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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
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Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes

Chenyang Ji1, Tong Jiang2, Luolin Liu3

  • 1Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.

Frontiers in Endocrinology
|June 10, 2025
PubMed
Summary

Continuous glucose monitoring (CGM) and artificial intelligence (AI) offer a personalized approach to managing prediabetes. This technology provides real-time data for precise diagnosis, tailored interventions, and improved patient outcomes in diabetes care.

Keywords:
artificial intelligencecontinuous glucose monitoringfasting blood glucoseprediabetestype 2 diabetes mellitus

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Area of Science:

  • Endocrinology and Metabolism
  • Medical Technology
  • Data Science in Healthcare

Background:

  • Prediabetes is a critical stage of glucose dysregulation with significant public health impact.
  • Conventional lifestyle interventions for prediabetes face limitations in personalization, real-time monitoring, and timely intervention.
  • Emerging technologies like continuous glucose monitoring (CGM) and artificial intelligence (AI) present novel solutions.

Purpose of the Study:

  • To systematically review the potential applications of integrating CGM and AI for prediabetes management.
  • To explore how this technological synergy can overcome the limitations of traditional interventions.
  • To highlight the benefits for diagnosis, personalized treatment, and decision support.

Main Methods:

  • Systematic review of current literature on CGM and AI in prediabetes.
  • Analysis of the combined capabilities of real-time glucose data from CGM and advanced analytics from AI.
  • Examination of integration benefits across diagnosis, intervention, and decision-making.

Main Results:

  • CGM provides dynamic, real-time glucose insights, addressing limitations of conventional monitoring.
  • AI enhances CGM data utility through deep learning and advanced analysis for precise diagnosis and personalized interventions.
  • Integration supports remote monitoring, shared decision-making, and patient empowerment.

Conclusions:

  • The combination of CGM and AI offers a more precise and efficient health management model for prediabetes.
  • Addressing challenges in data management, algorithm optimization, and ethical considerations is crucial for successful implementation.
  • Fostering multidisciplinary collaboration is recommended to advance the application of these innovations in diabetes care.