利用组学数据测量生物年龄。
Measuring biological age using omics data.
机构信息
Department of Genetics, Stanford University, Stanford, CA, USA.
Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
出版信息
Nat Rev Genet. 2022 Dec;23(12):715-727. doi: 10.1038/s41576-022-00511-7. Epub 2022 Jun 17.
Abstract
Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.
摘要
年龄是老年人疾病和残疾的关键风险因素。为了解决与年龄相关的疾病并延长健康寿命，人们提出了针对衰老过程本身的目标，以“恢复”生理功能。然而，要实现这一目标，需要在分子水平上测量生物年龄和衰老速度。在高通量组学技术的最新进展的推动下，新一代测量生物衰老的工具现在可以实现分子分辨率下衰老的定量描述。可以利用表观基因组学、转录组学、蛋白质组学和代谢组学数据，结合机器学习，构建具有证明能力的“衰老时钟”，以识别生物衰老的新生物标志物。
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