Abstract 21015: Coronary Inflammation in Humans Drives Spatial Changes of Perivascular Adipose Tissue Composition Detectable by a Novel Computed Tomography-Based Technology

Abstract

Background: Non-invasive detection of vascular inflammation remains an unmet goal. We hypothesized that phenotypic changes in perivascular adipose tissue (PVAT) induced by vascular inflammation can be quantified using a new computed tomography angiography (CTA) methodology. Methods: In Arm 1, human PVAT adipocytes were cultured +/- inflammatory cytokines (n=7) or co-cultured with vascular tissue (+/-Angiotensin-II, n=6) to assess the effects of vascular inflammation on PVAT differentiation. In Arm 2, AT explants (epicardial, subcutaneous and thoracic AT) from 453 cardiac surgery patients were used in histology and gene expression studies to relate the ex vivo images with in vivo CT scan information (n=105) on the biology of the explants. In Arm 3, in 267 patients undergoing diagnostic CTA, PVAT attenuation (Fat Attenuation Index (FAI) defined as the average CT attenuation of AT), was analysed around the proximal right coronary artery. In Arm 4, PVAT FAI around unstable (culprit) and stable coronary plaques was calculated in 22 CAD patients undergoing CTA. Results: In Arm 1, Angiotensin-II (A) and proinflammatory cytokines (B) prevented lipid accumulation and adipocyte differentiation in cultured PVAT. In Arm 2, adipocyte size by histology was inversely correlated with FAI in vivo (C). Both FAI of AT explants (not shown) and FAI in-vivo (n=105) were negatively associated with epicardial AT differentiation as assessed by FABP4 expression (D). In Arm 3, PVAT FAI change over distance from RCA wall was significantly different in CAD compared to no CAD patients (E). In Arm 4, PVAT FAI was significantly increased around unstable plaques (F). Conclusions: Human vessels exert paracrine effects on surrounding PVAT, affecting local intracellular lipid accumulation in preadipocytes, which can then be monitored using a CT imaging approach. This novel methodology can be implemented in clinical practice to detect unstable plaques in the human coronary vasculature.