Research Article
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Year 2023, Volume: 3 Issue: 4, 318 - 334, 30.12.2023
https://doi.org/10.53391/mmnsa.1397575

Abstract

References

  • [1] Wu, H., Eggleston, K.N., Zhong, J., Hu, R., Wang, C., Xie, K. et al. How do type 2 diabetes mellitus (T2DM)-related complications and socioeconomic factors impact direct medical costs? A cross-sectional study in rural Southeast China. BMJ Open, 8(11), e020647, (2018).
  • [2] World Health Organization, Health Topics, Diabetes. https://www.who.int/healthtopics/diabetes, [Accessed: 15.10.2023].
  • [3] The American Diabetes Association®(ADA). https://www.diabetes.org/diabetes, [Accessed: 15.10.2023].
  • [4] Scheen, A. Pathophysiology of type 2 diabetes. Acta Clinica Belgica, 58(6), 335-341, (2003).
  • [5] International diabetes federation, IDF Diabetes Atlas 2021. https://diabetesatlas.org/, [Accessed: 15.10.2023].
  • [6] Institute for Public Health. "National Health Morbidity Survey 2015 (NHMS 2015). Vol. II: Non-Communicable Diseases, Risk Factors & Other Health Problems." Minist. Health Malays. 2: 185-186 (2015). Retrieved from https://www.moh.gov.my/moh/resources/ nhmsreport2015vol2.pdf
  • [7] Malaysian Ministry of Health (MMH), 2016. National Strategic Plan for Non-Communicable Disease (NSP- NCD) 2016-2025. Retrieved from https://www.iccp-portal.org/system/ files/plans/MYS_B3_NSP%20NCD%202016-2025%2C%20FINAL.pdf
  • [8] Dal Canto, E., Ceriello, A., Rydén, L., Ferrini, M., Hansen, T.B., Schnell, O. et al. Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. European Journal of Preventive Cardiology, 26(2suppl), 25-32, (2019).
  • [9] Forbes, J.M. and Cooper, M.E. Mechanisms of diabetic complications. Physiological Reviews, 93(1), 137-188 (2013).
  • [10] Alonso-Magdalena, P., Quesada, I. and Nadal, A. Endocrine disruptors in the etiology of type 2 diabetes mellitus. Nature Reviews Endocrinology, 7, 346-353, (2011).
  • [11] Zoeller, R.T., Brown, T.R., Doan, L.L., Gore, A.C., Skakkebaek, N.E., Soto, A.M. et al. Endocrine-disrupting chemicals and public health protection: a statement of principles from The Endocrine Society. Endocrinology, 153(9), 4097-4110, (2012).
  • [12] Kunysz, M., Mora-Janiszewska, O. and Darmochwał-Kolarz, D. Epigenetic modifications associated with exposure to endocrine disrupting chemicals in patients with gestational diabetes mellitus. International Journal of Molecular Sciences, 22(9), 4693, (2021).
  • [13] Street, M.E., Angelini, S., Bernasconi, S., Burgio, E., Cassio, A., Catellani, C. et al. Current knowledge on endocrine disrupting chemicals (EDCs) from animal biology to humans, from pregnancy to adulthood: highlights from a national Italian meeting. International Journal of Molecular Sciences, 19(6), 1647, (2018).
  • [14] Sargis, R.M. and Simmons, R.A. Environmental neglect: endocrine disruptors as underappreciated but potentially modifiable diabetes risk factors. Diabetologia, 62, 1811-1822, (2019).
  • [15] Beszterda, M. and Fra´nski, R. Endocrine disruptor compounds in environment: As a danger for children health. Pediatric Endocrinology Diabetes and Metabolism, 24(2), 88-95, (2018).
  • [16] Selevan, S.G., Kimmel, C.A. and Mendola, P. Identifying critical windows of exposure for children’s health. Environmental Health Perspectives, 108(suppl 3), 451-455, (2000).
  • [17] Sun, Q., Zong, G., Valvi, D., Nielsen, F., Coull, B. and Grandjean, P. Plasma concentrations of perfluoroalkyl substances and risk of type 2 diabetes: A prospective investigation among US women. Environmental Health Perspectives, 126(3), 037001, (2018).
  • [18] Lind, P.M. and Lind, L. Endocrine-disrupting chemicals and risk of diabetes: an evidencebased review. Diabetologia, 61, 1495-1502, (2018).
  • [19] Boutayeb, A., Twizell, E.H., Achouayb, K. and Chetouani, A. A mathematical model for the burden of diabetes and its complications. BioMedical Engineering OnLine, 3, 20, (2004).
  • [20] Boutayeb, A., Chetouani, A., Achouyab, A. and Twizell, E.H. A non-linear population model of diabetes mellitus. Journal of Applied Mathematics and Computing, 21, 127-139, (2006).
  • [21] Derouich, M., Boutayeb, A., Boutayeb, W. and Lamlili, M. Optimal control approach to the dynamics of a population of diabetics. Applied mathematical sciences, 8(56), 2773-2782, (2104).
  • [22] Widyaningsih, P., Affan, R.C. and Saputro, D.R.S. A mathematical model for the epidemiology of diabetes mellitus with lifestyle and genetic factors. In In Proceedings, Journal of Physics: Conference Series (Vol. 1028), pp. 012110, Makassar, Indonesia, (2018, October).
  • [23] Bassey, B.E. Optimal control model for dual treatment of delayed type-II diabetes infection in human population. Open Science Journal of Mathematics and Application, 7(1), 34-49, (2019).
  • [24] Jajarmi, A., Ghanbari, B. and Baleanu, D. A new and efficient numerical method for the fractional modeling and optimal control of diabetes and tuberculosis co-existence. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29, 093111, (2019).
  • [25] Akinsola, V.O. and Oluyo, T.O. Mathematical analysis with numerical solutions of the mathematical model for the complications and control of diabetes mellitus. Journal of Statistics and Management systems, 22(5), 845-869, (2019).
  • [26] Ndii, M.Z., Berkanis, F.R., Tambaru, D., Lobo, M., Ariyanto and Djahi, B.S. Optimal control strategy for the effects of hard water consumption on kidney-related diseases. BMC Research Notes, 13, 201, (2020).
  • [27] Anusha, S. and Athithan, S. Mathematical modelling co-existence of diabetes and COVID-19: Deterministic and tochastic approach. Research Square, (2021).
  • [28] Özköse, F. and Yavuz, M. Investigation of interactions between COVID-19 and diabetes with hereditary traits using real data: A case study in Turkey. Computers in biology and medicine, 141, 105044, (2022).
  • [29] Agwu, C.O., Omame, A. and Inyama, S.C. Analysis of mathematical model of diabetes and Tuberculosis co-infection. International Journal of Applied and Computational Mathematics, 9, 36, (2023).
  • [30] Mollah, S. and Biswas, S. Optimal control for the complication of Type 2 diabetes: the role of awareness programs by media and treatment. International Journal of Dynamics and Control, 11, 877-891, (2023).
  • [31] Singh, T. and Adlakha, N. Numerical investigations and simulation of calcium distribution in the alpha-cell. Bulletin of Biomathematics, 1(1), 40-57, (2023).
  • [32] Balakrishnan, G.P., Chinnathambi, R. and Rihan, F.A. A fractional-order control model for diabetes with restraining and time-delay. Journal of Applied Mathematics and Computing, 69, 3403–3420, (2023).
  • [33] Nasir, H. Stability analysis and optimal control of a five-state diabetic population model. Journal of Statistics and Management Systems, 25(1), 245-267, (2022).
  • [34] Boutayeb, W., Lamlili, M.E.N., Boutayeb, A. and Derouich, M. The dynamics of a population of healthy people, pre-diabetics and diabetics with and without complications with optimal control. In Proceedings of the Mediterranean Conference on Information & Communication Technologies: MedCT 2015, (Vol. 1), pp. 463-471, Springer International Publishing, (2016, April).
  • [35] Kouidere, A., Youssoufi, L.E., Ferjouchia, H., Balatif, O. and Rachik, M. Optimal control of mathematical modeling of the spread of the COVID-19 pandemic with highlighting the negative impact of quarantine on diabetics people with cost-effectiveness. Chaos, Solitons & Fractals, 145, 110777, (2021).
  • [36] Fleming, W.H. and Rishel, R.W. Deterministic and Stochastic Optimal Control (Vol. 1). SpringerVerlag: New York, (2012).

Optimal control of diabetes model with the impact of endocrine-disrupting chemical: an emerging increased diabetes risk factor

Year 2023, Volume: 3 Issue: 4, 318 - 334, 30.12.2023
https://doi.org/10.53391/mmnsa.1397575

Abstract

Diabetes, a persistent pathological condition characterized by disruptions in insulin hormone regulation, has exhibited a noteworthy escalation in its prevalence over recent decades. The surge in incidence is notably associated with the proliferation of endocrine-disrupting chemicals (EDCs), which have emerged as primary contributors to the manifestation of insulin resistance and the consequent disruption of beta cell function, ultimately culminating in the onset of diabetes. Consequently, this study endeavors to introduce a model for diabetes that aims to elucidate the ramifications of exposure to EDCs within the diabetic population. In the pursuit of mitigating the deleterious effects of EDC-induced diabetes, we propose a framework for optimal control strategies. The utilization of Pontryagin’s maximum principle serves to explicate the principles governing the optimal control mechanisms within the proposed model. Our findings underscore that heightened concentrations of EDCs play a pivotal role in exacerbating the prevalence of diabetes. To substantiate our model, we employ parameter estimation techniques utilizing a diabetes dataset specific to the demographic context of India. This research contributes valuable insights into the imperative need for proactive measures to regulate and diminish EDC exposure, thereby mitigating the escalating diabetes epidemic.

References

  • [1] Wu, H., Eggleston, K.N., Zhong, J., Hu, R., Wang, C., Xie, K. et al. How do type 2 diabetes mellitus (T2DM)-related complications and socioeconomic factors impact direct medical costs? A cross-sectional study in rural Southeast China. BMJ Open, 8(11), e020647, (2018).
  • [2] World Health Organization, Health Topics, Diabetes. https://www.who.int/healthtopics/diabetes, [Accessed: 15.10.2023].
  • [3] The American Diabetes Association®(ADA). https://www.diabetes.org/diabetes, [Accessed: 15.10.2023].
  • [4] Scheen, A. Pathophysiology of type 2 diabetes. Acta Clinica Belgica, 58(6), 335-341, (2003).
  • [5] International diabetes federation, IDF Diabetes Atlas 2021. https://diabetesatlas.org/, [Accessed: 15.10.2023].
  • [6] Institute for Public Health. "National Health Morbidity Survey 2015 (NHMS 2015). Vol. II: Non-Communicable Diseases, Risk Factors & Other Health Problems." Minist. Health Malays. 2: 185-186 (2015). Retrieved from https://www.moh.gov.my/moh/resources/ nhmsreport2015vol2.pdf
  • [7] Malaysian Ministry of Health (MMH), 2016. National Strategic Plan for Non-Communicable Disease (NSP- NCD) 2016-2025. Retrieved from https://www.iccp-portal.org/system/ files/plans/MYS_B3_NSP%20NCD%202016-2025%2C%20FINAL.pdf
  • [8] Dal Canto, E., Ceriello, A., Rydén, L., Ferrini, M., Hansen, T.B., Schnell, O. et al. Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. European Journal of Preventive Cardiology, 26(2suppl), 25-32, (2019).
  • [9] Forbes, J.M. and Cooper, M.E. Mechanisms of diabetic complications. Physiological Reviews, 93(1), 137-188 (2013).
  • [10] Alonso-Magdalena, P., Quesada, I. and Nadal, A. Endocrine disruptors in the etiology of type 2 diabetes mellitus. Nature Reviews Endocrinology, 7, 346-353, (2011).
  • [11] Zoeller, R.T., Brown, T.R., Doan, L.L., Gore, A.C., Skakkebaek, N.E., Soto, A.M. et al. Endocrine-disrupting chemicals and public health protection: a statement of principles from The Endocrine Society. Endocrinology, 153(9), 4097-4110, (2012).
  • [12] Kunysz, M., Mora-Janiszewska, O. and Darmochwał-Kolarz, D. Epigenetic modifications associated with exposure to endocrine disrupting chemicals in patients with gestational diabetes mellitus. International Journal of Molecular Sciences, 22(9), 4693, (2021).
  • [13] Street, M.E., Angelini, S., Bernasconi, S., Burgio, E., Cassio, A., Catellani, C. et al. Current knowledge on endocrine disrupting chemicals (EDCs) from animal biology to humans, from pregnancy to adulthood: highlights from a national Italian meeting. International Journal of Molecular Sciences, 19(6), 1647, (2018).
  • [14] Sargis, R.M. and Simmons, R.A. Environmental neglect: endocrine disruptors as underappreciated but potentially modifiable diabetes risk factors. Diabetologia, 62, 1811-1822, (2019).
  • [15] Beszterda, M. and Fra´nski, R. Endocrine disruptor compounds in environment: As a danger for children health. Pediatric Endocrinology Diabetes and Metabolism, 24(2), 88-95, (2018).
  • [16] Selevan, S.G., Kimmel, C.A. and Mendola, P. Identifying critical windows of exposure for children’s health. Environmental Health Perspectives, 108(suppl 3), 451-455, (2000).
  • [17] Sun, Q., Zong, G., Valvi, D., Nielsen, F., Coull, B. and Grandjean, P. Plasma concentrations of perfluoroalkyl substances and risk of type 2 diabetes: A prospective investigation among US women. Environmental Health Perspectives, 126(3), 037001, (2018).
  • [18] Lind, P.M. and Lind, L. Endocrine-disrupting chemicals and risk of diabetes: an evidencebased review. Diabetologia, 61, 1495-1502, (2018).
  • [19] Boutayeb, A., Twizell, E.H., Achouayb, K. and Chetouani, A. A mathematical model for the burden of diabetes and its complications. BioMedical Engineering OnLine, 3, 20, (2004).
  • [20] Boutayeb, A., Chetouani, A., Achouyab, A. and Twizell, E.H. A non-linear population model of diabetes mellitus. Journal of Applied Mathematics and Computing, 21, 127-139, (2006).
  • [21] Derouich, M., Boutayeb, A., Boutayeb, W. and Lamlili, M. Optimal control approach to the dynamics of a population of diabetics. Applied mathematical sciences, 8(56), 2773-2782, (2104).
  • [22] Widyaningsih, P., Affan, R.C. and Saputro, D.R.S. A mathematical model for the epidemiology of diabetes mellitus with lifestyle and genetic factors. In In Proceedings, Journal of Physics: Conference Series (Vol. 1028), pp. 012110, Makassar, Indonesia, (2018, October).
  • [23] Bassey, B.E. Optimal control model for dual treatment of delayed type-II diabetes infection in human population. Open Science Journal of Mathematics and Application, 7(1), 34-49, (2019).
  • [24] Jajarmi, A., Ghanbari, B. and Baleanu, D. A new and efficient numerical method for the fractional modeling and optimal control of diabetes and tuberculosis co-existence. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29, 093111, (2019).
  • [25] Akinsola, V.O. and Oluyo, T.O. Mathematical analysis with numerical solutions of the mathematical model for the complications and control of diabetes mellitus. Journal of Statistics and Management systems, 22(5), 845-869, (2019).
  • [26] Ndii, M.Z., Berkanis, F.R., Tambaru, D., Lobo, M., Ariyanto and Djahi, B.S. Optimal control strategy for the effects of hard water consumption on kidney-related diseases. BMC Research Notes, 13, 201, (2020).
  • [27] Anusha, S. and Athithan, S. Mathematical modelling co-existence of diabetes and COVID-19: Deterministic and tochastic approach. Research Square, (2021).
  • [28] Özköse, F. and Yavuz, M. Investigation of interactions between COVID-19 and diabetes with hereditary traits using real data: A case study in Turkey. Computers in biology and medicine, 141, 105044, (2022).
  • [29] Agwu, C.O., Omame, A. and Inyama, S.C. Analysis of mathematical model of diabetes and Tuberculosis co-infection. International Journal of Applied and Computational Mathematics, 9, 36, (2023).
  • [30] Mollah, S. and Biswas, S. Optimal control for the complication of Type 2 diabetes: the role of awareness programs by media and treatment. International Journal of Dynamics and Control, 11, 877-891, (2023).
  • [31] Singh, T. and Adlakha, N. Numerical investigations and simulation of calcium distribution in the alpha-cell. Bulletin of Biomathematics, 1(1), 40-57, (2023).
  • [32] Balakrishnan, G.P., Chinnathambi, R. and Rihan, F.A. A fractional-order control model for diabetes with restraining and time-delay. Journal of Applied Mathematics and Computing, 69, 3403–3420, (2023).
  • [33] Nasir, H. Stability analysis and optimal control of a five-state diabetic population model. Journal of Statistics and Management Systems, 25(1), 245-267, (2022).
  • [34] Boutayeb, W., Lamlili, M.E.N., Boutayeb, A. and Derouich, M. The dynamics of a population of healthy people, pre-diabetics and diabetics with and without complications with optimal control. In Proceedings of the Mediterranean Conference on Information & Communication Technologies: MedCT 2015, (Vol. 1), pp. 463-471, Springer International Publishing, (2016, April).
  • [35] Kouidere, A., Youssoufi, L.E., Ferjouchia, H., Balatif, O. and Rachik, M. Optimal control of mathematical modeling of the spread of the COVID-19 pandemic with highlighting the negative impact of quarantine on diabetics people with cost-effectiveness. Chaos, Solitons & Fractals, 145, 110777, (2021).
  • [36] Fleming, W.H. and Rishel, R.W. Deterministic and Stochastic Optimal Control (Vol. 1). SpringerVerlag: New York, (2012).
There are 36 citations in total.

Details

Primary Language English
Subjects Biological Mathematics, Dynamical Systems in Applications
Journal Section Research Articles
Authors

P. Logaprakash 0009-0007-8330-901X

C. Monica 0000-0001-7580-5389

Publication Date December 30, 2023
Submission Date November 29, 2023
Acceptance Date December 25, 2023
Published in Issue Year 2023 Volume: 3 Issue: 4

Cite

APA Logaprakash, P., & Monica, C. (2023). Optimal control of diabetes model with the impact of endocrine-disrupting chemical: an emerging increased diabetes risk factor. Mathematical Modelling and Numerical Simulation With Applications, 3(4), 318-334. https://doi.org/10.53391/mmnsa.1397575


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