Biomarkers of aging

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Biomarkers of aging are biomarkers that could predict functional capacity at some later age better than will chronological age.[1] Stated another way, biomarkers of aging would give the true "biological age", which may be different from the chronological age.

Validated biomarkers of aging would allow for testing interventions to extend lifespan, because changes in the biomarkers would be observable throughout the lifespan of the organism.[1] Although maximum lifespan would be a means of validating biomarkers of aging, it would not be a practical means for long-lived species such as humans because longitudinal studies would take far too much time.[2] Ideally, biomarkers of aging should assay the biological process of ageing and not a predisposition to disease, should cause a minimal amount of trauma to assay in the organism, and should be reproducibly measurable during a short interval compared to the lifespan of the organism.[1]

Although graying of hair increases with age,[3] hair graying cannot be called a biomarker of ageing. Similarly, skin wrinkles and other common changes seen with aging are not better indicators of future functionality than chronological age. Biogerontologists have continued efforts to find and validate biomarkers of aging, but success thus far has been limited. Levels of CD4 and CD8 memory T cells and naive T cells have been used to give good predictions of the expected lifespan of middle-aged mice.[4]

Advances in big data analysis allowed for the new types of "aging clocks" to be developed. The epigenetic clock is a promising biomarker of aging and can accurately predict human chronological age.[5] Basic blood biochemistry and cell counts can also be used to accurately predict the chronological age.[6] Further studies of the hematological clock on the large datasets from South Korean, Canadian, and Eastern European populations demonstrated that biomarkers of aging may be population-specific and predictive of mortality.[7] It is also possible to predict the human chronological age using the transcriptomic clock.[8]

A broader acceptance of biomarkers of aging, however, will depend on a better understanding of the observed correlations to the incidence of specific diseases, improved transferability of the models across populations and reduction of costs of the studies. The recent introduction of low-power and compact sensors, based on micro-electromechanical systems (MEMS) has led to a new breed of the wearable and affordable devices providing unparalleled opportunities for the collecting and cloud-storing personal digitized activity records. Consequently, modern deep learning techniques could be used to produce a proof-of-concept digital biomarker of age in the form of all-causes-mortality predictor from a sufficiently large collection of one week long human physical activity streams augmented by the rich clinical data (including the death register, as provided by, e.g., the NHANES study).[9]

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References[edit]

  1. ^ a b c George T. Baker, III and Richard L. Sprott (1988). "Biomarkers of aging". Experimental Gerontology. 23 (4–5): 223–239. doi:10.1016/0531-5565(88)90025-3. PMID 3058488.
  2. ^ Harrison, Ph.D., David E. (November 11, 2011). "V. Life span as a biomarker". Jackson Laboratory. Archived from the original on April 26, 2012. Retrieved 2011-12-03.
  3. ^ Van Neste D, Tobin DJ (2004). "Hair cycle and hair pigmentation: dynamic interactions and changes associated with aging". MICRON. 35 (3): 193–200. doi:10.1016/j.micron.2003.11.006. PMID 15036274.
  4. ^ Miller RA (2001). "Biomarkers of aging: prediction of longevity by using age-sensitive T-cell subset determinations in a middle-aged, genetically heterogeneous mouse population". Journals of Gerontology. 56 (4): B180–B186. doi:10.1093/gerona/56.4.b180. PMID 11283189.
  5. ^ Horvath S (2013). "DNA methylation age of human tissues and cell types". Genome Biology. 14 (10): R115. doi:10.1186/gb-2013-14-10-r115. PMC 4015143. PMID 24138928.
  6. ^ Zhavoronkov A (2016). "Deep biomarkers of human aging: Application of deep neural networks to biomarker development". Aging. 8 (5): 1021–33. doi:10.18632/aging.100968. PMC 4931851. PMID 27191382.
  7. ^ Polina Mamoshina, Kirill Kochetov, Evgeny Putin, Franco Cortese, Alexander Aliper, Won-Suk Lee, Sung-Min Ahn, Lee Uhn, Neil Skjodt, Olga Kovalchuk, Morten Scheibye-Knudsen, Alex Zhavoronkov (2018). "Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations". The Journals of Gerontology: Series A. 6 (11): 1482–1490. doi:10.1093/gerona/gly005. PMC 6175034. PMID 29340580 – via (text: link).
  8. ^ Peters M (2015). "The transcriptional landscape of age in human peripheral blood". Nature Communications. 6: 8570. doi:10.1038/ncomms9570. PMC 4639797. PMID 26490707.
  9. ^ Tim Pyrkov, Konstantin Slipensky, Mikhail Barg, Alexey Kondrashin, Boris Zhurov, Alexander Zenin, Mikhail Pyatnitskiy, Leonid Menshikov, Sergei Markov, and Peter O. Fedichev (2018). "Extracting biological age from biomedical data via deep learning: too much of a good thing?". Scientific Reports. 8 (1): 5210. doi:10.1038/s41598-018-23534-9. PMC 5980076. PMID 29581467.

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