Title:Deciphering the Genetic Landscape: Exploring the Relationship Between HLA-DQA1, HLA-DQB1, and HLA-DRB1 Genes in Diabetes Mellitus
Volume: 21
Issue: 3
Author(s): Kuldeep Singh*, Jeetendra Kumar Gupta, Dilip Kumar Chanchal, Shahbaz Khan, Arti Varma, Kumari Shanno, Shivendra Kumar and Shamim
Affiliation:
- Department of Pharmacology, Institute of Pharmaceutical Research, GLA University, Mathura, Uttar Pradesh, India
Keywords:
Diabetes mellitus, genetic predisposition, human leukocyte antigen, polymorphisms, immune response, beta-cell function.
Abstract: Diabetes mellitus (DM) is a complex and multifactorial metabolic disorder
with a significant genetic component. The human leukocyte antigen (HLA) genes, specifically
HLA-DQA1, HLA-DQB1, and HLA-DRB1, have been implicated in the susceptibility
and pathogenesis of DM. This review delves into the intricate interplay of these
HLA genes, seeking to unravel the genetic tapestry that contributes to the development
and progression of diabetes. We begin by providing an overview of the HLA system and
its critical role in immune regulation. Subsequently, we explore the current state of
knowledge regarding the association between HLA-DQA1, HLA-DQB1, and HLADRB1
polymorphisms and susceptibility to both type 1 and type 2 diabetes. Emphasis is
placed on recent advancements in genetic research methodologies, including genomewide
association studies and next-generation sequencing, that have provided deeper insights
into the genetic architecture of DM. The review also scrutinizes the functional implications
of specific HLA alleles in modulating immune responses and the potential
mechanisms by which they contribute to the autoimmune processes observed in type 1 diabetes.
Additionally, we examine the role of HLA genes in the context of insulin resistance
and beta-cell dysfunction in type 2 diabetes, shedding light on the shared and distinct
genetic underpinnings of these two major forms of DM. Furthermore, we discuss the
clinical implications of HLA genotyping in predicting disease risk, prognosis, and personalized
treatment strategies. The integration of genetic information into clinical practice
holds promise for precision medicine approaches in diabetes management.
