StackAge: an ensemble-based clock for precise quantification of biological age using multi-omics data
Yingyi Jiang, Lei Jia, Yuan Fei, Xiaoguang Li, Xiaoqi Zheng, Yufang Qin · 2026
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Description
Your biological age, read from blood molecules, can outrun or lag your birthday count, and the gap predicts disease. Yingyi Jiang and co-authors build StackAge, a 2026 clock that stacks protein and metabolite profiles from over thirty thousand UK Biobank participants.
The ensemble matched chronological age closely and sharpened risk forecasts for twelve chronic diseases. For diabetes, Alzheimer disease, and kidney disease its discrimination passed a high bar. Adding each person's aging speed improved forecasts beyond raw molecule profiles and basic demographics. The strongest signals clustered in inflammation, metabolic stress, and tissue-remodeling pathways. Lifestyle factors appeared to push the clock forward, a hint that the pace is partly changeable.
Catalog record
| CREATOR | Yingyi Jiang, Lei Jia, Yuan Fei, Xiaoguang Li, Xiaoqi Zheng, Yufang Qin |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 39 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |