Abstract

Type 2 diabetes (T2D) represents a growing global health challenge, with early detection of at-risk individuals 
being critical for prevention and management. Traditional risk factors and biomarkers, while informative, have 
limited predictive accuracy, particularly for younger populations. Multi-omic risk scores integrating genomic, 
transcriptomic, proteomic, metabolomic, and epigenomic data offer a promising avenue for refined risk prediction. 
Evidence to date indicates moderate analytical and clinical validity, with incremental predictive value over 
conventional risk factors demonstrated in large population-based cohorts such as Whitehall II and LifeLines. 
Challenges remain regarding standardization, cross-platform integration, population transferability, and 
implementation at scale. Ethical, legal, and social considerations, as well as cost-effectiveness, must guide 
deployment. Integration with electronic health systems and ongoing pilot studies are essential to establish 
population-level clinical utility and to inform public health policy for targeted T2D prevention.