Abstract
Background: Epicardial adipose tissue (EAT) has been related to increased cardiovascular risk in chronic kidney disease
patients. However, prospective studies of EAT thickness in prediction of cardiovascular events in CKD patients are lacking.
Moreover, there are inconsistencies in literature regarding cut-off of EAT thickness, standard technique and phase of
measurement. Objectives: This study was undertaken to compare systolic and diastolic EAT thickness in prediction of
CV events in non-dialysis dependent CKD patients. Methods: In this prospective, observational study, transthoracic echocardiography
(TTE) was used to assess systolic and diastolic EAT thickness in 210 consecutive non-dialysis dependent
CKD patients and followed up for at least one year for pre-defined end-points. Results: The mean systolic and diastolic
EAT thickness in the CKD group (5.6±1.2mm and 4.2±1.1mm) was significantly higher than the non-CKD participants
(4.3±1.0mm and 3.1±1.1mm), both P

Key words: Coronary artery disease, CV risk, Echocardiography, Epicardial fat, Reproducibility

Similar Articles

Exploring the Alzheimer's disease neuroepigenome: recent advances and future trends.
Zhang H, Elefant F
Neural regeneration research. 2022; 17(2): 325-327

Editorial: Novel therapeutic approaches in chronic kidney disease and kidney transplantation: the draw of evolving integrated multimodal approaches in the targeted therapy era.
Tantisattamo E, Hanna RM, Kalantar-Zadeh K
Current opinion in nephrology and hypertension. 2022; 31(1): 1-5

Growth differentiation factor 5: a neurotrophic factor with neuroprotective potential in Parkinson's disease.
Goulding SR, Anantha J, Collins LM, Sullivan AM, O'Keeffe GW
Neural regeneration research. 2022; 17(1): 38-44

Environmental stocks, CEO health risk and COVID-19.
Fernández-Méndez C, Pathan S
Research in international business and finance. 2022; 59(): 101509

Differentiating Human Pluripotent Stem Cells to Vascular Endothelial Cells for Regenerative Medicine, Tissue Engineering, and Disease Modeling.
Bertucci T, Kakarla S, Kim D, Dai G
Methods in molecular biology (Clifton, N.J.). 2022; 2375(): 1-12