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Measuring biological age using omics data
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AI
DOI：10.1038/s41576-022-00511-7.png)
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Abstract
En 中文
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. Molecular measures of biological ageing based on high-throughput omics technologies are enabling the quantitative characterization of ageing. The authors review how epigenomic, transcriptomic, proteomic, metabolomic and other omics data can be harnessed using machine learning to build 'ageing clocks'.
Keywords:
SENESCENT CELLS
LIFE-SPAN
METHYLATION LEVELS
EPIGENETIC AGE
T-CELLS
BIOMARKER
HEALTH
REJUVENATION
GENETICS
IMMUNODEFICIENCY