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

Epicardial adipose tissue as a prognostic marker in acute pulmonary embolism

BACKGROUND

Epicardial adipose tissue (EAT) has been established as a quantitative imaging biomarker associated with disease severity in coronary heart disease. Our aim was to use this prognostic marker derived from computed tomography pulmonary angiography (CTPA) for the prediction of mortality and prognosis in patients with acute pulmonary embolism.

METHODS

The clinical database was retrospectively screened for patients with acute pulmonary embolism between 2015 and 2021. Overall, 513 patients (216 female, 42.1%) were included in the analysis. The study end-point was 30-day mortality. Epicardial adipose tissue was measured on the diagnostic CTPA in a semiquantitative manner. The volume and density of EAT were measured for every patient.

RESULTS

Overall, 60 patients (10.4%) died within the 30-day observation period. The mean EAT volume was 128.3 ± 65.0 cm3 in survivors and 154.6 ± 84.5 cm3 in nonsurvivors (p = 0.02). The density of EAT was -79.4 ± 8.3 HU in survivors and -76.0 ± 8.4 HU in nonsurvivors (p = 0.86), and EAT density was associated with 30-day mortality (odds ratio [OR] = 1.07; 95% confidence interval [CI]: 1.03; 1.1, p < 0.001) but did not remain statistically significant in multivariable analysis. No association was identified between EAT volume and 30-day mortality (OR = 1.0; 95% CI: 1.0; 1.0, p = 0.48).

CONCLUSION

There might be an association between EAT density and mortality in patients with acute pulmonary embolism. Further studies are needed to elucidate the prognostic relevance of EAT parameters in patients with acute pulmonary embolism.

New Mechanisms to Prevent Heart Failure with Preserved Ejection Fraction Using Glucagon-like Peptide-1 Receptor Agonism (GLP-1 RA) in Metabolic Syndrome and in Type 2 Diabetes: A Review

This review examines the impact of obesity on the pathophysiology of heart failure with preserved ejection fraction (HFpEF) and focuses on novel mechanisms for HFpEF prevention using a glucagon-like peptide-1 receptor agonism (GLP-1 RA). Obesity can lead to HFpEF through various mechanisms, including low-grade systemic inflammation, adipocyte dysfunction, accumulation of visceral adipose tissue, and increased pericardial/epicardial adipose tissue (contributing to an increase in myocardial fat content and interstitial fibrosis). Glucagon-like peptide 1 (GLP-1) is an incretin hormone that is released from the enteroendocrine L-cells in the gut. GLP-1 reduces blood glucose levels by stimulating insulin synthesis, suppressing islet α-cell function, and promoting the proliferation and differentiation of β-cells. GLP-1 regulates gastric emptying and appetite, and GLP-1 RA is currently indicated for treating type 2 diabetes (T2D), obesity, and metabolic syndrome (MS). Recent evidence indicates that GLP-1 RA may play a significant role in preventing HFpEF in patients with obesity, MS, or obese T2D. This effect may be due to activating cardioprotective mechanisms (the endogenous counter-regulatory renin angiotensin system and the AMPK/mTOR pathway) and by inhibiting deleterious remodeling mechanisms (the PKA/RhoA/ROCK pathway, aldosterone levels, and microinflammation). However, there is still a need for further research to validate the impact of these mechanisms on humans.

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.

Establishment and application of the BRP prognosis model for idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial lung disease. Clinical models to accurately evaluate the prognosis of IPF are currently lacking. This study aimed to construct an easy-to-use and robust prediction model for transplant-free survival (TFS) of IPF based on clinical and radiological information.

METHODS

A multicenter prognostic study was conducted involving 166 IPF patients who were followed up for 3 years. The end point of follow-up was death or lung transplantation. Clinical information, lung function tests, and chest computed tomography (CT) scans were collected. Body composition quantification on CT was performed using 3D Slicer software. Risk factors in blood routine examination-radiology-pulmonary function (BRP) were identified by Cox regression and utilized to construct the "BRP Prognosis Model". The performance of the BRP model and the gender-age-physiology variables (GAP) model was compared using time-ROC curves, calibration curves, and decision curve analysis (DCA). Furthermore, histopathology fibrosis scores in clinical specimens were compared between the different risk stratifications identified by the BRP model. The correlations among body composition, lung function, serum inflammatory factors, and profibrotic factors were analyzed.

RESULTS

Neutrophil percentage > 68.3%, pericardial adipose tissue (PAT) > 94.91 cm3, pectoralis muscle radiodensity (PMD) ≤ 36.24 HU, diffusing capacity of the lung for carbon monoxide/alveolar ventilation (DLCO/VA) ≤ 56.03%, and maximum vital capacity (VCmax) < 90.5% were identified as independent risk factors for poor TFS among patients with IPF. We constructed a BRP model, which showed superior accuracy, discrimination, and clinical practicability to the GAP model. Median TFS differed significantly among patients at different risk levels identified by the BRP model (low risk: TFS > 3 years; intermediate risk: TFS = 2-3 years; high risk: TFS ≈ 1 year). Patients with a high-risk stratification according to the BRP model had a higher fibrosis score on histopathology. Additionally, serum proinflammatory markers were positively correlated with visceral fat volume and infiltration.

CONCLUSIONS

In this study, the BRP prognostic model of IPF was successfully constructed and validated. Compared with the commonly used GAP model, the BRP model had better performance and generalization with easily obtainable indicators. The BRP model is suitable for clinical promotion.

Association between epicardial adipose tissue and myocardial work by non-invasive left ventricular pressure-strain loop in people with suspected metabolic syndrome

Given the inconsistent results on the prognostic significance of epicardial adipose tissue (EAT), the purpose of the present study was to investigate the association of EAT thickness and myocardial work by non-invasive left ventricular pressure-strain loop in people with suspected metabolic syndrome (MS). A total of 194 participants imaged with echocardiography were evaluated. In accordance with the median EAT thickness, MS patients fell into thin EAT group and thick EAT group. Conventional echocardiographic parameters, global longitudinal strain (GLS) and the global myocardial work parameters obtained by pressure-strain loop analysis, comprising the global work index (GWI), global work efficiency (GWE), global constructive work (GCW) and global wasted work (GWW) were compared between the two groups. In comparison with the thin EAT group, thick EAT group achieved significantly higher values in interventricular septal thickness, end-diastolic left ventricular posterior wall thickness, left ventricular mass index and GWW (p < 0.05). while the absolute value of GLS, GWI, GCW, and GWE were notably lower in the thick EAT group (p < 0.001). EAT thickness showed a significant correlation with GWI and GCW (r = - 0.328, p = 0.001; r = - 0.253, p = 0.012), and also independently correlated with GWI and GCW in the multivariate regression analysis (β = - 0.310, p = 0.001; β = - 0.199, p = 0.049). EAT thickness is associated with left ventricular myocardial function in subjects with suspected metabolic syndrome, independently of other risk factors. Further studies are supposed to ensure the causal associations and related mechanisms.

Computed tomography and nuclear medicine for the assessment of coronary inflammation: clinical applications and perspectives

There is increasing evidence that in patients with atherosclerotic cardiovascular disease (ASCVD) under optimal medical therapy, a persisting dysregulation of the lipid and glucose metabolism, associated with adipose tissue dysfunction and inflammation, predicts a substantial residual risk of disease progression and cardiovascular events. Despite the inflammatory nature of ASCVD, circulating biomarkers such as high-sensitivity C-reactive protein and interleukins may lack specificity for vascular inflammation. As known, dysfunctional epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) produce pro-inflammatory mediators and promote cellular tissue infiltration triggering further pro-inflammatory mechanisms. The consequent tissue modifications determine the attenuation of PCAT as assessed and measured by coronary computed tomography angiography (CCTA). Recently, relevant studies have demonstrated a correlation between EAT and PCAT and obstructive coronary artery disease, inflammatory plaque status and coronary flow reserve (CFR). In parallel, CFR is well recognized as a marker of coronary vasomotor function that incorporates the haemodynamic effects of epicardial, diffuse and small-vessel disease on myocardial tissue perfusion. An inverse relationship between EAT volume and coronary vascular function and the association of PCAT attenuation and impaired CFR have already been reported. Moreover, many studies demonstrated that 18F-FDG PET is able to detect PCAT inflammation in patients with coronary atherosclerosis. Importantly, the perivascular FAI (fat attenuation index) showed incremental value for the prediction of adverse clinical events beyond traditional risk factors and CCTA indices by providing a quantitative measure of coronary inflammation. As an indicator of increased cardiac mortality, it could guide early targeted primary prevention in a wide spectrum of patients. In this review, we summarize the current evidence regarding the clinical applications and perspectives of EAT and PCAT assessment performed by CCTA and the prognostic information derived by nuclear medicine.

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.