Epigenetic Biomarker for Measuring Aging Through Fitness

A new biomarker for measuring biological aging based on physical fitness has been published in Aging, and it has been found to be useful in predicting health issues.

A clock with a different purpose

Epigenetic clocks are most notable for their direct relationship to biological age, but some have been trained on metrics such as mortality risk (GrimAge) [1] and the rate of aging (DunedinPACE) [2]. As would be expected, these aging biomarkers have been shown to be modifiable by biological factors. Obesity is related to epigenetic aging [3], and there are quantifiable differences in epigenetic markers between athletes and other people [4].

Since directly testing for fitness parameters often a time-consuming and difficult endeavor [5], this study’s authors decided to use such epigenetic differences to their advantage. Instead of seeing these differences as side effects or interesting data points, these researchers decided to create a clock with them, one that uses fitness as a basis for biological age.

Four metrics of fitness

To develop this clock, the researchers collected data from a new Budapest study along with the Baltimore Longitudinal Study on Aging and the Offspring cohort of the well-known Framingham Heart Study. They also conducted a validation analysis using six entirely different datasets, including the well-known CALERIE study. Some metrics were substituted in validation cohorts that did not include them.

Separate clocks were built on four metrics: gait speed, grip strength, a lung measurement of  forced expiratory volume in one second (FEV1), and VO2max, a key measurement of cardiovascular fitness. Combining these clocks together with GrimAge, the researchers created male and female versions of a unified clock, DNAmFitAge, and its acceleration-oriented counterpart, FitAgeAcceleration.

Validating each of these DNA metrics yielded results that were shown to be significantly, but only slightly, correlated in most of the cohorts. Most notably, results from the CALERIE trial were uncorrelated in this respect, which the researchers hypothesize is due to that trial’s stringent enrollment requirements. While these biomarkers were built around fitness, and are correlated with fitness, it appears that they cannot accurately measure the fitness of completely healthy people.

These particular biomarkers were also notable predictors of mortality risk. On average, having a handgrip strength one kilogram greater than peers of the same age and sex meant a 5% decrease in all-cause mortality risk. Gait speed and FEV were found to be good predictors of diabetes and comorbidities.

DNAmFitAge correlated fairly well with chronological age, with an r of 0.77. In some age-restricted cohorts, this correlation was much weaker, but over larger and broader datasets such as CALERIE, the correlation was stronger. Male bodybuilders were found to be an average of 2.74 years biologically younger according to DNAmFitAge, but there were too few female bodybuilders in the study to measure properly.

Conclusion

While this set of biomarkers isn’t perfect, this approach has its strengths. The researchers hold that FitAgeAcceleration is suitable as a supplement, rather than a replacement, for the corresponding GrimAge acceleration clock. They also note that this clock can serve as a strong motivator for people to become more physically active and so help preserve their health.

Literature

[1] Lu, A. T., Quach, A., Wilson, J. G., Reiner, A. P., Aviv, A., Raj, K., … & Horvath, S. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (albany NY), 11(2), 303.

[2] Belsky, D. W., Caspi, A., Corcoran, D. L., Sugden, K., Poulton, R., Arseneault, L., … & Moffitt, T. E. (2022). DunedinPACE, a DNA methylation biomarker of the pace of aging. Elife, 11, e73420.

[3] Horvath, S., Erhart, W., Brosch, M., Ammerpohl, O., von Schönfels, W., Ahrens, M., … & Hampe, J. (2014). Obesity accelerates epigenetic aging of human liver. Proceedings of the National Academy of Sciences, 111(43), 15538-15543.

[4] Spólnicka, M., Pośpiech, E., Adamczyk, J. G., Freire-Aradas, A., Pepłońska, B., Zbieć-Piekarska, R., … & Branicki, W. (2018). Modified aging of elite athletes revealed by analysis of epigenetic age markers. Aging (Albany NY), 10(2), 241.

[5] Huggett, D. L., Connelly, D. M., & Overend, T. J. (2005). Maximal aerobic capacity testing of older adults: a critical review. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 60(1), 57-66.