Researchers have developed SASP Score, a deep learning–derived biomarker that interprets circulating senescence-associated secretory proteins in blood as a single measure of systemic senescent cell burden. Because senescent cells drive chronic inflammation, tissue dysfunction, and elevated cancer risk with age, a blood-based quantification offers a practical way to track this burden and to assess whether interventions targeting it are working.
Key Points
- Deep learning model scores circulating SASP proteins as one burden metric
- Senescent cells spread dysfunction through inflammatory secreted signals
- Standard blood draw supplies all input data required
Longevity Analysis
Senescent cell accumulation sits upstream of much of the low-grade inflammation that degrades immune competence, vascular function, and tissue repair capacity with age. A composite blood readout converts a diffuse, multi-tissue process into an interpretable signal, allowing clinicians to distinguish inflammatory noise from genuine senescent burden and to verify whether senolytic strategies, exercise protocols, or sleep and metabolic corrections actually reduce it. Value comes from serial measurement over time rather than a single result, since the metric is most informative as a trajectory.
Original published by LifeSpan.io, by Josh Conway.