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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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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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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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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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Biguanides, particularly metformin (Glucophage), are insulin sensitizers that enhance glucose uptake, thereby reducing insulin resistance. Unlike sulfonylureas, metformin doesn't prompt insulin secretion, which helps to curb hypoglycemia risk. Metformin is beneficial in treating conditions like polycystic ovary syndrome due to its insulin-resistance reduction capability. The drug's primary action involves curtailing hepatic gluconeogenesis, a significant contributor to high blood...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Updated: Jun 25, 2025

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Identification and validation of gestational diabetes subgroups by data-driven cluster analysis.

Benedetta Salvatori1, Silke Wegener2, Grammata Kotzaeridi3

  • 1CNR Institute of Neuroscience, Padua, Italy.

Diabetologia
|May 27, 2024
PubMed
Summary

Gestational diabetes mellitus (GDM) is heterogeneous. Cluster analysis identified three GDM subtypes using routine clinical data, enabling tailored treatments for better maternal and neonatal outcomes.

Keywords:
Cluster analysisData-driven clusteringGestational diabetes mellitusOral glucose tolerance testPregnancy outcomesTreatment stratificationUnsupervised machine learning

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Area of Science:

  • Reproductive Endocrinology
  • Clinical Genetics
  • Public Health

Background:

  • Gestational diabetes mellitus (GDM) presents significant heterogeneity.
  • Personalized treatment strategies are needed due to variability in GDM patient profiles.
  • Identifying distinct GDM subgroups can optimize clinical management.

Purpose of the Study:

  • To identify distinct gestational diabetes mellitus (GDM) subtypes using cluster analysis.
  • To analyze treatment needs and pregnancy outcomes across identified GDM subgroups.
  • To leverage routine clinical variables for personalized GDM management.

Main Methods:

  • A cohort study involving 2682 women with GDM across two European hospitals (2015-2022).
  • Evaluation of clustering models (k-means, k-medoids, agglomerative hierarchical clustering) with internal and external validation.
  • Analysis of clinical outcomes, including treatment requirements and maternal/fetal complications, across identified clusters.

Main Results:

  • Three GDM clusters were identified using maternal age, pre-pregnancy BMI, and OGTT glucose levels.
  • Cluster 1 (high OGTT, obesity) required more glucose-lowering medications and had higher risk of large-for-gestational-age infants.
  • Distinct treatment needs and outcomes were observed across the three identified GDM subgroups.

Conclusions:

  • Gestational diabetes mellitus (GDM) is confirmed to be a heterogeneous condition.
  • Subgroup identification through cluster analysis can guide tailored treatment approaches.
  • Personalized GDM management has the potential to improve maternal and neonatal outcomes.