Journal of the CardioMetabolic Syndrome

Magnetic Resonance Imaging for Quantifying Regional Adipose Tissue in Human Immunodeficiency Virus-Infected Persons With the Cardiometabolic Syndrome


  • Adil Bashir PhD,

    1. From the Department of Radiology, Mallinckrodt Institute of Radiology,1 and the Department of Medicine, Division of Metabolism, Endocrinology, and Lipid Research,2 Washington University School of Medicine, St Louis, MO
    Search for more papers by this author
  • Erin Laciny MSEd,

    1. From the Department of Radiology, Mallinckrodt Institute of Radiology,1 and the Department of Medicine, Division of Metabolism, Endocrinology, and Lipid Research,2 Washington University School of Medicine, St Louis, MO
    Search for more papers by this author
  • Sherry Lassa-Claxton MS, RD,

    1. From the Department of Radiology, Mallinckrodt Institute of Radiology,1 and the Department of Medicine, Division of Metabolism, Endocrinology, and Lipid Research,2 Washington University School of Medicine, St Louis, MO
    Search for more papers by this author
  • Kevin E. Yarasheski PhD

    1. From the Department of Radiology, Mallinckrodt Institute of Radiology,1 and the Department of Medicine, Division of Metabolism, Endocrinology, and Lipid Research,2 Washington University School of Medicine, St Louis, MO
    Search for more papers by this author

Adil Bashir, PhD, Mallinckrodt Institute of Radiology, Washington University in St Louis, Campus Box 8227, 4525 Scott Avenue, East Building, St Louis, MO 63110

In this technical brief, we describe a magnetic resonance imaging (MRI) technique to quantify regional adipose tissue content using commercially available magnetic resonance (MR) systems. An advantage of MRI over more conventional methods, such as anthropometry and dual energy x-ray absorptiometry, for quantifying body fat content or body composition is that regional differences in fat or muscle distribution can be assessed. Computed tomography can also provide reasonable estimates of regional adipose content, but it exposes the patient to potentially harmful ionizing radiation. MRI is a noninvasive technique that can quantify adipose tissue content using established clinical imaging sequences on standard 1.5T clinical MR systems in approximately 30 minutes. One potential shortcoming is that image analysis currently requires off-line processing with advanced nonstandard software. These advanced image analysis tools are becoming available as a standard tool on newer clinical MR systems, however.


The amount and regional distribution of adipose tissue on the body is considered an important biomarker for the cardiometabolic syndrome.1,2 Excessive visceral fat (visceral adiposity) is associated with impaired glucose tolerance, insulin resistance, an increased risk of type 2 diabetes, atherosclerosis, and hypertension—components of the cardiometabolic syndrome.3–5

Approximately 50% of persons with human immunodeficiency virus (HIV) infection treated with highly active antiretroviral therapy (HAART) that includes certain protease inhibitors and nucleoside reverse transcriptase inhibitors develop changes in fat distribution, including regional lipohypertrophy (eg, visceral adiposity, breast enlargement in women, cervical adipose deposition [buffalo hump], and benign lipomas) and lipoatrophy (eg, subcutaneous fat loss in the abdomen, limbs, face, and buttocks); these changes are sometimes referred to as lipodystrophy. Changes in the distribution of abdominal fat include loss of subcutaneous adipose tissue (SAT) between the skin and the outer margin of the abdominal wall and an accumulation of visceral adipose tissue (VAT) within the inner margin of the abdominal wall and surrounding the abdominal organs.6–9 In HIV infection, the presence of visceral adiposity and peripheral lipoatrophy tends to be associated with insulin resistance.7 In the general population, visceral adiposity and abdominal subcutaneous adipose accumulation appear more frequently than peripheral lipoatrophy, and they are associated with insulin resistance. The ability to image and partition the total adipose tissue, SAT, and VAT compartments and to accurately and reproducibly quantify SAT, VAT, and peripheral SAT content is crucial to the diagnosis of adipose tissue redistribution syndromes and is necessary to evaluate the effectiveness of interventions focused on reducing visceral adiposity, restoring peripheral SAT and, potentially, reducing cardiometabolic disease risk in HIV infection.

Magnetic Resonance Imaging

MRI is a widely used imaging technique that uses a strong magnetic field and radiofrequency (rf) waves to produce detailed images of the body's organs and structures without the use of ionizing radiation. Typically, MRI scans are created by exciting the labile hydrogen (1H) atoms in tissues using a short-duration (a few milliseconds) rf pulse in the presence of the strong magnetic field. The excited protons emit radio waves that are detected by receivers and processed to generate gray-scale images. By applying additional magnetic field gradients, information can be directly related to the signal position in the body, thus generating an image.10

The signals emitted by the 1H atoms after rf excitation decay with two characteristic time constants, the longitudinal relaxation time constant (T1) and the transverse relaxation time constant (T2). Different tissues and pathology exhibit different relaxation time constants, and these differences are used to generate contrast in MR images. By changing data acquisition parameters, the MRI system can generate images that high-light or alter the gray-scale intensity or appearance of the different tissues in the body. Water and fat 1H atoms have very different relaxation time constants and therefore will have different appearances on relaxation-weighted MR images. Specifically, water has a longer T1 than protons in fat, so on a T1-weighted image, where the image intensity is inversely proportional to T1, the fat appears brighter than water.

In addition to different characteristic time constants, fat and water protons exist in different chemical environments. Therefore, when placed in a strong magnetic field they resonate at slightly different frequencies. This frequency difference can also be used to selectively highlight water (muscle) or fat tissue in an image. For example, muscle tissue can be highlighted in an image by using either fat suppression (in which the signal from fat tissue is selectively eliminated by rf pulses) or water excitation (in which only water protons in muscle are excited by rf pulses). These selective water and fat tissue images are advantageous in signal processing for visually partitioning and quantifying muscle and fat volumes.

Study Participants

In an effort to highlight the divergence in regional adipose tissue distribution that can be quantified using MRI in HIV-infected persons, 2 men with HIV infection treated with HAART were recruited for the study (Table). The adipose tissue distribution of Patient A is similar to that associated with the metabolic syndrome in the general population, while that of Patient B is typical of the HIV-related metabolic syndrome or lipodystrophy (Figure 1 and Figure 2). The Washington University School of Medicine Human Research Protection Office approved the study. The study risks and benefits were explained, and each volunteer signed an approved consent document.

Table Table.  Descriptive Characteristics.
ParameterPatient APatient B
Age, y2754
EthnicityAfrican AmericanCaucasian
Current anti-HIV medicationsRitonavir, atazanavir, tenofovir/emtricitabineLamivudine/zidovudine, efavirenz
Height, cm175.3175.3
Weight, kg12384
BMI, kg/m240.027.3
Waist circumference, cm12096
Body fat, %3122
Trunk-to-limb fat ratio1.12.9
Glucose, mg/dL9992.5
Insulin, µU/mL1819
Total cholesterol, mg/dL163172
LDL cholesterol, mg/dL10898
HDL cholesterol, mg/dL3453
Triglycerides, mg/dL104107
Blood pressure, mm Hg140/74112/77
Abbreviations: BMI, body mass index; HDL, high-density lipoprotein; HIV, human immunodeficiency virus; HOMA-IR, homeostasis model assessment for insulin resistance; LDL, low-density lipoprotein. All hormone, metabolite, and blood pressure measurements were made after an overnight fast; trunk-to-limb fat ratio and body fat (%) were measured using dual energy x-ray absorptiometry.
Figure 1.

T1-weighted abdominal magnetic resonance images from 2 men with human immunodeficiency virus infection. On the left, Patient A is without visceral adiposity but has a high subcutaneous adipose tissue content. On the right, Patient B has visceral adiposity and subcutaneous lipoatrophy. Fat appears bright white-gray. Total abdominal fat volume in Patient A is greater than in Patient B (5166 cm3 vs 3325 cm3); however, the ratio of visceral to total abdominal fat volume is lower in Patient A (30%) than in Patient B (72%).

Figure 2.

T1-weighted thigh magnetic resonance images from 2 men infected with human immunodeficiency virus without (panels A and C, Patient A) and with (panels B and D, Patient B) peripheral lipoatrophy. Panels C and D show the same images using a fat suppression signal acquisition protocol where the fat signal is eliminated and only the water signal from muscle is visualized. Patient B has lipoatrophy because panels B and D show almost complete absence of thigh subcutaneous adipose tissue. By comparison, Patient A (panels A and C) does not have lipoatrophy (thigh fat-to-muscle ratio, 60%), but in Patient B (panels B and D) the thigh fat-to-muscle ratio is 6%. The small circular regions of high intensity on the fat-suppressed images represent blood vessels and are not included in the quantification of fat.

Study Protocol

Thigh subcutaneous fat and abdominal fat volumes (SAT and VAT) were quantified using 1H MRI. Data were acquired on a 1.5T whole-body Siemens Sonata system (Siemens Medical Systems, Erlangen, Germany) using the body coil. The participants were in the supine position for scanning. Three-plane reference images were obtained to identify anatomic landmarks and to optimize the participant's position in the MR scanner. For the quantitation of abdominal fat, 27 contiguous axial slices of the abdomen were obtained during a single breath hold using a standard T1-weighted, 2-dimensional, multislice, spoiled gradient-echo sequence. The inferior border of the imaging volume was centered over the spine at the L5-sacrum intervertebral space. The other scan parameters used were repetition time, 209 ms; echo time, 4.1 ms; slice thickness, 10 mm; and flip angle, 70°. The field of view was 400 mm, with an in-plane image resolution of 2.4 mm × 1.6 mm. Scan time was 20 seconds. Fat suppression was not used for abdominal scanning; it requires additional time and can be limited by the participant's ability to sustain a breath hold for >20 seconds.

For the quantitation of thigh fat and muscle content, 2 sets of 10 serial axial images, with and without fat suppression, were obtained starting from 10 cm above the superior border of the medial condyle of the tibia. Ten 8-mm-thick slices were obtained. Other scan parameters were repetition time, 1500 ms; echo time, 13 ms; and flip angle, 90°. In-plane resolution was 2.1 mm × 1.4 mm.

Data Analysis and Results

Images were analyzed using Analyze 7.0 software package (AnalyzeDirect, Inc, Overland Park, KS). Abdominal fat volumes were obtained from 8 (sequential) of the 27 axial images in a semiautomated fashion based upon the pixel intensity and location and separated into VAT and SAT regions. The VAT and SAT volumes were calculated on the basis of operator-defined adipose tissue location and pixel intensities, and the total abdominal adipose tissue volume was calculated as the sum of VAT plus SAT (Figure 1). Using 8 serial axial images of the thighs, right and left thigh muscle and fat volumes were quantified (separately) using Analyze software and operator-defined threshold pixel intensities for muscle and fat (Figure 2). In our hands, the day-to-day variability for quantifying fat and muscle volumes is <6% when the same operator identifies and quantifies the regions of interest in the same series of abdomen or thigh images.


The quantity and distribution of adipose tissue and muscle are important biomarkers in our understanding of cardiometabolic disorders. The examples presented here clearly illustrate the utility of MRI for providing insight into relationships between body fat content and distribution and metabolism. Of note, both HIV-infected participants were insulin-resistant on the basis of their fasting insulin level and homeostasis model assessment for insulin resistance index, but their adipose tissue distribution was very different. Patient A was obese, met the Third Report of the National Cholesterol Education Program Adult Treatment Panel (NCEP ATP III) criteria for the metabolic syndrome, and had a large volume of SAT. Patient B was leaner but had visceral adiposity and subcutaneous lipoatrophy. In Patient B, abdominal MRI was useful for making an important distinction. On the basis of NCEP ATP III criteria, the waist circumference of Patient B (96 cm) would not be considered central adiposity, and he would not be considered obese on the basis of his body mass index (27 kg/m2). On the basis of quantitative MRI, however, Patient B clearly has visceral adiposity that would not have been captured using the typical indirect indicators (waist circumference or body mass index). This is especially important in HIV-related alterations in regional adipose tissue distribution. Compared with subjective estimates, MRI provides a noninvasive quantitation of regional adipose tissue volume, including the differentiation of subcutaneous and visceral fat volumes. These measurements use established imaging protocols and may be used as clinical or research tools to identify HIV-infected persons with visceral adiposity and peripheral lipoatrophy and to evaluate the effectiveness of therapeutic interventions (exercise, diet, hormonal, and anti-HIV medication changes and glucose-, lipid-, and blood pressure-lowering medications) aimed at reducing visceral adiposity and potentially reducing cardiometabolic disease risk.


These studies were supported by NIH grants DK049393, DK059531, AT003083, P30 DK56341, DK020579, RR000954, and AI25903.