Detecting human coronary inflammation by imaging perivascular fat

Abstract

Early detection of vascular inflammation is a long-standing goal that would allow deployment of targeted strategies for the prevention or treatment of multiple disease states. Since vascular inflammation is not detectable with commonly used imaging modalities, we hypothesized that phenotypic changes in perivascular adipose tissue (PVAT) induced by vascular inflammation could be quantified using a new computerized tomography angiography (CTA) methodology. We show that inflamed human vessels release cytokines that prevent lipid accumulation in PVAT-derived preadipocytes in vitro, ex vivo and in vivo. We developed a 3D PVAT analysis method and studied CT images of human adipose tissue explants from 453 patients undergoing cardiac surgery, relating the ex vivo images with in vivo CT scan information on the biology of the explants. We have developed a new imaging biomarker, CT Fat attenuation index (FAI), that describes adipocyte lipid content and size. FAI has excellent sensitivity and specificity for detecting tissue inflammation as assessed by tissue uptake of 18FFDG in positron emission tomography (PET). In a validation cohort of 273 subjects, the FAI gradient around the human coronary arteries identified early subclinical coronary artery disease in vivo, and detected dynamic changes of PVAT in response to variations of vascular inflammation, and inflamed, vulnerable atherosclerotic plaques during acute coronary syndromes. Our study revealed that human vessels exert paracrine effects on the surrounding PVAT, affecting local intracellular lipid accumulation in preadipocytes, which can then be monitored using a CT imaging approach. This methodology can be implemented in clinical practice to allow the non-invasive detection of unstable plaques in the human coronary vasculature