Design and In Silico Validation of Primers Targeting Diabetes-Associated Genetic Regions (ABCC8, IGF2BP2, and HNF4A)

Introduction: Diabetes mellitus is a growing global health concern with an alarming rise in prevalence, particularly in developing countries. Early identification of individuals with increased genetic susceptibility is critical to enable timely lifestyle or therapeutic interventions and reduce the progression to type 2 diabetes. This study focuses on the design and in silico validation gene-specific primers targeting three key diabetes-associated genetic regions in the genes ABCC8, IGF2BP2, and HNF4A, as potential molecular markers for genetic risk screening of prediabetes. Methods: Candidate genes were identified through a comprehensive literature review and evidence from genome-wide association and functional studies linking them to β-cell function and glucose regulation. Gene sequences associated with reported diabetes-linked variants were retrieved from GenBank, and primers were designed using Primer3 software based on defined parameters including melting temperature and GC content and evaluated using NCBI Primer- BLAST and UCSCin silico PCR simulations to verify amplification specificity and expected amplicon profiles. Results: The selected primer pairs demonstrated optimal thermodynamic parameters and high specificity, consistently yielding single, gene-specific amplicons in in silico validation. No significant off-target amplification was observed, supporting their suitability for targeted genetic screening applications. Discussion: The findings highlight the potential of integrating bioinformatics-driven primer design with molecular diagnostics to support early identification of diabetic predisposition. The study emphasises a qualitative screening approach based on the amplification of gene regions associated with reported variants rather than quantitative risk prediction. Conclusion: This proof-of-concept study demonstrates the feasibility of designing and validating gene-specific primers as components of a PCR-based panel for the analysis of diabetes-associated genetic regions, with potential applications in pre-diabetes research. The proposed approach provides a foundation for developing cost-effective and accessible tools for early-stage risk identification, particularly in resource-limited settings.