2022,
Nature Reviews Genetics.
DOI:
10.1038/s41576-022-00511-7
Measuring biological age using omics data
Jarod Rutledge
, Hamilton Oh
, Tony Wyss-Coray
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.
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2023-03-31 16:57:00
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#paper doi：https://doi.org/10.1038/s41576-022-00511-7 Measuring biological age using omics data.
生物老化是随着实际年龄的增长而发生的系统完整性进行性下降 ，最终导致疾病、残疾和死亡。本文是利用组学数据量化生物衰老方法（衰老时钟）研究的综述文章，介绍了表观遗传、转录组学、蛋白质组学、代谢组学等方面衰老时钟的研究原理、局限性和优化。比较有意思的一点，第一代表观时钟彼此之间只有轻微的相关性，增大样本量的情况下训练第一代甲基化时钟过拟合，又消除了年龄和生物学年龄之间的联系。这里提出对之后开发衰老时钟的展望，定义衰老时钟的应用场景，通过有目的的特征选择或通过开发复合训练指标，将衰老生物学的特定方面纳入其中的建模方法应有助于提高模型的可解释性，并指导它们识别衰老的因果特征。一个大胆的方法是在时钟的训练中排除年龄，从而更接近于测量老化生物学特征。