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- Title
LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality.
- Authors
Li, Ruiyang; Chen, Wenben; Li, Mingyuan; Wang, Ruixin; Zhao, Lanqin; Lin, Yuanfan; Chen, Xinwei; Shang, Yuanjun; Tu, Xueer; Lin, Duoru; Wu, Xiaohang; Lin, Zhenzhe; Xu, Andi; Wang, Xun; Wang, Dongni; Zhang, Xulin; Dongye, Meimei; Huang, Yunjian; Chen, Chuan; Zhu, Yi
- Abstract
Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans. Age is closely related to health, but chronologically defined age often disagrees with biological age. Here, the authors develop an indicator of biological age - LensAge index - to reveal individuals' aging level, and it can be implemented with smartphones, showing potential for self-monitoring of aging.
- Subjects
DEEP learning; AGE; CRYSTALLINE lens; BIOINDICATORS; MORTALITY; EYE diseases; BLACKBERRIES
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-42934-8