Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China; Department of Pharmacology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China.
Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China; CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Ageing Res Rev. 2021 Aug;69:101350. doi: 10.1016/j.arr.2021.101350. Epub 2021 Apr 30.
Healthy aging is the prime goal of aging research and interventions. Healthy aging or not can be quantified by biological aging rates estimated by aging clocks. Generation and accumulation of large scale high-dimensional biological data together with maturation of artificial intelligence among other machine learning techniques, have enabled and spurred the rapid development of various aging rate estimators (aging clocks). Here we review the data sources and compare the algorithms of recent human aging clocks, and the applications of these clocks in both researches and daily life. We envision that not only more and multiscale data on cross-sectional data will add momentum to the aging clock development, new longitudinal and interventional data will further raise the aging clock development to the next level to be trained by true biological age such as morbidity and mortality age.
健康老龄化是衰老研究和干预的首要目标。健康老龄化或非健康老龄化可以通过衰老钟估计的生物衰老率来量化。大量高维生物数据的产生和积累，以及人工智能等机器学习技术的成熟，使得各种衰老率估计器（衰老钟）得以迅速发展和推动。在这里，我们回顾了最近人类衰老钟的数据源和算法，并比较了这些时钟在研究和日常生活中的应用。我们设想，不仅更多和多尺度的横断面数据将为衰老钟的发展提供动力，新的纵向和干预数据将进一步将衰老钟的发展提升到一个新的水平，通过真正的生物年龄（如发病率和死亡率年龄）进行训练。
