Why the study?
Type 2 diabetes causes multisystem complications, but comprehensive multi-omics analyses across diverse clinical outcomes remain limited.
Do proteomic and metabolomic predictive models improve risk prediction for major clinical outcomes in patients with type 2 diabetes compared to the SCORE2-Diabetes clinical model?
Population
Individuals with proteomic and metabolomic data in the UK Biobank
Comparison
LASSO-based predictive models vs SCORE2-Diabetes
Design
Cross-sectional and longitudinal analysis
Key result
Protein-based predictive models substantially outperformed clinical models for predicting Type 2 diabetes complications, achieving a median delta C-index of 0.117.
Authors
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May improve T2D complication risk prediction; leaves open prospective validation before clinical adoption.
Observational
Do proteomic and metabolomic predictive models improve risk prediction for major clinical outcomes in patients with type 2 diabetes compared to the SCORE2-Diabetes clinical model?
Effect estimate: median delta C-index 0.117
A simplified panel of 200 proteins significantly improves the prediction of multisystem complications in type 2 diabetes compared to standard clinical models like SCORE2-Diabetes.
Hongqiang Zhang (2026) conducted an observational in Type 2 diabetes. Proteomic and metabolomic profiles vs. SCORE2-Diabetes clinical model was evaluated on Predictive performance for major T2D-related clinical outcomes (median delta C-index 0.117). Protein-based predictive models substantially outperformed clinical models for predicting Type 2 diabetes complications, achieving a median delta C-index of 0.117.