Detecting human coronary inflammation by imaging perivascular fat.

Area of Science:

• Cardiovascular Imaging
• Biomedical Engineering
• Adipose Tissue Biology

Background:

• Vascular inflammation is a key driver of cardiovascular disease but is difficult to detect with current imaging.
• Perivascular adipose tissue (PVAT) is influenced by vascular inflammation, offering a potential indirect detection method.
• Existing imaging modalities lack the sensitivity to detect early-stage vascular inflammation.

Purpose of the Study:

• To develop and validate a novel computerized tomography (CT) angiography methodology for quantifying phenotypic changes in PVAT.
• To establish a new imaging metric, the CT fat attenuation index (FAI), for detecting vascular inflammation.
• To assess the FAI's ability to identify subclinical coronary artery disease and vulnerable atherosclerotic plaques.

Main Methods:

• Developed a 3D PVAT analysis method using CT angiography.
• Studied CT images from 453 cardiac surgery patients' adipose tissue explants.
• Validated the CT fat attenuation index (FAI) against 18F-fluorodeoxyglucose positron emission tomography (PET) and clinical outcomes in 273 subjects.

Main Results:

• Inflamed vessels release cytokines that inhibit lipid accumulation in PVAT-derived preadipocytes.
• The CT fat attenuation index (FAI) demonstrated high sensitivity and specificity for detecting inflammation.
• FAI gradient around coronary arteries identified early subclinical coronary artery disease and plaque instability.

Conclusions:

• Human vessels exert paracrine effects on PVAT, altering adipocyte lipid content.
• CT imaging of PVAT, using the FAI metric, provides a noninvasive method for detecting vascular inflammation.
• This methodology holds promise for clinical implementation in identifying plaque instability and guiding cardiovascular disease prevention strategies.