Epicardial adipose tissue characteristics, obesity and clinical outcomes in COVID-19: A post-hoc analysis of a prospective cohort study

Correlation between epicardial adipose tissue and myocardial injury in patients with COVID-19

Background: Many people infected with COVID-19 develop myocardial injury. Epicardial adipose tissue (EAT) is among the various risk factors contributing to coronary artery disease. However, its correlation with myocardial injury in patients diagnosed with COVID-19 remains uncertain. Methods: We examined myocardial biomarkers in population affected by COVID-19 during the period from December 2022 to January 2023. The patients without myocardial injury were referred to as group A (n = 152) and those with myocardial injury were referred to as group B (n = 212). Results: 1) The A group and the B group exhibitedstatistically significant differences in terms of age, TC, CRP, Cr, BUN, LDL-C, IL-6, BNP, LVEF and EAT (p < 0.05). 2) EAT volumehad a close relationship with IL-6, LDL-C, cTnI, and CRP (p < 0.05); the corresponding correlation coefficient values were 0.24, 0.21, 0.24, and 0.16. In contrast to those with lower EAT volume, more subjects with a higher volume of EAT had myocardial injury (p < 0.05). Regression analysis showed that EAT, LDL-C, Age and Cr were established as independent risk variables for myocardial injury in subjects affected by COVID-19. 3) In COVID-19 patients, the likelihood of myocardial injury rised notably as EAT levels increase (p < 0.001). Addition of EAT to the basic risk model for myocardial injury resulted in improved reclassification. (Net reclassification index: 58.17%, 95% CI: 38.35%, 77.99%, p < 0.001). Conclusion: Patients suffering from COVID-19 with higher volume EAT was prone to follow myocardial injury and EAT was an independent predictor of heart damage in these individuals.

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.

Prevalence of overweight and obesity and their effect on COVID-19 severity and hospitalization among younger than 50 years versus older than 50 years population: A systematic review and meta-analysis

Cohort studies have shown that both overweight and obesity have their impact by increasing hospitalization with COVID-19. We conducted a systematic literature search in PubMed, Google Scholar, and MedRxiv databases following the PRISMA guidelines. Statistical analyses were performed using STATA software version 16 MP (Stata Corp, College Station, TX, USA) and Med Calc software version 22.009(Med Calc software Ltd, Ostend, Belgium). The primary outcome was to measure the prevalence of overweight and obesity and their impact on the risk of hospitalization among COVID-19 patients under and above 50 years of age. In total, 184 studies involving 2,365,377 patients were included. The prevalence of overweight was highest among those younger than 50 years of age over those older than 50 years of age, (26.33% vs. 30.46%), but there was no difference in obesity (36.30% vs. 36.02%). Overall, the pooled prevalence of overweight and obesity among hospitalized COVID-19 patients was 31.0% and 36.26%, respectively. Compared with normal weight, the odds of hospitalization with overweight (odds ratio [OR] 2.186, 95% confidence interval [CI] [1.19, 3.99], p < 0.01) and obesity (OR 3.069, 95% CI [1.67, 5.61], p < 0.001) in those younger than 50 years and obesity (OR 3.977, 95% CI [2.75, 5.73], p < 0.001) in the older than 50 years age group were significantly high. The increased prevalence of overweight and obesity among the under 50 years age group and obesity among the older than 50 years age group significantly increased the rate of COVID-19 infections, severity and hospitalization.

Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study

OBJECTIVE

To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients.

METHODS

In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test.

RESULTS

The final cohort included 1669 patients (age 67.5 [58.5-77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88-95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001).

CONCLUSION

Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients.

CLINICAL RELEVANCE STATEMENT

In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification.

KEY POINTS

• In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death. • A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection. • Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients' risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.

Is There a Relationship Between Epicardial Adipose Tissue, Inflammatory Markers, and the Severity of COVID-19 Pneumonia?

Objectives

Epicardial adipose tissue (EAT) is a type of visceral adipose tissue with pro-inflammatory properties. We sought to examine the relationship between the EAT volume and attenuation measured on non-contrast chest computed tomography (CT), inflammation markers, and the severity of COVID-19 pneumonia.

Methods

One hundred and twenty-five patients who are over 18 years old who applied to our hospital and were found to have COVID-19 polymerase chain reaction (+) on nasopharyngeal swab sample and COVID-19 pneumonia on chest CT were included in the study. At admission, C-reactive protein (CRP), procalcitonin, fibrinogen, leukocytes, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, lactate dehydrogenase (LDH), ferritin, and d-dimer were evaluated. EAT volume and attenuation were measured on chest CT. Patients who were hospitalized and discharged from the ward were categorized as Group 1, whereas patients who required intensive care admission and/or died were classified as Group 2. The primary endpoint of our study was defined as death, hospitalization in the intensive care unit, and discharge. The relationship between disease severity and EAT and other inflammatory markers was investigated.

Results

One hundred and six individuals were in Group 1 and 19 patients were in Group 2. Of the 125 individuals, 46 were women and 79 were men. The mean age was 58.5±15.9 years. Group 2 patients were older. Regarding measurements of the EAT volume and attenuation; there was no statistically significant difference between the groups determined. The patients in Group 2 had statistically substantially higher values for urea, creatinine, LDH, d-dimer, troponin T, procalcitonin, CRP, and neutrophil/lymphocyte ratio in their laboratory tests. When compared to patients in Group 1, patients in Group 2 had statistically significantly lower albumin values (p<0.001). In obese patients, EAT volume was statistically significantly higher and EAT attenuation was found to be lower.

Conclusion

In our study, no relationship was found between critical COVID-19 disease and EAT volume and attenuation, which is an indicator of EAT inflammation. Inflammatory markers from routine laboratory tests can be used to predict critical COVID-19 disease. No relationship was found between obesity and critical COVID-19 disease.

Cardiac magnetic resonance findings in acute and post-acute COVID-19 patients with suspected myocarditis

INTRODUCTION

Cardiac injury is commonly reported in COVID-19 patients, resulting associated to pre-existing cardiovascular disease, disease severity, and unfavorable outcome. Aim is to report cardiac magnetic resonance (CMR) findings in patients with myocarditis-like syndrome during the acute phase of SARS-CoV-2 infection (AMCovS) and post-acute phase (cPACS).

METHODS

Between September 2020 and January 2022, 39 consecutive patients (24 males, 58%) were referred to our department to perform a CMR for the suspicion of myocarditis related to AMCovS (n = 17) and cPACS (n = 22) at multimodality evaluation (clinical, laboratory, ECG, and echocardiography). CMR was performed for the assessment of volume, function, edema and fibrosis with standard sequences and mapping techniques. CMR diagnosis and the extension and amount of CMR alterations were recorded.

RESULTS

Patients with suspected myocarditis in acute and post-COVID settings were mainly men (10 (59%) and 12 (54.5%), respectively) with older age in AMCovS (58 [48-64]) compared to cPACS (38 [26-53]). Myocarditis was confirmed by CMR in most of cases: 53% of AMCovS and 50% of cPACS with negligible LGE burden (3 [IQR, 1-5] % and 2 [IQR, 1-4] %, respectively). Myocardial infarction was identified in 4/17 (24%) patients with AMCovS. Cardiomyopathies were identified in 12% (3/17) and 27% (6/22) of patients with AMCovS and cPACS, including DCM, HCM and mitral valve prolapse.

CONCLUSIONS

In patients with acute and post-acute COVID-19 related suspected myocarditis, CMR improves diagnostic accuracy characterizing ischemic and non-ischemic injury and unraveling subclinical cardiomyopathies.

Global prevalence and effect of comorbidities and smoking status on severity and mortality of COVID-19 in association with age and gender: a systematic review, meta-analysis and meta-regression

A COVID-19 patient often presents with multiple comorbidities and is associated with adverse outcomes. A comprehensive assessment of the prevalence of comorbidities in patients with COVID-19 is essential. This study aimed to assess the prevalence of comorbidities, severity and mortality with regard to geographic region, age, gender and smoking status in patients with COVID-19. A systematic review and multistage meta-analyses were reported using PRISMA guidelines. PubMed/MEDLINE, SCOPUS, Google Scholar and EMBASE were searched from January 2020 to October 2022. Cross-sectional studies, cohort studies, case series studies, and case-control studies on comorbidities reporting among the COVID-19 populations that were published in English were included. The pooled prevalence of various medical conditions in COVID-19 patients was calculated based on regional population size weights. Stratified analyses were performed to understand the variations in the medical conditions based on age, gender, and geographic region. A total of 190 studies comprising 105 million COVID-19 patients were included. Statistical analyses were performed using STATA software, version 16 MP (StataCorp, College Station, TX). Meta-analysis of proportion was performed to obtain pooled values of the prevalence of medical comorbidities: hypertension (39%, 95% CI 36-42, n = 170 studies), obesity (27%, 95% CI 25-30%, n = 169 studies), diabetes (27%, 95% CI 25-30%, n = 175), and asthma (8%, 95% CI 7-9%, n = 112). Moreover, the prevalence of hospitalization was 35% (95% CI 29-41%, n = 61), intensive care admissions 17% (95% CI 14-21, n = 106), and mortality 18% (95% CI 16-21%, n = 145). The prevalence of hypertension was highest in Europe at 44% (95% CI 39-47%, n = 68), obesity and diabetes at 30% (95% CI, 26-34, n = 79) and 27% (95%CI, 24-30, n = 80) in North America, and asthma in Europe at 9% (95% CI 8-11, n = 41). Obesity was high among the ≥ 50 years (30%, n = 112) age group, diabetes among Men (26%, n = 124) and observational studies reported higher mortality than case-control studies (19% vs. 14%). Random effects meta-regression found a significant association between age and diabetes (p < 0.001), hypertension (p < 0.001), asthma (p < 0.05), ICU admission (p < 0.05) and mortality (p < 0.001). Overall, a higher global prevalence of hypertension (39%) and a lower prevalence of asthma (8%), and 18% of mortality were found in patients with COVID-19. Hence, geographical regions with respective chronic medical comorbidities should accelerate regular booster dose vaccination, preferably to those patients with chronic comorbidities, to prevent and lower the severity and mortality of COVID-19 disease with novel SARS-CoV-2 variants of concern (VOC).

Epicardial adipose tissue volume and CT-attenuation as prognostic factors for pulmonary embolism and mortality in critically ill patients affected by COVID-19

BACKGROUND/OBJECTIVES

The aim of this post-hoc analysis was to evaluate if epicardial adipose tissue (EAT) quantity and quality, as evaluated by computed tomography (CT), have a different role in the risk of mortality and pulmonary embolism in critically ill COVID-19 patients admitted to an intensive care unit (ICU).

SUBJECTS/METHODS

CT derived EAT volume and density, as well as anthropometric and blood biomarkers, were evaluated in a sample of 138 subjects, 109 men and 29 women, for whom CT images and information on pulmonary embolism were available from a total of 313 subjects who were consecutively admitted to the ICU for COVID-19 from the REINSURE-ARDS prospective registry.

RESULTS

A total of 28 patients (20.3%) died during the first 28 days after ICU admission. 26 subjects out of 138 had pulmonary embolism (18.8%). Age, weight, BMI, IL-6 levels and pulmonary embolism prevalence were significantly higher across EAT volume tertiles. Subjects who died in the first 28 days from ICU admission were older, had higher EAT volume, D-dimer, LDH and IL-6 level. After adjustment for age and gender, participants in tertile 3 of EAT volume had lower survival at 28 days from ICU admission as compared to subjects in the tertile 1, HR 2.95 (95% C.I. 1.02-8.49), but after adjusting for potential confounders the relation was no longer significant. No relation between EAT density and mortality was observed. From a binary logistic regression, subjects in tertile 3 of EAT volume and in tertile 1 of EAT density showed a 4 times and 3.6 times increased risk of pulmonary embolism, respectively.

CONCLUSIONS

ICU subjects affected by severe COVID-19 with higher EAT volume and low EAT density should be carefully monitored and managed with a prompt and aggressive approach, to prevent serious and life-threatening consequences and the increase of hospital treatment costs.

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.

The osteo-metabolic phenotype of COVID-19: an update

CONTEXT

In the multifaceted COVID-19 clinical scenario characterized by a multi-system disorder with negative implications not only on respiratory function but also on cardiac, hematological, neurological and endocrine-metabolic systems, a distinctive osteo-metabolic phenotype with an independent influence on disease severity and recovery of patients affected was early reported.

AIM

To summarize and update the main evidences regarding the distinct components of this phenotype in acute and Long COVID-19, reinforcing its clinical relevance and discussing the main pathophysiological and clinical-therapeutic implications of the most recent reported findings.

RESULTS

This emerging phenotype is characterized by a widespread acute hypocalcemia and hypovitaminosis D with an impaired compensatory parathyroid hormone response, and a high prevalence of skeletal complications such as vertebral fractures. The clinical relevance of this osteo-metabolic phenotype on acute COVID-19 is well characterized, and novel seminal evidences are progressively highlighting its importance also in predicting patient's long-term outcomes and Long COVID-19 occurrence.

CONCLUSIONS

These findings reinforced the central role of a multidisciplinary team, including endocrinologists, in evaluating these patients for a proactive search of each aspect of the osteo-metabolic phenotype components since they may represent suitable therapeutic targets to prevent SARS-CoV-2 infection, poor COVID-19 outcomes, Long COVID-19 occurrence and even possibly better responses to COVID-19 vaccination.

AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients

Purpose

To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification.

Material and Methods

In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled.

In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival.

A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020).

Results

The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766).

Conclusions

AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11547-022-01518-0.

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.

Advanced cardiac imaging in the spectrum of COVID-19 related cardiovascular involvement

Cardiovascular involvement is a common complication of COVID-19 infection and is associated to increased risk of unfavorable outcome. Advanced imaging modalities (coronary CT angiography and Cardiac Magnetic Resonance) play a crucial role in the diagnosis, follow-up and risk stratification of patients affected by COVID-19 pneumonia with suspected cardiovascular involvement. In the present manuscript we firstly review current knowledge on the mechanisms by which SARS-CoV-2 can trigger endothelial and myocardial damage. Secondly, the implications of the cardiovascular damage on patient's prognosis are presented. Finally, we provide an overview of the main findings at advanced cardiac imaging characterizing COVID-19 in the acute setting, in the post-acute syndrome, and after vaccination, emphasizing the potentiality of CT and CMR, the indication and their clinical implications.

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.

Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19

Background: To develop a deep-learning (DL) pipeline that allowed an automated segmentation of epicardial adipose tissue (EAT) from low-dose computed tomography (LDCT) and investigate the link between EAT and COVID-19 clinical outcomes. Methods: This monocentric retrospective study included 353 patients: 95 for training, 20 for testing, and 238 for prognosis evaluation. EAT segmentation was obtained after thresholding on a manually segmented pericardial volume. The model was evaluated with Dice coefficient (DSC), inter-and intraobserver reproducibility, and clinical measures. Uni-and multi-variate analyzes were conducted to assess the prognosis value of the EAT volume, EAT extent, and lung lesion extent on clinical outcomes, including hospitalization, oxygen therapy, intensive care unit admission and death. Results: The mean DSC for EAT volumes was 0.85 ± 0.05. For EAT volume, the mean absolute error was 11.7 ± 8.1 cm3 with a non-significant bias of −4.0 ± 13.9 cm3 and a correlation of 0.963 with the manual measures (p < 0.01). The multivariate model providing the higher AUC to predict adverse outcome include both EAT extent and lung lesion extent (AUC = 0.805). Conclusions: A DL algorithm was developed and evaluated to obtain reproducible and precise EAT segmentation on LDCT. EAT extent in association with lung lesion extent was associated with adverse clinical outcomes with an AUC = 0.805.

Role of a lower cutoff of high sensitivity troponin I in identification of early cardiac damage in non-severe patients with COVID-19

Cardiac damage in non-severe patients with coronavirus disease 2019 (COVID-19) is poorly explored. This study aimed to explore the manifestations of cardiac damage at presentation in non-severe patients with COVID-19. In this study, 113 non-severe patients with COVID-19 were grouped according to the duration from symptoms onset to hospital admission: group 1 (≤ 1 week, n = 27), group 2 (> 1 to 2 weeks, n = 28), group 3 (> 2 to 3 weeks, n = 27), group 4 (> 3 weeks, n = 31). Clinical, cardiovascular, and radiological data on hospital admission were compared across the four groups. The level of high sensitivity troponin I (hs-cTnI) in group 2 [10.25 (IQR 6.75-15.63) ng/L] was significantly higher than those in group 1 [1.90 (IQR 1.90-8.80) ng/L] and group 4 [1.90 (IQR 1.90-5.80) ng/L] (all P_bonferroni < 0.05). The proportion of patients who had a level of hs-cTnI ≥ 5 ng/L in group 2 (85.71%) was significantly higher than those in the other three groups (37.04%, 51.85%, and 25.81%, respectively) (all P_bonferroni < 0.05). Compared with patients with hs-cTnI under 5 ng/L, those with hs-cTnI ≥ 5 ng/L had lower lymphocyte count (P = 0.000) and SpO₂ (P = 0.002) and higher CRP (P = 0.000). Patients with hs-cTnI ≥ 5 ng/L had a higher incidence of bilateral pneumonia (P = 0.000) and longer hospital length of stay (P = 0.000). In conclusion, non-severe patients with COVID-19 in the second week after symptoms onset were most likely to suffer cardiac damage. A detectable level of hs-cTnI ≥ 5 ng/L might be a manifestation of early cardiac damage in the patients.

Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review

PURPOSE OF THE REVIEW

We aim to review the methods, current research evidence, and future directions in body composition analysis (BCA) with CT imaging.

RECENT FINDINGS

CT images can be used to evaluate muscle tissue, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) compartments. Manual and semiautomatic segmentation methods are still the gold standards. The segmentation of skeletal muscle tissue and VAT and SAT compartments is most often performed at the level of the 3rd lumbar vertebra. A decreased amount of CT-determined skeletal muscle mass is a marker of impaired survival in many patient populations, including patients with most types of cancer, some surgical patients, and those admitted to the intensive care unit (ICU). Patients with increased VAT are more susceptible to impaired survival / worse outcomes; however, those patients who are critically ill or admitted to the ICU or who will undergo surgery appear to be exceptions. The independent significance of SAT is less well established. Recently, the roles of the CT-determined decrease of muscle mass and increased VAT area and epicardial adipose tissue (EAT) volume have been shown to predict a more debilitating course of illness in patients suffering from severe acute respiratory syndrome coronavirus 2 (COVID-19) infection.

SUMMARY

The field of CT-based body composition analysis is rapidly evolving and shows great potential for clinical implementation.

Epicardial Fat Expansion in Diabetic and Obese Patients With Heart Failure and Preserved Ejection Fraction-A Specific HFpEF Phenotype

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome with diverse etiologies and pathophysiological factors. Obesity and type 2 diabetes mellitus (T2DM), conditions that coexist frequently, induce a cluster of metabolic and non-metabolic signaling derangements which are in favor to induce inflammation, fibrosis, myocyte stiffness, all hallmarks of HFpEF. In contrast to other HFpEF risk factors, obesity and T2DM are often associated with the generation of enlarged epicardial adipose tissue (EAT). EAT acts as an endocrine tissue that may exacerbate myocardial inflammation and fibrosis via various paracrine and vasocrine signals. In addition, an abnormally large EAT poses mechanical stress on the heart via pericardial restrain. HFpEF patients with enlarged EAT may belong to a unique phenotype that can benefit from specific EAT-targeted interventions, including life-style modifications and pharmacologically via statins and fat modifying anti-diabetics drugs; like metformin, sodium-glucose cotransporter 2 inhibitors, or glucagon-like peptide-1 receptor agonists, respectively.

Relationship between 25 Hydroxyvitamin D, Overweight/Obesity Status, Pro-Inflammatory and Oxidative Stress Markers in Patients with Type 2 Diabetes: A Simplified Empirical Path Model

Vitamin D deficiency is highly prevalent in patients with overweight/obesity and type 2 diabetes (T2DM). Herein, we investigated the relationship between vitamin D status and overweight/obesity status, insulin resistance (IR), systemic inflammation as well as oxidative stress (OS). Anthropometric and laboratory assessments of 25-hydroxyvitamin D (25(OH)D) and glycemic, pro-inflammatory and OS biomarkers were performed in a sample of 47 patients with T2DM who were divided into categories based on overweight and degree of obesity. The main findings were: the overweight/obesity status correlated negatively with the degree of serum 25(OH)D deficiency (ρ = −0.27) with a trend towards statistical significance (p = 0.069); the homeostasis model assessment of insulin resistance (HOMA-IR) was significantly different (p = 0.024) in patients with 25(OH)D deficiency, as was total oxidant status (TOS) and oxidative stress index (OSI) in patients with severe serum 25(OH)D deficiency as compared to those with 25(OH)D over 20 ng/mL (TOS: p = 0.007, OSI: p = 0.008); and 25(OH)D had a negative indirect effect on TOS by body mass index (BMI), but BMI was not a significant mediator of the studied relationship. In a setting of overweight and increasing degree of obesity, patients with T2DM did not display decreasing values of 25(OH)D. Subjects with the lowest values of 25(OH)D presented the highest values of BMI. Patients with 25(OH)D deficiency were more insulin resistant and showed increased OS but no elevated systemic inflammation. The negative effect of 25(OH)D on TOS did not seem to involve BMI as a mediator.