Pericoronary Adipose Tissue as a Predictor of Diastolic Dysfunction:A New Paradigm in Coronary Artery Disease Assessment
- Author:
Ritika AGARWAL
1
;
Jahnavi GADUPUTI
;
Karthik R. SHETTY
;
Neeraj SHIVAKUMAR
;
V. S. PRAKASH
Author Information
- Publication Type:Original Article
- From: International Journal of Heart Failure 2025;7(4):229-239
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background and Objectives:Pericoronary adipose tissue (PCAT) is linked to coronary artery disease (CAD) progression through compositional changes that disrupt adipose homeostasis and affect vascular biology. This study explores how PCAT characteristics - volume and fat attenuation index (FAI)-relate to CAD severity and left ventricular (LV) function.
Methods:This study included 100 patients with suspected CAD who underwent coronary computed tomography angiography (CCTA) and echocardiography between September 2023 and July 2024. PCAT volume, FAI and thickness were measured along coronary arteries (right coronary artery [RCA], left anterior descending [LAD], and left circumflex [LCX]). CAD severity was assessed using the Gensini score, while LV structural and functional metrics, including diastolic function, were obtained via echocardiography. Correlations between PCAT parameters, CAD burden, and LV function were analyzed using Spearman’s correlation and p value <0.05 was considered significant.
Results:RCA FAI exhibited the strongest association with CAD severity (Gensini score: r=0.416, p=0.026) and diastolic dysfunction (r=0.536, p<0.001), with progressively less negative values from Grade 1 to Grade 3 dysfunction. LAD and LCX (r=0.278, p=0.03) FAI displayed moderate associations. RCA volume correlated with LV mass index, fractional shortening and diastolic parameters. Hypertension was associated with elevated RCA FAI and volume (p<0.05), while diabetes showed no significant impact on PCAT metrics.
Conclusions:PCAT FAI is a sensitive marker of CAD severity and diastolic dysfunction, especially in the RCA, highlighting its value for detection, risk stratification, and personalized CAD management. Integration of PCAT metrics into routine CCTA analysis could enhance cardiovascular risk assessment and treatment planning.
