Proteomic clocks combined with deep learning phenotypes track eye aging and diseases
Shaopeng Yang, Zhuoyao Xin, Huangdong Li, Zhuoting Zhu, Ziyu Zhu, Yanping Chen, Yongjie Zhang, Ruilin Xiong, Minwen Zhou, Mingguang He, Wei Wang · 2026
Read Proteomic clocks combined with deep learning phenotypes track eye aging and diseases by Shaopeng Yang, Zhuoyao Xin, Huangdong Li, Zhuoting Zhu, Ziyu Zhu, Yanping Chen, Yongjie Zhang, Ruilin Xiong, Minwen Zhou, Mingguang He, Wei Wang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13572482.txt.
Description
Shaopeng Yang and colleagues built protein-based clocks that track how fast eyes age, and whether eye disease is on the way. Chronological age misleads, since some very old eyes stay healthy while younger ones fail. Drawing on three large cohorts of more than 55,000 people across countries and ethnicities, their 2026 study combined high-throughput blood-protein measures with deep-learning analysis of eye images.
People with cataract, diabetic retinopathy, macular degeneration, or glaucoma showed premature protein ageing, and the clock predicted those diseases beyond age alone. A streamlined, cheaper version kept its performance. Linking accelerated ageing to both nerve and blood-vessel decline in the retina, the authors present protein ageing as a scalable tool for eye-health screening.
Catalog record
| CREATOR | Shaopeng Yang, Zhuoyao Xin, Huangdong Li, Zhuoting Zhu, Ziyu Zhu, Yanping Chen, Yongjie Zhang, Ruilin Xiong, Minwen Zhou, Mingguang He, Wei Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 67 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |