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Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

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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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Glucose Homeostasis: Regulation of Blood Glucose01:02

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Carbohydrates consumed through foods are converted into glucose, a crucial energy source for the body. In the prandial state, high blood glucose levels stimulate the secretion of insulin from the pancreas. Insulin inhibits hepatic glucose production and stimulates glucose uptake and metabolism by muscle and adipose tissue. The excess glucose is converted into glycogen and stored in the liver and muscles.
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Diabetes Mellitus: Overview and Type I Subtype01:22

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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Diabetes: Management and Pharmacotherapy01:15

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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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Diabetes: Symptoms, Diagnosis, and Complications01:15

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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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Hormones Regulating Blood Glucose01:16

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Insulin is released by beta cells of the pancreas when blood glucose levels are high. It facilitates glucose absorption and utilization in insulin-dependent cells with insulin receptors on their plasma membranes. Insulin promotes glucose uptake by increasing the number of glucose transport proteins in the cell membrane, allowing glucose to enter the cell. As a result, glucose utilization and ATP production are enhanced.
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Use of Continuous Glucose Monitoring With Machine Learning to Identify Metabolic Subphenotypes and Inform Precision

Ahmed A Metwally1,2,3, Heyjun Park4, Yue Wu1

  • 1Department of Genetics, Stanford University, Stanford, CA, USA.

Journal of Diabetes Science and Technology
|April 14, 2026
PubMed
Summary

Continuous glucose monitoring (CGM) and wearables enable dynamic metabolic phenotyping, moving beyond static glucose levels. This allows for personalized diabetes prevention by identifying distinct metabolic subtypes and tailoring interventions.

Keywords:
CGMartificial intelligencediabeteswearables

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Area of Science:

  • Metabolic disease research
  • Personalized medicine
  • Biotechnology

Background:

  • Current diabetes classification using static glucose thresholds overlooks underlying metabolic heterogeneity.
  • Key drivers of dysglycemia include insulin resistance (IR), beta-cell dysfunction, and incretin deficiency.

Purpose of the Study:

  • To review how continuous glucose monitoring (CGM) and wearable technologies facilitate dynamic metabolic phenotyping.
  • To explore the use of machine learning with CGM data for predicting metabolic function.
  • To highlight the potential for personalized nutrition and lifestyle interventions in diabetes prevention.

Main Methods:

  • Leveraging high-resolution glucose data from CGM-enabled oral glucose tolerance tests.
  • Applying machine learning models to predict muscle IR and beta-cell function.
  • Integrating wearable data on diet, sleep, and physical activity patterns.
  • Analyzing individual postprandial glycemic responses (PPGR) to standardized meals.

Main Results:

  • Machine learning models accurately predict gold-standard measures of IR and beta-cell function using CGM data.
  • Individualized PPGR to meals serves as a biomarker for metabolic subtypes.
  • Wearable data reveals associations between lifestyle patterns (diet, sleep, activity timing) and specific metabolic dysfunctions.
  • Dietary interventions show phenotype-dependent efficacy in attenuating PPGR.

Conclusions:

  • CGM enables the deconstruction of early dysglycemia into actionable subphenotypes.
  • This approach moves beyond glycemic control towards precision interventions.
  • Paves the way for a new era of precision diabetes prevention through targeted strategies based on core metabolic defects.