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Measuring biological age using omics data

Nature Reviews Genetics volume 23, pages 715–727 (2022)Cite this article

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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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Fig. 1: Classes of ageing biomarkers.
Fig. 2: Select machine learning concepts important in the field of omics ageing clocks.
Fig. 3: Timeline of major advances and studies in ageing clocks research between 2008 and 2021.
Fig. 4: Associations between clock age gaps and ageing phenotypes.
Fig. 5: Measuring ageing across the body.

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Acknowledgements

This work was funded by the Stanford Alzheimer’s Disease Research Centers (AG047366), the National Institute on Ageing (AG072255), the Milky Way Research Foundation, the Stanford Graduate Fellowship (H.O. and J.R.), and the National Science Foundation Graduate Research Fellowship (H.O.).

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  1. These authors contributed equally: Jarod Rutledge, Hamilton Oh.

Authors and Affiliations

  1. Department of Genetics, Stanford University, Stanford, CA, USA

    Jarod Rutledge

  2. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Jarod Rutledge, Hamilton Oh & Tony Wyss-Coray

  3. Paul F. Glenn Center for the Biology of Ageing, Stanford University School of Medicine, Stanford, CA, USA

    Jarod Rutledge, Hamilton Oh & Tony Wyss-Coray

  4. Graduate Program in Stem Cell and Regenerative Medicine, Stanford University, Stanford, CA, USA

    Hamilton Oh

  5. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Tony Wyss-Coray

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Correspondence to Tony Wyss-Coray.

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T.W.-C. is a co-founder and scientific adviser of Alkahest and Qinotto. T.W.-C., J.R. and H.O. are co-founders and scientific advisers of Teal Omics.

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Glossary

Heterochronic parabiosis

An experimental paradigm where the circulatory systems of a young and old animal are surgically joined together.

Partial epigenetic reprogramming

Delivery of factors that can de-differentiate cells into induced pluripotent stem cells, typically short term, to de-age the epigenetic state of cells.

Biological age

The level of biological functioning of an organism, organ or cell as assessed in comparison to an expected level of function for a given chronological age.

Chronological age

The amount of time an organism has been alive for, typically measured in years for humans and tracked by birthdays.

Age gap

The difference between a biological age measurement and the expectation of that measurement for a given chronological age.

Overfit

When a machine learning model learns patterns that are actually the result of random noise in a dataset and which do not reflect the underlying distribution of the data.

LASSO

A ‘regularized’ linear regression algorithm that enforces an L1 norm penalty on regression parameters.

Linear discriminant analysis

A machine learning method used on categorical data that identifies linear hyperplanes in a dataset that can best split data into different groups, similar to the more commonly used logistic regression method.

r2

A metric that measures the amount of variance in the data that can be explained by a statistical model, ranging from cannot explain anything (r2 = 0) to perfect explanation (r2 = 1).

Black-box model

A model with parameters that cannot be easily interpreted or understood.

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Rutledge, J., Oh, H. & Wyss-Coray, T. Measuring biological age using omics data. Nat Rev Genet 23, 715–727 (2022). https://doi.org/10.1038/s41576-022-00511-7

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