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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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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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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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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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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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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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StUdy of Gestational diabetes And Risk using Electronic Data (SUGARED): a population-based cohort study-study

Deborah Randall1,2, Ibinabo Ibiebele1,3, Tanya Nippita1,4

  • 1Reproduction and Perinatal Centre - Northern Precinct, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.

BMJ Open
|December 26, 2024
PubMed
Summary

Gestational diabetes mellitus (GDM) is rising, increasing high-risk pregnancies. The SUGARED project uses linked health data to personalize GDM risk prediction and optimize birth timing for better maternal and infant outcomes.

Keywords:
Diabetes in pregnancyEPIDEMIOLOGYOBSTETRICSPregnant WomenSTATISTICS & RESEARCH METHODS

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Area of Science:

  • Public Health
  • Epidemiology
  • Reproductive Health

Background:

  • Gestational diabetes mellitus (GDM) incidence has tripled in Australia over 20 years, leading to increased high-risk pregnancies and interventions.
  • Over 40,000 high-risk pregnancies annually necessitate enhanced antenatal surveillance and early delivery.
  • The rising GDM rates impact obstetric care and pregnancy outcomes, highlighting the need for improved management strategies.

Purpose of the Study:

  • To personalize risk prediction for adverse pregnancy outcomes in women undergoing glucose tolerance testing.
  • To guide optimal birth timing for women with diet-controlled GDM.
  • To examine variations in GDM management and associated pregnancy outcomes within New South Wales (NSW), Australia.

Main Methods:

  • A retrospective cohort study utilizing linked, routinely collected health data from NSW, Australia (January 2016 - December 2020).
  • Inclusion of approximately 475,000 pregnancies, with over 70,000 diagnosed with GDM, linked across birth, hospital, and registry data.
  • Application of logistic regression, K-fold cross-validation for risk prediction, propensity-score matching for birth timing analysis, and multilevel modeling for hospital variation.

Main Results:

  • The study is designed to provide personalized risk predictions for adverse pregnancy outcomes.
  • It will offer evidence-based guidance on optimal birth timing for diet-controlled GDM cases.
  • Analysis will reveal variations in GDM management and outcomes across different health services in NSW.

Conclusions:

  • The SUGARED project will leverage large-scale linked health data to improve GDM care.
  • Findings aim to enhance personalized risk assessment and optimize delivery timing for improved maternal-fetal health.
  • Evidence generated will inform clinical practice and policy to address rising GDM rates and outcomes.