21st Century Advances in Multimodality Imaging of Obesity for Care of the Cardiovascular Patient

Abstract

Although obesity is typically defined by body mass index criteria, this does not differentiate true body fatness, as this includes both body fat and muscle. Therefore, other fat depots may better define cardiometabolic and cardiovascular disease (CVD) risk imposed by obesity. Data from translational, epidemiological, and clinical studies over the past 3 decades have clearly demonstrated that accumulation of adiposity in the abdominal viscera and within tissue depots lacking physiological adipose tissue storage capacity (termed "ectopic fat") is strongly associated with the development of a clinical syndrome characterized by atherogenic dyslipidemia, hyperinsulinemia/glucose intolerance/type 2 diabetes mellitus, hypertension, atherosclerosis, and abnormal cardiac remodeling and heart failure. This state-of-the-art paper discusses the impact of various body fat depots on cardiometabolic parameters and CVD risk. Specifically, it reviews novel and emerging imaging techniques to evaluate adiposity and the risk of cardiometabolic diseases and CVD.

Keywords: adiposity; cardiovascular disease; imaging techniques; multimodality imaging,obesity; waist circumference.

Figure 1.. Pericardial and epicardial fat by echocardiography and related pathophysiology.

Echocardiographic parasternal long axis view showing both epicardial and pericardial fat thickness. Epicardial fat (within the yellow line) is the echo-free space between the outer wall of the myocardium and the bright visceral layer of the pericardium that separates it from the pericardial fat (within the red line). Pericardial fat is anterior to the epicardial fat, as it is located between the visceral and parietal layer of the pericardium. Both epicardial and pericardial fat depots can present as either hypo- (more commonly) or slightly hyper-echoic spaces. Both pericardial and epicardial fat may secrete adipocytokines and other factors that directly interact with the underlying myocardium and epicardial coronary arteries. Image courtesy of Gianluca Iacobellis.

Figure 2.. Variation in muscle fat infiltration in three obese men.

Volumetric water and fat separated MR images of three males with obesity (BMI ~31 kg/m2), and similar age (~68 years), and visceral adipose tissue (VAT) volume (~6.3 L) but vastly different muscle fat infiltration (MFI) in the anterior thigh muscles ranging from 5.9 % (left) to 15.7 % (right). The images were analyzed using AMRA Researcher, AMRA Medical AB, Linköping, Sweden. Image courtesy of UK Biobank under access application 6569.

Central Illustration.. Segmentation of abdominal adipose tissue depots by CT and MRI.

A-B: Examples of cross-sectional segmentation of visceral (red) and subcutaneous (green) adipose tissue (VAT, SAT) on unenhanced CT (A) and proton-density fat fraction map from MRI (B). Adipose tissue and lean tissue can be separated based on low attenuation values of fat on CT (less than -50 Hounsfield Unit) and high fat fraction values of fat on MRI (70-100%). C-F: Examples of organ steatosis measurements on unenhanced CT (C, E) and MRI (D, F). Liver steatosis can range from lean (50–60 Hounsfield Unit [HU] or 0% fat fraction) to severe (below 10 HU or >50% fat fraction). Pancreas steatosis can range from lean (30–50 HU or near 0% fat fraction) to nearly all fat (approx. −100 HU or 100% fat fraction). C – no hepatic or pancreatic steatosis by CT. D – mild hepatic steatosis and no pancreas steatosis by MRI. E – no hepatic steatosis and moderate-severe pancreatic steatosis by CT. F – moderate hepatic and pancreatic steatosis by MRI.

Video Legend:. MRI-based body fat distribution analysis.

Quantitative body composition analysis of whole body water and fat separated MRI images demonstrating segmentation into thigh muscles (orange), lower body (thigh) and abdominal subcutaneous adipose tissue (blue), visceral adipose tissue (pink), pancreas (white), liver (red), and heart (yellow) using a single neck-to-knee MRI scan. The images were analyzed using AMRA Researcher, AMRA Medical AB, Linköping, Sweden.