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Related Concept Videos

Type II Diabetes I: Introduction01:26

Type II Diabetes I: Introduction

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Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance, in which target tissues such as the liver, muscle, and adipose tissue respond poorly to insulin. It is also associated with inadequate compensatory insulin secretion, where pancreatic β-cells fail to produce sufficient insulin. Together, these abnormalities lead to persistent hyperglycemia.EtiologyT2DM develops through a complex interaction of genetic predisposition and environmental or...
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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 II Diabetes II: Pathophysiology01:24

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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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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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Type I Diabetes I: Introduction01:12

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Type 1 diabetes mellitus is a chronic metabolic disorder characterized by an absolute deficiency of insulin resulting from the autoimmune destruction of pancreatic β-cells. Although it can occur at any age, it is most commonly diagnosed in childhood, adolescence, or early adulthood. The loss of insulin production impairs cellular glucose uptake, resulting in persistent hyperglycemia and necessitating lifelong insulin therapy.Autoimmune Destruction of β-CellsThe hallmark of type 1...
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Type I Diabetes II: Pathophysiology01:26

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Type 1 diabetes mellitus arises from an immune-mediated destruction of pancreatic β-cells, resulting in an absolute deficiency of insulin. This process develops in genetically susceptible individuals when autoimmunity, environmental exposures, and immunologic dysregulation converge to trigger a targeted attack on the insulin-producing cells of the pancreas. The β-cells are located within the islets of Langerhans and are essential for regulating blood glucose by facilitating cellular...
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Related Experiment Video

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Generation of High Quality Chromatin Immunoprecipitation DNA Template for High-throughput Sequencing ChIP-seq
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Genomic data and disease forecasting: application to type 2 diabetes (T2D).

Lawrence Sirovich1

  • 1Center for Studies in Physics & Biology, Rockefeller University, New York, New York, United States of America.

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|January 28, 2014
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Summary

This study introduces a new method for identifying disease risk from large genetic datasets. The approach enhances the discovery of important genomic regions, revealing potential unconventional disease mechanisms.

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Area of Science:

  • Genomics
  • Computational Biology
  • Disease Risk Prediction

Background:

  • Large-scale genetic databases are crucial for understanding disease etiology.
  • Identifying predictive genomic loci for complex diseases remains a challenge.
  • Current methods may not fully capture the subtle genetic signals associated with disease risk.

Purpose of the Study:

  • To develop a novel computational approach for extracting disease risk classifiers from large genetic datasets.
  • To improve the identification of high-value genomic loci associated with disease.
  • To investigate the hypothesis that disease signals are often small and latent within data.

Main Methods:

  • Data reorganization into a regularized standard form, emphasizing individual alleles.
  • A procedure to enhance the discovery of significant genomic loci.
  • Analysis based on a small signal-to-noise hypothesis for disease detection.

Main Results:

  • Application to the FUSION Type 2 Diabetes (T2D) database identified thousands of genomic loci for disease classification.
  • A large genomic kernel was shared by both diabetic and non-diabetic individuals, with a small, distinct separation observed.
  • The FUSION database size limited predictability, with only a fraction of loci directly related to T2D, suggesting confounding factors.

Conclusions:

  • The novel approach can identify a broad set of genomic loci, some potentially linked to unconventional disease mechanisms.
  • Database size significantly impacts the accuracy of disease predictability.
  • Further research with larger datasets is needed to disentangle disease-specific loci from confounding population features.