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ODE models for the management of diabetes: A review

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Abstract

Diabetes also known as diabetes mellitus is a chronic and complex metabolic disease due to the persistent raised blood glucose concentration for long duration. The mechanism behind the disturbed glucose-insulin dynamics is still not fully understood. The mathematical models which describe the glucose homeostasis, different aspects of diabetes and its consequences are growing rapidly, provide new insights into the biological mechanism involved and help in the management of diabetes. Here, contribution of diabetic’s modelling using ordinary differential equations over the past five decades is discussed. Some parameter estimation techniques, softwares involved and some computational results are also presented.

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References

  1. Jaidane H, Hober D Role of coxsackievirus B4 in the pathogenesis of type 1 diabetes. Diabete Metab. 2008;34:537–48.

    Article  CAS  PubMed  Google Scholar 

  2. Lupi R, Del Prato S B-cell apoptosis in type 2 diabetes: quantitative and functional conse-quences. Diabete Metab. 2008;34:556–64.

    Article  Google Scholar 

  3. Oschatz E, Mullner M, Herkner H, Laggner AN Multiple organ failure and prognosis in adult patients with diabetic ketoacidosis. Wien Klin Wochenschr. 1999;111:590–5.

    CAS  PubMed  Google Scholar 

  4. Bolie VW Coefficients of normal blood glucose regulation. J Appl Physiol. 1961;16:783–8.

    CAS  PubMed  Google Scholar 

  5. Bergman RN, Ider YZ, Bowden CR, Cobelli C Quantitative estimation of insulin sensitivity. Am J Phys. 1979;236:667–77.

    Google Scholar 

  6. Bergman RN, Phillips LS, Cobelli C Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. J Clin Invest. 1981;68:1456–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. R.N. Bergman, D.T. Finegood, and M. Ader, Assessment of insulin sensitivity in vivo, the endocrine system 6 (1985).

  8. Bergman RN Analysis of endocrine systems with feedback: the glucose/insulin prototype. In: Rod-bard D, Forti G, editors. Computers in endocrinology. New York: Raven Press; 1983.

    Google Scholar 

  9. Pacini G, Bergman RN MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test. Comput Methods Prog Biomed. 1986;23:113–22.

    Article  CAS  Google Scholar 

  10. Welch S, Gebhart SS, Bergman RN, Phillips LS Minimal model analysis of intravenous glucose tolerance test derived insulin sensitivity in diabetic subjects. J Clin Endocrinol Metab. 1990;71:1508–18.

    Article  CAS  PubMed  Google Scholar 

  11. Vicini P, Caumo A, Cobelli C, The hot IVGTT two-compartment minimal model: indexes of glucose effectiveness and insulin sensitivity. Am Physiol Soc. 1997:E1024–32.

  12. Caumo A, Giacca A, Morgese M, Pozza G, Micossi P, Cobelli C Minimal models of glucose disappearance: lessons from the labelled IVGTT. Diabet Med. 1991;8:822–32.

    Article  CAS  PubMed  Google Scholar 

  13. Sturis J, Polonsky KS, Mosekilde E, Cauter EV Computer model for mechanisms underlying ultradian oscillations of insulin and glucose. Amer J Physiol. 1991;260:E801–9.

    CAS  PubMed  Google Scholar 

  14. Topp B, Promislow K, de. Vries G, Miura MR, Finegood DT A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes. J Theor Biol. 2000;206:605–19.

    Article  CAS  PubMed  Google Scholar 

  15. Li J, Kuang Y, Mason C Modeling the glucose-insulin regulatory system and ultradian insulin secretory oscillations with two time delays. J Theor Biol. 2006;242:722–35.

    Article  CAS  PubMed  Google Scholar 

  16. Makroglou A, Li J, Kuang Y Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: an overview. Appl Numer Math. 2006;56:559–73.

    Article  Google Scholar 

  17. Boutayeb A, Chetouani A A critical review of mathematical models and data used in dia-betology. BioMedical Eng OnLine. 2006;5:43. doi:10.1186/1475-925X-5-43.

    Article  CAS  Google Scholar 

  18. Ajmera I, Swat M, Laibe C, Novre NL, Chelliah V The impact of mathematical modeling on the understanding of diabetes and related complications. Pharmacometrics Sys Pharmacol. 2013;2:e54. doi:10.1038/psp.2013.30.

    Article  CAS  Google Scholar 

  19. Bergman RN Minimal model: perspective from 2005. Horm Res. 2005;64(suppl 3):815. doi:10.1159/000089312.

    Google Scholar 

  20. Ackerman E, Rosevear JW, McGucking WF A mathematical model of the glucose tolerance test. Phys Med Biol. 1964:203–13.

  21. Segre G, Turco GL, Vercellone G, Siena, Torino. Modeling blood glucose and Insulin kinetics in normal, diabetic and obese subjects. Diabetes. 1973;22:94–103.

    Article  CAS  PubMed  Google Scholar 

  22. Celeste R, Ackerman E, Gatewood LLL, Reynolds C, Molnar GD The role of glucagon in the regulation of blood glucose: model studies. Bull Math Biol. 1978;40:59–77.

    Article  CAS  PubMed  Google Scholar 

  23. R.N. Bergman, G. Toffolo, C.R. Bowden, and C. Cobelli, Minimal modeling, partition analysis, and identification of glucose disposal in animals and man. Fed Proc. 1979;39:110–115.

  24. G. Toffolo, R.N. Bergman, D.R. Bowden, and C. Cobelli, Quantitative estimation of beta cell sensitivity to glucose in the intact organism, a minimal model of insulin kinetics in the dog, Diabetes 29 (1980) 979-990

  25. Defronzo RA, Hendler R, Simonson D Insulin resistance is a prominent feature of insulin-dependent diabetes. Diabetes. 1982;31:795–801.

    Article  CAS  PubMed  Google Scholar 

  26. Reaven GM, Bernstein R, Davis B, Olefsky JM Nonketotic diabetes mellitus: insulin deficiency or insulin resistance. Am J Med. 1976;60:80–8.

    Article  CAS  PubMed  Google Scholar 

  27. DeFronzo RA, Tobin JD, Andres R Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Phys. 1979;237:214–23.

    Google Scholar 

  28. Sherwin RS, Kramer KJ, Tobin JD, Insel PA, Liljenquist JE, Berman M, Andres R A model of the kinetics of insulin in man. J Clin Invest. 1974;53:1481–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tolic IM, Mosekilde E, Sturis J Modeling the insulin-glucose feedback system: the significance of pulsatile insulin secretion. J Theor Biol. 2000;207:361–75.

    Article  CAS  PubMed  Google Scholar 

  30. Wang H, Li J, Kuang Y Mathematical modeling and qualitative analysis of insulin therapies. Math Biosci. 2007;210:17–33.

    Article  CAS  PubMed  Google Scholar 

  31. Rathee S, Nilam. Quantitative analysis of time delays of glucose-insulin dynamics using artificial pancreas. Discrete Contin Dyn Syst Series B. 2015;20(9):3115–29. doi:10.3934/dcdsb.2015.20.3115.

    Article  Google Scholar 

  32. Huang M, Li J, Song X, Guo H Modeling impulsive injections of insuin: towards artificial pancreas. Siam J App Math Soc Ind Appl Math. 2012;72(5):1524–48.

    Article  Google Scholar 

  33. Song X, Huang M, Li J Modeling impulsive insulin delivery in insulin pump with time delays. Siam J App Math, 2014 Soc Ind Appl Math. 2014;74(6):1763–1785.

    Article  Google Scholar 

  34. Fisher ME A semiclosed-loop algorithm for the control of blood glucose levels in diabetics. IEEE Trans Biomed Eng. 1991;38(I).

  35. Coates PA, Luzio SD, Brunei P, Owens DR Comparison of estimates of insulin sensitivity from minimal model analysis of the insulin-modified frequently sampled intravenous glucose tolerance test and the isoglycemic hyperinsulinemic clamp in subjects with NIDDM. Diabetes. 1995;44:631–5.

    Article  CAS  PubMed  Google Scholar 

  36. R.D. Hernandez, D.J. Lyles, D.B. Rubin, T.B. Voden, and S.A. Wirkus, A model of β-cell mass, insulin, glucose and receptor dynamics with applications to diabetes, Cornell Univ., Dept. of Biometrics Technical Report (2001) BU-1579-M.

  37. De Gaetano A, Arino O Mathematical modelling of the intravenous glucose tolerance test. J Math Biol. 2000;40:136–68.

    Article  PubMed  Google Scholar 

  38. J. Li and Y. Kuang, Analysis of IVGTT glucose-insulin interaction models with time delays, Discrete and Continuous Dynamical Systems-series B (1) (2001)

  39. Dalla Man C, Caumo A, Cobelli C The oral glucose minimal model: estimation of insulin sensitivity from a meal test. IEEE Trans Biomed Eng Mar. 2002;49:419–29.

    Article  Google Scholar 

  40. Derouich M, Boutayeb A The effect of physical exercise on the dynamics of glucose and insulin. J Biomech. 2002;35:911–7.

    Article  CAS  PubMed  Google Scholar 

  41. Mari O, Schmitz A, Gastaldelli T, Oestergaard B, Nyholm, Ferrannini. Meal and Oral glucose tests for assessment of beta-cell function: modeling analysis in normal subjects. Am J Physiol Endocrinol Metab. 2002;283:1159–66.

    Article  Google Scholar 

  42. Toffolo G, Cobelli C The hot IVGTT two-compartment minimal model: an improved version. Amer J Physiol Endocrinol Metab. 2003;284:317–21.

    Article  Google Scholar 

  43. Dalla Man C, Caumo A, Basu R, Rizza RA, Toffolo G, Cobelli C Minimal model estimation of glucose absorption and insulin sensitivity from oral test: validation with a tracer method. Amer J Physiol. 2004;287:637–43.

    Google Scholar 

  44. Wallace TM, Levy JC, Matthews DR Use and abuse of HOMA modeling. Diabetes Care. 2004;27:1487–95.

    Article  PubMed  Google Scholar 

  45. Caumo A, Perseghin G, Brunani A, Luzi L New insights on the simultaneous assessment of insulin sensitivity and beta-cell function with the HOMA2 method. Diabetes Care. 2006;29:2733–4.

    Article  CAS  PubMed  Google Scholar 

  46. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab. 2000;85:2402–10.

    Article  CAS  PubMed  Google Scholar 

  47. Matsuda M, Defronzo RA Insulin sensitivity indices obtained from oral glucose tolerance testing. Diabetes Care. 1999;22:1462–70.

    Article  CAS  PubMed  Google Scholar 

  48. Dalla Man C, Yarasheski KE, Caumo A, Robertson H, Toffolo G, Polonsky KS, Cobelli C Insulin sensitivity by oral glucose minimal models: validation against clamp. Amer J Physiol. 2005;289:954–959.

    Google Scholar 

  49. Boutayeb A, Chetouani A, Achouyab A, Twizell DEH A non-linear population model of diabetes mellitus. J. Appl. Math Comput. 2006;21(1–2):127–39 Website: http://jamc.net.

    Article  Google Scholar 

  50. Nittala S, Ghosh D, Stefanovski RN, Bergman, Wang X Dimensional analysis of MINMOD leads to definition of the disposition index of glucose regulation and improved simulation algorithm. BioMedical Engineering OnLine. 2006;5:44. doi:10.1186/1475-925X-5-44.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wang X, He Z, Ghosh S Investigation of the age-at-onset heterogeneity in type 1 diabetes through mathematical modeling. Math Biosci. 2006;203:79–99.

    Article  PubMed  Google Scholar 

  52. Silber HE, Jauslin PM, Frey N, Gieschke R, Simonsson USH, Karlsson MO An integrated model for glucose and insulin regulation in healthy volunteers and type 2 diabetic patients following intravenous glucose provocations. J Clin Pharmacol. 2007;47:1159–71.

    Article  CAS  PubMed  Google Scholar 

  53. Silber HE, Frey N, Karlsson MO An integrated glucose-insulin model to describe oral glucose tolerance test data in healthy volunteers. J Clin Pharmacol. 2000;50:246–56.

    Article  Google Scholar 

  54. Anirban Roy MS, Parker RS Dynamic modeling of exercise effects on plasma glucose and insulin levels. J Diabetes Sci Technol. 2007;1:338–47.

    Article  PubMed  PubMed Central  Google Scholar 

  55. De Gaetano A, Hardy T, Beck B, Raddad EA, Palumbo P, Valleskey JB, Prksen N Mathematical models of diabetes progression. Am J Physiol Endocrinol Metab. 2008;295:1462–79.

    Article  Google Scholar 

  56. Stahl F, Johansson R Diabetes mellitus modeling and short-term prediction based on blood glucose measurements. Math Biosci. 2009;217:101117.

    Article  Google Scholar 

  57. Periwal V, Chow CC, Bergman RN, Ricks M, Vega GL, Sumner AE Evaluation of quantitative models of the effect of insulin on lipolysis and glucose disposal. Am J Physiol Regul Integr Comp Physiol. 2008;295:1089–96. doi:10.1152/ajpregu.90426.2008.

    Article  Google Scholar 

  58. Pacini G, Tonolo G, Sambataro M, Maioli M, Ciccarese M, Brocco E, Avogaro A, Nosadini R Insulin sensitivity and glucose effectiveness: minimal model analysis of regular and insulin modified FSIGT. Am J Physiol Endocrinol Metab. 1998;274:E592–9.

    CAS  Google Scholar 

  59. J. Baez, T. Gonzalez, A. Murillo, D. Toupo, R. Zarate, and E.T. Camacho, My βIG fat math model: β-cell compensation and type 2 diabetes (2011).

  60. K.E. Andersen and M. Hjbjerre, A Bayesian approach to Bergmans minimal model, in: C.M. Bishop, B.J. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence, http://research.microsoft.com/conferences/ aistats2003/proceedings/183.pdf.

  61. Pillonetto G, Sparacino G, Cobelli C Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation. Math Biosci. 2003;184:53–67.

    Article  CAS  PubMed  Google Scholar 

  62. K.E. Andersen, S.P. Brooks, and M. Hjbjerre, Bayesian model discrimination for glucose-insulin homeostasis, Technical Report R-2004-15, Aalborg University, Denmark.

  63. Bleckert G, Oppel UG, Salzsieder E Mixed graphical models for simultaneous model identification and control applied to the glucose-insulin metabolism. Comput Methods Prog Biomed. 1998;56:141–55.

    Article  CAS  Google Scholar 

  64. Vicini P, Sparacino G, Caumo A, Cobelli C Estimation of endogenous glucose production after a glucose perturbation by nonparametric stochastic deconvolution. Comput Methods Prog Biomed. 1997;52:147–56.

    Article  CAS  Google Scholar 

  65. Caumo A, Cobelli C Hepatic glucose production during labeled IVGTT: estimation by deconvolution with a new minimal model. Amer J Physiol Endocrinol Metab. 1993;264:829–41.

    Google Scholar 

  66. De Nicolao G, Sparacino G, Cobelli C Nonparametric input estimation in physiological systems: problems, methods, and case studies. Automatica. 1997;33:851–70.

    Article  Google Scholar 

  67. Shen SW, Reaven GM, Farquhar JW Comparison of impedance to insulin mediated glucose uptake in normal and diabetic subjects. J Clin Invest. 1970;49:2151–2160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ulefsky J, Farquhat W, Reaven GM Relationship between fasting plasma insulin level and resistance to insulin mediated glucose uptake in normal and diabetic subjects. Diabetes. 1973;22:507–13.

    Article  Google Scholar 

  69. Reaven GM, Sageman WS, Swenson RS Development of insulin resistance in normal dogs following alloxan-induced insulin deficiency. Diabetologia. 1977;13:459–62.

    Article  CAS  PubMed  Google Scholar 

  70. Marquardt DW An algorithim for least-squares estimation of non-linear parameters. J Soc Ind Appl Math. 1963;11:431–41.

    Article  Google Scholar 

  71. Beal SL, Sheiner LS NONMEM users guide. University of California at San Francisco: San Francisco, Calif: NONMEM Project Group; 1994.

    Google Scholar 

  72. S.L. Beal, L.B. Sheiner, and A.J. Boeckmann, NONMEM users guides, (1989–2006), Icon Development Solutions, Ellicott City, Maryland, USA

  73. Vicini P, Caumo A, Cobelli C Glucose effectiveness and insulin sensitivity from the minimal models: consequence of undermodeling assessed by Monte Carlo simulation. IEEE Trans Biomed Eng. 1999;46:130–7[PubMed: 9932334].

    Article  CAS  PubMed  Google Scholar 

  74. Barrett PHR, Bell BM, Cobelli C, Golde H, Schumitzky A, Vicini P, Foster DM SAAM II: simulation analysis, and modeling software for tracer and pharmacokinetic studies. Metabolism. 1988;47:484–92.

    Article  Google Scholar 

  75. SAAM II user guide, SAAM Institute, Seattle, WA (1997).

  76. E.J. Doedel, A.R. Champneys, T.F. Fairgrieve, Yu.A. Kuznetsov, B. Sandstede, and X.J. Wang, auto97: continuation and bifurcation software for ordinary differential equations (with HomCont), users guide, Concordia University, Montreal, Canada (1997) (http://indy.cs.concordia.ca).

  77. A. Dhooge, W. Govaerts, Yu.A. Kuznetsov, W. Mestrom, A.M. Riet, B. Sautois, MATCONT, and CL MATCONT: continuation toolboxes in MATLAB (2006).

  78. Stefanovski D, Moate PJ, Boston RC WinSAAM: a windows-based compartmental modeling system. Metabolism. 2003;52:1153–566.

    Article  CAS  PubMed  Google Scholar 

  79. Sparacino G, Pillonetto G, Capello M, De Nicolao G, Cobelli C WINSTODEC: a stochastic deconvolution interactive program for physiological and pharmacokinetic systems. Comput Methods Prog Biomed. 2001;67:67–77.

    Article  Google Scholar 

  80. Ermentrout B Simulating, analyzing, and animating dynamical systems: a guide to XPPAUT for researchers and students. Philadelphia, PA: SIAM; 2002.

    Book  Google Scholar 

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Acknowledgments

The authors are thankful to Delhi Technological University, Delhi for the financial support.

Authors’ contribution

Ms. Saloni Rathee has contributed to the study design, numerical analysis and manuscript preparation. Ms. Nilam has contributed to the manuscript editing and review. Both have made equal contribution in the literature search.

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Correspondence to Nilam.

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Rathee, S., Nilam ODE models for the management of diabetes: A review. Int J Diabetes Dev Ctries 37, 4–15 (2017). https://doi.org/10.1007/s13410-016-0475-8

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