Non-Invasive Quantification of White and Brown Adipose Tissues and Liver Fat Content by Computed Tomography in MiceReport as inadecuate

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Obesity and its distribution pattern are important factors for the prediction of the onset of diabetes in humans. Since several mouse models are suitable to study the pathophysiology of type 2 diabetes the aim was to validate a novel computed tomograph model Aloka-Hitachi LCT-200 for the quantification of visceral, subcutaneous, brown and intrahepatic fat depots in mice.


Different lean and obese mouse models C57BL-6, B6.V-Lepob, NZO were used to determine the most adequate scanning parameters for the detection of the different fat depots. The data were compared with those obtained after preparation and weighing the fat depots. Liver fat content was determined by biochemical analysis.


The correlations between weights of fat tissues on scale and weights determined by CT were significant for subcutaneous r2 = 0.995, visceral r2 = 0.990 and total white adipose tissue r2 = 0.992. Moreover, scans in the abdominal region, between lumbar vertebrae L4 to L5 correlated with whole-body fat distribution allowing experimenters to reduce scanning time and animal exposure to radiation and anesthesia. Test-retest reliability and measurements conducted by different experimenters showed a high reproducibility in the obtained results. Intrahepatic fat content estimated by CT was linearly related to biochemical analysis r2 = 0.915. Furthermore, brown fat mass correlated well with weighted brown fat depots r2 = 0.952. In addition, short-term cold-expose 4°C, 4 hours led to alterations in brown adipose tissue attributed to a reduction in triglyceride content that can be visualized as an increase in Hounsfield units by CT imaging.


The 3D imaging of fat by CT provides reliable results in the quantification of total, visceral, subcutaneous, brown and intrahepatic fat in mice. This non-invasive method allows the conduction of longitudinal studies of obesity in mice and therefore enables experimenters to investigate the onset of complex diseases such as diabetes and obesity.

Author: Marko Lubura, Deike Hesse, Nancy Neumann, Stephan Scherneck, Petra Wiedmer, Annette Schürmann



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