MicroRNA single-nucleotide polymorphisms and diabetes mellitus: A comprehensive review

Yanfen Zhang1, Ruocen Bai1, Chong Liu2

  • 1Department of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.

Clinical Genetics
|December 12, 2018
PubMed

Insights

This review explores how variations in microRNA (miRNA) single-nucleotide polymorphisms influence diabetes mellitus (DM) development and complications. Understanding these genetic factors is crucial for improving DM treatment and prognosis.

Area of Science:

  • Genetics
  • Endocrinology
  • Molecular Biology

Background:

  • Diabetes mellitus (DM) is a leading global chronic non-communicable disease.
  • MicroRNAs (miRNAs) are implicated in DM development, with existing research focusing on expression profiles and mechanisms.
  • A gap exists in reviews summarizing the impact of miRNA single-nucleotide polymorphisms (SNPs) on DM.

Purpose of the Study:

  • To review the role of miRNA single-nucleotide polymorphisms in the development and complications of diabetes mellitus.
  • To highlight the significance of genetic variations in understanding DM pathogenesis.
  • To underscore the implications for DM treatment and prognosis.

Main Methods:

  • Comprehensive literature search for studies on miRNA SNPs and diabetes mellitus.
  • Analysis of existing research on miRNA expression and mechanisms in DM.
  • Synthesis of findings related to genetic variations in miRNA and their association with DM stages and complications.

Main Results:

  • miRNA SNPs represent a significant genetic factor in DM etiology.
  • Specific miRNA variations are linked to altered DM risk and progression.
  • Understanding miRNA SNP effects provides insights into DM pathophysiology.

Conclusions:

  • miRNA genetic variations, specifically SNPs, play a critical role in diabetes mellitus.
  • Further research into miRNA SNPs is essential for advancing DM understanding.
  • This knowledge has direct implications for personalized DM treatment strategies and predicting disease outcomes.

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