Genetic Profiling and Early Detection of Type 2 Diabetes Subtypes through Sex-Stratified GWAS and Explainable AI

Type 2 diabetes (T2D) is a complex and clinically heterogeneous disease. Although clustering approaches have defined clinical subtypes, their genetic and molecular architectures remain poorly understood. Efforts using genome-wide association studies (GWAS) have been constrained by modest sample sizes and conservative significance thresholds, limiting subgroup resolution. Leveraging recent expansions in cohort scale and diversity, we developed a new analytical framework that integrates GWAS with Machine Learning and eXplainable Artificial Intelligence. Applied to the UK Biobank, this strategy enabled the first comprehensive genetic and molecular characterization of T2D subtypes, identifying 184 genes that define each subgroup’s molecular landscape and associated tissue profiles. In addition, the framework improved detection of T2D risk compared to current Polygenic Risk Scores. The findings underscore the importance of clinical stratification to uncover the complex pathophysiology of T2D and to pave the way for more precise prevention and treatment strategies.