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Related Concept Videos

Type II Diabetes I: Introduction01:26

Type II Diabetes I: Introduction

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...
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

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...
Type II Diabetes II: Pathophysiology01:24

Type II Diabetes II: Pathophysiology

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.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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Carbohydrate Metabolism01:36

Carbohydrate Metabolism

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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Type I Diabetes II: Pathophysiology01:26

Type I Diabetes II: Pathophysiology

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 uptake of...

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Related Experiment Video

Updated: Jul 9, 2026

Breeding by Design for Functional Rice with Genome Editing Technologies
09:43

Breeding by Design for Functional Rice with Genome Editing Technologies

Published on: January 3, 2025

Diet-Genetic Interactions in Type 2 Diabetes: Comparing Polygenic Risk in Rice- and Wheat-Consuming Populations.

Shahla Naran Chirakkal1, Ananthakrishnan Anilkumar Indu1, Ranajit Das1

  • 1Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, Karnataka, India.

Nutrition and Metabolic Insights
|July 8, 2026
PubMed
Summary

Long-term rice-based diets are linked to higher genetic susceptibility for type 2 diabetes (T2D) globally compared to wheat-based diets. This suggests gene-diet coevolution influences T2D risk across populations.

Keywords:
gene–diet interactionglycaemic loadpolygenic risk score (PRS)population genomicsrice versus wheat diettype 2 diabetes

Related Experiment Videos

Last Updated: Jul 9, 2026

Breeding by Design for Functional Rice with Genome Editing Technologies
09:43

Breeding by Design for Functional Rice with Genome Editing Technologies

Published on: January 3, 2025

Area of Science:

  • Population genomics
  • Human genetics
  • Nutritional science

Background:

  • Rice and wheat are primary global cereal staples with distinct starch compositions and glycemic impacts.
  • Rice-rich diets promote rapid glucose responses, while wheat-rich diets lead to slower digestion due to higher amylose and fiber content.
  • The relationship between long-term staple diet exposure and inherited genetic susceptibility to type 2 diabetes (T2D) at a population level is not well understood.

Purpose of the Study:

  • To investigate potential differences in polygenic risk scores (PRS) for T2D between populations with historically rice-dominant versus wheat-dominant diets.
  • To explore the concept of gene-diet coevolution in the context of T2D susceptibility.

Main Methods:

  • Utilized a population genomics approach comparing PRS for T2D.
  • Derived SNP weights from GWAS summary statistics (IEU OpenGWAS resource).
  • Analyzed genotype data (GenomeAsia 100K, HumanOrigins) and computed PRS using PRSice, assessing group differences with Welch's t-test.

Main Results:

  • Globally, rice-based populations showed significantly higher and more dispersed PRS distributions for T2D compared to wheat-based populations (P < 2.2x10⁻¹⁶).
  • In South Asia, analyses revealed substantial overlap and only a modest difference in PRS between dietary groups (P = 0.02).
  • Observed differences in South Asia may be attributed to dietary heterogeneity, admixture, and complex population structure.

Conclusions:

  • Findings support a gene-diet coevolutionary model where long-term carbohydrate environments influence T2D genetic susceptibility.
  • Emphasizes the significance of carbohydrate quality for metabolic health and the need for tailored dietary strategies.
  • Highlights that PRS are not suitable for individual clinical prediction, especially across diverse ancestries.