Glucose metabolism modeling of diabetes patients with different intensities of aerobic exercise: an in silico study_Journal of Biomedical Engineering

Authors：

JIN Jun ^1,2 , YU Lei ^1,2 ,  JIN Zhen ^1,2

1. Complex Systems Research Center, Shanxi University, Taiyuan 030006, P.R.China;
2. Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan 030006, P.R.China;

Corresponding author：

JIN Zhen, Email: jinzhn@263.net

Keywords：

diabetes mellitus; aerobic exercise; glucose-insulin metabolic model

DOI：

10.7507/1001-5515.201805066

Video：

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Abstract Full text Figures/Tables Video References Cited by

Exercise is vital for diabetics to improve their blood glucose level. However, the quantitative relationship between exercise modes (including types, intensity, time, etc.) and the blood glucose is still not clear. In order to answer these questions, this paper established a blood glucose metabolic model based on ordinary differential equation method. Furthermore, a silico method was adopted to study the effects of different aerobic exercise intensities (light, moderate and vigorous) on blood glucose and optimal strategies of insulin infusion for type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Additionally, the universality of proposed model and insulin infusion strategies was verified based on 1 000 virtual diabetes patients’ simulation. The experimental results showed that: (1) Vigorous-intensity aerobic exercise may result in hypoglycemia (< 3.89 mmol/L), which was so harmful to health that diabetics should avoid. Compared with moderate-intensity exercise, the light-intensity aerobic exercise intuitively lowered blood glucose slowly and caused a relative long high-blood-glucose (> 6.11 mmol/L) period, however, its overall blood glucose risk index (BGRI) was lower. (2) Insulin dosage of the optimized strategies decreased by 50% and 84% for T1DM and T2DM when they did moderate intensity exercise. As for light intensity exercise, the dosage of insulin was almost the same as they didn’t do exercise, but BGRI decreased significantly. (3) The simulations of 1 000 virtual diabetic patients manifested that the proposed model and the insulin infusion strategies had good universality. The results of this study can not only help to improve the quantitative understanding about the effects of aerobic exercise on blood glucose of diabetic patients, but also contribute to the regulation and management of blood glucose in exercise mode.

Citation： JIN Jun, YU Lei, JIN Zhen. Glucose metabolism modeling of diabetes patients with different intensities of aerobic exercise: an in silico study. Journal of Biomedical Engineering, 2019, 36(2): 274-280. doi: 10.7507/1001-5515.201805066 Copy

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10.	Derouich M, Boutayeb A. The effect of physical exercise on the dynamics of glucose and insulin. J Biomech, 2002, 35(7): 911-917.
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12.	Breton M D, Brown S A, Karvetski C H, et al. Adding heart rate signal to a control-to-range artificial pancreas system improves the protection against hypoglycemia during exercise in type 1 diabetes. Diabetes Technol Ther, 2014, 16(8): 506-511.
13.	Zecchin C, Facchinetti A, Sparacino G, et al. Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. Diabetes Technol Ther, 2013, 15(10): 836-844.
14.	李鹏, 祝楠楠, 郁磊, 等. 人工胰脏中数据驱动个体血糖代谢模型的辨识. 仪器仪表学报, 2016, 37(3): 714-720.
15.	喻成侠. 面向人工胰脏的快速建模方法与血糖预测研究. 杭州: 浙江大学, 2016.
16.	Turksoy K, Monforti C, Park M, et al. Use of wearable sensors and biometric variables in an artificial pancreas system. Sensors, 2017, 17(3): 532-548.
17.	Kovatchev B P, Straume M, Cox D J, et al. Risk analysis of blood glucose data: a quantitative approach to optimizing the control of insulin dependent diabetes. J Theor Med, 2000, 3(1): 1-10.
18.	Riddell M C, Zaharieva D P, Yavelberg L, et al. Exercise and the development of the artificial pancreas: one of the more difficult series of hurdles. J Diabetes Sci Technol, 2015, 9(6): 1217-1226.
19.	Mackenzie R, Maxwell N, Castle P, et al. Intermittent exercise with and without hypoxia improves insulin sensitivity in individuals with type 2 diabetes. J Clin Endocrinol Metab, 2012, 97(4): E546-E555.

1. Ambrose M. Diabetes: Examining insulin and blood sugar. New Jersey: Enslow Publishers, 2015.
2. 宁光. 中国糖尿病防治的现状及展望. 中国科学: 生命科学, 2018, 48(08): 810-811.
3. 王友清. 人工胰脏: 现状、挑战与展望. 中国生物医学工程学报, 2013, 32(3): 363-372.
4. Haidar A. The artificial pancreas: How closed-loop control is revolutionizing diabetes. IEEE Control Systems, 2016, 36(5): 28-47.
5. Cobelli C, Man C D, Sparacino G, et al. Diabetes: models, signals, and control. IEEE Rev Biomed Eng, 2009, 2(1): 54-96.
6. Rathee S. ODE models for the management of diabetes: A review. Int J Diabetes Dev Ctries, 2017, 37(1): 4-15.
7. Huang M, Li J, Song X, et al. Modeling impulsive injections of insulin: towards artificial pancreas. SIAM J Appl Math, 2012, 72(5): 1524-1548.
8. Riddell M C, Gallen I W, Smart C E, et al. Exercise management in type 1 diabetes: a consensus statement. Lancet Diabetes & Endocrinology, 2017, 5(5): 377-390.
9. Lukács A, Barkai L. Effect of aerobic and anaerobic exercises on glycemic control in type 1 diabetic youths. World J Diabetes, 2015, 6(3): 534-542.
10. Derouich M, Boutayeb A. The effect of physical exercise on the dynamics of glucose and insulin. J Biomech, 2002, 35(7): 911-917.
11. Breton M D. Physical activity-the major unaccounted impediment to closed loop control. J Diabetes Sci Technol, 2008, 2(1): 169-174.
12. Breton M D, Brown S A, Karvetski C H, et al. Adding heart rate signal to a control-to-range artificial pancreas system improves the protection against hypoglycemia during exercise in type 1 diabetes. Diabetes Technol Ther, 2014, 16(8): 506-511.
13. Zecchin C, Facchinetti A, Sparacino G, et al. Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. Diabetes Technol Ther, 2013, 15(10): 836-844.
14. 李鹏, 祝楠楠, 郁磊, 等. 人工胰脏中数据驱动个体血糖代谢模型的辨识. 仪器仪表学报, 2016, 37(3): 714-720.
15. 喻成侠. 面向人工胰脏的快速建模方法与血糖预测研究. 杭州: 浙江大学, 2016.
16. Turksoy K, Monforti C, Park M, et al. Use of wearable sensors and biometric variables in an artificial pancreas system. Sensors, 2017, 17(3): 532-548.
17. Kovatchev B P, Straume M, Cox D J, et al. Risk analysis of blood glucose data: a quantitative approach to optimizing the control of insulin dependent diabetes. J Theor Med, 2000, 3(1): 1-10.
18. Riddell M C, Zaharieva D P, Yavelberg L, et al. Exercise and the development of the artificial pancreas: one of the more difficult series of hurdles. J Diabetes Sci Technol, 2015, 9(6): 1217-1226.
19. Mackenzie R, Maxwell N, Castle P, et al. Intermittent exercise with and without hypoxia improves insulin sensitivity in individuals with type 2 diabetes. J Clin Endocrinol Metab, 2012, 97(4): E546-E555.

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Glucose metabolism modeling of diabetes patients with different intensities of aerobic exercise: an in silico study

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