Predicting Type 1 Diabetes Progression Using Deep Learning on Continuous Glucose Monitoring Data
Main Article Content
Abstract
This study presents a predictive framework for assessing the progression of Type 1 Diabetes by leveraging key glycemic variability metrics, Dynamic Stress Factor, Mean Amplitude of Glycemic Excursions, Mean of Daily Differences, Continuous Overall Net Glycemic Action alongside patient age. Two modeling approaches are explored: a machine learning model using a Random Forest Classifier and a deep learning model based on Recurrent Neural Networks. Patients are classified into three clinically significant categories: Stage 2 Type 1 Diabetes, Stage 1 Type 1 Diabetes, and Low Risk. After addressing class imbalance with Synthetic Minority Oversampling Technique, the Random Forest model achieved 99.6% accuracy, while the RNN model reached 100% accuracy on the test dataset.
Feature importance analysis revealed that Dynamic Stress Factor and Continuous Overall Net Glycemic Action were the most predictive features, emphasizing the critical role of glycemic volatility in early diabetes detection. In contrast, static parameters like age and mean glucose showed minimal contribution. These findings underscore the effectiveness of deep learning models in capturing temporal glucose patterns and support the clinical utility of time-series-based metrics for personalized diabetes management. The proposed framework offers a promising tool for improving early diagnosis, guiding intervention strategies, and enhancing clinical decision support in Type 1 Diabetes care.
Article Details
How to Cite
WISAM, Bukaita; ORIEHI, Anyaiwe; PATRICK, Nelson.
Predicting Type 1 Diabetes Progression Using Deep Learning on Continuous Glucose Monitoring Data.
Medical Research Archives, [S.l.], v. 13, n. 5, may 2025.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/6522>. Date accessed: 21 june 2025.
doi: https://doi.org/10.18103/mra.v13i5.6522.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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15. Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB, Devries JH. Glucose variability is associated with intensive care unit mortality. Crit Care Med. 2010 Mar;38(3):838-42. doi: 10.1097/CCM.0b013e3181cc4be9. PMID: 20035218.
16. Krinsley JS, Chase JG, Gunst J, Martensson J, Schultz MJ, Taccone FS, Wernerman J, Bohe J, De Block C, Desaive T, Kalfon P, Preiser JC. Continuous glucose monitoring in the ICU: clinical considerations and consensus. Crit Care. 2017 Jul 31;21(1):197. doi: 10.1186/s13054-017-1784-0. PMID: 28756769; PMCID: PMC5535285.
17. Kitae, Aya, Tetsuya Kimura, Chihiro Munekawa, Yukako Hosomi, Takafumi Osaka, Noriyuki Kitagawa, Emi Ushigome, Masahiro Yamazaki, Masahide Hamaguchi, and Michiaki Fukui. 2021. “Development of Application to Automatically Calculate Mean Amplitude of Glycaemic Excursions Using Intermittently Scanned Continuous Glucose Monitoring Data.” Diabetes, Obesity & Metabolism 23 (9): 2155–60. https://doi.org/10.1111/dom.14457.
18. Jędrzej Chrzanowski, Szymon Grabia, Arkadiusz Michalak, Anna Wielgus, Julia Wykrota, Beata Mianowska, Agnieszka Szadkowska, Wojciech Fendler; GlyCulator 3.0: A Fast, Easy-to-Use Analytical Tool for CGM Data Analysis, Aggregation, Center Benchmarking, and Data Sharing. Diabetes Care 2 January 2023; 46 (1): e3–e5. https://doi.org/10.2337/dc22-0534
19. Piersanti A, Giurato F, Göbl C, Burattini L, Tura A, Morettini M. Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data. Diabetes Technol Ther. 2023 Jan;25(1):69-85. doi: 10.1089/dia.2022.0237. Epub 2022 Nov 4. PMID: 36223198.
20. Akasaka, Tomonori, Daisuke Sueta, Noriaki Tabata, Seiji Takashio, Eiichiro Yamamoto, Yasuhiro Izumiya, Kenichi Tsujita, et al. 2017. “Effects of the Mean Amplitude of Glycemic Excursions and Vascular Endothelial Dysfunction on Cardiovascular Events in Nondiabetic Patients with Coronary Artery Disease.” Journal of the American Heart Association 6 (5). https://doi.org/10.1161/JAHA.116.004841.
21. Kazuya Tateishi, Yuichi Saito, Hideki Kitahara, Yoshio Kobayashi; 2021. “Impact of glycemic variability on coronary and peripheral endothelial dysfunction in patients with coronary artery disease”; Journal of Cardiology, 79, (2022) 65-70; https://www.journal-of-cardiology.com/article/S0914-5087(21)00205-7/pdf.
22. Imai, S., S. Kajiyama, Y. Hashimoto, A. Nitta, T. Miyawaki, S. Matsumoto, N. Ozasa, M. Tanaka, and M. Fukui. 2018. “Consuming Snacks Mid-Afternoon Compared with Just after Lunch Improves Mean Amplitude of Glycaemic Excursions in Patients with Type 2 Diabetes: A Randomized Crossover Clinical Trial.” Diabetes and Metabolism 44 (6): 482–87. https://doi.org/10.1016/j.diabet.2018.07.001.
23. Wilson, D. M., Pietropaolo, S. L., Acevedo-Calado, M., et al. (2023). CGM Metrics Identify Dysglycemic States in Participants From the TrialNet Pathway to Prevention Study. Diabetes Care, 46(3), 526–534. https://doi.org/10.2337/dc22-1297
24. Steck AK, Dong F, Taki I, Hoffman M, Klingensmith GJ, Rewers MJ. Early hyperglycemia detected by continuous glucose monitoring in children at risk for type 1 diabetes. Diabetes Care. 2014 Jul;37(7):2031-3. doi: 10.2337/dc13-2965. Epub 2014 May 1. PMID: 24784826; PMCID: PMC4067399.
25. Tokutsu, Akemi, Yosuke Okada, Keiichi Torimoto, and Yoshiya Tanaka. 2020. “Relationship between Interstitial Glucose Variability in Ambulatory Glucose Profile and Standardized Continuous Glucose Monitoring Metrics; a Pilot Study.” Diabetology & Metabolic Syndrome 12 (1). https://doi.org/10.1186/s13098-020-00577-5.
26. Shivaprasad, Channabasappa, Yalamanchi Aiswarya, Shah Kejal, Atluri Sridevi, Biswas Anupam, Barure Ramdas, Kolla Gautham, and Premchander Aarudhra. 2021. “Comparison of CGM-Derived Measures of Glycemic Variability between Pancreatogenic Diabetes and Type 2 Diabetes Mellitus.” Journal of Diabetes Science and Technology 15 (1): 134–40. https://doi.org/10.1177/1932296819860133.
27. Mao, Yishen, Xingfei Zhao, Lihui Zhou, Bin Lu, Chen Jin, Deliang Fu, Lie Yao, and Ji Li. 2021. “Evaluating Perioperative Glycemic Status after Different Types of Pancreatic Surgeries via Continuous Glucose Monitoring System: A Pilot Study.” Gland Surgery 10 (10): 2945–55. https://doi.org/10.21037/gs-21-495.
28. Type 1 Diabetes TrialNet (TrialNet). 2025, April; https://repository.niddk.nih.gov/network/30
2. Ziegler AG, Rewers M, Simell O, Simell T, Lempainen J, Steck A, Winkler C, Ilonen J, Veijola R, Knip M, Bonifacio E, Eisenbarth GS. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA. 2013 Jun 19;309(23):2473-9. doi: 10.1001/jama.2013.6285. PMID: 23780460; PMCID: PMC4878912.
3. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006 Apr 12;295(14):1681-7. doi: 10.1001/jama.295.14.1681. PMID:16609090.
4. Rodbard, D. (2009). Interpretation of continuous glucose monitoring data: Glycemic variability and quality of glycemic control. Diabetes Technology & Therapeutics, 11(S1), S-55–S-67.
5. Rawlings R, Yuan L, Shi H, Brehm W, Pop-Busui R, Nelson P. Dynamic Stress Factor (DySF): A Significant Predictor of Severe Hypoglycemic Events in Children with Type 1 Diabetes. J Diabetes Metab. 2012 Feb 28;3:177. doi: 10.4172/2155-6156.1000177. PMID: 24349871; PMCID: PMC3859451.
6. Battelino, T., Danne, T., Bergenstal, R. M., et al. (2019). Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the International Consensus on Time in Range. Diabetes Care, 42(8), 1593–1603. https://doi.org/10.2337/dci19-0028
7. Breyton AE, Lambert-Porcheron S, Laville M, Vinoy S, Nazare JA. CGMS and Glycemic Variability, Relevance in Clinical Research to Evaluate Interventions in T2D, a Literature Review. Front Endocrinol (Lausanne). 2021 Sep 9;12:666008. doi: 10.3389/fendo.2021.666008. PMID: 34566883; PMCID: PMC8458933.
8. Wilmot EG, Choudhary P, Leelarathna L, Baxter M. Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care. Diabetes Obes Metab. 2019 Dec;21(12):2599-2608. doi: 10.1111/dom.13842. Epub 2019 Aug 26. PMID: 31364268; PMCID: PMC6899456.
9. Tuppad A, Patil SD. Machine learning for diabetes clinical decision support: a review. Adv Comput Intell. 2022;2(2):22. doi: 10.1007/s43674-022-00034-y. Epub 2022 Apr 13. PMID: 35434723; PMCID: PMC9006199.
10. Jin, M., Zhang, H., & Huang, D. (2024). Deep Learning-Based Early Warning Model for Continuous Glucose Monitoring Data in Diabetes Management. Spectrum of Research, 4(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/4.
11. Chiang JL, Kirkman MS, Laffel LM, Peters AL; Type 1 Diabetes Sourcebook Authors. Type 1 diabetes through the life span: a position statement of the American Diabetes Association. Diabetes Care. 2014 Jul;37(7):2034-54. doi: 10.2337/dc14-1140. PMID: 24935775; PMCID: PMC5865481.
12. Sunghwan Suh, Jae Hyeon Kim; Glycemic Variability: How Do We Measure It and Why Is It Important? Diabetes & Metabolism Journal 2015;39(4):273-282. DOI: https://doi.org/10.4093/dmj.2015.39.4.273
13. Rawlings RA, Shi H, Yuan LH, Brehm W, Pop-Busui R, Nelson PW. Translating glucose variability metrics into the clinic via Continuous Glucose Monitoring: a Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©). Diabetes Technol Ther. 2011 Dec;13(12):1241-8. doi: 10.1089/dia.2011.0099. Epub 2011 Sep 20. PMID: 21932986; PMCID: PMC3263307.
14. Furushima, Nana, Moritoki Egi, Norihiko Obata, Hitoaki Sato, and Satoshi Mizobuchi. 2021. “Mean Amplitude of Glycemic Excursions in Septic Patients and Its Association with Outcomes: A Prospective Observational Study Using Continuous Glucose Monitoring.” Journal of Critical Care 63: 218–22. https://doi.org/10.1016/j.jcrc.2020.08.021.
15. Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB, Devries JH. Glucose variability is associated with intensive care unit mortality. Crit Care Med. 2010 Mar;38(3):838-42. doi: 10.1097/CCM.0b013e3181cc4be9. PMID: 20035218.
16. Krinsley JS, Chase JG, Gunst J, Martensson J, Schultz MJ, Taccone FS, Wernerman J, Bohe J, De Block C, Desaive T, Kalfon P, Preiser JC. Continuous glucose monitoring in the ICU: clinical considerations and consensus. Crit Care. 2017 Jul 31;21(1):197. doi: 10.1186/s13054-017-1784-0. PMID: 28756769; PMCID: PMC5535285.
17. Kitae, Aya, Tetsuya Kimura, Chihiro Munekawa, Yukako Hosomi, Takafumi Osaka, Noriyuki Kitagawa, Emi Ushigome, Masahiro Yamazaki, Masahide Hamaguchi, and Michiaki Fukui. 2021. “Development of Application to Automatically Calculate Mean Amplitude of Glycaemic Excursions Using Intermittently Scanned Continuous Glucose Monitoring Data.” Diabetes, Obesity & Metabolism 23 (9): 2155–60. https://doi.org/10.1111/dom.14457.
18. Jędrzej Chrzanowski, Szymon Grabia, Arkadiusz Michalak, Anna Wielgus, Julia Wykrota, Beata Mianowska, Agnieszka Szadkowska, Wojciech Fendler; GlyCulator 3.0: A Fast, Easy-to-Use Analytical Tool for CGM Data Analysis, Aggregation, Center Benchmarking, and Data Sharing. Diabetes Care 2 January 2023; 46 (1): e3–e5. https://doi.org/10.2337/dc22-0534
19. Piersanti A, Giurato F, Göbl C, Burattini L, Tura A, Morettini M. Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data. Diabetes Technol Ther. 2023 Jan;25(1):69-85. doi: 10.1089/dia.2022.0237. Epub 2022 Nov 4. PMID: 36223198.
20. Akasaka, Tomonori, Daisuke Sueta, Noriaki Tabata, Seiji Takashio, Eiichiro Yamamoto, Yasuhiro Izumiya, Kenichi Tsujita, et al. 2017. “Effects of the Mean Amplitude of Glycemic Excursions and Vascular Endothelial Dysfunction on Cardiovascular Events in Nondiabetic Patients with Coronary Artery Disease.” Journal of the American Heart Association 6 (5). https://doi.org/10.1161/JAHA.116.004841.
21. Kazuya Tateishi, Yuichi Saito, Hideki Kitahara, Yoshio Kobayashi; 2021. “Impact of glycemic variability on coronary and peripheral endothelial dysfunction in patients with coronary artery disease”; Journal of Cardiology, 79, (2022) 65-70; https://www.journal-of-cardiology.com/article/S0914-5087(21)00205-7/pdf.
22. Imai, S., S. Kajiyama, Y. Hashimoto, A. Nitta, T. Miyawaki, S. Matsumoto, N. Ozasa, M. Tanaka, and M. Fukui. 2018. “Consuming Snacks Mid-Afternoon Compared with Just after Lunch Improves Mean Amplitude of Glycaemic Excursions in Patients with Type 2 Diabetes: A Randomized Crossover Clinical Trial.” Diabetes and Metabolism 44 (6): 482–87. https://doi.org/10.1016/j.diabet.2018.07.001.
23. Wilson, D. M., Pietropaolo, S. L., Acevedo-Calado, M., et al. (2023). CGM Metrics Identify Dysglycemic States in Participants From the TrialNet Pathway to Prevention Study. Diabetes Care, 46(3), 526–534. https://doi.org/10.2337/dc22-1297
24. Steck AK, Dong F, Taki I, Hoffman M, Klingensmith GJ, Rewers MJ. Early hyperglycemia detected by continuous glucose monitoring in children at risk for type 1 diabetes. Diabetes Care. 2014 Jul;37(7):2031-3. doi: 10.2337/dc13-2965. Epub 2014 May 1. PMID: 24784826; PMCID: PMC4067399.
25. Tokutsu, Akemi, Yosuke Okada, Keiichi Torimoto, and Yoshiya Tanaka. 2020. “Relationship between Interstitial Glucose Variability in Ambulatory Glucose Profile and Standardized Continuous Glucose Monitoring Metrics; a Pilot Study.” Diabetology & Metabolic Syndrome 12 (1). https://doi.org/10.1186/s13098-020-00577-5.
26. Shivaprasad, Channabasappa, Yalamanchi Aiswarya, Shah Kejal, Atluri Sridevi, Biswas Anupam, Barure Ramdas, Kolla Gautham, and Premchander Aarudhra. 2021. “Comparison of CGM-Derived Measures of Glycemic Variability between Pancreatogenic Diabetes and Type 2 Diabetes Mellitus.” Journal of Diabetes Science and Technology 15 (1): 134–40. https://doi.org/10.1177/1932296819860133.
27. Mao, Yishen, Xingfei Zhao, Lihui Zhou, Bin Lu, Chen Jin, Deliang Fu, Lie Yao, and Ji Li. 2021. “Evaluating Perioperative Glycemic Status after Different Types of Pancreatic Surgeries via Continuous Glucose Monitoring System: A Pilot Study.” Gland Surgery 10 (10): 2945–55. https://doi.org/10.21037/gs-21-495.
28. Type 1 Diabetes TrialNet (TrialNet). 2025, April; https://repository.niddk.nih.gov/network/30