MATHEMATICAL MODEL FOR DETECTING DIABETES IN BLOOD CELLS WITH THE INFLUENCE OF CORTISOL

  • Imam Sujarwo Department of Mathematics Education, Faculty of Islamic Education and Teacher Training, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia https://orcid.org/0009-0007-6744-6030
  • Juhari Juhari Department of Mathematics, Faculty of Science and Technologi, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia https://orcid.org/0000-0001-5668-7428
  • Adelia Irma Feby Ariyanti Department of Mathematics, Faculty of Science and Technologi, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia https://orcid.org/0009-0002-3761-9475
  • Sutrisno Sutrisno Department of Mathematics Education, Faculty of Islamic Education and Teacher Training, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia https://orcid.org/0009-0005-2426-4013
Keywords: Cortisol, Diabetes, Epinephrine, Glucose, Insulin, Mathematical model

Abstract

Diabetes is a disease that occurs when the body is unable to produce enough insulin or cannot effectively use the insulin it produces, resulting in an increase in blood glucose levels. One of the factors that affects the stability of glucose and insulin is the hormone cortisol, which is produced in response to stress. The purpose of this study is to develop a mathematical model of diabetes detection in blood cells by considering the influence of cortisol. The model is formulated as a system of linear differential equations and analyzed through equilibrium points and eigenvalue analysis. The results show that two eigenvalues form asymptotically stable spirals, while the other two are asymptotically stable nodes, indicating system stability. The novelty of this study lies in the inclusion of cortisol, which delays stabilization of glucose–insulin dynamics and provides a more realistic representation of physiological conditions under stress. A limitation of this study is that the model relies on simplifying assumptions without clinical validation. This research is expected to serve as a foundation for further model development by considering other regulatory factors, with implications for improving diabetes prevention and intervention strategies in stress-related conditions.

Downloads

Download data is not yet available.

References

R. Kwach, B., Ongati, O., and Simwa, “MATHEMATICAL MODEL FOR DETECTING DIABETES IN THE BLOOD,” Appl. Math. Sci., vol. 5, no. 6, pp. 279–286, 2011, [Online]. Available: http://ir.mksu.ac.ke/handle/123456780/4414

D. H. Hellhammer, S. Wüst, and B. M. Kudielka, “SALIVARY CORTISOL AS A BIOMARKER IN STRESS RESEARCH,” Psychoneuroendocrinology, vol. 34, no. 2, pp. 163–171, Feb. 2009, doi: https://doi.org/10.1016/j.psyneuen.2008.10.026.

M. Al Ahdab, J. Leth, T. Knudsen, P. Vestergaard, and H. G. Clausen, “GLUCOSE-INSULIN MATHEMATICAL MODEL FOR THE COMBINED EFFECT OF MEDICATIONS AND LIFE STYLE OF TYPE 2 DIABETIC PATIENTS,” Biochem. Eng. J., vol. 176, p. 108170, Dec. 2021, doi: https://doi.org/10.1016/j.bej.2021.108170.

M. Breton and B. Kovatchev, “ANALYSIS, MODELING, AND SIMULATION OF THE ACCURACY OF CONTINUOUS GLUCOSE SENSORS,” J. Diabetes Sci. Technol., vol. 2, no. 5, pp. 853–862, Sep. 2008, doi: https://doi.org/10.1177/193229680800200517.

A. De Gaetano and O. Arino, “MATHEMATICAL MODELLING OF THE INTRAVENOUS GLUCOSE TOLERANCE TEST,” J. Math. Biol., vol. 40, no. 2, pp. 136–168, Feb. 2000, doi: https://doi.org/10.1007/s002850050007.

J. Juhari, “ON THE BEHAVIOR ANALYSIS OF SUSCEPTIBLE, INFECTION, RECOVERY (SIR) MEASLES SPREAD MODEL WITH AGE STRUCTURE,” BAREKENG J. Ilmu Mat. dan Terap., vol. 16, no. 2, pp. 427–442, Jun. 2022, doi: https://doi.org/10.30598/barekengvol16iss2pp427-442.

R. K. Leung, Y. Wang, R. C. Ma, A. O. Luk, V. Lam, M. Ng, W. Y. So, S. K. Tsui and J. C. Chan., “USING A MULTI-STAGED STRATEGY BASED ON MACHINE LEARNING AND MATHEMATICAL MODELING TO PREDICT GENOTYPE-PHENOTYPE RISK PATTERNS IN DIABETIC KIDNEY DISEASE: A PROSPECTIVE CASE–CONTROL COHORT ANALYSIS,” BMC Nephrol., vol. 14, no. 1, p. 162, Dec. 2013, doi: https://doi.org/10.1186/1471-2369-14-162.

M. Ma and J. Li, “DYNAMICS OF A GLUCOSE–INSULIN MODEL,” J. Biol. Dyn., vol. 16, no. 1, pp. 733–745, Dec. 2022, doi: https://doi.org/10.1080/17513758.2022.2146769.

N. D. Pallab, “PANCREATIC Β-CELL DYNAMICS WITH THREE-TIME-SCALE SYSTEMS,” arXiv Prepr., May 2025, [Online]. Available: http://arxiv.org/abs/2505.18837

N. E. López-Palau and J. M. Olais-Govea, “MATHEMATICAL MODEL OF BLOOD GLUCOSE DYNAMICS BY EMULATING THE PATHOPHYSIOLOGY OF GLUCOSE METABOLISM IN TYPE 2 DIABETES MELLITUS,” Sci. Rep., vol. 10, no. 1, p. 12697, Jul. 2020, doi: https://doi.org/10.1038/s41598-020-69629-0.

D. N. R. D. B. Gabriela Urbina, “MATHEMATICAL MODELING OF NONLINEAR BLOOD GLUCOSE-INSULIN DYNAMICS WITH BETA CELLS EFFECT,” Appl. Appl. Math. An Int. J., vol. 15, no. 1, pp. 171–191, 2020, [Online]. Available: https://digitalcommons.pvamu.edu/aam/vol15/iss1/10%0A

I. I. Mohammed, I. I. Adamu, and S. J. Barka, “MATHEMATICAL MODEL FOR THE DYNAMICS OF GLUCOSE, INSULIN AND Β-CELL MASS UNDER THE EFFECT OF TRAUMA, EXCITEMENT AND STRESS,” Model. Numer. Simul. Mater. Sci., vol. 09, no. 04, pp. 71–96, 2019, doi: https://doi.org/10.4236/mnsms.2019.94005.

S. Khani and J. A. Tayek, “CORTISOL INCREASES GLUCONEOGENESIS IN HUMANS: ITS ROLE IN THE METABOLIC SYNDROME,” Clin. Sci., vol. 101, no. 6, p. 739, Dec. 2001, doi: https://doi.org/10.1042/CS20010180.

M. Degering, R. Linz, L. M. C. Puhlmann, T. Singer, and V. Engert, “REVISITING THE STRESS RECOVERY HYPOTHESIS: DIFFERENTIAL ASSOCIATIONS OF CORTISOL STRESS REACTIVITY AND RECOVERY AFTER ACUTE PSYCHOSOCIAL STRESS WITH MARKERS OF LONG-TERM STRESS AND HEALTH,” Brain, Behav. Immun. - Heal., vol. 28, p. 100598, Mar. 2023, doi: https://doi.org/10.1016/j.bbih.2023.100598.

K. Sriram, M. Rodriguez-Fernandez, and F. J. Doyle, “MODELING CORTISOL DYNAMICS IN THE NEURO-ENDOCRINE AXIS DISTINGUISHES NORMAL, DEPRESSION, AND POST-TRAUMATIC STRESS DISORDER (PTSD) IN HUMANS,” PLoS Comput. Biol., vol. 8, no. 2, p. e1002379, Feb. 2012, doi: https://doi.org/10.1371/journal.pcbi.1002379.

D. P. Rooney, R. D. G. Neely, O. Beatty, N. P. Bell, B. Sheridan, A. B. Atkinson, E. R. Trimble and P. M. Bell., “CONTRIBUTION OF GLUCOSE/GLUCOSE 6-PHOSPHATE CYCLE ACTIVITY TO INSULIN RESISTANCE IN TYPE 2 (NON-INSULIN-DEPENDENT) DIABETES MELLITUS,” Diabetologia, vol. 36, no. 2, pp. 106–112, Feb. 1993, doi: https://doi.org/10.1007/BF00400689.

R. C. Andrews and B. R. Walker, “GLUCOCORTICOIDS AND INSULIN RESISTANCE: OLD HORMONES, NEW TARGETS,” Clin. Sci., vol. 96, no. 5, pp. 513–523, May 1999, doi: https://doi.org/10.1042/cs0960513.

A. Holmáng, E. Jennische, and P. BjÖrntorp, “THE EFFECTS OF LONG‐TERM HYPERINSULINAEMIA ON INSULIN SENSITIVITY IN RATS,” Acta Physiol. Scand., vol. 153, no. 1, pp. 67–73, Jan. 1995, doi: https://doi.org/10.1111/j.1748-1716.1995.tb09835.x.

H. Shamoon, R. Hendler, and R. S. Sherwin, “ALTERED RESPONSIVENESS TO CORTISOL, EPINEPHRINE, AND GLUCAGON IN INSULIN-INFUSED JUVENILE-ONSET DIABETICS: A MECHANISM FOR DIABETIC INSTABILITY,” Diabetes, vol. 29, no. 4, pp. 284–291, Apr. 1980, doi: https://doi.org/10.2337/diab.29.4.284.

D. L. Wong, “EPINEPHRINE BIOSYNTHESIS: HORMONAL AND NEURAL CONTROL DURING STRESS,” Cell. Mol. Neurobiol., vol. 26, no. 4–6, pp. 889–898, Jul. 2006, doi: https://doi.org/10.1007/s10571-006-9056-6.

M. Salehi., “COMPARISON OF SALIVARY CORTISOL LEVEL IN TYPE 2 DIABETIC PATIENTS AND PRE-DIABETICS WITH HEALTHY PEOPLE,” Open Access Maced. J. Med. Sci., vol. 7, no. 14, pp. 2321–2327, Jul. 2019, doi: https://doi.org/10.3889/oamjms.2019.340.

C. Anton, H., & Rorres, “ALJABAR LINEAR ELEMENTER VERSI APLIKASI,” PT Gelora Aksara Pratama, 2000, [Online]. Available: https://lib.ui.ac.id/detail?id=20359146&lokasi=lokal

Published
2026-01-26
How to Cite
[1]
I. Sujarwo, J. Juhari, A. I. Feby Ariyanti, and S. Sutrisno, “MATHEMATICAL MODEL FOR DETECTING DIABETES IN BLOOD CELLS WITH THE INFLUENCE OF CORTISOL”, BAREKENG: J. Math. & App., vol. 20, no. 2, pp. 1569–1582, Jan. 2026.