Background: Diabetes mellitus and cardiovascular disorders are among the most prevalent and interrelated complex diseases worldwide, contributing significantly to global morbidity and mortality. Both conditions are influenced by a combination of genetic and environmental factors, with single nucleotide polymorphisms (SNPs) playing a critical role in disease susceptibility, progression, and therapeutic response. SNPs, the most common form of genetic variation in the human genome, can alter gene function or regulation, thereby impacting metabolic and cardiovascular pathways. Objectives: This study aims to investigate SNPs associated with diabetes and cardiovascular disorders using computational SNP analysis tools. Specific objectives include: a) Identifying significant SNPs linked to disease phenotypes, b) Annotating functional relevance of variants in coding and regulatory regions, c) Exploring shared and distinct genetic pathways between diabetes and cardiovascular disorders, d) Assessing the potential of SNP-based findings for precision medicine applications. Methodology: This study focuses on the application of SNP analysis to identify genetic variants associated with diabetes and cardiovascular disorders in humans. Using genome-wide association studies (GWAS) and specialized SNP tools such as Ensembl Variant Effect Predictor (VEP), the research aims to uncover significant SNPs, annotate their functional relevance, and explore their contribution to disease mechanisms. Quality control measures, statistical association testing, and pathway enrichment analyses are employed to ensure robust findings. Results: The study identified key SNPs in genes such as TCF7L2, PPARG, FTO, SLC30A8 for T2DM and APOE, PCSK9, IL6, NOS3 for CVD, along with several shared variants (APOE, PCSK9, and FTO) in pathways related to inflammation, insulin signalling, oxidative stress, and lipid metabolism. Conclusion: The integration of multi database variant evidence supports the hypothesis that overlapping genetic mechanisms contribute to metabolic syndrome and cardio metabolic complications. SNP analysis provides valuable insights into the genetic architecture of diabetes and cardiovascular disorders. The identification of shared and disease-specific variants underscores the interconnected nature of these conditions and highlights opportunities for targeted interventions. Integrating SNP data with bioinformatics tools enhances understanding of disease mechanisms and supports the development of personalized strategies for risk prediction, diagnosis, and therapy.
KUSUMA et al. (Thu,) studied this question.