Publications

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.