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 of what 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. While everyone ages due to the same processes, the speed at which these different processes occur can vary between individuals.
While individual biomarkers are good for measuring a certain aspect of aging in a very focused way, and they are indeed useful in this capacity, they do not give an overall picture of how someone is aging and where to focus preventative efforts [1].
The 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, while useful, it is not really an aging biomarker in the strict sense [14].
A system analysis approach to aging biomarkers
In order to get the bigger picture, we need to move beyond simple approaches 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 with a wider set of biomarkers, and curious people do 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 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 his or her health. 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
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[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.
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[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.
