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The Genetic Landscape of Diabetes Mellitus: Lessons from Monogenic and Polygenic Forms
Davide Nilo1, Roberto Nilo2, Marta Chiara Sircana3
1Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli'', 80138 Naples, Italy.
None:
Diabetes mellitus is increasingly recognized as a biologically heterogeneous disorder that extends beyond traditional phenotype-based classifications. Advances in human genetics have revealed that monogenic and polygenic forms of diabetes are not discrete entities, but rather represent points along a continuum of genetic architectures that converge on shared molecular pathways governing pancreatic β-cell identity, function, and survival. Rare monogenic forms, including maturity-onset diabetes of the young and neonatal diabetes, arise from highly penetrant single-gene defects that directly impair transcriptional regulation, glucose sensing, insulin biosynthesis, or stimulus-secretion coupling. Although individually uncommon, these disorders provide high-resolution models of β-cell dysfunction and have demonstrated the clinical value of genotype-guided diagnosis and therapy. At the opposite end of the spectrum, type 1 and type 2 diabetes result from complex interactions between multiple genetic variants and environmental factors, with genome-wide association studies highlighting a central role for genetically determined β-cell vulnerability alongside immune-mediated and metabolic stress pathways. Importantly, intermediate phenotypes such as latent autoimmune diabetes in adults further illustrate the overlap between autoimmune and metabolic mechanisms, challenging rigid diagnostic boundaries. This review synthesizes current evidence on the genetic architecture of diabetes across monogenic and polygenic forms, emphasizing convergent molecular mechanisms and their translational implications. By integrating insights from rare genetic disorders with findings from large-scale population studies, we propose a continuum-based framework that supports a shift from phenotype-driven labels toward a mechanistic, biology-informed approach to diabetes classification, risk stratification, and personalized care.
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