Genetic Mechanisms in Diabetes Pathogenesis

Diabetes mellitus is a prevalent metabolic disorder characterized by a complex interplay of genetic, environmental, and lifestyle factors. While it is commonly classified into type 1 (T1D) and type 2 diabetes (T2D), with additional monogenic forms such as Maturity Onset Diabetes of the Young (MODY) and neonatal diabetes, the genetic drivers of each differ markedly. T1D involves autoimmune pathways with strong HLA associations, whereas T2D is linked to polygenic variation affecting insulin action and beta-cell function. Advances in genome-wide association studies (GWAS), whole-genome sequencing, and functional genomics have identified numerous susceptibility loci, yet the biological interpretation of these findings and their integration into coherent models of disease pathogenesis remain incomplete. This Research Topic aims to address critical gaps in our understanding of how genetic variation contributes to the development and heterogeneity of diabetes. Despite growing genomic datasets, much remains unknown about the mechanisms by which specific variants influence risk, how they interact with environmental factors, and how they may explain differences across populations. This collection seeks to consolidate high-quality research that clarifies the molecular pathways shaped by genetic factors, supports cross-disciplinary integration, and promotes a deeper understanding of diabetes as a genetically complex disease. We invite original research, reviews, and perspectives addressing topics including but not limited to: • Rare and common genetic variants associated with T1D and T2D risk • Epigenetic regulation of insulin sensitivity and beta-cell function • Gene-environment and gene-lifestyle interactions in diabetes susceptibility • Regulatory networks, non-coding RNAs, and transcriptional control mechanisms • Cross-population genetic risk analyses • Multi-omics approaches to elucidate genetic contributions to beta-cell and insulin regulation By bringing together current research on the genetic drivers of diabetes, this collection aims to provide a comprehensive resource for understanding diabetes as a genetically influenced disease, and to support the development of robust predictive frameworks for disease risk and prevention.