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The History, Evolution and Future of Continuous Glucose Monitoring (CGM)

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Bender, Clara, Vestergaard, Peter, Cichosz, Simon Lebech·2025·Diabetology
DOI 10.3390/diabetology6030017
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Abstract

Continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) systems have revolutionized diabetes management by delivering real-time, dynamic insights into blood glucose levels. This article provides a concise overview of the evolution of CGM technology, highlights emerging innovations in the field and explores current and potential future applications (including insulin management, early diagnostics, predictive modeling, diabetes education and integration into automated insulin delivery (AID) systems) of CGM in healthcare.

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References 78 · Cited by 43

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  1. Rodbard, 2016, Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities, Diabetes Technol. Ther., 18, S23, 10.1089/dia.2015.0417 · DOI
  2. Carlson, 2017, Clinical use of continuous glucose monitoring in adults with type 2 diabetes, Diabetes Technol. Ther., 19, S4, 10.1089/dia.2017.0024 · DOI
  3. Lacy, 2024, Patterns and Trends in Continuous Glucose Monitoring Utilization Among Commercially Insured Individuals with Type 1 Diabetes: 2010–2013 to 2016–2019, Clin. Diabetes, 42, 388, 10.2337/cd23-0051 · DOI
  4. Ajjan, 2017, How can we realize the clinical benefits of continuous glucose monitoring?, Diabetes Technol. Ther., 19, S27, 10.1089/dia.2017.0021 · DOI
  5. Mortensen, 1984, Reference intervals for urinary glucose in pregnancy, Scand. J. Clin. Lab. Investig., 44, 409, 10.3109/00365518409083829 · DOI
  6. Rock, 1966, Dextrostix Method for Determination of Blood Glucose Levels: A Statistical Evaluation, JAMA, 198, 231, 10.1001/jama.1966.03110160059021 · DOI
  7. Yamada, 2011, Historical Achievements of Self-Monitoring of Blood Glucose Technology Development in Japan, J. Diabetes Sci. Technol., 5, 1300, 10.1177/193229681100500541 · DOI
  8. Kesavadev, 2023, History and Evolution of Capillary Glucose Monitoring, Int. J. Diabetes Technol., 2, 37, 10.4103/ijdt.ijdt_30_23 · DOI
  9. Lee, I., Probst, D., Klonoff, D., and Sode, K. (2021). Continuous glucose monitoring systems—Current status and future perspectives of the flagship technologies in biosensor research. Biosens. Bioelectron., 181. · DOI
  10. Diabetes Research in Children Network (DirecNet) Study Group (2008). The accuracy of the Guardian RT continuous glucose monitor in children with type 1 diabetes. Diabetes Technol. Ther., 10, 266–272. · DOI
  11. Olczuk, 2018, A history of continuous glucose monitors (CGMs) in self-monitoring of diabetes mellitus, Diabetes Metab. Syndr., 12, 181, 10.1016/j.dsx.2017.09.005 · DOI
  12. Didyuk, 2021, Continuous Glucose Monitoring Devices: Past, Present, and Future Focus on the History and Evolution of Technological Innovation, J. Diabetes Sci. Technol., 15, 676, 10.1177/1932296819899394 · DOI · Paper page
  13. Hirsch, 2018, Introduction: History of Glucose Monitoring, Compendia, 2018, 1, 10.2337/db20181-1 · DOI
  14. Alva, 2023, Accuracy of the Third Generation of a 14-Day Continuous Glucose Monitoring System, Diabetes Ther., 14, 767, 10.1007/s13300-023-01385-6 · DOI
  15. Kim, 2024, Accuracy and Safety of the 15-Day CareSens Air Continuous Glucose Monitoring System, Diabetes Technol. Ther., 26, 222, 10.1089/dia.2023.0468 · DOI
  16. Glatzer, 2024, Concept and Implementation of a Novel Continuous Glucose Monitoring Solution With Glucose Predictions on Board, J. Diabetes Sci. Technol., 18, 1004, 10.1177/19322968241269927 · DOI
  17. Herrero, 2024, Enhancing the Capabilities of Continuous Glucose Monitoring with a Predictive App, J. Diabetes Sci. Technol., 18, 1014, 10.1177/19322968241267818 · DOI · Paper page
  18. Woldaregay, 2019, Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes, Artif. Intell. Med., 98, 109, 10.1016/j.artmed.2019.07.007 · DOI · Paper page
  19. Cichosz, 2021, Short-term prediction of future continuous glucose monitoring readings in type 1 diabetes: Development and validation of a neural network regression model, Int. J. Med. Inform., 151, 104472, 10.1016/j.ijmedinf.2021.104472 · DOI
  20. Cichosz, S.L., Kronborg, T., Jensen, M.H., and Hejlesen, O. (2021). Penalty weighted glucose prediction models could lead to better clinically usage. Comput. Biol. Med., 138. · DOI
  21. Woldaregay, 2019, Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes, J. Med. Internet Res., 21, e11030, 10.2196/11030 · DOI
  22. Cichosz, 2024, Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes, Diabetes Technol. Ther., 26, 403, 10.1089/dia.2023.0531 · DOI
  23. Fleischer, J., Hansen, T.K., and Cichosz, S.L. (2022). Hypoglycemia event prediction from CGM using ensemble learning. Front. Clin. Diabetes Healthc., 3. · DOI
  24. Islam, 2021, Long Term HbA1c Prediction Using Multi-Stage CGM Data Analysis, IEEE Sens. J., 21, 15237, 10.1109/JSEN.2021.3073974 · DOI
  25. Cichosz, 2024, Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring, Diabetes Technol. Ther., 26, 457, 10.1089/dia.2023.0532 · DOI
  26. Cichosz, S.L., Olesen, S.S., and Jensen, M.H. (2024). Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring. J. Diabetes Sci. Technol., online ahead of print. · DOI
  27. Hollmann, 2025, Accurate predictions on small data with a tabular foundation model, Nature, 637, 319, 10.1038/s41586-024-08328-6 · DOI
  28. Shchur, O., Turkmen, A.C., Erickson, N., Shen, H., Shirkov, A., Hu, T., and Wang, B. (2023, January 12–15). AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting. Proceedings of the Second International Conference on Automated Machine Learning, PMLR, Potsdam, Germany.
  29. Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A. (2020). AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. arXiv.
  30. Soliman, 2014, Continuous glucose monitoring system and new era of early diagnosis of diabetes in high risk groups, Indian J. Endocrinol. Metab., 18, 274, 10.4103/2230-8210.131130 · DOI
  31. Haynes, 2024, Early Dysglycemia Is Detectable Using Continuous Glucose Monitoring in Very Young Children at Risk of Type 1 Diabetes, Diabetes Care, 47, 1750, 10.2337/dc24-0540 · DOI
  32. Cichosz, 2024, From Stability to Variability: Classification of Healthy Individuals, Prediabetes, and Type 2 Diabetes using Glycemic Variability Indices from Continuous Glucose Monitoring Data, Diabetes Technol. Ther., 27, 34, 10.1089/dia.2024.0226 · DOI
  33. Ahmadzai, 2022, Continuous Glucose Monitoring for the Diagnosis of Gestational Diabetes Mellitus: A Pilot Study, J. Diabetes Res., 2022, 5142918
  34. Ayers, 2024, Artificial Intelligence to Diagnose Complications of Diabetes, J. Diabetes Sci. Technol., 19, 246, 10.1177/19322968241287773 · DOI
  35. Cichosz, 2016, Toward Big Data Analytics, J. Diabetes Sci. Technol., 10, 27, 10.1177/1932296815611680 · DOI · Paper page
  36. Phillip, 2012, Use of continuous glucose monitoring in children and adolescents (*), Pediatr. Diabetes, 13, 215, 10.1111/j.1399-5448.2011.00849.x · DOI
  37. Tobiaszewska, 2013, Monthly use of a real-time continuous glucose monitoring system as an educational and motivational tool for poorly controlled type 1 diabetes adolescents, Adv. Med. Sci., 58, 344, 10.2478/ams-2013-0024 · DOI
  38. Alfadhli, 2016, Use of a real time continuous glucose monitoring system as an educational tool for patients with gestational diabetes, Diabetol. Metab. Syndr., 8, 48, 10.1186/s13098-016-0161-5 · DOI
  39. Miller, 2020, Using Continuous Glucose Monitoring in Clinical Practice, Clin. Diabetes, 38, 429, 10.2337/cd20-0043 · DOI
  40. Mensing, 2003, Group Education in Diabetes: Effectiveness and Implementation, Diabetes Spectr., 16, 96, 10.2337/diaspect.16.2.96 · DOI
  41. Bailey, 2016, Self-Monitoring Using Continuous Glucose Monitors with Real-Time Feedback Improves Exercise Adherence in Individuals with Impaired Blood Glucose: A Pilot Study, Diabetes Technol. Ther., 18, 185, 10.1089/dia.2015.0285 · DOI · Paper page
  42. Battelino, 2011, Effect of continuous glucose monitoring on hypoglycemia in type 1 diabetes, Diabetes Care, 34, 795, 10.2337/dc10-1989 · DOI
  43. Riddlesworth, 2017, Effect of initiating use of an insulin pump in adults with type 1 diabetes using multiple daily insulin injections and continuous glucose monitoring (DIAMOND): A multicentre, randomised controlled trial, Lancet Diabetes Endocrinol., 5, 700, 10.1016/S2213-8587(17)30217-6 · DOI · Paper page
  44. Castle, 2017, Future of automated insulin delivery systems, Diabetes Technol. Ther., 19, S67, 10.1089/dia.2017.0012 · DOI
  45. Knoll, 2022, Real-world evidence on clinical outcomes of people with type 1 diabetes using open-source and commercial automated insulin dosing systems: A systematic review, Diabet. Med., 39, e14741, 10.1111/dme.14741 · DOI
  46. Blauw, 2024, Bihormonal fully closed-loop system for the treatment of type 1 diabetes: A real-world multicentre, prospective, single-arm trial in the Netherlands, Lancet Digit. Health, 6, e272, 10.1016/S2589-7500(24)00002-5 · DOI
  47. Wilson, 2019, Role of glucagon in automated insulin delivery, Endocrinol. Metab. Clin. N. Am., 49, 179, 10.1016/j.ecl.2019.10.008 · DOI
  48. Chehregosha, 2019, A View Beyond HbA1c: Role of Continuous Glucose Monitoring, Diabetes Ther., 10, 853, 10.1007/s13300-019-0619-1 · DOI
  49. Battelino, 2023, Continuous glucose monitoring and metrics for clinical trials: An international consensus statement, Lancet Diabetes Endocrinol., 11, 42, 10.1016/S2213-8587(22)00319-9 · DOI · Paper page
  50. Fox, 2021, Continuous Glucose Monitoring Use in Clinical Trials for On-Market Diabetes Drugs, Clin. Diabetes, 39, 160, 10.2337/cd20-0049 · DOI
  51. Cichosz, 2024, Beyond A1c: Investigating the Contribution of Red Blood Cell Parameters to Dysglycemia Diagnostics, J. Diabetes Sci. Technol., 18, 1519, 10.1177/19322968241228541 · DOI
  52. Loy, S.L., Lin, J., Cheung, Y.B., Sreedharan, A.V., Chin, X., Godfrey, K.M., Tan, K.H., Shek, L.P.-C., Chong, Y.S., and Leow, M.K.-S. (2021). Influence of red blood cell indices on HbA1c performance in detecting dysglycaemia in a Singapore preconception cohort study. Sci. Rep., 11. · DOI
  53. Schnell, 2017, Role of Continuous Glucose Monitoring in Clinical Trials: Recommendations on Reporting, Diabetes Technol. Ther., 19, 391, 10.1089/dia.2017.0054 · DOI
  54. Huang, 2021, Association of time in range with postoperative wound healing in patients with diabetic foot ulcers, Int. Wound J., 19, 1309, 10.1111/iwj.13725 · DOI
  55. Yin, 2022, Association of continuous glucose monitoring-derived time in range with major amputation risk in diabetic foot osteomyelitis patients undergoing amputation, Ther. Adv. Endocrinol. Metab., 13, 20420188221099337, 10.1177/20420188221099337 · DOI
  56. Reddy, 2024, The role of real-time continuous glucose monitoring in diabetes management and how it should link to integrated personalized diabetes management, Diabetes Obes. Metab., 26, 46, 10.1111/dom.15504 · DOI
  57. Underwood, 2021, Virtual visits and the use of continuous glucose monitoring for diabetes care in the era of COVID-19, J. Am. Assoc. Nurse Pract., 34, 586, 10.1097/JXX.0000000000000659 · DOI
  58. 2022, Telemedicine for the Clinical Management of Diabetes; Implications and Considerations After COVID-19 Experience, High Blood Press. Cardiovasc. Prev., 29, 319, 10.1007/s40292-022-00524-7 · DOI
  59. Reddy, 2023, Personalized Virtual Care Using Continuous Glucose Monitoring in Adults With Type 2 Diabetes Treated with Less Intensive Therapies, Clin. Diabetes, 41, 452, 10.2337/cd22-0128 · DOI
  60. Shah, 2018, Neonatal hypoglycemia: Continuous glucose monitoring, Curr. Opin. Pediatr., 30, 204, 10.1097/MOP.0000000000000592 · DOI
  61. McKinlay, 2017, Continuous glucose monitoring in neonates: A review, Matern. Health Neonatol. Perinatol., 3, 18, 10.1186/s40748-017-0055-z · DOI
  62. Beardsall, 2021, Real-time continuous glucose monitoring in preterm infants (REACT): An international, open-label, randomised controlled trial, Lancet Child. Adolesc. Health, 5, 265, 10.1016/S2352-4642(20)30367-9 · DOI

Metadata

Authors
Bender, C., Vestergaard, P., & Cichosz, S. L.
Affiliations
  • Aalborg University (DK)
  • Aalborg University Hospital (DK)
Corresponding author
Cichosz, Simon Lebech
ORCID
Venue
Diabetology
Publisher
MDPI AG
Published
2025-03-03
Volume
6
Issue
3
Pages
17
ISSN
2673-4540 (ISSN-L)
DOI
10.3390/diabetology6030017
OpenAlex
W4408108843
Cited by
43
References
78
Citations by year
2026: 29 · 2025: 14
FWCI
29.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Diabetology (publishedVersion, cc-by)
  • VBN Forskningsportal (Aalborg Universitet) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords
continuous glucose monitoring, flash glucose monitoring, diabetes management, blood glucose monitoring, insulin management, predictive modeling, diabetes education, artificial pancreas

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