A deep-learning based biomarker of systemic cellular senescence burden to predict mortality and health outcomes
A blood-based aging score predicts disease risk and responds to exercise, offering a measurable target for healthspan interventions.
Researchers developed a deep learning-derived SASP Score from UK Biobank proteomics that predicts mortality and multiple chronic diseases including dementia, COPD, and stroke. The score was responsive to exercise intervention in an independent clinical trial, supporting its use as a geroscience biomarker.
What the study was
- Study design
- Biomarker development + external validation
- Population
- UK Biobank + independent RCT cohort
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- medRxiv
Why it surfaced
Innovative deep learning biomarker for cellular senescence burden with UK Biobank development and RCT validation. High novelty. Preprint cap limits score to 7.
A plain-language summary of published research — not medical advice. Talk to a clinician about your own care.