Xia Xian, Chen Weiyang, McDermott Joseph, Han Jing-Dong Jackie
Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
School of Information, Qilu University of Technology, Jinan, China.
F1000Res. 2017 Jun 9;6:860. doi: 10.12688/f1000research.10692.1. eCollection 2017.
Individuals of the same age may not age at the same rate. Quantitative biomarkers of aging are valuable tools to measure physiological age, assess the extent of 'healthy aging', and potentially predict health span and life span for an individual. Given the complex nature of the aging process, the biomarkers of aging are multilayered and multifaceted. Here, we review the phenotypic and molecular biomarkers of aging. Identifying and using biomarkers of aging to improve human health, prevent age-associated diseases, and extend healthy life span are now facilitated by the fast-growing capacity of multilevel cross-sectional and longitudinal data acquisition, storage, and analysis, particularly for data related to general human populations. Combined with artificial intelligence and machine learning techniques, reliable panels of biomarkers of aging will have tremendous potential to improve human health in aging societies.
同龄个体的衰老速度可能不尽相同。衰老的定量生物标志物是衡量生理年龄、评估“健康衰老”程度以及潜在预测个体健康寿命和寿命的宝贵工具。鉴于衰老过程的复杂性，衰老的生物标志物是多层次、多方面的。在此，我们综述衰老的表型和分子生物标志物。目前，多层次横断面和纵向数据采集、存储及分析能力的快速增长，特别是与普通人群相关的数据，有助于识别和使用衰老生物标志物来改善人类健康、预防与年龄相关的疾病并延长健康寿命。结合人工智能和机器学习技术，可靠的衰老生物标志物组合在老龄化社会中改善人类健康方面将具有巨大潜力。
