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Diabetes Mellitus: Type 2 and Gestational01:22

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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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Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated...
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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: 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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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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PathophysiologyType 2 diabetes mellitus (T2DM ) is a chronic metabolic disorder characterized by insulin resistance and progressive pancreatic β-cell dysfunction, leading to impaired glucose homeostasis. It results from interactions among genetic predisposition, environmental factors, and metabolic stressors, such as overnutrition and a sedentary lifestyle.Insulin Resistance and Glucose DysregulationEarly T2DM involves insulin resistance in skeletal muscle, adipose tissue, and the liver.
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Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and

Timor Glatzer1, Ajandek Peak2, Eemeli Leppäaho3

  • 1Roche Diabetes Care GmbH, Mannheim, Germany.

Journal of Diabetes Science and Technology
|April 26, 2026
PubMed
Summary
This summary is machine-generated.

Identifying recurring glucose patterns in continuous glucose monitoring (CGM) data can significantly improve time in range (TIR) for individuals with type 1 and type 2 diabetes. Addressing these actionable patterns offers a path to more personalized diabetes management.

Keywords:
artificial intelligencecontinuous glucose monitoring (CGM)glucose patternsglucose variabilityhyperglycemiahypoglycemiapattern recognition

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Area of Science:

  • Endocrinology
  • Metabolic Diseases
  • Digital Health

Background:

  • Continuous glucose monitoring (CGM) generates extensive data, posing challenges for diabetes self-management.
  • Identifying glucose patterns is crucial for effective diabetes management but often difficult for patients.
  • Accu-Chek SmartGuide was utilized to systematically identify glucose patterns and assess their impact.

Purpose of the Study:

  • To systematically identify glucose patterns from CGM data.
  • To quantify the impact of these glucose patterns on diabetes management.
  • To evaluate the potential for improving time in range (TIR) by resolving identified patterns.

Main Methods:

  • Retrospective analysis of real-world CGM data from 3379 individuals with type 1 (T1D) and type 2 diabetes (T2D).
  • An algorithm identified 29 predefined glucose patterns weekly.
  • Analysis included pattern prevalence, demographic influence, persistence, and attribution to time above range (TAR)/time below range (TBR).

Main Results:

  • Actionable glucose patterns significantly contributed to TAR (T1D: 66.2%, T2D: 58.0%) and TBR (T1D: 56.3%, T2D: 42.2%).
  • Resolving daytime hyperglycemia patterns could improve TIR by up to +10.72% in T1D and +5.16% in T2D.
  • These patterns represented an average of 9.3 events per week, indicating frequent glucose excursions.

Conclusions:

  • Most glucose excursions in T1D and T2D are attributable to recurring glucose patterns.
  • Detecting and resolving actionable patterns presents a significant opportunity to enhance TIR.
  • Targeting interventions to resolve these patterns is key for personalized diabetes care.