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Related Concept Videos

Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

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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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Pathophysiology of Diabetes01:20

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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
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Carbohydrates are polymers composed of molecules containing atoms of carbon, hydrogen and oxygen. One gram of carbohydrate can provide four kilo-calories of energy, which makes it the most efficient instant energy source.
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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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Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

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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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Obesity01:24

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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Body Composition Differentiates Prediabetes Phenotype Clusters in the Diabetes Prevention Program Study.

Benjamin M Stroebel1, Meghana Gadgil2, Kimberly Lewis1

  • 1Department of Physiological Nursing, School of Nursing, University of California, San Francisco, CA 94143, USA.

The Journal of Clinical Endocrinology and Metabolism
|March 11, 2025
PubMed
Summary
This summary is machine-generated.

Clustering prediabetes patients reveals distinct phenotypes, aiding in identifying those at higher risk for type 2 diabetes (T2D). This approach can personalize prevention strategies and improve treatment responses.

Keywords:
body compositionclustering modelsprediabetestype 2 diabetes

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Area of Science:

  • Metabolic health research
  • Clinical data analysis
  • Biostatistics

Background:

  • Type 2 diabetes (T2D) poses a significant public health challenge.
  • Current T2D risk reduction strategies are not universally effective.

Purpose of the Study:

  • To identify and characterize prediabetes phenotypes using clustering methods.
  • To examine the relationship between these phenotypes, treatment groups, and incident T2D.

Main Methods:

  • Secondary analysis of the Diabetes Prevention Program (DPP) trial data.
  • Unsupervised k-means clustering applied to metabolic risk factors and body composition measures.
  • Analysis included 994 participants with body composition data.

Main Results:

  • Five distinct prediabetes clusters were identified using both clinical factors alone and comprehensive measures.
  • Comprehensive models better differentiated overweight phenotypes by metabolic health.
  • The metformin arm showed the greatest differentiation in predicting time to T2D across clusters.

Conclusions:

  • Data-driven clustering can identify prediabetes phenotypes with higher progression risk.
  • Phenotypic differences may influence response to interventions, enabling personalized prevention.
  • Further research into treatment response variations can optimize T2D prevention and management.