Association of epicardial adipose tissue and left atrial size on non-contrast CT with atrial fibrillation: the Heinz Nixdorf Recall Study

Epicardial adipose tissue density predicts the presence of atrial fibrillation and its recurrence after catheter ablation: three-dimensional reconstructed image analysis

Epicardial adipose tissue (EAT) induces inflammation in the atria and is associated with atrial fibrillation (AF). Several studies have examined the relationship between EAT volume (EAT-V) and density (EAT-D) and the presence of AF after catheter ablation. However, conclusions have been inconsistent. This study included 43 consecutive patients who underwent catheter ablation for AF and 30 control patients. EAT-V and EAT-D around the entire heart, entire atrium, left atrium (LA), and right atrium (RA) were measured in detail using reconstructed three-dimensional (3D) EAT images from dual-source computed tomography (CT). None of the measurements of EAT-V differed significantly between patients with AF and controls or between patients with recurrent AF and those without. On the other hand, all measurements of EAT-D were higher in patients with AF than in controls (entire atrium, p < 0.001; RA, p < 0.001; LA, p = 0.002). All EAT-D measurements were associated with the presence of AF. Among patients with AF who underwent ablation, all EAT-D measurements were higher in patients with recurrent AF than in those without. The difference was significant for EATRA-D (p = 0.032). All atrial EAT-D values predicted recurrent AF (EATRA-D: hazard ratio [HR], 1.208; 95% confidence interval [95% CI], 1.053-1.387; p = 0.007; EATLA-D: HR, 1.108; 95% CI 1.001-1.225; p = 0.047; EATatrial-D: HR, 1.174; 95% CI 1.040-1.325; p = 0.010). The most sensitive cutoffs for predicting recurrent AF were highly accurate for EATRA-D (area under the curve [AUC], 0.76; p < 0.01) and EATatrial-D (AUC = 0.75, p < 0.05), while the cutoff for EATLA-D had low accuracy (AUC, 0.65; p = 0.209). For predicting the presence of AF and recurrent AF after catheter ablation, 3D analysis of atrial EAT-D, rather than EAT-V, is useful.

AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CACTM) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis

BACKGROUND

Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported.

METHODS

We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CACTM) to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 ​s per CAC scan. We used the 5-year outcomes data for incident atrial fibrillation (AF) and assessed discrimination using the time-dependent area under the curve (AUC) of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP. The mean follow-up time to an AF event was 2.9 ​± ​1.4 years.

RESULTS

At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p ​< ​0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p ​< ​0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p ​< ​0.01).

CONCLUSION

AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.

Quality of epicardial adipose tissue predicts major adverse cerebral and cardiovascular events following transcatheter aortic valve implantation

Epicardial adipose tissue (EAT) have been shown to be associated with several heart disease, including coronary artery disease (CAD), atrial fibrillation (AF), and heart failure (HF). It is reported that the quality of EAT, represented by fat attenuation determined using computed tomography (CT) imaging, can detect the histologically-assessed remodeled EAT. We tested the hypothesis that quality of EAT would predict major adverse cerebral and cardiovascular events (MACCE) following transcatheter aortic valve implantation (TAVI) in patients with aortic stenosis (AS). A total of 125 consecutive severe AS patients who underwent TAVI were enrolled (39 male, mean 85.4 ± 4.0 years). Using CT imaging before TAVI, we measured the average CT fat attenuation of EAT (EAT attenuation) and investigated the association with MACCE. During the mean follow up period of 567 ± 371 days, 21 cases of MACCE were observed. Patients with MACCE had greater levels of EAT attenuation compared to those without (- 74 ± 3.7 Hounsfield Units (HU) vs - 77 ± 5.5 HU, p = 0.010). Based on the ROC curves, the high EAT attenuation was defined as > - 74.3 HU. According to this cut-off index, 44 patients were classified into the high EAT attenuation group (28 female, mean age 87 ± 3.6 years), whereas 81 patients were classified into the low EAT attenuation group (13 female, 85 ± 4.1 years). Kaplan-Meier survival curve demonstrated that the patients in the high EAT attenuation group showed greater prevalence of MACCE (log-rank 6.64, p = 0.010). Cox proportional hazards regression analysis revealed that EAT attenuation and Logistic EuroSCORE were independently associated with the incidence of MACCE. Our results suggest that quality of EAT, assessed by EAT attenuation detected by CT imaging, can predict the cerebral and cardiovascular events after TAVI in patients with AS.

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis

INTRODUCTION

Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).

METHODS

We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.

RESULTS

Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​< ​0.0001), and comparable to clinical risk factors (0.85, p ​= ​0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p ​< ​0.0001).

CONCLUSION

AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis

Background

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths.

Methods

We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score.

Results

During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342).

Conclusion

In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.

Obesity and Transcatheter Aortic Valve Replacement

Amidst an aging population and escalating obesity prevalence, elucidating the impact of obesity on transcatheter aortic valve replacement (TAVR) outcomes becomes paramount. The so-called "obesity paradox"-a term denoting the counterintuitive association of obesity, typically a risk factor for cardiovascular diseases, with improved survival outcomes in TAVR patients relative to their leaner or normal-weight counterparts-merits rigorous examination. This review comprehensively investigates the complex relationship between obesity and the clinical outcomes associated with TAVR, with a specific focus on mortality and periprocedural complications. This study aims to deepen our understanding of obesity's role in TAVR and the underlying mechanisms of the obesity paradox, thereby optimizing management strategies for this patient demographic, tailored to their unique physiological and metabolic profiles.

Epicardial Atrial Fat at Cardiac Magnetic Resonance Imaging and AF Recurrence after Transcatheter Ablation

The relationship between epicardial adipose tissue (EAT) and atrial fibrillation (AF) has gained interest in recent years. The previous literature on the topic presents great heterogeneity, focusing especially on computed tomography imaging. The aim of the present study is to determine whether an increased volume of left atrial (LA) EAT evaluated at routine pre-procedural cardiac magnetic resonance imaging (MRI) relates to AF recurrences after catheter ablation. A total of 50 patients undergoing AF cryoballoon ablation and pre-procedural cardiac MRI allowing quantification of LA EAT were enrolled. In one patient, the segmentation of LA EAT could not be achieved. After a median follow-up of 16.0 months, AF recurrences occurred in 17 patients (34%). The absolute volume of EAT was not different in patients with and without AF recurrences (10.35 mL vs. 10.29 mL; p-value = 0.963), whereas the volume of EAT indexed on the LA volume (EATi) was lower, albeit non-statistically significant, in patients free from arrhythmias (12.77% vs. 14.06%; p-value = 0.467). The receiver operating characteristic curve testing the ability of LA EATi to predict AF recurrence after catheter ablation showed sub-optimal performance (AUC: 0.588). The finest identified cut-off of LA EATi was 10.65%, achieving a sensitivity of 0.5, a specificity of 0.82, a positive predictive value of 0.59 and a negative predictive value of 0.76. Patients with values of LA EATi lower than 10.65% showed greater survival, free from arrhythmias, than patients with values above this cut-off (84% vs. 48%; p-value = 0.04). In conclusion, EAT volume indexed on the LA volume evaluated at cardiac MRI emerges as a possible independent predictor of arrhythmia recurrence after AF cryoballoon ablation. Nevertheless, prospective studies are needed to confirm this finding and eventually sustain routine EAT evaluation in the management of patients undergoing AF catheter ablation.

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis

Background

Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported.

Methods

We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC), taking on average 21 seconds per CAC scan, to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). We used the 5-year outcomes data for incident atrial fibrillation (AF) and compared the time-dependent AUC of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP (BNP). The mean follow-up time to an AF event was 2.9±1.4 years.

Results

At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF or BNP at year 1 (0.836, 0.742, 0.742), year 2 (0.842, 0.807,0.772), and year 3 (0.811, 0.785, 0.745) (p<0.02), but similar for year 4 (0.785, 0.769, 0.725) and year 5 (0.781, 0.767, 0.734) respectively (p>0.05). AI-CAC LA volume significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CAC score (0.74, 0.49, 0.53, 0.39, 0.44), CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and BNP (0.68, 0.44, 0.42, 0.30, 0.37) respectively (p<0.01).

Conclusion

AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and BNP.

Sex differences in the association between epicardial adipose tissue volume and left atrial volume index

BACKGROUND

Sex disparities in the association between epicardial adipose tissue volume (EATV) and cardiovascular disease have been reported. The sex-dependent effects of EATV on left atrial (LA) size have not been elucidated.

METHODS

Consecutive 247 subjects (median 65 [interquartile range 57, 75] years; 67% of men) who underwent multi-detector computed tomography without significant coronary artery disease or moderate to severe valvular disease were divided into two groups: patients with sinus rhythm (SR) or atrial fibrillation (AF). Sex differences in the association between the EATV index (EATVI) (mL/m2) and LA volume index (LAVI) in 63 SR (28 men and 35 women) and 184 AF (137 men and 47 women) patients were evaluated using univariate and multivariate regression analyses.

RESULTS

In overall that includes both men and women, the relationship between EATVI and LAVI was not significantly correlated for patients with SR and AF. The relationship between EATVI and LAVI differed between men and women in both SR and AF groups. In SR patients, there was a positive relationship between EATVI and LAVI in men, but not in women. In contrast, in patients with AF, a negative relationship was found between EATVI and LAVI in women, whereas no association was found in men.

CONCLUSIONS

We evaluated sex differences in the association between EATVI and LAVI in patients with either SR or AF, and found a positive relationship in men with SR and a negative relationship in women with AF. This is the first report to evaluate sex differences in the relationship between EATVI and LAVI, suggesting that EAT may play a role, at least in part, in sex differences in the etiology of AF.

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 Adipose Tissue in Myocardial Disease: From Physiology to Heart Failure Phenotypes

Epicardial adipose tissue (EAT) is increasingly being recognized as a determinant of myocardial biology. The EAT-heart crosstalk suggests causal links between dysfunctional EAT and cardiomyocyte impairment. Obesity promotes EAT dysfunction and shifts in secreted adipokines which adversely affect cardiac metabolism, induce cardiomyocyte inflammation, redox imbalance and myocardial fibrosis. Thus, EAT determines cardiac phenotype via effects on cardiac energetics, contractility, diastolic function, and atrial conduction. Vice-versa the EAT is altered in heart failure (HF), and such phenotypic changes can be detected by noninvasive imaging or incorporated in Artificial Intelligence-enhanced tools to aid the diagnosis, subtyping or risk prognostication of HF. In the present article, we summarize the links between EAT and the heart, explaining how the study of epicardial adiposity can improve the understanding of cardiac disease, serve as a source of diagnostic and prognostic biomarkers, and as a potential therapeutic target in HF to improve clinical outcomes.

Impact of Obesity on Atrial Electrophysiological Substrate

(1) Background. Obesity is a well-established worldwide recognised risk factor for atrial fibrillation (AF). Prior review papers reported on the associations between obesity and AF development, but not on the relation between obesity and atrial electrophysiology. We therefore conducted a systematic review to describe the current knowledge of the characteristics of the atrial electrophysiological substrate in obese individuals and how they relate to the development of AF. (2) Methods. A search was conducted in Pubmed, Embase, and the Cochrane Library for publications evaluating the impact of obesity on atrial electrophysiology, electrical substrates, and their relation to the development of AF. (3) Results. A systematic literature search retrieved 477 potential publications based on the inclusion criteria; 76 full-text articles were selected for the present systematic review. The literature demonstrated that obesity predisposes to not only a higher AF incidence but also to more extensive atrial electrophysiological abnormalities increasing susceptibility to AF development. (4) Conclusion. Obesity may predispose to an overall increase in atrial electropathology, consisting of an increase in the slowing of the conduction, conduction block, low-voltage areas, and complex fractionated electrograms. To determine the impact of obesity-induced atrial electrical abnormalities on the long-term clinical outcome, further prospective studies are mandatory.

State of the art paper: Cardiac computed tomography of the left atrium in atrial fibrillation

The clinical spectrum of atrial fibrillation means that a patient-individualized approach is required to ensure optimal treatment. Cardiac computed tomography can accurately delineate atrial structure and function and could contribute to a personalized care pathway for atrial fibrillation patients. The imaging modality offers excellent spatial resolution and has been utilised in pre-, peri- and post-procedural care for patients with atrial fibrillation. Advances in temporal resolution, acquisition times and analysis techniques suggest potential expanding roles for cardiac computed tomography in the future management of patients with atrial fibrillation. The aim of the current review is to discuss the use of cardiac computed tomography in atrial fibrillation in pre-, peri- and post-procedural settings. Potential future applications of cardiac computed tomography including atrial wall thickness assessment and epicardial fat volume quantification are discussed together with emerging analysis techniques including computational modelling and machine learning with attention paid to how these developments may contribute to a personalized approach to atrial fibrillation management.

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.

Depot-specific adipose tissue modulation by SGLT2 inhibitors and GLP1 agonists mediates their cardioprotective effects in metabolic disease

Sodium-glucose transporter-2 inhibitors (SGLT-2i) and glucagon-like peptide 1 (GLP-1) receptor agonists are newer antidiabetic drug classes, which were recently shown to decrease cardiovascular (CV) morbidity and mortality in diabetic patients. CV benefits of these drugs could not be directly attributed to their blood glucose lowering capacity possibly implicating a pleotropic effect as a mediator of their impact on cardiovascular disease (CVD). Particularly, preclinical and clinical studies indicate that SGLT-2i(s) and GLP-1 receptor agonists are capable of differentially modulating distinct adipose pools reducing the accumulation of fat in some depots, promoting the healthy expansion of others, and/or enhancing their browning, leading to the suppression of the metabolically induced inflammatory processes. These changes are accompanied with improvements in markers of cardiac structure and injury, coronary and vascular endothelial healing and function, vascular remodeling, as well as reduction of atherogenesis. Here, through a summary of the available evidence, we bring forth our view that the observed CV benefit in response to SGLT-2i or GLP-1 agonists therapy might be driven by their ameliorative impact on adipose tissue inflammation.

In Silico Identification of Key Genes and Immune Infiltration Characteristics in Epicardial Adipose Tissue from Patients with Coronary Artery Disease

Background

The present study is aimed at identifying the differentially expressed genes (DEGs) and relevant biological processes and pathways associated with epicardial adipose tissue (EAT) from patients with coronary artery disease (CAD). We also explored potential biomarkers using two machine-learning algorithms and calculated the immune cell infiltration in EAT.

Materials and Methods

Three datasets (GSE120774, GSE64554, and GSE24425) were obtained from the Gene Expression Omnibus (GEO) database. The GSE120774 dataset was used to evaluate DEGs between EAT of CAD patients and the control group. Functional enrichment analyses were conducted to study associated biological functions and mechanisms using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA). After this, the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were performed to identify the feature genes related to CAD. The expression level of the feature genes was validated in GSE64554 and GSE24425. Finally, we calculated the immune cell infiltration and evaluated the correlation between the feature genes and immune cells using CIBERSORT.

Results

We identified a total of 130 upregulated and 107 downregulated genes in GSE120774. Functional enrichment analysis revealed that DEGs are associated with several pathways, including the calcium signaling pathway, complement and coagulation cascades, ferroptosis, fluid shear stress and atherosclerosis, lipid and atherosclerosis, and regulation of lipolysis in adipocytes. TCF21, CDH19, XG, and NNAT were identified as feature genes and validated in the GSE64554 and GSE24425 datasets. Immune cell infiltration analysis showed plasma cells are significantly more numerous in EAT than in the control group (p = 0.001), whereas macrophage M0 (p = 0.024) and resting mast cells (p = 0.036) were significantly less numerous. TCF21, CDH19, XG, and NNAT were correlated with immune cells, including plasma cells, M0 macrophages, and resting mast cells.

Conclusion

TCF21, CDH19, XG, and NNAT might serve as feature genes for CAD, providing new insights for future research on the pathogenesis of cardiovascular diseases.

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 Predictive Value of Epicardial Fat Tissue Volume in the Occurrence and Development of Atrial Fibrillation: A Systematic Review and Meta-Analysis

Background

Atrial fibrillation (AF) is one of the most common arrhythmias in clinical practice. Although fat is currently considered to be a risk factor for AF and a pathogenic link between epicardial fat tissue (EFT) and AF has been speculated, there are currently few clinical studies and literature data domestically or abroad.

Objective

This study conducted a meta-analysis of observational case series studies to verify the relationship between atrial fibrillation and EFT and to strengthen the predictive value of EFT in the occurrence, development, and postablative recurrence of AF.

Methods

We conducted a systematic search of the literature in electronic databases until December 2021 and supplemented this through manual searches of individual studies, reviewed articles, and reference lists in conference proceedings. This study conducted a meta-analysis to compare the differences between different populations, such as healthy participants and AF patients, healthy subjects and AF subtype cases, and paroxysmal and persistent AF with AF recurrence and without AF recurrence after ablation.

Results

Following the retrieval of 828 articles, only 22 articles were selected as research results. Accordingly, the meta-analysis results show that the volume of EFT in AF is greater than that in healthy subjects (MD = 39.34 ml, 95% CI = 27.11, 51.58); persistent AF is greater than paroxysmal AF (MD = 14.37 ml, 95% CI = 7.46, 21.27); and recurrence after ablation is greater than without recurrence (MD = 14.37 ml, 95% CI = 7.46, 21.27).

Conclusion

The results of this study further confirm the connection between EFT and AF and that EFT has a certain predictive value for the occurrence and development of AF.

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.

Atrial Fibrillation after Coronary Bypass Surgery? Is Epicardial Fat a Risk Factor?

BACKGROUNDS

 Postoperative atrial fibrillation (POAF) is one of the most common complication of cardiac surgery. Epicardial fat tissue may play a role in the development of atrial fibrillation (AF). The aim of this study was to evaluate relationship between epicardial fat volume (EFV) and the appearance of new-onset AF in patients undergoing isolated coronary artery bypass graft (CABG) with normal echocardiographic functions.

METHODS

 Between January 2017 and June 2020, 281 coronary artery disease patients undergone isolated CABG surgery with normal echocardiographic functions were included in the study. Patient characteristics, medical history, and perioperative variables were retrospectively collected. Patients with AF predisposition factors were excluded.

RESULTS

 Sixty-seven patients (23.8%) developed postoperatively AF during hospital stay. In univariate analysis, patients with postoperative AF were older compared with sinus rhythm patients (60.78 ± 9.03 vs. 65.46 ± 9.22, p = 0.001). There are no statistically significant differences between groups and EFV compared (107.78 ± 41.04 vs. 106.66 ± 34.98 p = 0.84). Large left atrial diameter, female patient, cardiopulmonary bypass and longer cross-clamp time showed correlation between POAF without statistical significance.

CONCLUSION

 Aging is the only associated factor with AF in this study. There was no EFV difference between POAF and non-AF groups in patients undergoing isolated CABG with normal echocardiographic functions.

Epicardial adipose tissue is associated with higher recurrence risk after catheter ablation in atrial fibrillation patients: a systematic review and meta-analysis

OBJECTIVE

Our study aimed to investigate the association between epicardial adipose tissue (EAT) and atrial fibrillation (AF) recurrence risk after catheter ablation.

METHODS

We searched PubMed, Embase, and Cochrane Library databases up to November 30, 2021 without language restrictions. Outcome was the relative risk (RR) of EAT contributes to AF recurrence after catheter ablation. The RR and 95% confidence interval (CI) was pooled by the random-effect model.

RESULTS

10 studies that contained 1840 AF patients were included in our study. The result of this study showed that EAT amount was associated with higher risk of AF recurrence after catheter ablation (RR = 1.06, 95% CI 1.02-1.11, P = 0.005) and EAT related thickness was a risk factor for AF recurrence after catheter ablation (RR = 1.73, 95% CI 1.04-2.87, P = 0.040). Sub-analysis showed that EAT was strongly associated with higher risk of AF recurrence common in Asian population (RR = 1.25, 95% CI 1.10-1.43, P < 0.001), patients aged ≤ 60 years old (RR = 2.01, 95% CI 1.18-3.44, P = 0.010), and follow-up more than 1 year (RR = 1.06, 95% CI 1.01-1.11, P = 0.020).

CONCLUSION

The meta-analysis demonstrated that EAT related thickness seems to be the marker most strongly associated with a greater risk of AF recurrences after catheter ablation. It should be included into risk stratification for predicting AF recurrent before catheter ablation.

Epicardial fat and incident heart failure with preserved ejection fraction in patients with coronary artery disease

BACKGROUND

We aimed to determine, whether epicardial adipose tissue (EAT) as local source of inflammation, as well as its change over time, associates with the development of heart failure with preserved ejection fraction (HFpEF) in patients with coronary artery disease.

METHODS AND RESULTS

We retrospectively included 379patients (aged 65.2 ± 11.7 years, 70.2%male) with coronary artery disease but without heart failure at baseline, undergoing clinical and echocardiographic assessment in 2010-2013 and receiving a second assessment in 2014-2018. EAT thickness was defined as space between the myocardium and the pericardium and indexed (EATi) by body surface area. Change in EATi was calculated as the difference of follow-up and baseline EATi. HFpEF was defined according to presence of dyspnea, elevated natriuretic peptides, and structural and/or functional alterations on echocardiography in accordance with current European Society of Cardiology guidelines. During a median follow-up of 4.3 years, 142patients (37.5%) developed HFpEF. Patients with onset of HFpEF had higher EATi at baseline (2.4 ± 1.3 vs. 1.9 ± 0.9 mm/m², p = 0.001). In multivariable regression analysis, EATi associated with onset of HFpEF (1.25 [1.01-1.54], p = 0.04). Likewise, an increase in EATi over time was linked HFpEF development, independent of other risk factors and baseline EATi (1.39 [1.04-1.87], p = 0.03). EATi was significantly associated with follow-up b-type natriuretic peptide (BNP) levels (4.31[0.58-8.05], p = 0.024), but not with baseline BNP (2.24[-0.27-4.76], p = 0.08).

CONCLUSION

EATi is associated with the development of HFpEF. The finding of changes in EATi altering the risk of HFpEF manifestation support the rationale for further research on epicardial fat modulation as a treatment target for HFpEF.

Association between left atrial epicardial fat, left atrial volume, and the severity of atrial fibrillation

AIMS

Left atrial (LA) volume and LA epicardial fat are both substrates for atrial fibrillation (AF), but may relate with AF at different (early vs. late) stages in the AF disease process. We evaluated associations between LA epicardial fat and LA volume in patients with sinus rhythm (SR), paroxysmal AF (PAF), and persistent/permanent AF.

METHODS AND RESULTS

In total, 300 patients (100 with SR, 100 with PAF, and 100 with persistent/permanent AF) who underwent cardiac computed tomography angiography (CTA) were included. The epicardial fat mass posterior to the LA and the LA volume were quantified from CTA and compared between patients with SR, PAF, and persistent/permanent AF. Furthermore, four groups were created by classifying LA epicardial fat and LA volume into large or small according to their median. The mean age of the population was 58.9 ± 10.5 years and 69.7% was male. Left atrial epicardial fat mass was larger in patients with PAF compared with SR, but did not further increase from PAF to persistent/permanent AF. Left atrial volume increased significantly from SR to PAF and to persistent/permanent AF. Left atrial epicardial fat and LA volume were both concordantly large or small in 184 (61%) patients, and discordant in 116 (39%). When both were small, 65.2% of the patients had SR, 23.9% PAF, and 10.9% persistent/permanent AF. When the LA epicardial fat mass was large and the LA volume small (compared with both being small), patients were significantly more often in PAF (55.2 vs. 23.9, P < 0.05), less frequently in SR (32.8% vs. 65.2%, P < 0.05) but showed comparable rates of persistent/permanent AF (12.0% vs. 10.9%, P < 0.05). When the LA volume was large, most patients had persistent/permanent AF.

CONCLUSION

Left atrial epicardial fat mass was larger in PAF vs. SR, possibly indicating a marker of early disease, while large LA volumes were associated with a high prevalence of persistent/permanent AF. Elevated LA epicardial fat mass without large LA volume may reflect the early AF disease process.

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

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

Changes in epicardial adipose tissue among women treated with trastuzumab for breast cancer

BACKGROUND

Epicardial adipose tissue (EAT) as an endocrine organ, secreting hormones, and inflammatory cytokines is associated with adverse cardiovascular outcomes and may have a role in trastuzumab-induced cardiotoxicity (TIC). We sought to assess changes in EAT volume and radiodensity after receiving trastuzumab and if they are associated with TIC.

METHODS

A total of 185 women treated with trastuzumab for human epidermal growth factor receptor 2-positive breast cancer were retrospectively recruited. All patients underwent echocardiography and CT before and during trastuzumab therapy. The time interval between CT and echocardiography was <10 days. EAT volume and density were quantified by CT. TIC was defined as a left ventricular ejection fraction (LVEF) decrease of >10% and < 53%.

RESULTS

Of the 185 patients, 18 (9.7%) experienced TIC. After receiving trastuzumab, EAT volume and radiodensity were increased, despite similar BMI. TIC group showed a significantly higher increment of EAT volume (21.2 ± 6.3 vs. 11.7 ± 10.5 ml, p < 0.001) and radiodensity (2.7 ± 1.8 vs. 1.5 ± 2.0HU, p < 0.05) than no TIC group. There was a high negative correlation between changes in EAT volume and LVEF (r = -0.70; p < 0.001) and a moderately negative correlation between changes in EAT radiodensity and LVEF (r = -0.50; p < 0.001). Increased EAT volume, but not radiodensity appeared to be a good imaging biomarker for TIC (AUC: 0.79 vs. 0.65, p < 0.05).

CONCLUSIONS

EAT volume and radiodensity were increased after receiving trastuzumab particularly in the TIC patients despite similar BMI. Notably, the increased EAT volume rather than radiodensity was strongly negatively associated with LVEF and appeared to be a good imaging biomarker of TIC.

CT-derived epicardial adipose tissue density: Systematic review and meta-analysis

PURPOSE

The aim of our work was to systematically review and meta-analyze epicardial adipose tissue (EAT) density values reported in literature, assessing potential correlations of EAT density with segmentation thresholds and other technical and clinical variables.

METHOD

A systematic search was performed, aiming for papers reporting global EAT density values in Hounsfield Units (HU) in patients undergoing chest CT for any clinical indication. After screening titles, abstract and full text of each retrieved work, studies reporting mean and standard deviation for EAT density were ultimately included. Technical, clinical and EAT data were extracted, and divided into subgroups according to clinical conditions of reported subjects. Pooled density analyses were performed both overall and for subgroups according to clinical conditions. Metaregression analyses were done to appraise the impact of clinical and technical variables on EAT volume.

RESULTS

Out of 152 initially retrieved works, 13 were ultimately included, totaling for 7683 subjects. EAT density showed an overall pooled value of -85.86 HU (95% confidence interval [95% CI] -91.84, -79.89 HU), being -86.40 HU (95% CI -112.69, -60.12 HU) in healthy subjects and -80.71 HU (95% CI -87.43, -73.99 HU) in patients with coronary artery disease. EAT volume and lower and higher segmentation thresholds were found to be significantly correlated with EAT density (p = 0.044, p < 0.001 and p< 0.001 respectively).

CONCLUSIONS

Patients with coronary artery disease appear to present with higher EAT density values, while the correlations observed at metaregression highlight the need for well-established, shared thresholds for EAT segmentation.

Associations of body size and composition with subclinical cardiac dysfunction in older individuals: the cardiovascular health study

BACKGROUND

Focused studies in younger to middle-aged populations have demonstrated a relationship between obesity and adverse cardiac mechanics. We examined whether measures of overall and central adiposity are associated with cardiac mechanics, assessed by speckle-tracking echocardiography, in an older population without prevalent coronary heart disease or heart failure.

METHODS

Body composition was measured by anthropometry, bioelectrical impedance, and dual-energy x-ray absorptiometry among participants in the Cardiovascular Health Study, a population-based cohort of adults aged 65 years or older. Systolic and diastolic cardiac mechanics were measured with speckle-tracking analysis of echocardiograms. Multi-variable adjusted linear regression models were used to investigate associations of body composition measures and cardiac mechanics.

RESULTS

Mean age for the 3525 included participants was 72.6 years, 39% were male, and 10% were black. Mean body-mass index (BMI) was 26.3 ± 4.4 kg/m², waist circumference (WC) was 93.2 ± 12.9 cm, and waist-to-hip ratio was 0.92 ± 0.09. In fully adjusted analyses, all adiposity measures were associated with worse LV longitudinal strain, LV early diastolic strain rate, and left atrial reservoir strain; however, associations were strongest for WC and BMI (p < 0.001). When both BMI and WC were included in the same model, only WC remained associated with each cardiac strain measure.

CONCLUSION

In this cross-sectional study of older adults, central obesity was most robustly associated with impaired left ventricular systolic and diastolic strain as well as left atrial strain. The adverse effects of central obesity appear to extend even into older age.

Contemporary narrative review on left atrial strain mechanics in echocardiography: cardiomyopathy, valvular heart disease and beyond

Left atrial (LA) strain mechanics refer to the measurement of LA myocardial deformation expressed as a percentage, and have been gathering interest over the last decade with expanding research supporting their utility in multiple cardiovascular disorders. Measured through advanced dynamic imaging techniques which include tissue Doppler imaging (TDI) and two-dimensional (2D) speckle tracking echocardiography (STE), LA strain mechanics are affected by left ventricular diastolic dysfunction prior to the onset of functional and structural changes in the left ventricle (LV). There is a need for practising cardiologists to become more familiar with the clinical utility of LA strain mechanics. In this article, we begin by reviewing the physiologic function of the LA, using this as a basis for understanding LA strain mechanics. The focus of this review article is to provide a contemporary update on the utility of LA strain mechanics in a range of cardiovascular disorders, including atrial fibrillation (AF), hypertrophic cardiomyopathy (HCM), valvular pathologies, coronary artery disease (CAD) as well as systemic diseases, such as hypertension (HTN), obesity and diabetes mellitus (DM). This article also highlights the current limitations in more widespread clinical applications of LA strain mechanics, as well as outlining the future perspectives on the clinical applications of LA strain mechanics.

Epicardial fat and the risk of atrial tachy-arrhythmia recurrence post pulmonary vein isolation: a computed tomography study

Epicardial and Pericardia fat have been hypothesized to exert local and systemic pathogenic effects on nearby cardiac structures. The present study aimed to evaluate the impact of epicardial and pericardial fat volumes on the outcome of patients that underwent a first pulmonary vein isolation (PVI) with cryoablation. We included 130 consecutive patients with atrial fibrillation (AF) that underwent contrast enhanced ECG-gated cardiac computed tomography (CCT) before a PVI. The control group included 50 patients in normal sinus rhythm that underwent ECG-gated CT to rule out coronary artery disease. Epicardial and pericardial fat volumes were quantified with CCT. Patients with AF compared to patients with normal sinus rhythm (control group) had significantly larger epicardial (140.3 ± 58.1 vs. 55.9 ± 17.7 ml; respectively, P < 0.001) and pericardial (77.0 ± 35.5 ml vs. 27.2 ± 9.5 ml; respectively, P < 0.001) fat volumes. Among patients that underwent PVIs, those with AF recurrence had a greater epicardial (175.0 ± 54.4 ml vs. 130.7 ± SD 54.2 ml; respectively, P < 0.001) and pericardial (93.7 ± SD 42.8 vs. 72.5 ± SD 31.9 ml; respectively, P < 0.001) fat volumes, compared to patients with no AF recurrence. Multivariate analyses revealed that epicardial fat was an independent predictor of recurrence post-ablation (HR = 1.08, 95% CI 1.02-1.16 per 10-ml increase in volume; P = 0.009). Pericardial fat was associated with 7% increase in risk of recurrent AF (HR = 1.07, 95% CI 0.98-1.18; P = 0.117). Epicardial fat, assessed with contrast enhanced CCT, is an independent predictor of AF recurrence after PVI ablation.

Cardiac Adiposity and Arrhythmias: The Role of Imaging

Increased cardiac fat depots are metabolically active tissues that have a pronounced pro-inflammatory nature. Increasing evidence supports a potential role of cardiac adiposity as a determinant of the substrate of atrial fibrillation and ventricular arrhythmias. The underlying mechanism appears to be multifactorial with local inflammation, fibrosis, adipocyte infiltration, electrical remodeling, autonomic nervous system modulation, oxidative stress and gene expression playing interrelating roles. Current imaging modalities, such as echocardiography, computed tomography and cardiac magnetic resonance, have provided valuable insight into the relationship between cardiac adiposity and arrhythmogenesis, in order to better understand the pathophysiology and improve risk prediction of the patients, over the presence of obesity and traditional risk factors. However, at present, given the insufficient data for the additive value of imaging biomarkers on commonly used risk algorithms, the use of different screening modalities currently is indicated for personalized risk stratification and prognostication in this setting.

Association of pericardial adipose tissue with left ventricular structure and function: a region-specific effect?

BACKGROUND

The independent role of pericardial adipose tissue (PAT) as an ectopic fat associated with cardiovascular disease (CVD) remains controversial. This study aimed to determine whether PAT is associated with left ventricular (LV) structure and function independent of other markers of general obesity.

METHODS

We studied 2471 participants (50.9 % women) without known CVD from the Korean Genome Epidemiology Study, who underwent 2D-echocardiography with tissue Doppler imaging (TDI) and computed tomography measurement for PAT.

RESULTS

Study participants with more PAT were more likely to be men and had higher cardiometabolic indices, including blood pressure, glucose, and cholesterol levels (all P < 0.001). Greater pericardial fat levels across quartiles of PAT were associated with increased LV mass index and left atrial volume index (all P < 0.001) and decreased systolic (P = 0.015) and early diastolic (P < 0.001) TDI velocities, except for LV ejection fraction. These associations remained after a multivariable-adjusted model for traditional CV risk factors and persisted even after additional adjustment for general adiposity measures, such as waist circumference and body mass index. PAT was also the only obesity index independently associated with systolic TDI velocity (P < 0.001).

CONCLUSIONS

PAT was associated with subclinical LV structural and functional deterioration, and these associations were independent of and stronger than with general and abdominal obesity measures.

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

(1) Background: Epi- and Paracardial Adipose Tissue (EAT, PAT) have been spotlighted as important biomarkers in cardiological assessment in recent years. Since biomarker quantification is an increasingly important method for clinical use, we wanted to examine fully automated EAT and PAT quantification for possible use in cardiovascular risk stratification. (2) Methods: 966 patients with intermediate Framingham risk scores for Coronary Artery Disease referred for coronary calcium scans were included in clinical routine retrospectively. The Coronary Artery Calcium Score (CACS) was extracted and tissue quantification was performed by a deep learning network. (3) Results: The Computed Tomography (CT) segmentations predicted by the network indicated no significant correlation between EAT volume and EAT radiodensity when compared to Agatston score (r = 0.18, r = -0.09). CACS 0 category patients showed significantly lower levels of total EAT and PAT volumes and higher EAT and PAT densities than CACS 1-99 category patients (p < 0.01). Notably, this difference did not reach significance regarding EAT attenuation in male patients. Women older than 50 years, thus more likely to be postmenopausal, were shown to be at higher risk of coronary calcification (p < 0.01, OR = 4.59). CACS 1-99 vs. CACS ≥100 category patients remained below significance level (EAT volume: p = 0.087, EAT attenuation: p = 0.98). (4) Conclusions: Our study proves the feasibility of a fully automated adipose tissue analysis in clinical cardiac CT and confirms in a large clinical cohort that volume and attenuation of EAT and PAT are not correlated with CACS. Broadly available deep learning based rapid and reliable tissue quantification should thus be discussed as a method to assess this biomarker as a supplementary risk predictor in cardiac CT.

Epicardial adipose tissue differentiates in patients with and without coronary microvascular dysfunction

BACKGROUND/OBJECTIVES

Coronary microvascular dysfunction (CMD) is a common disorder, leading to symptoms similar to obstructive coronary artery disease and bears important prognostic implications. Local inflammation is suggested to promote development of CMD. Epicardial adipose tissue (EAT) is a local visceral fat depot surrounding the heart and the coronary arteries, modifying the inflammatory environment of the heart. We compared EAT in patients with and without CMD.

METHODS

We retrospectively included consecutive patients undergoing diagnostic coronary angiography as well as transthoracic echocardiography between March and October 2016. EAT thickness was defined as space between the epicardial wall of the myocardium and the visceral layer of the pericardium and EAT index was calculated as EAT thickness/body surface area. Logistic regression analysis was used to determine the association of EAT index with the presence of CMD.

RESULTS

Overall, 399 patients (mean age 60.2 ± 14.0 years, 46% male) were included. EAT thickness was significantly higher in patients with CMD compared to patients without CMD (EAT thickness 4.4 ± 1.8 vs. 4.9 ± 2.4 mm, p = 0,048 for patients without and with CMD, respectively). In univariate regression analysis, EAT index was associated with a 30% higher frequency of CMD (odds ratio [95% confidence interval]: 1.30 [1.001-1.69], p = 0.049). Effect sizes remained stable upon adjustment for body mass index (BMI, 1.30 [1.003-1.70], p = 0.048), but were attenuated when ancillary adjusting for age and gender (1.17 [0.90-1.54, p = 0.25). The effect was more pronounced in patients >65 years of age and independent of BMI and sex (1.85 [1.14-3.00], p = 0.013).

CONCLUSION

EAT thickness is independently associated with CMD and can differentiate between patients with and without CMD especially in older age groups. Our results support the hypothesis that modulation of local inflammation by epicardial fat is involved in the development of CMD.

Prognostic importance of left atrial size measured by non-contrast cardiac computed tomography - A DANCAVAS study

BACKGROUND

Enlargement of left atrium (LA) is a valuable marker of cardiovascular events, and LA size is readily available while performing non-contrast cardiac computed tomography (NCCT) for preventive purposes. We aimed to evaluate the predictive value of a single LA area from NCCT in a population-based cohort.

METHOD

Mainly men aged 60-75 years from DANCAVAS were included. Traditional risk factors were recorded, and an NCCT scan performed at baseline. Coronary artery calcifications (CAC) score and the largest LA area were measured. LA was indexed to body surface area and categorised into four groups. Data on incident atrial fibrillation (AF), thromboembolic events, heart failure (HF) and death were obtained from Danish national registries.

RESULTS

In total, 14,557 individuals were eligible, excluding those without LA measurement (N = 232) and with heart valve replacement (N = 197). Known AF or HF were respectively excluded from follow-up. Median follow-up time was 2.1 to 3.4 years. In total, 304 developed AF, 149 had thromboembolism, 129 developed HF and 482 died. In adjusted analysis, LA enlargement was associated with AF (HR (95% CI): large 1.99 (1.46-2.71) and very large LA 3.77 (2.31-6.14)) and HF (large 2.40 (1.50-3.85) and very large LA 6.54 (4.07-10.51)). A very large LA significantly increased mortality (HR: 2.01 (1.44-2.82)), and was associated with a two-fold increased risk of thromboembolism; however, not significantly in adjusted analysis (p = 0.09).

CONCLUSION

We demonstrated that determination of LA area from NCCT was an important predictor of AF, HF and death. This knowledge could inform current risk assessment beyond CAC score.

Inflammation and adiposity: new frontiers in atrial fibrillation

The aetiology of atrial fibrillation (AF) remains poorly understood, despite its growing prevalence and associated morbidity, mortality, and healthcare costs. Obesity is implicated in myriad different disease processes and is now recognized a major risk factor in the pathogenesis of AF. Moreover, the role of distinct adipose tissue depots is a matter of intense scientific interest with the depot directly surrounding the heart—epicardial adipose tissue (EAT) appearing to have the greatest correlation with AF presence and severity. Similarly, inflammation is implicated in the pathophysiology of AF with EAT thought to act as a local depot of inflammatory mediators. These can easily diffuse into atrial tissue with the potential to alter its structural and electrical properties. Various meta-analyses have indicated that EAT size is an independent risk factor for AF with adipose tissue expansion being inevitably associated with a local inflammatory process. Here, we first briefly review adipose tissue anatomy and physiology then move on to the epidemiological data correlating EAT, inflammation, and AF. We focus particularly on discussing the mechanistic basis of how EAT inflammation may precipitate and maintain AF. Finally, we review how EAT can be utilized to help in the clinical management of AF patients and discuss future avenues for research.

The Relationship Between Epicardial Adipose Tissue Thickness and Presence of Left Atrial Thrombus in Mitral Stenosis Patients

Objective

To examine the relationship between the left atrial (LA) thrombus presence and the epicardial adipose tissue (EAT) thickness.

Methods

Three hundred and twelve consecutive rheumatic mitral valve stenosis (RMVS) patients with mitral valve area (MVA) < 2 cm2 were included in this cross-sectional study. Patients were divided into two groups, those with and those without LA thrombus. Routine biochemical analysis and electrocardiographic examinations were carried out. EAT was measured using transthoracic echocardiography.

Results

LA thrombus was determined in 84 (26.9%) RMVS patients. In echocardiographic examinations, higher mean gradient and LA diameter as well as lower MVA were found in the group with LA thrombus (P<0.001). In this group, higher C-reactive protein (CRP) and EAT values were also determined (P<0.001). There was significant correlation between EAT and MVA, CRP, LA appendage peak flow velocity, LA anteroposterior diameter, and mean gradient (P<0.001). Higher EAT values were identified as independently associated with the presence of LA thrombus (odds ratio 59.5; 95% confidence interval 12.1-290.10; P<0.001).

Conclusion

Transthoracic echocardiography, routinely used in patients with RMVS, can measure EAT to determine patients who are under risk for thrombus.

Epicardial adipose tissue: an emerging biomarker of cardiovascular complications in type 2 diabetes?

Type 2 diabetes (T2D) is associated with an increased risk of cardiovascular disease and heart failure, which highlights the need for improved understanding of factors contributing to the pathophysiology of these complications as they are the leading cause of mortality in T2D. Patients with T2D have high levels of epicardial adipose tissue (EAT). EAT is known to secrete inflammatory factors, lipid metabolites, and has been proposed to apply mechanical stress on the cardiac muscle that may accelerate atherosclerosis, cardiac remodeling, and heart failure. High levels of EAT in patients with T2D have been associated with atherosclerosis, diastolic dysfunction, and incident cardiovascular events, and this fat depot has been suggested as an important link coupling diabetes, obesity, and cardiovascular disease. Despite this, the predictive potential of EAT in general, and in patients with diabetes, is yet to be established, and, up until now, the clinical relevance of EAT is therefore limited. Should this link be established, importantly, studies show that this fat depot can be modified both by pharmacological and lifestyle interventions. In this review, we first introduce the role of adipose tissue in T2D and present mechanisms involved in the pathophysiology of EAT and pericardial adipose tissue (PAT) in general, and in patients with T2D. Next, we summarize the evidence that these fat depots are elevated in patients with T2D, and discuss whether they might drive the high cardiometabolic risk in patients with T2D. Finally, we discuss the clinical potential of cardiac adipose tissues, address means to target this depot, and briefly touch upon underlying mechanisms and future research questions.

The impact of catheter ablation of atrial fibrillation on the left atrial volume and function: study using three-dimensional echocardiography

Purpose

The exact correlation between the baseline left atrial (LA) volume (LAV) and atrial fibrillation (AF) radiofrequency catheter ablation (RFCA) outcomes and changes to the LA after AF RFCA has not yet been fully understood. We sought to evaluate the serial changes in the LAV and LA function after RFCA using 3D echocardiography.

Methods

Consecutive patients who received RFCA of paroxysmal (PAF) or persistent AF (PeAF) at our center between January 2013 and March 2016 were included. Real-time 3D apical full-volume images were acquired, and a 3D volumetric assessment was performed using an automated three-beat averaging method. The LAV index (LAVI) was calculated and the LA ejection fraction (LAEF) was calculated as [LAVmax − LAVmin]/LAVmax.

Results

Ninety-nine total patients were enrolled, and the mean age was 58.0 ± 8.2 years and 75 (74.7%) were male. There were 59 (59.6%) PAF patients and the remaining 40 (40.4%) had PeAF. AF recurred in 5 of 59 (8.5%) PAF and in 10 of 40 (25%) PeAF patients. The LAVImax increased on 1 day, decreased at 3 months, and then increased again at 1 year but was lower than that at baseline. The LAEF changes were similar to the volume changes but were more prominent in PeAF than PAF patients. The baseline 3D LAVImax was an independent predictor of AF recurrence after RFCA and the cut-off value was 44.13 ml/m².

Conclusion

In our study, even after 3 months of scar formation due to ablation, structural remodeling of the LA continued. The changes were more prominent in the non-recurrent, PeAF patients.

Electronic supplementary material

The online version of this article (10.1007/s10840-019-00696-8) contains supplementary material, which is available to authorized users.

Left Atrial Volume as a Biomarker of Atrial Fibrillation at Routine Chest CT: Deep Learning Approach

Purpose

To test the performance of a deep learning (DL) model in predicting atrial fibrillation (AF) at routine nongated chest CT.

Materials and Methods

A retrospective derivation cohort (mean age, 64 years; 51% female) consisting of 500 consecutive patients who underwent routine chest CT served as the training set for a DL model that was used to measure left atrial volume. The model was then used to measure atrial size for a separate 500-patient validation cohort (mean age, 61 years; 46% female), in which the AF status was determined by performing a chart review. The performance of automated atrial size as a predictor of AF was evaluated by using a receiver operating characteristic analysis.

Results

There was good agreement between manual and model-generated segmentation maps by all measures of overlap and surface distance (mean Dice = 0.87, intersection over union = 0.77, Hausdorff distance = 4.36 mm, average symmetric surface distance = 0.96 mm), and agreement was slightly but significantly greater than that between human observers (mean Dice = 0.85 [automated] vs 0.84 [manual]; P = .004). Atrial volume was a good predictor of AF in the validation cohort (area under the receiver operating characteristic curve = 0.768) and was an independent predictor of AF, with an age-adjusted relative risk of 2.9.

Conclusion

Left atrial volume is an independent predictor of the AF status as measured at routine nongated chest CT. Deep learning is a suitable tool for automated measurement.© RSNA, 2019See also the commentary by de Roos and Tao in this issue.

Novel imaging biomarkers: epicardial adipose tissue evaluation

Epicardial adipose tissue (EAT) is a metabolically activated beige adipose tissue, non-homogeneously surrounding the myocardium. Physiologically, EAT regulates toxic fatty acids, protects the coronary arteries against mechanical strain, regulates proinflammatory cytokines, stimulates the production of nitric oxide, reduces oxidative stress, and works as a thermogenic source against hypothermia. Conversely, EAT has pathologic paracrine interactions with the surrounded vessels, and might favour the onset of atrial fibrillation. In addition, initial atherosclerotic lesions can promote inflammation and trigger the EAT production of cytokines increasing vascular inflammation, which, in turn, may help the development of collateral vessels but also of self-stimulating, dysregulated inflammatory process, increasing coronary artery disease severity. Variations in EAT were also linked to metabolic syndrome. Echocardiography first estimated EAT measuring its thickness on the free wall of the right ventricle but does not allow accurate volumetric EAT estimates. Cardiac CT (CCT) and cardiac MR (CMR) allow for three-dimensional EAT estimates, the former showing higher spatial resolution and reproducibility but being limited by radiation exposure and long segmentation times, the latter being radiation-free but limited by lower spatial resolution and reproducibility, higher cost, and difficulties for obese patients. EAT radiodensity at CCT could to be related to underlying metabolic processes. The correlation between EAT and response to certain pharmacological therapies has also been investigated, showing promising results. In the future, semi-automatic or fully automatic techniques, machine/deep-learning methods, if validated, will facilitate research for various EAT measures and may find a place in CCT/CMR reporting.

Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study

Purpose

To evaluate the performance of deep learning for robust and fully automated quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data.

Materials and Methods

In this multicenter study, a convolutional neural network approach was trained to quantify EAT on non-contrast material-enhanced calcium-scoring CT scans from multiple cohorts, scanners, and protocols (n = 850). Deep learning performance was compared with the performance of three expert readers and with interobserver variability in a subset of 141 scans. The deep learning algorithm was incorporated into research software. Automated EAT progression was compared with expert measurements for 70 patients with baseline and follow-up scans.

Results

Automated quantification was performed in a mean (± standard deviation) time of 1.57 seconds ± 0.49, compared with 15 minutes for experts. Deep learning provided high agreement with expert manual quantification for all scans (R = 0.974; P < .001), with no significant bias (0.53 cm³; P = .13). Manual EAT volumes measured by two experienced readers were highly correlated (R = 0.984; P < .001) but with a bias of 4.35 cm³ (P < .001). Deep learning quantifications were highly correlated with the measurements of both experts (R = 0.973 and R = 0.979; P < .001), with significant bias for reader 1 (5.11 cm³; P < .001) but not for reader 2 (0.88 cm³; P = .26). EAT progression by deep learning correlated strongly with manual EAT progression (R = 0.905; P < .001) in 70 patients, with no significant bias (0.64 cm³; P = .43), and was related to an increased noncalcified plaque burden quantified from coronary CT angiography (5.7% vs 1.8%; P = .026).

Conclusion

Deep learning allows rapid, robust, and fully automated quantification of EAT from calcium scoring CT. It performs as well as an expert reader and can be implemented for routine cardiovascular risk assessment.© RSNA, 2019See also the commentary by Schoepf and Abadia in this issue.

PPAR γ/Nnat/NF-κB Axis Involved in Promoting Effects of Adiponectin on Preadipocyte Differentiation

A previous study has demonstrated that adiponectin (APN) could promote preadipocyte differentiation, and the present study further explored its mechanism. 3T3-L1 cells were infected with adenovirus holding human adiponectin gene apM1 and mouse neuronatin (Nnat) shRNA and initiated differentiation while coculturing with mature adipocytes stimulated with LPS. After 8 days, preadipocyte differentiation was observed by Oil Red O staining. Real-time quantitative PCR was used to evaluate mRNA expression levels of monocyte chemoattractant protein-1 (MCP-1), interleukin- (IL-) 6, IL-8, and tumor necrosis factor α (TNF-α). The levels of reactive oxygen species (ROS), total antioxidant capacity (T-AOC), malondialdehyde (MDA), and superoxide dismutase (SOD) in 3T3-L1 cells were detected. Western blotting was done to quantify the protein expression levels of Nnat, peroxisome proliferator-activated receptor (PPAR) γ, p65, and inhibitor of nuclear factor κB (IκB) α. Results demonstrated that APN overexpression markedly increased preadipocyte differentiation; inhibited gene expression of MCP-1, IL-6, IL-8, and TNF-α; reduced ROS and MDA release; increased T-AOC and SOD levels; upregulated Nnat, PPAR γ, and IκB α protein expressions; and downregulated p65 protein expression under LPS stimulation. However, the effects of APN were markedly attenuated when Nnat expression was knocked down. Taken together, the present study provided evidences that the effects of APN on promoting preadipocyte differentiation under inflammatory conditions via anti-inflammation and antioxidative stress may be regulated by the PPAR γ/Nnat/NF-κB signaling pathway.

Regional differences of fat depot attenuation using non-contrast, contrast-enhanced, and delayed-enhanced cardiac CT

BACKGROUND

Regional fat density assessed by computed tomography (CT) has been suggested as a marker of perivascular adipose tissue inflammation. Dual energy CT (DECT) allows improved tissue characterization compared to conventional CT.

PURPOSE

To explore whether DECT might aid regional fat density discrimination.

MATERIAL AND METHODS

We included patients who had completed a non-enhanced cardiac CT scan, CT coronary angiography (CTCA), and a delayed enhancement CT. Attenuation levels (Hounsfield units [HU]) were assessed at the epicardial, paracardial, visceral, and subcutaneous fat. The number of coronary segments with disease (SIS) was calculated.

RESULTS

A total of 36 patients were included in the analysis. Twenty-six (72%) patients had evidence of obstructive disease at CCTA and 25 (69%) patients had evidence of previous myocardial infarction. At non-contrast CT, we did not identify significant attenuation differences between epicardial, paracardial, subcutaneous, and visceral fat depots (-110.8 ± 9 HU, vs. -113.7 ± 9 HU, vs. -114.7 ± 8 HU, vs. -113.8 ± 11 HU, P = 0.36). Significant attenuation differences were detected between fat depots at mid and low energy levels, both at CTCA and delayed-enhancement scans ( P < 0.05 for all). Epicardial fat showed the least negative attenuation, irrespective of the acquisition mode; epicardial fat evaluated at 40 keV was related to the SIS (r = 0.37, P = 0.03).

CONCLUSIONS

In this study, regional fat depots amenable to examination during thoracic CT scans have distinctive regional attenuation values. Furthermore, such differences were better displayed using contrast-enhanced monochromatic imaging at low energy levels.

Effect of Obesity on Left Atrial Strain in Persons Aged 35-55 Years (The Asklepios Study)

Obesity increases the risk of heart failure and atrial fibrillation. Left atrial (LA) dysfunction is increasingly recognized as a mediator of cardiovascular disease. Early effects of obesity on LA function have not been examined in large population samples. We quantified LA strain and strain rate (SR) through speckle tracking echocardiography in 1,531 middle-aged community-based participants enrolled in the Asklepios study. We compared LA function between individuals with body mass index (BMI) < 25 kg/m² (n = 779), 25 to 29.9 kg/m² (n = 618) and ≥ 30 kg/m² (n = 134). Significant differences in reservoir longitudinal LA strain (BMI < 25 kg/m² = 35.3%, BMI 25-29.9 kg/m² = 33.1%, and BMI ≥ 30 kg/m² = 30.9%; p < 0.00001) strain rate ([SR] BMI < 25 kg/m² = 151; BMI 25 to 29.9 kg/m² = 141; and BMI ≥ 30 kg/m² = 135 %/s; p <0.00001) and expansion index (BMI < 25 kg/m² = 1.6, BMI 25 to 29.9 kg/m² = 1.4, and BMI ≥ 30 kg/m² = 1.4; p <0.00001) were seen, indicating reduced reservoir function with increasing BMI. Obesity was also associated with impaired LA conduit function, including conduit longitudinal LA strain (BMI < 25 kg/m² = 21.6%, BMI 25 to 29.9 kg/m² = 18.9%, and BMI ≥ 30 kg/m² = 16.7%; p <0.00001), SR (BMI < 25 kg/m² = -189, BMI 25 to 29.9 kg/m² = 166, and BMI ≥ 30 kg/m² = 150 %/s; p <0.0001) and passive LA emptying fraction (BMI < 25 kg/m² = 40.5, BMI 25 to 29.9 kg/m² = 36.5, and BMI ≥ 30 kg/m² = 36%, p <0.00001). These differences persisted after adjustment for age, gender and other potential confounders. In contrast to reservoir and conduit function, obesity was associated with increased booster pump function (active LA emptying fraction: BMI < 25 kg/m² = 19.4%, BMI 25 to 29.9 kg/m² = 20.5%, and BMI ≥ 30 kg/m² = 21.5%; p <0.00001). In middle-aged adults, obesity is associated with impaired reservoir and conduit LA function and higher booster function, which may be compensatory. Loss of booster LA function, either because of more advanced LA dysfunction or atrial fibrillation, may play an important role in precipitating heart failure in obese individuals.

Meta-Analysis of Relation of Epicardial Adipose Tissue Volume to Left Atrial Dilation and to Left Ventricular Hypertrophy and Functions

Many studies have explored the hypothesis that epicardial adipose tissue (EAT) accumulation adversely affects cardiac remodeling. We assessed, through a systematic review and meta-analysis, whether EAT is linked to left atrial (LA) and left ventricular (LV) structure and function, irrespective of global or abdominal visceral adiposity. We searched MEDLINE, Scopus, and Web of Science for studies evaluating the association of EAT volume quantified by computed tomography with cardiac morphology and function. We used DerSimonian and Laird random-effects models to summarize the adjusted-effect of 10 ml variation of EAT on LA size, LV mass, LV diastolic and systolic functions parameters, and presence of diastolic dysfunction. We quantified heterogeneity using I² statistic. We included 19 studies. Quantitative analysis by cardiac parameters, including LA dimension (n = 2,719), LV mass (n = 2,519), diastolic function (n = 3,741), and systolic function (n = 2,037) showed that EAT was associated with LA dilation (pooled B-coefficient: 0.12 mm; 95% confidence interval [CI] 0.08 to 0.17; I²: 97%), LV hypertrophy (pooled B-coefficient: 1.21 g; 95% CI 0.63 to 1.79; I²: 77%), diastolic dysfunction (odds ratio: 1.35; 95% CI 1.16 to 1.57; I²: 0%), higher E/E' ratio (pooled B-coefficient: 0.28 cm/s; 95% CI 0.08 to 0.49; I²: 67%), lower E' velocity (pooled B-coefficient: -0.16 cm/s; 95% CI -0.22 to -0.09; I²: 43%), and E/A ratio (pooled B-coefficient: -0.01; 95% CI -0.02 to -0.001; I²: 70%), independently of body mass index. There was no association between EAT and LV systolic function. In conclusion, EAT volume measured by computed tomography was independently associated with LA dilation, LV hypertrophy, and diastolic dysfunction.