Skip to main content
NCBI home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 Apr 11;120:33–40. doi: 10.1016/j.ijid.2022.04.013

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

Kaiwei Liu 1, Xin Wang 1, Guang Song 1,⁎
PMCID: PMC8996473  PMID: 35421580

Abstract

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.

Keywords: COVID-19, Epicardial adipose tissue, Severity, Prognosis, Risk Factors, Meta-analysis

Introduction

More and more COVID-19 cases have been confirmed since December 2019. As of December 21, 2021, there have been over 273 million confirmed cases and over 5.3 million deaths worldwide (World Health Organization, 2021). Hospitals, especially intensive care units (ICU), are currently overcrowded in some countries. Determining how to assess the severity quickly and predict the outcomes of COVID-19 (such as the need for hospitalization) after patients visiting an outpatient or emergency department began a research goal for doctors, which could efficiently allocate medical resources and help doctors make treatment-related decisions.

Early studies have revealed that obesity with a higher body mass index (BMI) is a risk factor for developing a critical condition in COVID-19 (Chang et al., 2020, Chu et al., 2020, Huang et al., 2020). Although widely used to assess obesity, BMI was not always an accurate prediction of adverse clinical outcomes of COVID-19 (Caussy et al., 2020, Yang et al., 2020). This suggested that the relationship between obesity and the severity and mortality of COVID-19 is more complex. The underlying pathophysiologic mechanisms remain unclear.

Epicardial adipose tissue (EAT), an ectopic heart adipose, modulates the metabolic environment of both the coronary arteries and myocardium. EAT has close relationships with coronary artery disease, metabolic syndrome, diabetes, obstructive sleep apnea, and human immunodeficiency virus. Iacobellis et al pioneered the research on EAT (Iacobellis, 2015, Iacobellis et al., 2005). EAT has been proposed as an independent predictor of visceral adiposity and may have a close relationship with adverse clinical outcomes of COVID-19; this was first reported by Malavazos, Goldberger, and Iacobellis in 2020 (Malavazos et al., 2020).

EAT can be measured by chest computed tomography (CT) scan and echocardiography. EAT thickness, volume, and density/attenuation can be easily and precisely measured by CT scan using commercial software. Image analysis took approximately 3 minutes per patient (Conte et al., 2021). Several studies have evaluated the relationship between EAT measures and COVID-19 severity (Deng et al., 2020, Iacobellis et al., 2020, Watanabe et al., 2020). However, small sample sizes and potential confounders, such as differences in EAT measures, can affect the strength of previous evidence. Therefore, this meta-analysis of studies was conducted to provide a more comprehensive summary of currently available research to explore the association of EAT measures with the severity and clinical outcomes of COVID-19.

Methods

We followed a reporting guideline (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, PRISMA) to perform this study. A review protocol (CRD42022302104) with search strategy was registered in the PROSPERO international prospective register of systematic reviews.

Search strategy

Two independent investigators (KL and XW) independently searched PubMed, MEDLINE, Web of Science, Embase, Cochrane library, and medRxiv.org from database inception to January 7, 2022 to identify relevant studies. The following search keywords included “COVID-19” and “epicardial adipose tissue”. At the same time, we read the references of articles to find the potential literature which may meet the criteria.

Study selection and exclusion

Two researchers (KL and XW) independently screened the titles and abstracts for eligibility. Full papers were assessed to confirm disagreement in existence according to the exclusion criteria by the 2 researchers. Disagreements were discussed and resolved by involving a third reviewer (GS) for adjudication.

Original studies were eligible if the following criteria were met: (i) COVID-19 with different related clinical outcomes or different severity, (ii) quantitative assessment of EAT measures (EAT thickness, volume, or attenuation) by CT or echocardiography as soon as possible after presenting to the hospital, and (iii) text in English available. Original studies were ineligible if the following criteria existed: (i) reviews, case reports, or case series; (ii) did not report the data necessary for calculating the mean and standard deviation of EAT measures; and (iii) animal studies. If there were several publications from the same study, the study with the most cases and relevant information was included.

The extracted data included the first author of involved studies, year of publication, country, participant number, gender, age, BMI, EAT measures, and outcomes. Numeric data were gathered directly from tables or, when presented in graphs only, were inferred by digitizing the figure with GetData Graph Digitizer 2.26 (Li et al., 2017). The quality assessment was performed by the Newcastle–Ottawa Scale (NOS) assessment tool.

Clinical outcomes and severity of COVID-19

The clinical outcomes involved in the meta-analysis were death/survival, the need for invasive mechanical ventilation, the need for hospitalization, the need for ICU admission, or combined adverse outcomes. Combined adverse outcomes were the need for ICU admission, invasive mechanical ventilation, vasopressor therapy, or death (Bihan et al., 2021, Grodecki et al., 2021, Phan et al., 2021). In our meta-analysis, severe or critical patients with COVID-19 were grouped into the severe group, and mild or moderate patients with COVID-19 were grouped into the nonsevere group. The severity criteria of the involved studies were shown in Supplementary Table S1.

Statistical analysis

The pooled effects are presented as the weighted mean difference (WMD) or standard mean difference (SMD) with 95% confidence intervals (CIs). Heterogeneity was assessed using the I 2 statistic. If there was no heterogeneity (P > 0.1 or I 2 < 50%), a fixed-effects model was used to estimate the pooled effect; otherwise, a random-effects model was used. When heterogeneity existed, we conducted subgroup analyses based on “EAT measures” or “type of clinical outcomes”. Sensitivity analyses were directed to assess the influence of the individual study on the overall estimate. We analyzed the symmetry of a funnel plot to evaluate possible small sample effects and used Begg and Egger tests to evaluate publication bias in the included studies. A p-value < 0.05 was considered statistically significant for asymmetry. Trim-and-fill analysis was performed to ‘normalize’ the asymmetric funnel plot. Statistical analyses were performed using Stata (version 16.0; StataCorp, College Station, TX, USA).

Results

Search selection

We had searched potentially relevant publications from 6 sources. After applying the inclusion and exclusion criteria, 13 studies were identified (Abrishami et al., 2021, Bihan et al., 2021, Conte et al., 2021, Deng et al., 2020, Eslami et al., 2021, Grodecki et al., 2021, Guarisco et al., 2021, Iacobellis et al., 2020, Phan et al., 2021, Sevilla et al., 2022, Slipczuk et al., 2021, Turker Duyuler et al., 2021, Watanabe et al., 2020) (Figure 1 ).

Figure 1.

Figure 1

Open in a new tab

Flow-chart of study selection.

Characteristics of studies

The baseline characteristics of the included studies are shown in Table 1 . All studies were published in these 3 years. Studies were conducted in Europe (France, Italy, and Spain), North America (USA), and Asia (China, Iran, and Turkey). A total of 2482 patients with COVID-19 were included, all of whom had positive reverse transcriptase-polymerase chain reaction results. All quantitative EAT measures in these 13 studies were CT-based. A total of 8 studies evaluated the EAT volume, 8 with EAT attenuation, and 3 with EAT thickness. A total of 6 studies evaluated the association between EAT measures and the severity of COVID-19. A total of 10 studies evaluated the association between EAT measures and clinical outcomes of COVID-19. The NOS score ranged from 7–9, indicating no low-quality study was involved (Supplementary Table S2).

Table 1.

The baseline characteristics of the included studies

Study Year Country Participant number Male (%) Age (year) BMI(kg/m2) EAT measures Outcomes NOS score
Deng 2020 China 65 55.4 34.5±5.8 24.0±4.4 Volume, attenuation Severity 9
Iacobellis 2020 Italy 41 73.2 67.0±13.0 26.9±4.4 Attenuation Severity, ICU, IMV 7
Watanabe 2020 Italy 150 64.7 64.2±15.7 NR Thickness IMV, ICU, hospitalization 8
Abrishami 2021 Iran 100 68.0 55.5±15.2 NR Volume, attenuation Survival 8
Bihan 2021 France 100 63.0 61.8±16.2 28.9±6.2 Volume Severity, combined adverse outcomes 9
Conte 2021 Italy 192 76.0 60.0±12.5 26.7±3.9 Volume, attenuation Severity 9
Eslami 2021 Iran 87 65.5 54.6±15.3 NR Thickness, attenuation Survival 7
Grodecki 2021 USA, Italy 109 62.4 63.7±16.0 26.2±3.9 Volume, attenuation Combined adverse outcomes 9
Guarisco 2021 Italy 229 57.8 62.1±17.4 27.2±4.6 Volume, attenuation Severity 9
Phan 2021 France 81 72.8 66.0±11.1 27.0±5.2 Volume Survival, combined adverse outcomes 9
Slipczuk 2021 USA 493 49.5 70.0±7.4 27.3±6.4 Volume Survival 9
Turker Duyuler 2021 Turkey 504 56.6 53.8±18.9 26.2±2.7 Thickness Severity, ICU, hospitalization 8
Sevilla 2022 Spain 331 56.0 71.0±11.0 NR Attenuation Survival, IMV, ICU 7
Open in a new tab

BMI, body mass index; EAT, epicardial adipose tissue; IMV, invasive mechanical ventilation; ICU, intensive care unit admission; NR, no reported; NOS, New castle Ottawa Scale.

Association of EAT measures with the severity of COVID-19 and subgroup analysis

A total of 1128 patients (336 in the severe group and 792 in the nonsevere group) were involved in the quantitative data synthesis. Patients in the severe group had higher EAT measures compared to the nonsevere group (SMD = 0.74, 95% CI: 0.29–1.18, P = 0.001).

To investigate the possible sources of this heterogeneity (I 2 = 92.6%, P < 0.001), we carried out the subgroup analysis (Figure 2 ). However, heterogeneity did not decrease after stratification by the type of EAT measures. Subgroup analysis indicated that significant results were observed in the EAT volume and EAT thickness subgroups (SMD = 0.62, 95% CI: 0.14–1.11, P = 0.012; SMD = 1.22, 95% CI: 0.97–1.47, P < 0.001).

Figure 2.

Figure 2

Open in a new tab

Subgroup analysis of standard mean difference in EAT measures between patients with severe COVID-19 and patients with nonsevere COVID-19. EAT, epicardial adipose tissue

Subgroup analysis for the association of EAT measures with clinical outcomes of COVID-19

After stratification by the type of clinical outcomes, patients with combined adverse outcomes had higher EAT volume (in milliliters) than those without combined adverse outcomes (WMD = 49.61, 95% CI: 31.45–67.78, P < 0.001, Figure 3 ).

Figure 3.

Figure 3

Open in a new tab

Subgroup analysis of weighted mean difference in EAT volume between different clinical outcomes of COVID-19. EAT, epicardial adipose tissue.

Patients admitted to ICU had higher EAT attenuation compared with patients without ICU admission (WMD = 13.07, 95% CI: 8.07–18.06, P < 0.001, Figure 4 ). Patients requiring IMV had higher EAT attenuation than patients without IMV requirement (WMD = 13.07, 95% CI: 8.07–18.06, P < 0.001, Figure 4).

Figure 4.

Figure 4

Open in a new tab

Subgroup analysis of weighted mean difference in EAT attenuation between different clinical outcomes of COVID-19. EAT, epicardial adipose tissue.

The EAT thickness (in millimeters) was significantly increased in the patients admitted to ICU than the patients without ICU admission (WMD = 0.89, 95% CI: 0.66–1.12, P < 0.001, Figure 5 ). Furthermore, the EAT thickness was significantly increased in patients with hospitalization requirements than patients without (WMD = 0.74, 95% CI: 0.45–1.03, P < 0.001, Figure 5).

Figure 5.

Figure 5

Open in a new tab

Subgroup analysis of weighted mean difference in EAT thickness between different clinical outcomes of COVID-19. EAT, epicardial adipose tissue.

There was no difference of EAT measures between the nonsurvivor and survivor group (EAT volume: WMD = 13.43, 95% CI: -14.51–41.36, P = 0.346; EAT attenuation: WMD = -7.88, 95% CI: -22.53–6.78, P = 0.292; EAT thickness: WMD = 1.80, 95% CI: -0.28–3.88, P = 0.091).

Sensitivity analysis and publication bias

To evaluate the robustness of the results, sensitivity analyses were performed by sequentially removing each study. No apparent change occurred for most outcomes when an individual study was omitted.

No publication bias was observed in our evaluation of the funnel plots for EAT measures with the severity of COVID-19, and this finding was confirmed by Begg (P = 0.466) and Egger (P = 0.991) tests. No publication bias was observed in our evaluation of the funnel plots for EAT volume with the clinical outcomes of COVID-19, and this finding was confirmed by Begg (P = 0.707) and Egger (P = 0.985) tests. No publication bias was observed in our evaluation of the funnel plots for EAT attenuation with the clinical outcomes of COVID-19, and this finding was confirmed by Begg (P = 0.764) and Egger (P = 0.069) tests. However, an obvious publication bias was revealed in our evaluation of the funnel plots for EAT attenuation with the clinical outcomes of COVID-19, confirmed by Begg (P = 0.348) and Egger (P = 0.001) tests; Thus, the trim-and-fill method was used to adjust the publication bias. After trimming, the results were similar, indicating that the results were statistically reliable (WMD = 0.84, 95% CI: 0.66–1.02, Figure 6 ).

Figure 6.

Figure 6

Open in a new tab

Evaluation of publication bias using the funnel plot. (A) EAT measures between severe and nonsevere groups. (B) EAT volume between different clinical outcomes of COVID-19. (C) EAT attenuation between different clinical outcomes of COVID-19. (D) Trim-and-fill analysis for EAT thickness between different clinical outcomes of COVID-19. EAT, epicardial adipose tissue.

Discussion

To the best of our knowledge, this is the first meta-analysis that comprehensively summarized the association of EAT measures with the severity of COVID-19. EAT measures had a close relationship with adverse clinical outcomes of COVID-19 and thus became a risk stratification tool for patients with COVID-19. The results were confirmed by subgroup analysis, sensitivity analysis, and publication bias test.

A previous study has confirmed that obesity increased the infection severity in patients with 2009 influenza A virus infection (Van Kerkhove et al., 2011). Similar results were revealed in patients with COVID-19 (Du et al., 2021, Pranata et al., 2021b). BMI is a simple index and commonly used to classify overweight and obesity in adults. Recent meta-analysis papers demonstrated that a nonlinear relationship was observed between BMI and adverse clinical outcomes in patients with COVID-19 (Du et al., 2021, Pranata et al., 2021b). One of the studies revealed that the odds ratios for overweight patients and obesity patients were 1.02 (95% CI:0.99–1.05) and 1.09 (95% CI:1.04–1.15) when using BMI of 20 kg/m2 as the reference (Pranata et al., 2021b). Overweight patients with COVID-19 with increased BMI probably didn't have a higher risk of adverse outcomes than normal-weight controls, consistent with several recently published studies (Kananen et al., 2021, Tamara and Tahapary, 2020, van Son et al., 2021). That conclusion also suggested that the relationship between body fat and the severity/adverse outcome of COVID-19 is more complex.

There are some anthropometric tools that are used as BMI complements to determine obesity, including waist circumference, waist-to-hip ratio, waist-to-height ratio, or visceral adiposity parameters (Sommer et al., 2020). Researchers found the Edmonton Obesity Staging System, a clinical classification tool, was associated with adverse COVID-19 outcomes. This system distinguished risks beyond BMI (Rodriguez-Flores et al., 2022). The limitation of this system is complicated and may not be suitable for patients who need urgent care. Recently studies have shown that visceral adiposity seems to be a better risk stratification tool in COVID-19, and patients with central obesity might need special attention (Foldi et al., 2021, Pranata et al., 2021a). However, no standard CT protocol for visceral adiposity measurement (quantified at the different levels) may cause high heterogeneity of studies. Abdominal CT is also not a routine exam for patients with COVID-19.

EAT, as ectopic visceral fat around the heart, makes up for the lack of BMI that does not reflect the mass of the visceral fat (Hamdy et al., 2006). EAT is a potential source of inflammatory mediators, including tumor necrosis factor-alpha (TNF-alpha), interleukins 1 and 6 (IL-1, IL-6), leptin, and monocyte chemoattractant protein-1, and is a potential marker of systemic inflammation (Shimabukuro et al., 2013, Song et al., 2020). Inflammation plays a major role in the pathogenesis and prognosis of COVID-19 (Feng et al., 2020). Therefore, many studies have begun to verify the hypothesis that there are close relationships between EAT and severity/adverse outcomes of COVID-19.

EAT can be measured by chest CT scan and echocardiography. EAT measurement by echocardiography was first developed by Iacobellis et al (Iacobellis et al., 2003). However, echocardiography can only measure the EAT thickness, and the imaging quality may reduce owing to COVID-19 pneumonia. Chest CT scan is a useful exam for suspected or confirmed patients with COVID-19, both for diagnosis and in clinical decision-making (Garg et al., 2021). Our meta-analysis revealed that EAT measures had a close relationship with the severity and adverse clinical outcomes of COVID-19. Therefore, EAT measures by chest CT scan on admission could be a risk stratification tool for patients with COVID-19. EAT measures were associated with multiple inflammatory biomarkers and PaO2/FiO2 ratio, which were proven to be effective predictors of COVID-19 progression (Abrishami et al., 2021, Guarisco et al., 2021). The integration of EAT volume measurements into the clinical risk scores system for patients with COVID-19 can potentially enhance adverse outcome prediction (Conte et al., 2021, Grodecki et al., 2021, Guarisco et al., 2021, Turker Duyuler et al., 2021).

The mechanism of EAT measures in patients with COVID-19 is not clear. EAT may exert the direct/paracrine and indirect/systemic effect in COVID-19. On the basis of the results of existing research (Conte et al., 2021, Grodecki et al., 2021, Watanabe et al., 2020), we propose the following: (i) EAT was associated with coronary artery disease, diabetes, chronic obstructive pulmonary disease, and old age and male sex which were risk factors for COVID-19 poor prognosis (Parohan et al., 2020); (ii) COVID-19 infection could trigger systemic inflammatory response (Coperchini et al., 2020). EAT may transduce this inflammation to the heart (Kim and Han, 2020, Malavazos et al., 2020). Furthermore, EAT may exert a direct effect on the neighboring lungs and enhance systemic inflammatory response to COVID-19 (Moore and June, 2020, Ryan and Caplice, 2020). The EAT, which is near the pulmonary artery, enables direct diffusion of the inflammatory mediators into the pulmonary circulation, which may then exert vasocrine or paracrine effects on the lung tissue (Grodecki et al., 2021); (iii) EAT accumulation caused growth hormone–insulin-like growth factor 1 axis impairment, which usually affects old male patients, may further influence the pathophysiology of COVID-19 (Lubrano et al., 2020); (iv) COVID-19 infection could trigger systemic inflammation, including EAT inflammation. Viruses infect cells through the angiotensin-converting enzyme 2 (ACE2) receptor, which is highly expressed in EAT (Couselo-Seijas et al., 2021). A recent animal study found that ACE2 deficiency could worsen EAT inflammation in diet-induced obesity in mice (Patel et al., 2016); and (v) the release of proinflammatory cytokines from EAT into the general circulation may contribute to the systemic inflammatory state in COVID-19; systemic inflammation, in turn, promotes accumulation of EAT, creating a positive feedback loop (Grodecki et al., 2021).

Recently, Iacobellis et al. reported that 33 patients with COVID-19 had reduced EAT attenuation after receiving the dexamethasone therapy compared with any of the other therapies (Iacobellis et al., 2021). Therefore, EAT could serve as a therapeutic target for anti-inflammatory treatment in patients with COVID-19. More studies about EAT attenuation in COVID-19 are suggested. For patients with an increased EAT measure, doctors may advise hospitalization and prompt closer clinical observation for the possibility of myocardial injury and/or cardiac dysfunction (Özer et al., 2021, Wei et al., 2020). Both statins and colchicine were proven to decrease EAT (Konwerski et al., 2022). Recent studies have found these 2 drugs may reduce the mortality risk of patients with COVID-19 (Chiu et al., 2021, Diaz-Arocutipa et al., 2021). There are several ongoing clinical trials (such as NCT04472611, NCT04904536, NCT05038449) that evaluate the efficacy and safety of these 2 drugs for patients with COVID-19. Therefore, statins and colchicine may be beneficial to patients with COVID-19 with an increased EAT measure.

Limitations

First, most studies were single-centered. Single race and the small sample size can't be ignored. All these disadvantages could reduce the credibility of the conclusion of this study. Second, high heterogeneity was found in most analyses. Subgroup analysis was performed. No source of heterogeneity was revealed. Third, some underlying confounders may not be adjustable in the involved studies, such as BMI in each group. Fourth, although the publication bias was adjusted by the trim-and-fill method, the scarcity of the involved studies with EAT thickness data may affect the credibility of the conclusion.

Conclusions

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

Ethical approval statement

Not applicable.

Funding sours

None.

Conflict of interest

The authors have no conflict of interest to declare.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijid.2022.04.013.

Appendix. Supplementary materials

mmc1.docx (28.7KB, docx)

Reference

  1. Abrishami A, Eslami V, Baharvand Z, Khalili N, Saghamanesh S, Zarei E, et al. Epicardial adipose tissue, inflammatory biomarkers and COVID-19: Is there a possible relationship? Int Immunopharmacol. 2021;90 doi: 10.1016/j.intimp.2020.107174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bihan H, Heidar R, Beloeuvre A, Allard L, Ouedraogo E, Tatulashvili S, et al. Epicardial adipose tissue and severe Coronavirus Disease 19. Cardiovasc Diabetol. 2021;20(1):147. doi: 10.1186/s12933-021-01329-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Caussy C, Pattou F, Wallet F, Simon C, Chalopin S, Telliam C, et al. Prevalence of obesity among adult inpatients with COVID-19 in France. Lancet Diabetes Endocrinol. 2020;8(7):562–564. doi: 10.1016/S2213-8587(20)30160-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Chang TH, Chou CC, Chang LY. Effect of obesity and body mass index on coronavirus disease 2019 severity: A systematic review and meta-analysis. Obes Rev. 2020;21(11):e13089. doi: 10.1111/obr.13089. [DOI] [PubMed] [Google Scholar]
  5. Chiu L, Lo CH, Shen M, Chiu N, Aggarwal R, Lee J, et al. Colchicine use in patients with COVID-19: A systematic review and meta-analysis. PLoS One. 2021;16(12) doi: 10.1371/journal.pone.0261358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chu Y, Yang J, Shi J, Zhang P, Wang X. Obesity is associated with increased severity of disease in COVID-19 pneumonia: a systematic review and meta-analysis. Eur J Med Res. 2020;25(1):64. doi: 10.1186/s40001-020-00464-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Conte C, Esposito A, De Lorenzo R, Di Filippo L, Palmisano A, Vignale D, et al. Epicardial adipose tissue characteristics, obesity and clinical outcomes in COVID-19: A post-hoc analysis of a prospective cohort study. Nutr Metab Cardiovasc Dis. 2021;31(7):2156–2164. doi: 10.1016/j.numecd.2021.04.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Coperchini F, Chiovato L, Croce L, Magri F, Rotondi M. The cytokine storm in COVID-19: An overview of the involvement of the chemokine/chemokine-receptor system. Cytokine Growth Factor Rev. 2020;53:25–32. doi: 10.1016/j.cytogfr.2020.05.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Couselo-Seijas M, Almenglo C, R MA-B, Luis Fernandez A, Alvarez E, J RG-J, et al. Higher ACE2 expression levels in epicardial cells than subcutaneous stromal cells from patients with cardiovascular disease: Diabetes and obesity as possible enhancer. Eur J Clin Invest. 2021;51(5):e13463. doi: 10.1111/eci.13463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Deng M, Qi Y, Deng L, Wang H, Xu Y, Li Z, et al. Obesity as a Potential Predictor of Disease Severity in Young COVID-19 Patients: A Retrospective Study. Obesity (Silver Spring) 2020;28(10):1815–1825. doi: 10.1002/oby.22943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Diaz-Arocutipa C, Melgar-Talavera B, Alvarado-Yarasca A, Saravia-Bartra MM, Cazorla P, Belzusarri I, et al. Statins reduce mortality in patients with COVID-19: an updated meta-analysis of 147 824 patients. Int J Infect Dis. 2021;110:374–381. doi: 10.1016/j.ijid.2021.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Du Y, Lv Y, Zha W, Zhou N, Hong X. Association of body mass index (BMI) with critical COVID-19 and in-hospital mortality: A dose-response meta-analysis. Metabolism. 2021;117 doi: 10.1016/j.metabol.2020.154373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Eslami V, Abrishami A, Zarei E, Khalili N, Baharvand Z, Sanei-Taheri M. The Association of CT-measured Cardiac Indices with Lung Involvement and Clinical Outcome in Patients with COVID-19. Acad Radiol. 2021;28(1):8–17. doi: 10.1016/j.acra.2020.09.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Feng X, Li S, Sun Q, Zhu J, Chen B, Xiong M, et al. Immune-Inflammatory Parameters in COVID-19 Cases: A Systematic Review and Meta-Analysis. Front Med (Lausanne) 2020;7:301. doi: 10.3389/fmed.2020.00301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Foldi M, Farkas N, Kiss S, Dembrovszky F, Szakacs Z, Balasko M, et al. Visceral Adiposity Elevates the Risk of Critical Condition in COVID-19: A Systematic Review and Meta-Analysis. Obesity (Silver Spring) 2021;29(3):521–528. doi: 10.1002/oby.23096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Garg M, Prabhakar N, Bhalla AS, Irodi A, Sehgal I, Debi U, et al. Computed tomography chest in COVID-19: When & why? Indian J Med Res. 2021;153(1 & 2):86–92. doi: 10.4103/ijmr.IJMR_3669_20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Grodecki K, Lin A, Razipour A, Cadet S, McElhinney PA, Chan C, et al. Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19. Metabolism. 2021;115 doi: 10.1016/j.metabol.2020.154436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Guarisco G, Fasolo M, Capoccia D, Morsello G, Carraro A, Zuccalà P, et al. Blood glucose and epicardial adipose tissue at the hospital admission as possible predictors for COVID-19 severity. Endocrine. 2021:1–9. doi: 10.1007/s12020-021-02925-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hamdy O, Porramatikul S, Al-Ozairi E. Metabolic obesity: the paradox between visceral and subcutaneous fat. Curr Diabetes Rev. 2006;2(4):367–373. doi: 10.2174/1573399810602040367. [DOI] [PubMed] [Google Scholar]
  20. Huang Y, Lu Y, Huang YM, Wang M, Ling W, Sui Y, et al. Obesity in patients with COVID-19: a systematic review and meta-analysis. Metabolism. 2020;113 doi: 10.1016/j.metabol.2020.154378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Iacobellis G. Local and systemic effects of the multifaceted epicardial adipose tissue depot. Nat Rev Endocrinol. 2015;11(6):363–371. doi: 10.1038/nrendo.2015.58. [DOI] [PubMed] [Google Scholar]
  22. Iacobellis G, Assael F, Ribaudo MC, Zappaterreno A, Alessi G, Di Mario U, et al. Epicardial fat from echocardiography: a new method for visceral adipose tissue prediction. Obes Res. 2003;11(2):304–310. doi: 10.1038/oby.2003.45. [DOI] [PubMed] [Google Scholar]
  23. Iacobellis G, Corradi D, Sharma AM. Epicardial adipose tissue: anatomic, biomolecular and clinical relationships with the heart. Nat Clin Pract Cardiovasc Med. 2005;2(10):536–543. doi: 10.1038/ncpcardio0319. [DOI] [PubMed] [Google Scholar]
  24. Iacobellis G, Malavazos AE, Basilico S, Tresoldi S, Rinaldo RF, Dubini C, et al. Epicardial fat inflammation response to COVID-19 therapies. Obesity (Silver Spring) 2021;29(9):1427–1433. doi: 10.1002/oby.23232. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Iacobellis G, Secchi F, Capitanio G, Basilico S, Schiaffino S, Boveri S, et al. Epicardial Fat Inflammation in Severe COVID-19. Obesity (Silver Spring) 2020;28(12):2260–2262. doi: 10.1002/oby.23019. [DOI] [PubMed] [Google Scholar]
  26. Kananen L, Eriksdotter M, Bostrom AM, Kivipelto M, Annetorp M, Metzner C, et al. Body mass index and Mini Nutritional Assessment-Short Form as predictors of in-geriatric hospital mortality in older adults with COVID-19. Clin Nutr. 2021 doi: 10.1016/j.clnu.2021.07.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kim IC, Han S. Epicardial adipose tissue: fuel for COVID-19-induced cardiac injury? Eur Heart J. 2020;41(24):2334–2335. doi: 10.1093/eurheartj/ehaa474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Konwerski M, Gasecka A, Opolski G, Grabowski M, Mazurek T. Role of Epicardial Adipose Tissue in Cardiovascular Diseases: A Review. Biology (Basel) 2022;11(3) doi: 10.3390/biology11030355. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Li SM, Kang MT, Wu SS, Meng B, Sun YY, Wei SF, et al. Studies using concentric ring bifocal and peripheral add multifocal contact lenses to slow myopia progression in school-aged children: a meta-analysis. Ophthalmic Physiol Opt. 2017;37(1):51–59. doi: 10.1111/opo.12332. [DOI] [PubMed] [Google Scholar]
  30. Lubrano C, Masi D, Risi R, Balena A, Watanabe M, Mariani S, et al. Is Growth Hormone Insufficiency the Missing Link Between Obesity, Male Gender, Age, and COVID-19 Severity? Obesity (Silver Spring) 2020;28(11):2038–2039. doi: 10.1002/oby.23000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Malavazos AE, Goldberger JJ, Iacobellis G. Does epicardial fat contribute to COVID-19 myocardial inflammation? Eur Heart J. 2020;41(24):2333. doi: 10.1093/eurheartj/ehaa471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Moore JB, June CH. Cytokine release syndrome in severe COVID-19. Science. 2020;368(6490):473–474. doi: 10.1126/science.abb8925. [DOI] [PubMed] [Google Scholar]
  33. Özer S, Bulut E, Özyıldız AG, Peker M, Turan OE. Myocardial injury in COVID-19 patients is associated with the thickness of epicardial adipose tissue. Kardiologiia. 2021;61(8):48–53. doi: 10.18087/cardio.2021.8.n1638. [DOI] [PubMed] [Google Scholar]
  34. Parohan M, Yaghoubi S, Seraji A, Javanbakht MH, Sarraf P, Djalali M. Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies. Aging Male. 2020;23(5):1416–1424. doi: 10.1080/13685538.2020.1774748. [DOI] [PubMed] [Google Scholar]
  35. Patel VB, Mori J, McLean BA, Basu R, Das SK, Ramprasath T, et al. ACE2 Deficiency Worsens Epicardial Adipose Tissue Inflammation and Cardiac Dysfunction in Response to Diet-Induced Obesity. Diabetes. 2016;65(1):85–95. doi: 10.2337/db15-0399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Phan F, Boussouar S, Lucidarme O, Zarai M, Salem J-E, Kachenoura N, et al. Cardiac adipose tissue volume and IL-6 level at admission are complementary predictors of severity and short-term mortality in COVID-19 diabetic patients. Cardiovascular Diabetology. 2021;20(1) doi: 10.1186/s12933-021-01327-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pranata R, Lim MA, Huang I, Yonas E, Henrina J, Vania R, et al. Visceral adiposity, subcutaneous adiposity, and severe coronavirus disease-2019 (COVID-19): Systematic review and meta-analysis. Clin Nutr ESPEN. 2021;43:163–168. doi: 10.1016/j.clnesp.2021.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Pranata R, Lim MA, Yonas E, Vania R, Lukito AA, Siswanto BB, et al. Body mass index and outcome in patients with COVID-19: A dose-response meta-analysis. Diabetes Metab. 2021;47(2) doi: 10.1016/j.diabet.2020.07.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Rodriguez-Flores M, Goicochea-Turcott EW, Mancillas-Adame L, Garibay-Nieto N, Lopez-Cervantes M, Rojas-Russell ME, et al. The utility of the Edmonton Obesity Staging System for the prediction of COVID-19 outcomes: a multi-centre study. Int J Obes (Lond) 2022 doi: 10.1038/s41366-021-01017-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Ryan PM, Caplice NM. Is Adipose Tissue a Reservoir for Viral Spread, Immune Activation, and Cytokine Amplification in Coronavirus Disease 2019? Obesity (Silver Spring) 2020;28(7):1191–1194. doi: 10.1002/oby.22843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sevilla T, Aparisi-Sanz Á, Aristizábal-Duque C, Gómez-Salvador I, Baladrón C, San Román A. Epicardial adipose tissue attenuation in admitted patients with COVID-19. Rev Esp Cardiol (Engl Ed) 2022;75(1):98–100. doi: 10.1016/j.rec.2021.07.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Shimabukuro M, Hirata Y, Tabata M, Dagvasumberel M, Sato H, Kurobe H, et al. Epicardial adipose tissue volume and adipocytokine imbalance are strongly linked to human coronary atherosclerosis. Arterioscler Thromb Vasc Biol. 2013;33(5):1077–1084. doi: 10.1161/ATVBAHA.112.300829. [DOI] [PubMed] [Google Scholar]
  43. Slipczuk L, Castagna F, Schonberger A, Novogrodsky E, Sekerak R, Dey D, et al. Coronary artery calcification and epicardial adipose tissue as independent predictors of mortality in COVID-19. Int J Cardiovasc Imaging. 2021;37(10):3093–3100. doi: 10.1007/s10554-021-02276-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sommer I, Teufer B, Szelag M, Nussbaumer-Streit B, Titscher V, Klerings I, et al. The performance of anthropometric tools to determine obesity: a systematic review and meta-analysis. Sci Rep. 2020;10(1):12699. doi: 10.1038/s41598-020-69498-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Song G, Sun F, Wu D, Bi W. Association of epicardial adipose tissues with obstructive sleep apnea and its severity: A meta-analysis study. Nutr Metab Cardiovasc Dis. 2020;30(7):1115–1120. doi: 10.1016/j.numecd.2020.03.016. [DOI] [PubMed] [Google Scholar]
  46. Tamara A, Tahapary DL. Obesity as a predictor for a poor prognosis of COVID-19: A systematic review. Diabetes Metab Syndr. 2020;14(4):655–659. doi: 10.1016/j.dsx.2020.05.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Turker Duyuler P, Duyuler S, Demirtaş B, Çayhan V. Epicardial and pericoronary adipose tissue in severe COVID-19 infection. Acta Cardiol. 2021:1–8. doi: 10.1080/00015385.2021.2010009. [DOI] [PubMed] [Google Scholar]
  48. Van Kerkhove MD, Vandemaele KA, Shinde V, Jaramillo-Gutierrez G, Koukounari A, Donnelly CA, et al. Risk factors for severe outcomes following 2009 influenza A (H1N1) infection: a global pooled analysis. PLoS Med. 2011;8(7) doi: 10.1371/journal.pmed.1001053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. van Son J, Oussaada SM, Sekercan A, Beudel M, Dongelmans DA, van Assen S, et al. Overweight and Obesity Are Associated With Acute Kidney Injury and Acute Respiratory Distress Syndrome, but Not With Increased Mortality in Hospitalized COVID-19 Patients: A Retrospective Cohort Study. Front Endocrinol (Lausanne) 2021;12 doi: 10.3389/fendo.2021.747732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Watanabe M, Caruso D, Tuccinardi D, Risi R, Zerunian M, Polici M, et al. Visceral fat shows the strongest association with the need of intensive care in patients with COVID-19. Metabolism-Clinical and Experimental. 2020;111 doi: 10.1016/j.metabol.2020.154319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Wei ZY, Qiao R, Chen J, Huang J, Wang WJ, Yu H, et al. Pre-existing Health Conditions and Epicardial Adipose Tissue Volume: Potential Risk Factors for Myocardial Injury in COVID-19 Patients. Front Cardiovasc Med. 2020;7 doi: 10.3389/fcvm.2020.585220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. World Health Organization. Weekly operational update on COVID-19 [Internet]. Available from: https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19-21-december-2021. Accessed December 21, 2021.
  53. Yang Y, Ding L, Zou X, Shen Y, Hu D, Hu X, et al. Visceral Adiposity and High Intramuscular Fat Deposition Independently Predict Critical Illness in Patients with SARS-CoV-2. Obesity (Silver Spring) 2020;28(11):2040–2048. doi: 10.1002/oby.22971. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.docx (28.7KB, docx)

Articles from International Journal of Infectious Diseases are provided here courtesy of Elsevier

RESOURCES