Abstract Background Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinic

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Large-scale multi-omics enhance risk prediction for type 2 diabetes (2026) · doi

  Abstract Background Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear.

  Keywords: risk abstract background polygenic scores metabolomics proteomics promise improving type diabetes prediction combined utility beyond
• Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations (2024) · doi

  Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients.

  Keywords: prss predictive performance polygenic risk scores improved several challenges remain addressed implemented clinic including reduced
• Quantifying the utility of type 2 diabetes polygenic risk score for predicting incident diabetes: an analysis of large US-based cohort studies (2026) · doi

  Abstract Background Genome-wide association analyses (GWASs) have identified numerous genetic variants associated with type 2 diabetes, but the utility of polygenic risk scores (PRSs) derived from these associations for predicting future incident diabetes remains uncertain.

  Keywords: diabetes abstract background genome wide association analyses gwass identified numerous genetic variants associated type utility