Large-Scale Proteomics Improve Risk Prediction for Type 2 Diabetes
Diabetes Care, 2025
Xie R., Vlaski T., Trares K., Herder C., Holleczek B., Brenner H., Schöttker B.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Metabolic Diseases | Patient Stratification | Plasma |  Olink Target 96  Olink Explore 3072/384 |
Abstract
OBJECTIVE
This study evaluated the incremental predictive value of proteomic biomarkers in assessing 10-year type 2 diabetes risk when added to the clinical Cambridge Diabetes Risk Score (CDRS).
RESEARCH DESIGN AND METHODS
Data from 21,898 UK Biobank participants were used for model derivation and internal validation, and 4,454 Epidemiologische Studie zu Chancen der Verhütung, Früherkennung und optimierten Therapie chronischer Erkrankungen in der älteren Bevölkerung (ESTHER) cohort (Germany) participants were used for external validation. Proteomic profiling included the Olink Explore (2,085 proteins) and Olink Target 96 Inflammation panel (73 proteins).
RESULTS
Adding 15 proteins from Olink Explore or 6 proteins from the Olink Inflammation panel improved the C-index of the CDRS by 0.029 or 0.016 in internal validation with net reclassification of 23.0% and 29.0%, respectively. External validation was only conducted for the six-protein-extended model, and the C-index improved by 0.014.
CONCLUSIONS
The Olink Explore–based 15-protein model enhanced the CDRS model performance most, and this promising prediction model should be externally validated. Our successful external validation of the Olink Inflammation panel–based six-protein model shows that this is a promising endeavor.