Syeda Umme Fahmida Malik1, A.T Reza Ahmed2, Begum Nazmus Sama Shimu3, Mushfika Rahman Chowdhury4
Professor & Head, Department of Biochemistry, North East Medical College, Sylhet, Bangladesh
Consultant, Department of Paediatric, Ibn Sina Hospital, Sylhet, Bangladesh
Associate Professor, Department of Biochemistry, North East Medical College, Sylhet, Bangladesh
Associate Professor, Department of Biochemistry, North East Medical College, Sylhet, Bangladesh
Abstract
Type 2 diabetes mellitus (T2DM) is a multifactorial disease influenced by genetic and environmental factors, with insulin resistance playing a crucial role in its pathogenesis. Single nucleotide polymorphisms (SNPs) in the insulin receptor (INSR) gene can significantly impact receptor function, thereby altering insulin signaling and increasing susceptibility to diabetes. This study investigates the impact of SNPs in the INSR gene by analyzing receptor gene sequences and predicting the effects of mutations using bioinformatics tools. These mutations are associated with altered transcriptional regulation and may contribute to INSR-mediated metabolic disorders. These findings provide insights into the genetic basis of insulin resistance and highlight the significance of SNP analysis for personalized diabetes risk assessment and targeted therapeutic approaches.
Type 2 diabetes mellitus (T2DM) is a multifactorial disease influenced by genetic and environmental factors, with insulin resistance playing a crucial role in its pathogenesis. Single nucleotide polymorphisms (SNPs) in the insulin receptor (INSR) gene can significantly impact receptor function, thereby altering insulin signaling and increasing susceptibility to diabetes. This study investigates the impact of SNPs in the INSR gene by analyzing receptor gene sequences and predicting the effects of mutations using bioinformatics tools. These mutations are associated with altered transcriptional regulation and may contribute to INSR-mediated metabolic disorders. These findings provide insights into the genetic basis of insulin resistance and highlight the significance of SNP analysis for personalized diabetes risk assessment and targeted therapeutic approaches.