Epicardial adipose tissue and severe Coronavirus Disease 19

Incremental Value of Radiomics Features of Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection

INTRODUCTION

Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, existing detection methods for COVID-19 severity assessment often lack consideration of organs and tissues other than the lungs, which limits the accuracy and reliability of these predictive models.

MATERIAL AND METHODS

The retrospective study included data from 515 COVID-19 patients (Cohort 1, n=415; Cohort 2, n=100) from two centers (Shanghai Public Health Center and Brazil Niteroi Hospital) between January 2020 and July 2020. Firstly, a three-stage EAT segmentation method was proposed by combining object detection and segmentation networks. Lung and EAT radiomics features were then extracted, and feature selection was performed. Finally, a hybrid model, based on seven machine learning models, was built for detecting COVID-19 severity. The hybrid model's performance and uncertainty were evaluated in both internal and external validation cohorts.

RESULTS

For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (±0.011) and 0.968 (±0.005), respectively. For severity detection, the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) of the hybrid model increased by 0.09 (p<0.001), 19.3 % (p<0.05), and 18.0 % (p<0.05) in the internal validation cohort, and by 0.06 (p<0.001), 18.0 % (p<0.05) and 18.0 % (p<0.05) in the external validation cohort, respectively. Uncertainty and radiomics features analysis confirmed the interpretability of increased certainty in case prediction after inclusion of EAT features.

CONCLUSION

This study proposed a novel three-stage EAT extraction method. We demonstrated that adding EAT radiomics features to a COVID-19 severity detection model results in increased accuracy and reduced uncertainty. The value of these features was also confirmed through feature importance ranking and visualization.

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.

Epicardial Adipose Tissue as a Prognostic Marker in COVID-19

BACKGROUND/AIM

Epicardial adipose tissue (EAT) has been established as a quantitative imaging biomarker associated with the prognosis of several diseases, especially cardiovascular diseases. The cardiac injury by coronavirus disease 2019 (COVID-19) might be linked to the EAT. This study aimed to use this prognostic marker derived from computed tomography (CT) images to predict 30-day mortality in patients with COVID-19.

PATIENTS AND METHODS

Consecutive patients with COVID-19 were retrospectively screened between 2020 and 2022. Overall, 237 patients (78 female, 32.9%) were included in the present study. The study end-point was the 30-day mortality. EAT was measured using the diagnostic CT in a semiquantitative manner. EAT volume and density were measured for each patient.

RESULTS

Overall, 70 patients (29.5%) died within the 30-day observation period and 143 patients (60.3%) were admitted to the intensive care unit (ICU). The mean EAT volume was 140.9±89.1 cm3 in survivors and 132.9±77.7 cm3 in non-survivors, p=0.66. The mean EAT density was -71.9±8.1 Hounsfield units (HU) in survivors, and -67.3±8.4 HU in non-survivors, p=0.0001. EAT density was associated with 30-day mortality (p<0.0001) and ICU admission (p<0.0001). EAT volume was not associated with mortality and/or ICU admission.

CONCLUSION

EAT density was associated with 30-day mortality and ICU admission in patients with COVID-19.

Epicardial adipose tissue measured from analysis of adipose tissue area using chest CT imaging is the best potential predictor of COVID-19 severity

BACKGROUND

Computed tomography (CT) imaging is widely used for diagnosing and determining the severity of coronavirus disease 2019 (COVID-19). Chest CT imaging can be used to calculate the epicardial adipose tissue (EAT) and upper abdominal visceral adipose tissue (Abd-VAT) areas. The EAT is the main source of inflammatory cytokines involved in chest inflammatory diseases; thus, the EAT area might be a more useful severity predictor than the Abd-VAT area for COVID-19. However, to the best of our knowledge, there are no large-scale reports that sufficiently consider this issue. In addition, there are no reports on the characteristics of patients with normal body mass index (BMI) and high adipose tissue.

AIM

The purpose of this study was to analyze whether the EAT area, among various adipose tissues, was the most associated factor with COVID-19 severity. Using a multicenter COVID-19 patient database, we analyzed the associations of chest subcutaneous, chest visceral, abdominal subcutaneous, and Abd-VAT areas with COVID-19 outcomes. In addition, the clinical significance of central obesity, commonly disregarded by BMI, was examined.

METHODS

This retrospective cohort study evaluated patients with COVID-19 aged ≥18 years In Japan. Data including from chest CT images collected between February 2020 and October 2022 in four hospitals of the Japan COVID-19 Task Force were analyzed. Patient characteristics and COVID-19 severity were compared according to the adipose tissue areas (chest and abdominal subcutaneous adipose tissue [Chest-SAT and Abd-SAT], EAT, and Abd-VAT) calculated from chest CT images.

RESULTS

We included 1077 patients in the analysis. Patients with risk factors of severe COVID-19 such as old age, male sex, and comorbidities had significantly higher areas of EAT and Abd-VAT. High EAT area but not high Abd-VAT area was significantly associated with COVID-19 severity (adjusted odds ratio (aOR): 2.66, 95 % confidence interval [CI]: 1.19-5.93). There was no strong correlation between BMI and VAT. Patients with high VAT area accounted for 40.7 % of the non-obesity population (BMI < 25 kg/m2). High EAT area was also significantly associated with COVID-19 severity in the non-obesity population (aOR: 2.50, 95 % CI: 1.17-5.34).

CONCLUSIONS

Our study indicated that VAT is significantly associated with COVID-19 severity and that EAT is the best potential predictor for risk stratification in COVID-19 among adipose tissue areas. Body composition assessment using EAT is an appropriate marker for identifying obesity patients overlooked by BMI. Considering the next pandemic of the global health crisis, our findings open new avenues for implementing appropriate body composition assessments based on CT imaging.

COVID-19 and Cardiovascular Diseases: A Literature Review From Pathogenesis to Diagnosis

The coronavirus disease 2019 (COVID-19) took the world by storm after the first case of COVID-19 emerged in China on December 8, 2019. The disease is generally considered as an infection of the respiratory system, but serious life-threatening myocardial injuries have been reported with this infection. Coronavirus can damage cardiac myocytes by entering the cell through angiotensin-converting enzyme 2 (ACE-2) receptor binding. Myocardial infarction, myocarditis, heart failure, cardiac arrhythmias, and Takotsubo cardiomyopathy are cardiac clinical manifestations commonly seen among patients affected by COVID-19. These cardiac pathologies are seen both during ongoing infection and post-infection. Elevated levels of myoglobin, troponin, creatine kinase-MB, plasma interleukin-6, lactate dehydrogenase (LDH), and N-terminal pro-b-type natriuretic peptide (NT-proBNP) have been found in COVID-19-associated myocardial injuries. The diagnostic modalities used in myocardial injuries due to COVID-19 include electrocardiography (ECG), cardiac magnetic resonance imaging (CMR), endomyocardial biopsy, echocardiography (Echo), and computerized tomography (CT-Scan). This literature review will discuss, in detail, the pathogenesis, clinical manifestations, and diagnosis of myocardial injuries due to COVID-19.

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.

Cardiac adipose tissue volume assessed by computed tomography is a specific and independent predictor of early mortality and critical illness in COVID-19 in type 2-diabetic patients

BACKGROUND

Patients with type 2-diabetes mellitus (T2D), are characterized by visceral and ectopic adipose tissue expansion, leading to systemic chronic low-grade inflammation. As visceral adiposity is associated with severe COVID-19 irrespective of obesity, we aimed to evaluate and compare the predictive value for early intensive care or death of three fat depots (cardiac, visceral and subcutaneous) using computed tomography (CT) at admission for COVID-19 in consecutive patients with and without T2D.

METHODS

Two hundred and two patients admitted for COVID-19 were retrospectively included between February and June 2020 and distributed in two groups: T2D or non-diabetic controls. Chest CT with cardiac (CATi), visceral (VATi) and subcutaneous adipose tissue (SATi) volume measurements were performed at admission. The primary endpoint was a composite outcome criteria including death or ICU admission at day 21 after admission. Threshold values of adipose tissue components predicting adverse outcome were determined.

RESULTS

One hundred and eight controls [median age: 76(IQR:59-83), 61% male, median BMI: 24(22-27)] and ninety-four T2D patients [median age: 70(IQR:61-77), 70% male, median BMI: 27(24-31)], were enrolled in this study. At day 21 after admission, 42 patients (21%) had died from COVID-19, 48 (24%) required intensive care and 112 (55%) were admitted to a conventional care unit (CMU). In T2D, CATi was associated with early death or ICU independently from age, sex, BMI, dyslipidemia, CRP and coronary calcium (CAC). (p = 0.005). Concerning T2D patients, the cut-point for CATi was  > 100 mL/m² with a sensitivity of 0.83 and a specificity of 0.50 (AUC = 0.67, p = 0.004) and an OR of 4.71 for early ICU admission or mortality (p = 0.002) in the fully adjusted model. Other adipose tissues SATi or VATi were not significantly associated with early adverse outcomes. In control patients, age and male sex (OR = 1.03, p = 0.04) were the only predictors of ICU or death.

CONCLUSIONS

Cardiac adipose tissue volume measured in CT at admission was independently predictive of early intensive care or death in T2D patients with COVID-19 but not in non-diabetics. Such automated CT measurement could be used in routine in diabetic patients presenting with moderate to severe COVID-19 illness to optimize individual management and prevent critical evolution.

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.

Is there a relationship between epicardial adipose tissue, inflammatory markers and prognosis in COVID-19 in patients under 65 years?

Aim: This study investigated the prognostic value of epicardial adipose tissue volume (EATV) attenuation (EATA) in patients admitted to the intensive care unit for COVID-19. Materials & methods: C-reactive protein (CRP), fasting blood glucose (FBG), neutrophil and lymphocyte counts, neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-CRP ratio (LCR) were recorded. Receiver operator characteristic analysis was performed for EATV and EATA. Results: The study included 190 patients (65 deceased, 125 discharged, mean age 52.01 ± 9.6 years). The deceased group had significantly higher FBG and CRP values and significantly lower platelet count and LCR values. EATA (cut-off: -92.38 HU) and EATV (cut-off: 15.74 cm²) were significantly higher in the deceased group. EATV had a correlation with age, FBG, CRP, neutrophil, NLR and LCR, whereas EATA correlated with involvement on CT scan. Conclusion: EATV is associated with inflammatory parameters, whereas EATA is associated with CT scan involvement and can be used to predict mortality in young adult patients.

Cut-off point of CT-assessed epicardial adipose tissue volume for predicting worse clinical burden of SARS-CoV-2 pneumonia

Objective

To identify a cut-off value of epicardial adipose tissue (EAT) volume quantified by CT associated with a worse clinical outcome in patients with SARS-CoV-2 pneumonia.

Materials and methods

In this retrospective study, sixty patients with a diagnosis of laboratory-confirmed COVID-19 pneumonia and a chest CT exam on admission were enrolled. Based on a total severity score (range 0–20), patients were divided into two groups: ordinary group (total severity score < 7) and severe/critical group (total severity score > 7). Clinical results and EAT volume were compared between the two groups.

Results

The severe/critical patients, compared to the ordinary ones, were older (66.83 ± 11.72 vs 58.57 ± 16.86 years; p = 0.031), had higher body mass index (27.77 ± 2.11 vs 25.07 ± 2.80 kg/m²; p < 0.001) and higher prevalence of comorbidities. EAT volume was higher in severe/critical group, compared with the ordinary group (151.40 ± 66.22 cm³ vs 92.35 ± 44.46 cm³, p < 0.001). In severe/critical group, 19 (73%) patients were admitted in intensive care unit (ICU), compared with 6 (20%) patients in the ordinary group (p < 0.001). The area under the ROC curve (AUC) is equal to 0.781 (p < 0.001) (95% CI: 0.662–0.900). The cut-off found, in correspondence with the highest value of the Youden Index, is 97 cm³: the sensitivity is equal to 83.3%, while the specificity is equal to 70% for predicting a worse outcome. The risk (odds ratio) of belonging to the severe/critical group in this population due to EAT ≥ 97 cm³ is 11.667 (95% CI: 3.384–40.220; p < 0.001).

Conclusion

An EAT volume of 97 cm³ has good sensitivity and specificity to predict a greater extent of pulmonary involvement and therefore a worse clinical outcome in patients with SARS-CoV-2 pneumonia.

Association of epicardial adipose tissue with the severity and adverse clinical outcomes of COVID-19: A meta-analysis

Objectives

Epicardial adipose tissue (EAT) has been proposed to be an independent predictor of visceral adiposity. EAT measures are associated with coronary artery disease, diabetes, and chronic obstructive pulmonary disease, which are risk factors for COVID-19 poor prognosis. Whether EAT measures are related to COVID-19 severity and prognosis is controversial.

Methods

We searched 6 databases for studies until January 7, 2022. The pooled effects are presented as the standard mean difference (SMD) or weighted mean difference with 95% confidence intervals (CIs). The primary end point was COVID-19 severity. Adverse clinical outcomes were also assessed.

Results

A total of 13 studies with 2482 patients with COVID-19 were identified. All patients had positive reverse transcriptase-polymerase chain reaction results. All quantitative EAT measures were based on computed tomography. Patients in the severe group had higher EAT measures compared with the nonsevere group (SMD = 0.74, 95% CI: 0.29–1.18, P = 0.001). Patients with hospitalization requirement, requiring invasive mechanical ventilation, admitted to intensive care unit, or with combined adverse outcomes had higher EAT measures compared to their controls (all P < 0.001).

Conclusions

EAT measures were associated with the severity and adverse clinical outcomes of COVID-19. EAT measures might help in prognostic risk stratification of patients with COVID-19.

Browning Epicardial Adipose Tissue: Friend or Foe?

The epicardial adipose tissue (EAT) is the visceral fat depot of the heart which is highly plastic and in direct contact with myocardium and coronary arteries. Because of its singular proximity with the myocardium, the adipokines and pro-inflammatory molecules secreted by this tissue may directly affect the metabolism of the heart and coronary arteries. Its accumulation, measured by recent new non-invasive imaging modalities, has been prospectively associated with the onset and progression of coronary artery disease (CAD) and atrial fibrillation in humans. Recent studies have shown that EAT exhibits beige fat-like features, and express uncoupling protein 1 (UCP-1) at both mRNA and protein levels. However, this thermogenic potential could be lost with age, obesity and CAD. Here we provide an overview of the physiological and pathophysiological relevance of EAT and further discuss whether its thermogenic properties may serve as a target for obesity therapeutic management with a specific focus on the role of immune cells in this beiging phenomenon.

Diabetes mellitus and COVID-19: review of a lethal interaction from the cellular and molecular level to the bedside

INTRODUCTION

While the main mode of transmission of coronavirus disease 2019 (COVID-19) is close contact with other individuals, the presence of chronic underlying diseases such as Diabetes Mellitus (DM) increases the chance of hospitalization and mortality rate due to infection.

AREAS COVERED

To investigate the effects of COVID-19 infection in DM patients, we reviewed literature from Google Scholar search engine and PubMed database from '2013 to 2020' using the terms "COVID-19; SARS-CoV-2; Diabetes mellitus; obesity; Angiotensin-converting enzyme 2; ACE2; Insulin and Metformin. Evidence suggests that COVID-19 exacerbates the course of diabetes. Presence of pro-inflammatory conditions, increased expression of receptors, and more difficult control of glucose levels in diabetics COVID-19 patients are some of the problems that diabetic patients may face. Also, psychological problems caused by the COVID-19 epidemic in diabetic patients is one of the most important problems in these patients, which is less covered.

EXPERT OPINION

DM is a strong and independent risk factor with a poor prognosis, which increases the risk of COVID-19 infection, the need for emergency services, the rate of hospitalization in the intensive care unit and also increases the mortality rate of COVID-19 patients.

Epicardial adipose tissue volume and myocardial ischemia in asymptomatic people living with diabetes: a cross-sectional study

Background

Epicardial adipose tissue (EAT) is considered a novel diagnostic marker for cardiometabolic disease. This study aimed to evaluate whether EAT volume was associated with stress-induced myocardial ischemia in asymptomatic people living with diabetes—independently of confounding factors—and whether it could predict this condition.

Methods

We included asymptomatic patients with diabetes and no coronary history, who had undergone both a stress a myocardial scintigraphy to diagnose myocardial ischemia, and a computed tomography to measure their coronary artery calcium (CAC) score. EAT volume was retrospectively measured from computed tomography imaging. Determinants of EAT volume and asymptomatic myocardial ischemia were evaluated.

Results

The study population comprised 274 individuals, including 153 men. Mean (± standard deviation) age was 62 ± 9 years, and 243, 23 and 8 had type 2, type 1, or another type of diabetes, respectively. Mean body mass index was 30 ± 6 kg/m², and mean EAT volume 96 ± 36 cm³. Myocardial ischemia was detected in 32 patients (11.7%). EAT volume was positively correlated with age, body mass index and triglyceridemia, but negatively correlated with HbA1c, HDL- and LDL-cholesterol levels. Furthermore, EAT volume was lower in people with retinopathy, but higher in men, in current smokers, in patients with nephropathy, those with a CAC score > 100 Agatston units, and finally in individuals with myocardial ischemia (110 ± 37 cm³ vs 94 ± 37 cm³ in those without myocardial ischemia, p < 0.05). The association between EAT volume and myocardial ischemia remained significant after adjustment for gender, diabetes duration, peripheral macrovascular disease and CAC score. We also found that area under the ROC curve analysis showed that EAT volume (AROC: 0.771 [95% confidence interval 0.683–0.858]) did not provide improved discrimination of myocardial ischemia over the following classic factors: gender, diabetes duration, peripheral macrovascular disease, retinopathy, nephropathy, smoking, atherogenic dyslipidemia, and CAC score (AROC 0.773 [0.683–0.862]).

Conclusions

EAT may play a role in coronary atherosclerosis and coronary circulation in patients with diabetes. However, considering EAT volume is not a better marker for discriminating the risk of asymptomatic myocardial ischemia than classic clinical data.