Measuring biological age using omics data

By J. Rutledge, H. Oh, T. Wyss‐Coray. Nature reviews genetics, 2022

434 citations recorded by the source corpus.

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’.

Subfields: Health, Toxicology and Mutagenesis, Molecular Biology, Physiology

View Paper: https://www.semanticscholar.org/paper/b6574ba4996308aeac4ad56a49e7c14457fec996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yanan Wang
2. Wanying Zhang
3. He Huang
4. Xia Li
5. Chentao Li
6. P. Qian

Citations in the corpus

• Inflammation and aging: signaling pathways and intervention therapies (2023, background): “This illustrates the ability of machine learning to identify novel biomarkers of biological aging and provides a boost to early warning and intervention in aging and precision medicine strategies.(372) In 2022, Li et al.”