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Measuring biological age using omics data.
Rutledge, Jarod; Oh, Hamilton; Wyss-Coray, Tony.
Afiliación
  • Rutledge J; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Oh H; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
  • Wyss-Coray T; Paul F. Glenn Center for the Biology of Ageing, Stanford University School of Medicine, Stanford, CA, USA.
Nat Rev Genet ; 23(12): 715-727, 2022 12.
Article en En | MEDLINE | ID: mdl-35715611
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Envejecimiento / Proteómica Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Humans Idioma: En Revista: Nat Rev Genet Asunto de la revista: GENETICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Envejecimiento / Proteómica Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Humans Idioma: En Revista: Nat Rev Genet Asunto de la revista: GENETICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos