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 thee 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] It is also possible to predict the human chronological age using the transcriptomic aging clocks.[7]

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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. PMID 3058488. doi:10.1016/0531-5565(88)90025-3. 
  2. ^ Harrison, Ph.D., David E. (November 11, 2011). "V. Life span as a biomarker". Jackson Laboratory. 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. PMID 15036274. doi:10.1016/j.micron.2003.11.006. 
  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. PMID 11283189. doi:10.1093/gerona/56.4.b180. 
  5. ^ Horvath S (2013). "DNA methylation age of human tissues and cell types". Genome Biology. 14 (10): R115. PMC 4015143Freely accessible. PMID 24138928. doi:10.1186/gb-2013-14-10-r115. 
  6. ^ Zhavoronkov A (2016). "Deep biomarkers of human aging: Application of deep neural networks to biomarker development". Aging. 8 (5): 1021. PMC 4931851Freely accessible. PMID 27191382. doi:10.18632/aging.100968. 
  7. ^ Peters M (2015). "The transcriptional landscape of age in human peripheral blood". 

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