Background: The aim of this study was to investigate the influence of marking methods on the outcomes of body composition analysis and provide guidance for the selection of marking methods in mouse body composition analysis.
Methods: Male C57BL/6J mice aged 6 weeks were randomly assigned for pre- and post- ear tagging measurements. The body composition of the mice was measured using a small animal body composition analyzer, which provided measurements of the mass of fat, lean, and free fluid. Then, the mass of fat, lean and free fluid to body weight ratio was gained. Further data analysis was conducted to obtain the range and coefficient of variation in body composition measurements for each mouse. The distribution of fat and lean tissue in the mice was also analyzed by comparing the fat-to-lean ratio.
Results: (1) The mass of all body composition components in the ear tagging group was significantly lower than that in the control group. (2) There was a significant increase in the range and coefficient of variation of body composition measurements between the ear tagging group and the control group. (3) The fat-to-lean ratio in the ear tagging group was significantly lower than that in the control group.
Conclusions: Ear tagging significantly lowered the results of body composition analysis in mice and higher the results of measurement error. Therefore, ear tagging should be avoided as much as possible when conducting body composition analysis experiments in mice.
| [1] |
Bellicha A, van Baak MA, Battista F, et al. Effect of exercise training on weight loss, body composition changes, and weight maintenance in adults with overweight or obesity: an overview of 12 systematic reviews and 149 studies. Obes Rev. 2021;22(Suppl 4):e13256.
|
| [2] |
Ashutosh K, Litao X, My TC, et al. SWELL1 regulates skeletal muscle cell size, intracellular signaling, adiposity and glucose metabolism. elife. 2020;9:e58941.
|
| [3] |
Kelly TL, Berger N, Richardson TL. DXA body composition: theory and practice. Appl Radiat Isot. 1998;49(5–6):511-513.
|
| [4] |
Jiang X, Zhou X, Xie Z, Ni Z, Lu R, Yi H. An adjustable TD-NMR method for rapid and quantitative analysis of body composition in awake mice. Appl Magn Reson. 2020;51:241-253.
|
| [5] |
Zhu M, Zhang Z, Zhao L. Construction and evaluation of animal models of simple obesity mice. J Med Theory Pract. 2020;33(3):345-350.
|
| [6] |
Kumar D, Rao PS. A review on magnetic assisted abrasive flow machining (MAAFM). IJRESM. 2019;2(1):2581-5792.
|
| [7] |
Masumi S, Arita M, Morikawa M, Toyoda S. Effect of dental metals on magnetic resonance imaging (MRI). J Oral Rehabil. 1993;20(1):97-106.
|
| [8] |
Kalia D, Kalia UDP, Batham PR. MRI and its implications in dentistry. IJMSDR. 2022;6(4):1-12.
|
| [9] |
Jin L, Li T, Wu B, et al. Rapid detection of Salmonella in milk by nuclear magnetic resonance based on membrane filtration superparamagnetic nanobiosensor. Food Control. 2020;110:107011.
|
| [10] |
Pedroso JAB, Camporez JP, Belpiede LT, Pinto RS, Cipolla-Neto J, Donato J. Evaluation of hepatic steatosis in rodents by time-domain nuclear magnetic resonance. Diagnostics. 2019;9(4):198.
|
| [11] |
Kraai TL, Loch RB, Shellock FG. Assessment of MRI safety issues for stainless steel sutures used for microtia reconstruction. J Plast Reconstr Aesthet Surg. 2018;71(10):1469-1475.
|
| [12] |
Bussmann S, Luechinger R, Froehlich JM, et al. Safety of intrauterine devices in MRI. PLoS One. 2018;13(10):e0204220.
|
| [13] |
Wang ZJ, Park YJ, Morriss MC, et al. Correcting B0 field distortions in MRI caused by stainless steel orthodontic appliances at 1.5 T using permanent magnets—a head phantom study. Sci Rep. 2018;8(1):5706.
|
| [14] |
Breit HC, Vosshenrich J, Clauss M, et al. Visual and quantitative assessment of hip implant-related metal artifacts at low field MRI: a phantom study comparing a 0.55-T system with 1.5-T and 3-T systems. Eur Radiol Exp. 2023;7(1):5.
|
| [15] |
Almuqbel MM, Leeper GJ, Petelo JF, Page TJ, Melzer TR. MRI artefact in the rectum caused by ingested orthodontic brackets [J]. Radiography. 2018;24(2): e48-e50.
|
| [16] |
Borga M, West J, Bell JD, et al. Advanced body composition assessment: from body mass index to body composition profiling. J Investig Med. 2018;66(5):1-9.
|
| [17] |
Hadjihambi A, Konstantinou C, Klohs J, et al. Partial MCT1 invalidation protects against diet-induced non-alcoholic fatty liver disease and the associated brain dysfunction. J Hepatol. 2023;78(1):180-190.
|
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2024 The Author(s). Animal Models and Experimental Medicine published by John Wiley & Sons Australia, Ltd on behalf of The Chinese Association for Laboratory Animal Sciences.
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