Abstract
This paper describes an adjustable method for body composition analysis of awake mice based on time domain nuclear magnetic resonance (TD-NMR) technology. A T1-weighted CPMG pulse sequence was introduced to acquire the NMR signals that contain both longitudinal (T1) and transversal (T2) relaxation information of mice. It took less than 90 s to dramatically lighten the burden on animals. A model training according to a well-designed factorial experiment was conducted by measuring a series of artificial samples, and the relations between NMR signals and body components (lean, fat and free fluid) were established. This NMR model gave a good fitting (R2 > 0.99) between real values and predictive values and measuring stability were also demonstrated. A calibration method was highlighted to figure out how to keep NMR model effective on another NMR scanner or avoid the error from fluctuation of machine without a time-consuming re-modeling process.
Similar content being viewed by others
References
J.C. Wells, M.S. Fewtrell, Arch. Dis. Child. 91, 612 (2006)
T. Erselcan, F. Candan, S. Saruhan, T. Ayca, Ann. Nutr. Metab. 44, 243 (2000)
Y.B. Yang, D.L. Smith, K.D. Keating, D.B. Allison, T.R. Nagy, Obesity 22, 2147 (2014)
A. Andres, A.D. Mitchell, T.M. Badger, Int. J. Obes. 34, 775 (2010)
J. Stenger, C. Bielajew, Physiol. Behav. 57, 319 (1995)
M.N. Metzinger, B. Miramontes, P. Zhou, Y. Liu, S. Chapman, L. Sun, T.A. Sasser, G.E. Duffield, M.S. Stack, W.M. Leevy, Sensors (Basel) 14, 18526 (2014)
S. Halldorsdottir, J. Carmody, C.N. Boozer, C.A. Leduc, R.L. Leibel, Int. J. Body Compos. Res. 7, 147 (2009)
J. Kullberg, J. Brandberg, J.E. Angelhed, H. Frimmel, E. Bergelin, L. Strid, H. Ahlstrom, L. Johansson, L. Lonn, Br. J. Radiol. 82, 123 (2009)
M.F. Marcone, S. Wang, W. Albabish, S. Nie, D. Somnarain, A. Hill, Food Res. Int. 51, 729 (2013)
J. Kruk, M. Doskocz, E. Jodlowska, A. Zacharzewska, J. Lakomiec, K. Czaja, J. Kujawski, Appl. Magn. Reson. 48, 1 (2017)
K.E. Washburn, E.R. McCarney, Appl. Magn. Reson. 49, 429 (2018)
B. Dai, S. Huang, Y. Deng, Food Res. Int. 116, 1239 (2019)
J.P. Nixon, M. Zhang, C. Wang, M.A. Kuskowski, C.M. Novak, J.A. Levine, C.J. Billington, C.M. Kotz, Obesity 18, 1652 (2010)
E. Rodríguez-Alonso, F.J. Vergeldt, A.J. van der Goot, Magn. Reson. Chem. (2019). https://doi.org/10.1002/mrc.4815
E. Kirtil, D. Dag, S. Guner, K. Unal, M.H. Oztop, Food Res. Int. 99, 807 (2017)
Y. Gao, K. Xu, H. Peng, J. Jiang, R. Zhao, J. Lu, Appl. Sci. 9, 78 (2018)
J.D. Ferreira, T.B. Moraes, L.A. Colnago, F.M.V. Pereira, Appl. Magn. Reson. 49, 653 (2018)
A.E. DeJong, R.W. Hartel, J. Food Eng. 178, 117 (2016)
H. Todt, G. Guthausen, W. Burk, D. Schmalbein, A. Kamlowski, Food Chem. 96, 436 (2006)
O.V. Petrov, J. Hay, I.V. Mastikhin, B.J. Balcom, Food Res. Int. 41, 758 (2008)
T. Rudi, G. Guthausen, W. Burk, C.T. Reh, H.D. Isengard, Food Chem. 106, 1375 (2008)
M.S. Johnson, D.L. Smith, T.R. Nagy, Int. J. Body Compos. Res. 7, 99 (2009)
A.S. Jones, M.S. Johnson, T.R. Nagy, Int. J. Body Compos. Res. 7, 67 (2009)
G.Z. Taicher, F.C. Tinsley, A. Reiderman, M.L. Heiman, Anal. Bioanal. Chem. 377, 990 (2003)
B. Künnecke, P. Verry, A. Bénardeau, M.V. Kienlin, Obesity 12, 1604 (2004)
F.C. Tinsley, G.Z. Taicher, M.L. Heiman, Obes. Res. 12, 150 (2004)
Acknowledgements
This project is supported by National Key Scientific Instrument and Equipment Development Project of China under Grant no. 51627808, National Natural Science Foundation of China under Grant no. 51605089, Jiangsu Province National Natural Science Foundation of China under Grant no. BK20150609. We thank advisors for their valuable scientific discussions, and lab partners for their support and help in writing analysis program and building the NMR scanners.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
It is to be noted that artificial samples should be similar to the living mice in NMR characteristics, so that these samples can represent the living mice effectively and ensure the reliability of the measurement results. To verify the validity of selected materials of artificial samples, T1 and T2 distributions of the mixture of fresh chicken and refined rapeseed oil were measured as shown in Fig. 7. The three T2 peaks of the mixture are, respectively, in 1–10 ms, 10–102 ms and 102–103 ms, and two T1 peaks are in 10–102 ms and 102–103 ms. The T1 and T2 distributions of artificial samples have strong correlation with those of living mice. It can be concluded that the artificial samples and living mice are consistent in NMR characteristics. Consequently, the method of simulating living mice with chicken breasts, rapeseed oil and saline is effective and reliable.
Rights and permissions
About this article
Cite this article
Jiang, X., Zhou, X., Xie, Z. et al. An Adjustable TD-NMR Method for Rapid and Quantitative Analysis of Body Composition in Awake Mice. Appl Magn Reson 51, 241–253 (2020). https://doi.org/10.1007/s00723-019-01180-2
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00723-019-01180-2