Relationship Between Epicardial Adipose Tissue and Biventricular Longitudinal Strain and Strain Rate in Patients with Type 2 Diabetes Mellitus

Zhu, Jing; Li, Wenjia; Xie, Zhen; Zhuo, Kaimin

doi:10.1016/j.acra.2022.08.019

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Multiple studies have assessed the impact of EAT on subclinical myocardial damage in T2DM patients. Zhu et al observed that EFV measured by MRI was independently linked to left ventricular global peak systolic longitudinal strain and left ventricular peak diastolic longitudinal strain rate ( 27 ). However, the measurement of EFV by MRI is limited in clinical application.…”

Section: Discussionmentioning

confidence: 99%

Association of epicardial fat volume with subclinical myocardial damage in patients with type 2 diabetes mellitus

Hu,

Yu,

Zhang

et al. 2024

Quant Imaging Med Surg

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Background In type 2 diabetes mellitus (T2DM) patients, left ventricular systolic dyssynchrony (LVSD) with normal left ventricular ejection fraction (LVEF) and normal myocardial perfusion could referred to as subclinical myocardial damage, which is difficult to diagnose at an early stage. Epicardial adipose tissue, a distinctive heart-specific visceral fat, is closely related to various cardiovascular diseases. The objective of this study was to investigate the correlation between epicardial fat volume (EFV) and subclinical myocardial damage in T2DM patients. Methods This retrospective cross-sectional study included 117 T2DM patients with normal myocardial perfusion by single photon emission computed tomography-computed tomography (SPECT-CT) and normal LVEF by echocardiography. The study was conducted from January 2018 to December 2022. Patient data were collected through electronic medical records including basic patient information, medical history, laboratory tests, and medication data. The EFV was quantified through a non-contrast CT scan. Quantitative indicators of LVSD including phase standard deviation (PSD) and phase histogram bandwidth (PBW) were obtained through phase analysis of the gated rest myocardial perfusion imaging (MPI). Additionally, 83 healthy individuals at the same time were selected to gain the reference threshold of LVSD indicators (13.1° for PSD and 37.6° for PBW). Univariate and multivariable logistic regression models were performed to analyze factors influencing LVSD. A generalized additive model (GAM) was applied to explore the relationship between EFV and LVSD. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of EFV for LVSD. Results Among all patients, 32 (27.4%) patients had LVSD. Compared with the non-LVSD group, the body mass index (BMI) and EFV were higher in the LVSD group (25.83±2.66 vs. 23.94±3.13 kg/m 2 ; 142.41±44.17 vs. 108.01±38.24 cm 3 , respectively, both P<0.05). Multivariate regression analysis revealed that EFV was independently associated with LVSD [odds ratio (OR) =1.19; 95% confidence interval (CI): 1.06–1.34; P=0.003]. Age, BMI, incidence of hypertension, and LVSD were increased with tertiles of EFV (all P<0.05). The GAM indicated a linear association between EFV and LVSD. The ROC curve analysis concluded that the area under the curve (AUC) of EFV for predicting subclinical myocardial damage in T2DM patients was 0.732 (95% CI: 0.633–0.831, P<0.001), with the optimal threshold of 122.26 cm 3 , sensitivity of 71.9%, and specificity of 69.4%. Conclusions EFV is an independent risk factor for LVSD in T2DM patients with normal LVEF and normal MPI, which could potentially serve as a novel imaging marker and a potential therapeutic target for subclinical myocardial damage.

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Section: Discussionmentioning

confidence: 99%

Association of epicardial fat volume with subclinical myocardial damage in patients with type 2 diabetes mellitus

Hu,

Yu,

Zhang

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

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Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography

Matsunaga,

Kono,

Matsuo

et al. 2024

Academic Radiology

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Role of epicardial adipose tissue in diabetic cardiomyopathy through the lens of cardiovascular magnetic resonance imaging – a narrative review

Kotha,

Plein,

Greenwood

et al. 2024

Therapeutic Advances in Endocrinology

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Accumulating evidence suggests that ectopic/visceral adiposity may play a key role in the pathogenesis of nonischaemic cardiovascular diseases associated with type 2 diabetes. Epicardial adipose tissue (EAT) is a complex visceral fat depot, covering 80% of the cardiac surface with anatomical and functional contiguity to the myocardium and coronary arteries. EAT interacts with the biology of the underlying myocardium by secreting a wide range of adipokines. Magnetic resonance imaging (MRI) is the reference modality for structural and functional imaging of the heart. The technique is now also emerging as the reference imaging modality for EAT quantification. With this narrative review, we (a) surveyed contemporary clinical studies that utilized cardiovascular MRI to characterize EAT (studies published 2010–2023); (b) listed the clinical trials monitoring the response to treatment in EAT size as well as myocardial functional and structural parameters and (c) discussed the potential pathophysiological role of EAT in the development of diabetic cardiomyopathy. We concluded that increased EAT quantity and its inflammatory phenotype correlate with early signs of left ventricle dysfunction and may have a role in the pathogenesis of cardiac disease in diabetes with and without coronary artery disease.

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Relationship Between Epicardial Adipose Tissue and Biventricular Longitudinal Strain and Strain Rate in Patients with Type 2 Diabetes Mellitus

Cited by 3 publications

References 43 publications

Association of epicardial fat volume with subclinical myocardial damage in patients with type 2 diabetes mellitus

Association of epicardial fat volume with subclinical myocardial damage in patients with type 2 diabetes mellitus

Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography

Role of epicardial adipose tissue in diabetic cardiomyopathy through the lens of cardiovascular magnetic resonance imaging – a narrative review

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