Research progress on automated insulin delivery system in the field of diabetes management_Journal of Biomedical Engineering

Authors：

YU Zhichao ¹ , SUN Yufan ¹ , HUANG Zhijian ¹ , LI Zhanhong ¹ , LONG Jianjun ² ,  ZHU Zhigang ¹

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Jiangsu Yuwell-POCT Biological Technology Co., Ltd., Danyang, Jiangsu 212300, P. R. China;

Corresponding author：

Keywords：

Automated insulin delivery; Diabetes mellitus; Insulin pump; Continuous glucose monitoring

DOI：

10.7507/1001-5515.202406060

Video：

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Diabetes and its complications pose a serious threat to human life and health. It has become a public health problem of wide concern worldwide. Currently, diabetes is mainly treated with insulin injection in clinic. However, manual insulin injection still has many shortcomings. In recent years, with the deepening of research, it has been found that an automated insulin delivery system (AID), which combines a continuous glucose monitoring device with an insulin pump, can significantly improve the effectiveness of diabetes treatment and reduce the incidence of complications in patients. This paper firstly introduces the composition of the AID system and its working principle, and then details the development history and current status of the related technologies from the aspects of continuous glucose monitoring technology, insulin pumps and the development of closed-loop control algorithms, etc. Finally, this paper looks forward to the application prospect and future development of AID system in the field of diabetes treatment, providing theoretical reference for further research.

Citation： YU Zhichao, SUN Yufan, HUANG Zhijian, LI Zhanhong, LONG Jianjun, ZHU Zhigang. Research progress on automated insulin delivery system in the field of diabetes management. Journal of Biomedical Engineering, 2024, 41(6): 1279-1285. doi: 10.7507/1001-5515.202406060 Copy

1.	Fralick M, Jenkins A J, Khunti K, et al. Global accessibility of therapeutics for diabetes mellitus. Nature Reviews Endocrinology, 2022, 18(4): 199-204.
2.	Ali M K, Pearson-Stuttard J, Selvin E, et al. Interpreting global trends in type 2 diabetes complications and mortality. Diabetologia, 2022, 65(1): 3-13.
3.	Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Research and Clinical Practice, 2022, 183: 109119.
4.	邓超, 李霞, 周智广. 《糖尿病分型诊断中国专家共识》解读. 中华糖尿病杂志, 2022, 14(2): 99-103.
5.	Wei J, Tian J, Tang C, et al. The influence of different types of diabetes on vascular complications. Journal of Diabetes Research, 2022, 2022: 3448618.
6.	Lian K, Feng H, Liu S, et al. Insulin quantification towards early diagnosis of prediabetes/diabetes. Biosensors and Bioelectronics, 2022, 203: 114029.
7.	Choudhary P, Kolassa R, Keuthage W, et al. Advanced hybrid closed loop therapy versus conventional treatment in adults with type 1 diabetes (ADAPT): a randomised controlled study. The Lancet Diabetes & Endocrinology, 2022, 10(10): 720-731.
8.	Lee I, Probst D, Klonoff D, et al. Continuous glucose monitoring systems-current status and future perspectives of the flagship technologies in biosensor research. Biosensors and Bioelectronics, 2021, 181: 113054.
9.	de Farias J L C B, Bessa W M. Intelligent control with artificial neural networks for automated insulin delivery systems. Bioengineering, 2022, 9(11): 664.
10.	Bergman R N, Ider Y Z, Bowden C R, et al. Quantitative estimation of insulin sensitivity. Am J Physiol, 1979, 236(6): E667-E677.
11.	高鑫禹, 徐泽楷, 陈力群. 微创及无创血糖检测方法研究现状. 生物医学工程学杂志, 2023, 40(2): 365-372.
12.	Larin K V, Eledrisi M S, Motamedi M, et al. Noninvasive blood glucose monitoring with optical coherence tomography: a pilot study in human subjects. Diabetes Care, 2002, 25(12): 2263-2267.
13.	Ali H, Bensaali F, Jaber F. Novel approach to non-invasive blood glucose monitoring based on transmittance and refraction of visible laser light. IEEE Access, 2017, 5: 9163-9174.
14.	Rachim V P, Chung W Y. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sensors and Actuators B: Chemical, 2019, 286: 173-180.
15.	Zhu B, Li X, Zhou L, et al. An overview of wearable and implantable electrochemical glucose sensors. Electroanalysis, 2022, 34(2): 237-245.
16.	Majdinasab M. Wearable electrochemical biosensors for glucose monitoring//Wearable Biosensing in Medicine and Healthcare. Singapore: Springer Nature Singapore, 2024: 35-66.
17.	余江渊, 李崭虹, 陈诚, 等. 血糖监测用植入式传感器的研究进展. 生物医学工程学杂志, 2016, 33(5): 991-997.
18.	Chen C, Zhao X L, Li Z H, et al. Current and emerging technology for continuous glucose monitoring. Sensors, 2017, 17(1): 182.
19.	Alam M M, Howlader M M R. Nonenzymatic electrochemical sensors via Cu native oxides (CuNOx) for sweat glucose monitoring. Sensing and Bio-Sensing Research, 2021, 34: 100453.
20.	Parrilla M, Detamornrat U, Domínguez-Robles J, et al. Wearable hollow microneedle sensing patches for the transdermal electrochemical monitoring of glucose. Talanta, 2022, 249: 123695.
21.	余江渊, 朱志刚, 陈诚, 等. 基于螺旋型铂铱合金电极的植入式葡萄糖生物传感器. 传感技术学报, 2016, 29(1): 9-14.
22.	Marks B E, Williams K M, Sherwood J S, et al. Practical aspects of diabetes technology use: Continuous glucose monitors, insulin pumps, and automated insulin delivery systems. Journal of Clinical & Translational Endocrinology, 2022, 27: 100282.
23.	Zhang J, Xu J, Lim J, et al. Wearable glucose monitoring and implantable drug delivery systems for diabetes management. Advanced Healthcare Materials, 2021, 10(17): 2100194.
24.	Lane W, Lambert E, George J, et al. Exploring the burden of mealtime insulin dosing in adults and children with type 1 diabetes. Clinical Diabetes, 2021, 39(4): 347-357.
25.	Yu J, Wang H, Zhu M, et al. MDI versus CSII in Chinese adults with type 1 diabetes in a real-world situation: based on propensity score matching method. Health and Quality of Life Outcomes, 2024, 22(1): 47.
26.	Wadwa R P, Reed Z W, Buckingham B A, et al. Trial of hybrid closed-loop control in young children with type 1 diabetes. New England Journal of Medicine, 2023, 388(11): 991-1001.
27.	Boughton C K, Hartnell S, Hobday N, et al. Implementation of fully closed‐loop insulin delivery for inpatients with diabetes: Real-world outcomes. Diabetic Medicine, 2023, 40(6): e15092.
28.	Gros Herguido N, Amuedo S, Bellido V, et al. Effectiveness and safety of an advanced hybrid closed-loop system in adolescents and adults with type 1 diabetes previously treated with continuous subcutaneous insulin infusion and flash glucose monitoring. Diabetes Technology & Therapeutics, 2023, 25(2): 151-156.
29.	胡志昊, 朱帅, 李玉华, 等. 血糖波动及血糖控制在2型糖尿病患者发生脑卒中事件中作用的研究. 中国糖尿病杂志, 2024, 32(2): 101-107.
30.	赵群, 王文绢. 2型糖尿病血管并发症危险因素控制研究进展. 中国慢性病预防与控制, 2022, 30(4): 302-306.
31.	Zhou K, Isaacs D. Closed-loop artificial pancreas therapy for type 1 diabetes. Current Cardiology Reports, 2022, 24(9): 1159-1167.
32.	Peacock S, Frizelle I, Hussain S. A systematic review of commercial hybrid closed-loop automated insulin delivery systems. Diabetes Therapy, 2023, 14(5): 839-855.
33.	Wilson L M, Jacobs P G, Riddell M C, et al. Opportunities and challenges in closed-loop systems in type 1 diabetes. The Lancet Diabetes & Endocrinology, 2022, 10(1): 6-8.
34.	Matamoros-Alcivar E, Ascencio-Lino T, Fonseca R, et al. Implementation of MPC and PID control algorithms to the artificial pancreas for diabetes mellitus type 1//2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). IEEE, 2021: 1-6.
35.	Li C, Hu R. PID control based on BP neural network for the regulation of blood glucose level in diabetes//2007 IEEE 7th International Symposium on Bioinformatics and Bioengineering. IEEE, 2007: 1168-1172.
36.	Eren-Oruklu M, Cinar A, Quinn L, et al. Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes. Journal of Process Control, 2009, 19(8): 1333-1346.
37.	Wu S, Furutani E, Sugawara T, et al. Glycemic control for critically ill patients using zone model predictive control. IEEJ Transactions on Electrical and Electronic Engineering, 2021, 16(2): 275-281.
38.	Nanayakkara N, Sharifi A, Burren D, et al. Hybrid closed loop using a do-it-yourself artificial pancreas system in adults with type 1 diabetes. Journal of Diabetes Science and Technology, 2024, 18(4): 889-896.

1. Fralick M, Jenkins A J, Khunti K, et al. Global accessibility of therapeutics for diabetes mellitus. Nature Reviews Endocrinology, 2022, 18(4): 199-204.
2. Ali M K, Pearson-Stuttard J, Selvin E, et al. Interpreting global trends in type 2 diabetes complications and mortality. Diabetologia, 2022, 65(1): 3-13.
3. Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Research and Clinical Practice, 2022, 183: 109119.
4. 邓超, 李霞, 周智广. 《糖尿病分型诊断中国专家共识》解读. 中华糖尿病杂志, 2022, 14(2): 99-103.
5. Wei J, Tian J, Tang C, et al. The influence of different types of diabetes on vascular complications. Journal of Diabetes Research, 2022, 2022: 3448618.
6. Lian K, Feng H, Liu S, et al. Insulin quantification towards early diagnosis of prediabetes/diabetes. Biosensors and Bioelectronics, 2022, 203: 114029.
7. Choudhary P, Kolassa R, Keuthage W, et al. Advanced hybrid closed loop therapy versus conventional treatment in adults with type 1 diabetes (ADAPT): a randomised controlled study. The Lancet Diabetes & Endocrinology, 2022, 10(10): 720-731.
8. Lee I, Probst D, Klonoff D, et al. Continuous glucose monitoring systems-current status and future perspectives of the flagship technologies in biosensor research. Biosensors and Bioelectronics, 2021, 181: 113054.
9. de Farias J L C B, Bessa W M. Intelligent control with artificial neural networks for automated insulin delivery systems. Bioengineering, 2022, 9(11): 664.
10. Bergman R N, Ider Y Z, Bowden C R, et al. Quantitative estimation of insulin sensitivity. Am J Physiol, 1979, 236(6): E667-E677.
11. 高鑫禹, 徐泽楷, 陈力群. 微创及无创血糖检测方法研究现状. 生物医学工程学杂志, 2023, 40(2): 365-372.
12. Larin K V, Eledrisi M S, Motamedi M, et al. Noninvasive blood glucose monitoring with optical coherence tomography: a pilot study in human subjects. Diabetes Care, 2002, 25(12): 2263-2267.
13. Ali H, Bensaali F, Jaber F. Novel approach to non-invasive blood glucose monitoring based on transmittance and refraction of visible laser light. IEEE Access, 2017, 5: 9163-9174.
14. Rachim V P, Chung W Y. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sensors and Actuators B: Chemical, 2019, 286: 173-180.
15. Zhu B, Li X, Zhou L, et al. An overview of wearable and implantable electrochemical glucose sensors. Electroanalysis, 2022, 34(2): 237-245.
16. Majdinasab M. Wearable electrochemical biosensors for glucose monitoring//Wearable Biosensing in Medicine and Healthcare. Singapore: Springer Nature Singapore, 2024: 35-66.
17. 余江渊, 李崭虹, 陈诚, 等. 血糖监测用植入式传感器的研究进展. 生物医学工程学杂志, 2016, 33(5): 991-997.
18. Chen C, Zhao X L, Li Z H, et al. Current and emerging technology for continuous glucose monitoring. Sensors, 2017, 17(1): 182.
19. Alam M M, Howlader M M R. Nonenzymatic electrochemical sensors via Cu native oxides (CuNOx) for sweat glucose monitoring. Sensing and Bio-Sensing Research, 2021, 34: 100453.
20. Parrilla M, Detamornrat U, Domínguez-Robles J, et al. Wearable hollow microneedle sensing patches for the transdermal electrochemical monitoring of glucose. Talanta, 2022, 249: 123695.
21. 余江渊, 朱志刚, 陈诚, 等. 基于螺旋型铂铱合金电极的植入式葡萄糖生物传感器. 传感技术学报, 2016, 29(1): 9-14.
22. Marks B E, Williams K M, Sherwood J S, et al. Practical aspects of diabetes technology use: Continuous glucose monitors, insulin pumps, and automated insulin delivery systems. Journal of Clinical & Translational Endocrinology, 2022, 27: 100282.
23. Zhang J, Xu J, Lim J, et al. Wearable glucose monitoring and implantable drug delivery systems for diabetes management. Advanced Healthcare Materials, 2021, 10(17): 2100194.
24. Lane W, Lambert E, George J, et al. Exploring the burden of mealtime insulin dosing in adults and children with type 1 diabetes. Clinical Diabetes, 2021, 39(4): 347-357.
25. Yu J, Wang H, Zhu M, et al. MDI versus CSII in Chinese adults with type 1 diabetes in a real-world situation: based on propensity score matching method. Health and Quality of Life Outcomes, 2024, 22(1): 47.
26. Wadwa R P, Reed Z W, Buckingham B A, et al. Trial of hybrid closed-loop control in young children with type 1 diabetes. New England Journal of Medicine, 2023, 388(11): 991-1001.
27. Boughton C K, Hartnell S, Hobday N, et al. Implementation of fully closed‐loop insulin delivery for inpatients with diabetes: Real-world outcomes. Diabetic Medicine, 2023, 40(6): e15092.
28. Gros Herguido N, Amuedo S, Bellido V, et al. Effectiveness and safety of an advanced hybrid closed-loop system in adolescents and adults with type 1 diabetes previously treated with continuous subcutaneous insulin infusion and flash glucose monitoring. Diabetes Technology & Therapeutics, 2023, 25(2): 151-156.
29. 胡志昊, 朱帅, 李玉华, 等. 血糖波动及血糖控制在2型糖尿病患者发生脑卒中事件中作用的研究. 中国糖尿病杂志, 2024, 32(2): 101-107.
30. 赵群, 王文绢. 2型糖尿病血管并发症危险因素控制研究进展. 中国慢性病预防与控制, 2022, 30(4): 302-306.
31. Zhou K, Isaacs D. Closed-loop artificial pancreas therapy for type 1 diabetes. Current Cardiology Reports, 2022, 24(9): 1159-1167.
32. Peacock S, Frizelle I, Hussain S. A systematic review of commercial hybrid closed-loop automated insulin delivery systems. Diabetes Therapy, 2023, 14(5): 839-855.
33. Wilson L M, Jacobs P G, Riddell M C, et al. Opportunities and challenges in closed-loop systems in type 1 diabetes. The Lancet Diabetes & Endocrinology, 2022, 10(1): 6-8.
34. Matamoros-Alcivar E, Ascencio-Lino T, Fonseca R, et al. Implementation of MPC and PID control algorithms to the artificial pancreas for diabetes mellitus type 1//2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). IEEE, 2021: 1-6.
35. Li C, Hu R. PID control based on BP neural network for the regulation of blood glucose level in diabetes//2007 IEEE 7th International Symposium on Bioinformatics and Bioengineering. IEEE, 2007: 1168-1172.
36. Eren-Oruklu M, Cinar A, Quinn L, et al. Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes. Journal of Process Control, 2009, 19(8): 1333-1346.
37. Wu S, Furutani E, Sugawara T, et al. Glycemic control for critically ill patients using zone model predictive control. IEEJ Transactions on Electrical and Electronic Engineering, 2021, 16(2): 275-281.
38. Nanayakkara N, Sharifi A, Burren D, et al. Hybrid closed loop using a do-it-yourself artificial pancreas system in adults with type 1 diabetes. Journal of Diabetes Science and Technology, 2024, 18(4): 889-896.

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Research progress on automated insulin delivery system in the field of diabetes management

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