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      Quantification of Abdominal Fat Depots in Rats and Mice during Obesity and Weight Loss Interventions

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          Background & Aims

          Obesity is a leading healthcare issue contributing to metabolic diseases. There is a great interest in non-invasive approaches for quantitating abdominal fat in obese animals and humans. In this work, we propose an automated method to distinguish and quantify subcutaneous and visceral adipose tissues (SAT and VAT) in rodents during obesity and weight loss interventions. We have also investigated the influence of different magnetic resonance sequences and sources of variability in quantification of fat depots.

          Materials and Methods

          High-fat diet fed rodents were utilized for investigating the changes during obesity, exercise, and calorie restriction interventions (N = 7/cohort). Imaging was performed on a 7T Bruker ClinScan scanner using fast spin echo (FSE) and Dixon imaging methods to estimate the fat depots. Finally, we quantified the SAT and VAT volumes between the L1–L5 lumbar vertebrae using the proposed automatic hybrid geodesic region-based curve evolution algorithm.


          Significant changes in SAT and VAT volumes (p<0.01) were observed between the pre- and post-intervention measurements. The SAT and VAT were 44.22±9%, 21.06±1.35% for control, −17.33±3.07%, −15.09±1.11% for exercise, and 18.56±2.05%, −3.9±0.96% for calorie restriction cohorts, respectively. The fat quantification correlation between FSE (with and without water suppression) sequences and Dixon for SAT and VAT were 0.9709, 0.9803 and 0.9955, 0.9840 respectively. The algorithm significantly reduced the computation time from 100 sec/slice to 25 sec/slice. The pre-processing, data-derived contour placement and avoidance of strong background–image boundary improved the convergence accuracy of the proposed algorithm.


          We developed a fully automatic segmentation algorithm to quantitate SAT and VAT from abdominal images of rodents, which can support large cohort studies. We additionally identified the influence of non-algorithmic variables including cradle disturbance, animal positioning, and MR sequence on the fat quantification. There were no large variations between FSE and Dixon-based estimation of SAT and VAT.

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          Most cited references 16

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          Body fat distribution and risk of cardiovascular disease: an update.

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            Subcutaneous abdominal fat and thigh muscle composition predict insulin sensitivity independently of visceral fat.

            Whether visceral adipose tissue has a uniquely powerful association with insulin resistance or whether subcutaneous abdominal fat shares this link has generated controversy in the area of body composition and insulin sensitivity. An additional issue is the potential role of fat deposition within skeletal muscle and the relationship with insulin resistance. To address these matters, the current study was undertaken to measure body composition, aerobic fitness, and insulin sensitivity within a cohort of sedentary healthy men (n = 26) and women (n = 28). The subjects, who ranged from lean to obese (BMI 19.6-41.0 kg/m2), underwent dual energy X-ray absorptiometry (DEXA) to measure fat-free mass (FFM) and fat mass (FM), computed tomography to measure cross-sectional abdominal subcutaneous and visceral adipose tissue, and computed tomography (CT) of mid-thigh to measure muscle cross-sectional area, muscle attenuation, and subcutaneous fat. Insulin sensitivity was measured using the glucose clamp technique (40 mU.m-2.min-1), in conjunction with [3-3H]glucose isotope dilution. Maximal aerobic power (VO2max) was determined using an incremental cycling test. Insulin-stimulated glucose disposal (Rd) ranged from 3.03 to 16.83 FFM. Rd was negatively correlated with FM (r = -0.58), visceral fat (r = -0.52), subcutaneous abdominal fat (r = -0.61), and thigh fat (r = -0.38) and positively correlated with muscle attenuation (r = 0.48) and VO2max (r = 0.26, P < 0.05). In addition to manifesting the strongest simple correlation with insulin sensitivity, in stepwise multiple regression, subcutaneous abdominal fat retained significance after adjusting for visceral fat, while the converse was not found. Muscle attenuation contributed independent significance to multiple regression models of body composition and insulin sensitivity, and in analysis of obese subjects, muscle attenuation was the strongest single correlate of insulin resistance. In summary, as a component of central adiposity, subcutaneous abdominal fat has as strong an association with insulin resistance as visceral fat, and altered muscle composition, suggestive of increased fat content, is an important independent marker of insulin resistance in obesity.
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              Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy.

              Hepatic steatosis is characterized by abnormal and excessive accumulation of lipids within hepatocytes. It is an important feature of diffuse liver disease, and the histological hallmark of nonalcoholic fatty liver disease (NAFLD). Other conditions associated with steatosis include alcoholic liver disease, viral hepatitis, human immunodeficiency virus (HIV) and genetic lipodystrophies, cystic fibrosis liver disease, and hepatotoxicity from various therapeutic agents. Liver biopsy, the current clinical gold standard for assessment of liver fat, is invasive and has sampling errors, and is not optimal for screening, monitoring, clinical decision-making, or well suited for many types of research studies. Noninvasive methods that accurately and objectively quantify liver fat are needed. Ultrasound (US) and computed tomography (CT) can be used to assess liver fat but have limited accuracy as well as other limitations. Magnetic resonance (MR) techniques can decompose the liver signal into its fat and water signal components and therefore assess liver fat more directly than CT or US. Most MR techniques measure the signal fat-fraction (the fraction of the liver MR signal attributable to liver fat), which may be confounded by numerous technical and biological factors and may not reliably reflect fat content. By addressing the factors that confound the signal fat-fraction, advanced MR techniques measure the proton density fat-fraction (the fraction of the liver proton density attributable to liver fat), which is a fundamental tissue property and a direct measure of liver fat content. These advanced techniques show promise for accurate fat quantification and are likely to be commercially available soon. Copyright © 2011 Wiley-Liss, Inc.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                13 October 2014
                : 9
                : 10
                Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, Agency for Science, Technology, and Research, Singapore, Singapore
                Stanford University School of Medicine, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: BPK VG SSV. Performed the experiments: BPK VG SSL. Analyzed the data: BPK VG. Contributed reagents/materials/analysis tools: SSV. Wrote the paper: BPK VG SSV.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Pages: 9
                This research was supported by the intramural funding from Singapore Bioimaging Consortium, A*STAR, Singapore. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Biology and Life Sciences
                Engineering and Technology
                Signal Processing
                Image Processing
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Metabolic Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Diet and Type 2 Diabetes
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. All the data are included in the manuscript. Any request for other information or segmentation programs please contact S. Sendhil Velan Head, MRS and Metabolic Imaging Group Singapore Bioimaging Consortium sendhil_velan@



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