Nonlinear Modeling and Stability Analysis of Glucose–Insulin Homeostasis

Authors

DOI:

https://doi.org/10.30526/39.4.4466

Keywords:

Glucose–insulin dynamics; Equilibrium geometry; Global asymptotic stability; Lyapunov methods; Phase-plane analysis; Type-2 diabetes modeling

Abstract

This study develops a nonlinear dynamic model for glucose-insulin regulation that includes glucose utilization, saturated insulin secretion, insulin clearance, and an auxiliary remote-effect variable capturing the delayed regulatory response. The proposed framework enables comprehensive investigation of glucose homeostasis from a dynamical systems perspective, emphasizing equilibrium structure, stability properties, and parameter-induced transitions. The positivity and bounds of the solution were established  ensuring the physical acceptability . Analytical characterization of the equilibrium points reveals that the steady-state   of glucose concentration satisfies a non-linear algebraic relationship, allowing parameter-dependent shifts in the metabolic regime. Local and global stability were investigated by Jacobian matrix analysis and the Lyapunov-cascade approach, showing that the system admits a unique globally asymptotically stable equilibrium under physically relevant conditions. Numerical simulations confirm the analytical results and show that variations in physiological parameters including external glucose input, insulin clearance rate, insulin secretion gain, and net insulin effectiveness consistently displace the equilibrium from normoglycemic to pathological hyperglycemic states without inducing oscillatory dynamics. It is important to mention that the transition to type 2 diabetes emerges as spontaneous equilibrium shifts and weakening of stability rather than bifurcation-driven oscillations as an mathematical simplification aiming to focus on the stability and equilibrium behavior of the system.These findings give a mathematically rigorous and physiologically interpretable explanation of metabolic flexibility and its gradual breakdown, providing a unifying framework for understanding glucose homeostasis and its impairment under adverse conditions.

Author Biographies

  • Maha Dakhil Hamuwd, Department of Mathematics, College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Baghdad, Iraq

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  • Mohanad Nafaa Jaafar, Department of Mathematics, College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Baghdad, Iraq

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References

1. Roden M, Shulman GI. The integrative biology of type 2 diabetes. Nature. 2019;576:51–60. https://doi.org/10.1038/s41586-019-1797-8

2. Ashcroft FM, Rorsman P. Diabetes mellitus and the β cell: The Last Ten Years. Cell. 2012;148(6):1160-1171. https://doi.org/10.1016/j.cell.2012.02.010

3. Gaetano A, Bertuzzi A, Ditlevsen S, PalumboP. Mathematical modeling of the glucose–insulin system: A review. Math Biosci. 2013;244(2):69-81. https://doi.org/10.1016/j.mbs.2013.05.006.

4. Abed BA, Farhan LO, Dawood AS. Relationship between serum Nesfatin-1, Adiponectin, Resistin concentration, and obesity with Type 2 Diabetes Mellitus. Baghdad Sci J. 2024;21(1):0117. https://doi.org/10.21123/bsj.2023.8119

5. Shah M, Stolbov L, Yakovleva T, Tang W, Sokolov V, Penland RC, Boulton D, Parkinson J. A model-based approach to investigating the relationship between glucose-insulin dynamics and dapagliflozin treatment effect in patients with type 2 diabetes. Diabetes Obes Metab. 2021;23(4):991-1000. https://doi.org/10.1111/dom.14305

6. Wu Q, Yu L, Li X, Li W. Dynamic analysis of a Filippov blood glucose insulin model. AIMS Math. 2024;9(7):18356-18373. https://doi.org/10.3934/math.2024895.

7. Yang B, Li J, Haller MJ, Schatz DA, Rong L. Modeling the progression of type 2 diabetes with underlying obesity. PLoS Comput Biol. 2023;19(2):e1010914. https://doi.org/10.1371/journal.pcbi.1010914

8. Banzi W, Kambutse I, Dusabejambo V, Ntaganda JM, Rutaganda E, Minani F, Niyobuhungiro J, Mpinganzima L. Mathematical modelling of glucose-insulin system and test of abnormalities of type 2 diabetic patients. Int J Math Math Sci. 2021:6660177. https://doi.org/10.1155/2021/6660177.

9. Vázquez G, Acho L, Báguena J. Commuted PD controller for nonlinear systems: glucose–insulin regulatory case. Appl Sci (Basel). 2023;13(14):8129. https://doi.org/10.3390/app13148129.

10. Nazirun NNN, Wahab Abdul A, Selamat A, Fujita H, Krejcar O, Kuca K, Seng G. Prediction models for type 2 diabetes progression: a systematic review. IEEE Access. 2024;12:161595-161619. https://doi.org/10.1109/ACCESS.2024.3432118 .

11. AlShurbaji M, Kader LA, Hannan H, Mortula M, Husseini GA. Comprehensive study of a diabetes mellitus mathematical model using numerical methods with stability and parametric analysis. Int J Environ Res Public Health. 2023;20(2):939. https://doi.org/10.3390/ijerph20020939.

12. Rao F, Zhang Z, Li J. Dynamical analysis of a glucose-insulin regulatory system with insulin-degrading enzyme and multiple delays. J Math Biol. 2023;87(5): 73. https://doi.org/10.1007/s00285-023-02003-6.

13. Ha J, Sherman A. Type 2 diabetes: one disease, many pathways. Am J Physiol Endocrinol Metab. 2020;319(2):E410–E426. https://doi.org/10.1152/ajpendo.00512.2019.

14. Mohabati F, Molaei MR. Bifurcation analysis in a delay model of IVGTT glucose-insulin interaction. Theory Biosci. 2020;139(1):9-20.

15. Albargi A, El Hajji M. Mathematical analysis of a two-tiered microbial food-web model for the anaerobic digestion process. Math Biosci Eng. 2023;20(4):6591-6611. https://doi.org/10.3934/mbe.2023283.

16. Bachar M. Sensitivity analysis for a delay mathematical model: the glucose-insulin model. Front Appl Math Stat. 2025 11:1562636. https://doi.org/10.3389/fams.2025.1562636.

17. Kumari P, Singh S, Singh HP. Bifurcation and stability analysis of glucose-insulin regulatory system in the presence of β-cells. Iran J Sci Technol Trans A Sci. 2021;45:1743–1756. https://doi.org/10.1007/s40995-021-01152-x

18. Zenkin A, Peregudin A, Bobtsov A. Lyapunov function search method for analysis of nonlinear systems stability using genetic algorithm. Sci Tech J Inf Technol. 2023;23(5): 886–893. https://doi.org/10.48550/arXiv.2307.03030.

19. El Koufi A, Bennar A, Yousfi N, Pitchaimani M. Threshold dynamics for a class of stochastic SIRS epidemic models with nonlinear incidence and Markovian switching. Math Model Nat Phenom. 2021; 16:55. https://doi.org/10.1051/mmnp/2021047.

20. Huard B, Kirkham G. Mathematical modelling of glucose dynamics. Curr Opin Endocrinol Diabetes Obes. 2022 ; 25:100379. https://doi.org/10.1016/j.coemr.2022.100379.

21. Binatari N, Adi-Kusumo F, Aryati L. Stability regions and bifurcation analysis of a delayed predator-prey model caused from gestation period. Int J Differ Equ. 2022;2022(1):3711158. https://doi.org/10.1155/2022/3711158.

22. Modu GU, Hadejia YA, Ahmed I, Thounthong P. Analysis of linear and nonlinear mathematical models for monitoring diabetic population with minor and major complications. Thai J Math. 2021 .19(3):1004-1027.

23. Mughal IS, Patané L, Caponetto R. A comprehensive review of models and nonlinear control strategies for blood glucose regulation in artificial pancreas. Annu Rev Control. 2024;57:100937. https://doi.org/10.1016/j.arcontrol.2024.100937.

24. Saber S, Dridi B, Alahmari A, Messaoudi M. Hyers-Ulam stability and control of fractional glucose-insulin system. Eur J Pure Appl Math. 2025;18(2):2-24 https://doi.org/10.29020/nybg.ejpam.v18i2.6152

25. Saber S, Solouma E, Alharb RA, Alalyani A. Chaos in fractional-order glucose–insulin models with variable derivatives: insights from the Laplace–Adomian decomposition method and generalized Euler techniques. Fractal Fract. 2025;9(3):149 https://doi.org/10.3390/fractalfract9030149.

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Published

20-Oct-2026

Issue

Section

Mathematics

How to Cite

[1]
Hamuwd, M.D. and Jaafar, M.N. 2026. Nonlinear Modeling and Stability Analysis of Glucose–Insulin Homeostasis. Ibn AL-Haitham Journal For Pure and Applied Sciences. 39, 4 (Oct. 2026), 314–323. DOI:https://doi.org/10.30526/39.4.4466.