A multi-omics ensemble approach combines plasma proteomics and metabolomics to quantify biological age and predict disease risk.
Key Takeaways
- StackAge predicted chronological age with high accuracy, achieving Pearson r ≈ 0.93
- Model improved risk prediction for 12 chronic diseases, with AUCs exceeding 0.90
- Estimated aging rates added predictive value beyond conventional omics and demographic features