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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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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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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 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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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 Risk Prediction in the Framingham Offspring Study and Large Population Analysis.

Masumi Ai1,2, Seiko Otokozawa1,3, Ching-Ti Liu4,5

  • 1Cardiovascular Nutrition Laboratory, Human Nutrition Research Center on Aging at Tufts University, Tufts University School of Medicine, Boston, MA 02111, USA.

Nutrients
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

A new diabetes risk model accurately predicts 10-year diabetes risk in large populations. Fasting glucose is key, with prediabetes significantly increasing risk, highlighting the need for early intervention.

Keywords:
C-peptidediabetes mellitusglucoseinsulinrisk prediction modelthe Framingham Offspring Study

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Area of Science:

  • Endocrinology
  • Epidemiology
  • Biochemistry

Background:

  • Diabetes mellitus is a leading cause of mortality and morbidity.
  • It is a significant risk factor for cardiovascular disease, kidney failure, neuropathy, and retinopathy.
  • Developing accurate diabetes risk prediction models is crucial for public health.

Purpose of the Study:

  • To develop a robust diabetes risk prediction model.
  • To apply this model to a large population for risk assessment.

Main Methods:

  • Prospective cohort study of non-diabetic adults (n=2416) followed for 10 years.
  • Baseline measurements included fasting serum glucose, adiponectin, insulin, glycated albumin, lipids, and health information.
  • Logistic regression was used to develop prediction models, applied to a population of 133,764.

Main Results:

  • 166 subjects (6.9%) developed new-onset diabetes over 10 years.
  • Key predictors identified: glucose, BMI, adiponectin, glycated albumin, parental diabetes, triglycerides, and cholesterol medication use (C-statistic: 0.924).
  • Predicted 10-year risk was 0.4% for non-diabetic and 5.5% for prediabetic individuals using the biochemical model.

Conclusions:

  • Accurate 10-year diabetes risk prediction is feasible and applicable to large populations.
  • Fasting glucose is a strong diagnostic and predictive marker; prediabetes increases risk sixfold.
  • Insulin and C-peptide levels aid in assessing insulin production and therapy needs in diabetic patients.