Large-scale proteomics improve risk prediction for type 2 diabetes

<p dir="ltr">OBJECTIVE</p><p dir="ltr">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). </p><p dir="ltr">RESEARCH DESIGN AND METHODS </p><p dir="ltr">Data from 21,898 UK Biobank participants were used for model derivation and internal validation, and 4,454 ESTHER cohort (Germany) participants for external validation. Proteomic profiling included the OLINK-Explore (2,085 proteins) and OLINK-Target-96-Inflammation panel (73 proteins). </p><p dir="ltr">RESULTS</p><p dir="ltr">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 6-protein-extended model and the C-index improved by 0.014. </p><p dir="ltr">CONCLUSIONS</p><p dir="ltr">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 6-protein-model shows that this is a promising endeavor.</p>