MANY MINDEDBlood test for senescent cell burden
Lifespan.io developed SASP Score—a deep learning model using blood proteins to measure senescent cell burden. It analyzes 38 proteins from the UK Biobank Pharma Proteomics Project’s 54,219 participants (50,997 analyzed), with 85% of data training the model and 15% validating it. Higher scores correlate with 1.4x increased all-cause mortality risk and elevated risks for dementia, stroke, and chronic kidney disease. Crucially, it tracks biological aging without chronological age and shows regular exercise maintains stable scores over 18 months in healthy older adults.
This metric moves health abundance by enabling earlier intervention for age-related diseases. By quantifying cellular senescence—a key driver of aging—SASP Score could help identify at-risk individuals before symptoms emerge, potentially reducing long-term costs for conditions like dementia and heart disease. It avoids the need for invasive tissue tests, making biological aging assessment more accessible.
However, external validation remains needed as the model was built solely on UK Biobank data. It does not predict cancer risk (showing inverse or no correlation in some cases) and focuses only on healthy older adults without specific diseases. The metric also measures cellular senescence, not overall biological aging, limiting its scope. Next steps include testing SASP Score across diverse populations to confirm its predictive power for real-world health outcomes.
Source: Lifespan.io