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As human life expectancy has increased throughout the 20th and 21st centuries, this has led to a steady increase in the population of older people. With that increase has come the rise of age-related diseases and disabilities.

As a result, it is becoming ever more important to develop preventative strategies to monitor and maintain health, as well as therapies that directly address the various aging processes to delay or prevent the onset of age-related diseases.

One of the ways we can do this is by developing more effective ways to measure how someone is aging; this means developing high quality aging biomarkers. The challenge in creating such biomarkers has always been the fundamental question – what do we measure?

Chronological age is a poor indication of how someone might be aging and is not a good way to ascertain an individual’s risk factor for various age-related diseases. This is simply because everyone ages differently and at different rates. Whilst everyone ages due to the same processes, the speed at which these different processes occur can vary between individuals.

Whilst individual biomarkers are good for measuring a certain aspect of aging in a very focused way, and indeed they are useful in this capacity, they do not give an overall picture of how someone is aging and where to focus preventative efforts[1].

Literature is replete with examples of biomarkers that measure physical function, anabolic response, inflammation levels, and immune system aging[2-10].

Biomarkers have their limitations

Taken individually, these are useful but many biomarkers have their limitations. Biomarkers such as β-galactosidase, which is very popular among researchers investigating cellular senescence, has some limits, especially if used as the only or one of few biomarkers during an experiment[11].

Another popular biomarker of aging is the measurement of telomeres. However this also has some limitations, depending on the particular method used[12-13]. Indeed some studies have investigated its validity as an aging biomarker, and argue that whilst useful it is not really an aging biomarker in the strict sense[14].

A system analysis approach to aging biomarkers

So, in order to get the bigger picture, we need to move beyond this simple approach to a systems analysis approach that examines multiple biomarkers at once[15].

A number of approaches to this issue have been proposed and even tested. Arguably one of the most well-known methods for ascertaining biological age is the DNA methylation clock developed by Horvath, it can in many ways be considered the gold standard for aging biomarkers[16].

Other approaches that consider multiple biomarkers have also been proposed; such systems evaluate a number of biomarkers to give a ‘score’ as an overall indication of aging rate[17-20]. More recently a package of 19 biomarkers has been suggested as another approach to evaluating age[21].

There are numerous similar proposals in literature to evaluate aging on a wider set of biomarkers, and one does not have to search far to find them.

There is an urgent need to not only develop more accurate biomarkers, but also to package them into a systems analysis approach. This would allow researchers developing drugs and therapies that target the aging processes to ascertain efficacy to a much greater degree. It could also allow better monitoring of an individual’s health state and allow physicians to identify and address areas of concern to a far greater degree of accuracy.

Conclusion

The development of better biomarkers and systems capable of packaging them into compact solutions is very important to aging research. The rising popularity of health wearables and other personal health monitoring equipment also has the potential to allow the average person to take more control over their health too. 

Such approaches could be combined with other functional aging tests such as the H-Scan or the updated version being developed as part of a fundraising project at Lifespan.io. 

The development of biomarkers and systems that deliver them efficiently and at an affordable cost should therefore be a high priority.

Literature

[1] Karasik, D., Demissie, S., Cupples, L. A., & Kiel, D. P. (2005). Disentangling the genetic determinants of human aging: biological age as an alternative to the use of survival measures. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60(5), 574-587.

[2] Gruenewald, T. L., Seeman, T. E., Ryff, C. D., Karlamangla, A. S., & Singer, B. H. (2006). Combinations of biomarkers predictive of later life mortality. Proceedings of the National Academy of Sciences, 103(38), 14158-14163.

[3] Walston, J., Hadley, E. C., Ferrucci, L., Guralnik, J. M., Newman, A. B., Studenski, S. A., … & Fried, L. P. (2006). Research agenda for frailty in older adults: toward a better understanding of physiology and etiology: summary from the American Geriatrics Society/National Institute on Aging Research Conference on Frailty in Older Adults. Journal of the American Geriatrics Society, 54(6), 991-1001.

[4] Stenholm, S., Maggio, M., Lauretani, F., Bandinelli, S., Ceda, G. P., Di Iorio, A., … & Ferrucci, L. (2010). Anabolic and catabolic biomarkers as predictors of muscle strength decline: the InCHIANTI study. Rejuvenation research, 13(1), 3-11.

[5] Banerjee, C., Ulloor, J., Dillon, E. L., Dahodwala, Q., Franklin, B., Storer, T., … & Montano, M. (2011). Identification of serum biomarkers for aging and anabolic response. Immunity & Ageing, 8(1), 5.

[6] Franceschi, C., & Campisi, J. (2014). Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 69(Suppl 1), S4-S9.

[7] Bürkle, A., Moreno-Villanueva, M., Bernhard, J., Blasco, M., Zondag, G., Hoeijmakers, J. H., … & Gonos, E. S. (2015). MARK-AGE biomarkers of ageing. Mechanisms of ageing and development, 151, 2-12.

[8] Cohen, A. A., Milot, E., Li, Q., Bergeron, P., Poirier, R., Dusseault-Belanger, F., … & Fried, L. P. (2015). Detection of a novel, integrative aging process suggests complex physiological integration. PLoS One, 10(3), e0116489.

[9] Catera, M., Borelli, V., Malagolini, N., Chiricolo, M., Venturi, G., Reis, C. A., … & Ostan, R. (2016). Identification of novel plasma glycosylation-associated markers of aging. Oncotarget, 7(7), 7455.

[10] Peterson, M. J., Thompson, D. K., Pieper, C. F., Morey, M. C., Kraus, V. B., Kraus, W. E., … & Cohen, H. J. (2015). A novel analytic technique to measure associations between circulating biomarkers and physical performance across the adult life span. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, glv007.

[11] Yang, N. C., & Hu, M. L. (2005). The limitations and validities of senescence associated-β-galactosidase activity as an aging marker for human foreskin fibroblast Hs68 cells. Experimental gerontology, 40(10), 813-819.

[12] Montpetit, A. J., Alhareeri, A. A., Montpetit, M., Starkweather, A. R., Elmore, L. W., Filler, K., … & Collins, J. B. (2014). Telomere length: a review of methods for measurement. Nursing research, 63(4), 289.

[13] Bernadotte, A., Mikhelson, V. M., & Spivak, I. M. (2016). Markers of cellular senescence. Telomere shortening as a marker of cellular senescence. Aging (Albany NY), 8(1), 3.

[14] Der, G., Batty, G. D., Benzeval, M., Deary, I. J., Green, M. J., McGlynn, L., … & Shiels, P. G. (2012). Is telomere length a biomarker for aging: cross-sectional evidence from the west of Scotland?. PLoS One, 7(9), e45166.

[15] Zierer, J., Menni, C., Kastenmüller, G., & Spector, T. D. (2015). Integration of ‘omics’ data in aging research: from biomarkers to systems biology. Aging cell, 14(6), 933-944.

[16] Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome biology, 14(10), 3156.

[17] Levine, M. E. (2013). Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 68(6), 667-674.

[18] Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., … & Sugden, K. (2015). Quantification of biological aging in young adults. Proceedings of the National Academy of Sciences, 112(30), E4104-E4110.

[19] Peterson, M. J., Thompson, D. K., Pieper, C. F., Morey, M. C., Kraus, V. B., Kraus, W. E., … & Cohen, H. J. (2015). A novel analytic technique to measure associations between circulating biomarkers and physical performance across the adult life span. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, glv007.

[20] Lara, J., Cooper, R., Nissan, J., Ginty, A. T., Khaw, K. T., Deary, I. J., … & Mathers, J. C. (2015). A proposed panel of biomarkers of healthy ageing. BMC medicine, 13(1), 222.

[21] Sebastiani, P., Thyagarajan, B., Sun, F., Schupf, N., Newman, A. B., Montano, M., & Perls, T. T. (2017). Biomarker signatures of aging. Aging Cell.

 

 

 

 

 

CategoryBlog
About the author

Steve Hill

As a scientific writer and a devoted advocate of healthy longevity technologies Steve has provided the community with multiple educational articles, interviews and podcasts, helping the general public to better understand aging and the means to modify its dynamics. His materials can be found at H+ Magazine, Longevity reporter, Psychology Today and Singularity Weblog. He is a co-author of the book “Aging Prevention for All” – a guide for the general public exploring evidence-based means to extend healthy life (in press).

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