Abstract
Diet-induced obesity is becoming more common all over the world, which is increasing the prevalence of obesity-induced chronic diseases such as diabetes, coronary heart disease, cancer, and sleep apnea. Many experimental results show that obesity is often associated with an elevated concentration of plasma leptin and triglycerides. Triglycerides inhibit the passage of leptin across the blood–brain barrier (BBB) to signal the hypothalamus to suppress appetite. However, it is still not clear how triglyceride concentration affects leptin transport across the BBB and energy balance. In this paper, we propose a novel ordinary differential equations model describing the role of leptin in the regulation of adipose tissue mass. Analytical and numerical results are analyzed using biologically relevant parameter values. Additionally, we perform sensitivity analysis of the equilibria and study the sensitivity of triglyceride production on leptin resistance. Equilibria analysis and simulation results show that triglyceride production plays an important role in determining the fat mass in an individual. As weight increases, the occurrence of leptin resistance increases. Obesity enhances the likelihood of creating a vicious circle, where more fat mass leads to greater leptin resistance. Thus, control of the triglyceride production may be effective in reducing the occurrence of leptin resistance.
Keywords
- Triglyceride
- Leptin resistance
- Sensitivity
- Bifurcation
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
WHO, http://www.who.int/mediacentre/factsheets/fs311/en/, 2016.
B. Swinburn and G. Sacks and K. Hall et al., Lancet 378, 804–814, 2011.
J. Friedman, Nature 404, 632–634, 2000.
J. O. Jørgensen et al., Christiansen, Metabolism, 47, 1134–1139, 1998.
M. W. Schwartz et al., Nature Medicine 2, 589–593, 1996.
W. Banks and A. Coon and S. Robinson et al., Diabetes, 53, 1253–1260, 2004.
R. B. Harris, Annual Review of Nutrition 20, 45–75, 2000.
G. Scholz and P. Englaro and I. Thiele et al., Hormo. Metabo. Resea. 28, 718–723, 1996.
M. K. Sinha et al., J. Clin. Invest. 98, 1277, 1996.
M. Jacquiera and H. Soulac and F. Crauste, Math. Bios. 267, 10–23, 2015.
J. Krieger and H. Sitren and M. Daniels et al., Am. J. Clin. Nutr. 83, 260–274, 2006.
T. Pearson and J. Wattis and J. King et al., Bull. Math. Biol. 76, 2091–2121, 2014.
J. Tam and D. Fukumura and R. Jain, Cell Metab. 9, 52–63, 2009.
B. Song and D. Thomas, J. Math. Biol., 54, 27–43, 2007.
M. Jacquiera and H. Soulac and F. Crauste, Math. Bios. 267, 10–23, 2015.
R. Pattaranit and H. Van den Berg, J. R. Soc. Interface, 5, 1119–1135, 2008.
R. Ku-Carrillo et al., Appl. Math. Model. 40, 4908–4920, 2016.
J. Friedman and J. Halaas, Nature 359, 763–770, 1998.
G. Yang and H. Ge and A. Boucher et al., Molec. Endocrinol. 18, 1354–1362, 2004.
J. Krieger and H. Sitren and M. Daniels et al., Am. J. Clin. Nutr. 83, 260–274, 2006.
N. Timpson and P. Emmett and T. Frayling et al., Am. J. Clin. Nutr. 88, 971–978, 2008.
A. Lammert et al., Bioch Biophy. Resea. Commun. 283, 982–988, 2001.
W. A. Banks and A. J. Kastin and W. Huang et al., Peptides 17, 305–311, 1996.
S. Klein and S. W. Coppack and V. Mohamed-Ali et al., Diabetes, 45, 984–987, 1996.
J. Dobbing and J. Sands, Arch. Dis. Child. 48, 757–767, 1973.
K. L. Stanhope et al., J. Clin. Endocrinol. Metab. 96, 1596–1605, 2011.
CDC, http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5751a4.htm, 2009.
Y. J. Ostlund et al., J Clin. Endocrinol. Metab, 81, 3903–3913, 1996.
M. Miller and N. Stone and C. Ballantyne, Circulation, 123, 2292–2333, 2011.
L. Arriola and J. Hyman, Mathematical and Statistical Estimation Approaches in Epidemiology, Springer, Dordrecht, 2009, pp. 195–247.
Acknowledgements
This research was conducted in Mathematical and Theoretical Biology Institute (MTBI) at the Simon A. Levin Mathematical, Computational and Modeling Sciences Center (SAL MCMSC) at Arizona State University (ASU). It was partially supported by grants from the National Science Foundation (DMS1263374), and Zhao’s work partially supported by the Ningxia Medical University Research Project (XT2017002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, Y., Burkow, D., Song, B. (2019). Mathematically Modeling the Role of Triglyceride Production on Leptin Resistance. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_35
Download citation
DOI: https://doi.org/10.1007/978-981-10-8944-2_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8943-5
Online ISBN: 978-981-10-8944-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)