Large-Scale Proteomics Improve Risk Prediction for Type 2 Diabetes.
Summary
In UK Biobank, adding a 15-protein panel (Olink Explore) to CDRS improved discrimination (ΔC-index 0.029) with substantial net reclassification. A simpler 6-protein inflammation panel improved discrimination (ΔC-index 0.016) and replicated in ESTHER (ΔC-index 0.014), supporting translational potential.
Key Findings
- A 15-protein Olink Explore panel improved CDRS discrimination by 0.029 with 23% net reclassification.
- A 6-protein inflammation panel improved CDRS by 0.016 and externally validated in ESTHER (ΔC-index 0.014).
- Both proteomic models outperformed CDRS alone, supporting biomarker-enhanced risk prediction.
Clinical Implications
Proteomic panels could refine risk stratification beyond clinical factors, guiding earlier interventions and trial enrichment; further external validation and cost-effectiveness analyses are needed.
Why It Matters
Demonstrates that targeted proteomics can meaningfully enhance established clinical risk scores, advancing precision prevention strategies for type 2 diabetes.
Limitations
- External validation performed only for the 6-protein model; the 15-protein model needs replication.
- Clinical utility and cost-effectiveness were not directly assessed.
Future Directions
Prospective impact studies, multi-ancestry validation, integration with genomics and metabolomics, and implementation studies assessing clinical utility and cost-effectiveness.
Study Information
- Study Type
- Cohort
- Research Domain
- Prognosis
- Evidence Level
- II - Large-scale cohort derivation with internal validation and independent external validation for prognostic model performance.
- Study Design
- OTHER