show that compared with some classical segmentation algorithms, the proposed method has the advantages of shorter time-consuming, less memory required for operations, and higher segmentation accuracy. The code is available at https://github.com/lvanlee/EAT_Seg/tree/main/EAT_seg. This article is protected by copyright. All rights reserved.