To learn more about how to use and implement a variety of methods for measuring and analyzing biological age, join Professor Lauren Gaydosh for Analysis of Biological Aging on June 10-13.
Everybody wants to lead happy and healthy lives. Typically, people go about this by adopting or modifying behaviors intended to improve health – like cutting back on alcohol, quitting smoking, eating better, and moving more. While such behaviors are undoubtedly good for you, can they actually make you younger?
The desire to prevent aging is a longstanding human pursuit, but in the last decade science has made tangible progress towards this goal by developing and refining ways to measure biological age. Biological age refers to the aging of cellular and molecular processes, and it does not always correspond to chronological (i.e. calendar) age. Methods for measuring biological aging have been improving in their predictive accuracy, and are cheaper and more accessible than ever before. In fact, for $400 you can now mail in a cheek swab and receive your own biological age (but should you?). And signs point to this trend continuing. Just consider that a recent competition will award hundreds of thousands of dollars to teams that can calculate measures of biological age that are best able to predict chronological age, mortality, and healthspan.
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As demonstrated by the biomarkers of aging challenge, there are a variety of ways to calculate biological age. The two main distinguishing characteristics of different measures of biological age are first, the data that are used as inputs, and second, the outcome that the data are used to predict. (A third key characteristic is the diversity and representation of the samples on which such measures are developed, as discussed in a previous post). With respect to the first characteristic, the most common inputs are biomarker measurements, oftentimes from blood samples. Given reduced costs of assays and increasing availability of molecular data, DNA methylation specifically is currently the most popular biomarker input, with the field generating dozens of algorithms, or “clocks”, to quantify biological age. With respect to the second characteristic, the selected inputs can be used to predict various outcomes. The field began by predicting chronological age, but has since progressed to predict events like mortality, morbidity, and physical and cognitive decline. Regardless of the chosen inputs and predicted outcomes, virtually all measures of biological age share a common motivation: to provide a quantification of biological risk that can be assessed without waiting for age-related outcomes to occur.
Measuring biological age also makes it possible to evaluate the efficacy of anti-aging and health promoting interventions. What is the current evidence that such interventions can reduce your biological age? Much of the existing research in humans concentrates on modifiable health behaviors, such as diet. A randomized trial on caloric restriction (aptly named CALERIE) assigned the treatment group to reduce caloric intake by 25%, while the control group maintained the same diet. In one study stemming from this project, researchers using blood protein measures of biological age found that in one year, the treatment group aged more slowly biologically (0.11 years) than the control group (0.71 years) (Belsky et al., 2018). Another study using DNA methylation measures of biological age found that in two years, the treatment group saw a 2-3% reduction in the pace of biological aging compared to the control group, though this effect was not observed using other algorithms for calculating biological age (Waziry et al., 2023). In a study of 22 identical twin pairs, an 8-week vegan diet reduced biological age by 0.3 to 0.7 years compared to no change among twins eating a healthy omnivorous diet (Dwaraka et al., 2024). Notably, the vegan group also ate fewer calories on average and experienced weight-loss during the 8-week period. These studies suggest that diet might play a role in slowing biological aging.
Another large group of interventions are pharmacological, though many of these are still in the animal phase of testing (Petr et al., 2024). Trials evaluating the effects of pharmaceutical interventions on biological aging in humans are often limited to individuals with disease, and so it is unknown whether or how they may apply to disease-free populations. Nevertheless, there is tremendous enthusiasm for the anti-aging potential of glucagon-like peptide-1 receptor agonists (GLP-1RA, known more by their common brand names like Ozempic and Wegovy), though the evidence of its effects on biological aging is currently limited. Another challenge will be disentangling the effects of the treatment from those of caloric restriction and weight-loss.
While the science is certainly exciting, there is good reason for caution, rooted in concerns about the quality of our measures of biological age. DNA methylation, for example, is a notoriously noisy measure; fewer than half of probes (sites of measurement) have an intraclass correlation >.5, though reliability is more acceptable for probes where there is greater variation (Xu and Taylor, 2021). This measurement error in the assay translates to the calculated epigenetic clocks, with differences between repeated measurements ranging from 3 to 9 years across algorithms (Higgins-Chen et al., 2022). Fortunately, this error does not tend to change the rank order of individuals within a sample, making relative comparisons easier. But the challenge to differentiating between true within-individual change and measurement error remains.
The science of slowing and reversing aging is still in its infancy and continues to grapple with fundamental questions about how best to measure biological age. But already there are promising signs that slowing the aging process is possible. Discovering how to make this a reality in people’s lives, and how to make such interventions equitably accessible, remains one the most exciting and important avenues of future scientific research.
References
Belsky, D.W., Huffman, K.M., Pieper, C.F., Shalev, I., Kraus, W.E., Anderson, R., 2018. Change in the Rate of Biological Aging in Response to Caloric Restriction: CALERIE Biobank Analysis. The Journals of Gerontology: Series A 73, 4–10. https://doi.org/10.1093/GERONA/GLX096
Dwaraka, V.B., Aronica, L., Carreras-Gallo, N., Robinson, J.L., Hennings, T., Carter, M.M., Corley, M.J., Lin, A., Turner, L., Smith, R., Mendez, T.L., Went, H., Ebel, E.R., Sonnenburg, E.D., Sonnenburg, J.L., Gardner, C.D., 2024. Unveiling the epigenetic impact of vegan vs. omnivorous diets on aging: insights from the Twins Nutrition Study (TwiNS). BMC Med 22, 1–19. https://doi.org/10.1186/S12916-024-03513-W
Higgins-Chen, A.T., Thrush, K.L., Wang, Y., Minteer, C.J., Kuo, P.L., Wang, M., Niimi, P., Sturm, G., Lin, J., Moore, A.Z., Bandinelli, S., Vinkers, C.H., Vermetten, E., Rutten, B.P.F., Geuze, E., Okhuijsen-Pfeifer, C., van der Horst, M.Z., Schreiter, S., Gutwinski, S., Luykx, J.J., Picard, M., Ferrucci, L., Crimmins, E.M., Boks, M.P., Hägg, S., Hu-Seliger, T.T., Levine, M.E., 2022. A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking. Nat Aging 2, 644. https://doi.org/10.1038/S43587-022-00248-2
Petr, M.A., Matiyevskaya, F., Osborne, B., Berglind, M., Reves, S., Zhang, B., Ezra, M. Ben, Carmona-Marin, L.M., Syadzha, M.F., Mediavilla, M.C., Keijzers, G., Bakula, D., Mkrtchyan, G. V., Scheibye-Knudsen, M., 2024. Pharmacological interventions in human aging. Ageing Res Rev 95, 102213. https://doi.org/10.1016/J.ARR.2024.102213
Waziry, R., Ryan, C.P., Corcoran, D.L., Huffman, K.M., Kobor, M.S., Kothari, M., Graf, G.H., Kraus, V.B., Kraus, W.E., Lin, D.T.S., Pieper, C.F., Ramaker, M.E., Bhapkar, M., Das, S.K., Ferrucci, L., Hastings, W.J., Kebbe, M., Parker, D.C., Racette, S.B., Shalev, I., Schilling, B., Belsky, D.W., 2023. Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial. Nature Aging 2023 3:3 3, 248–257. https://doi.org/10.1038/s43587-022-00357-y
Xu, Z., Taylor, J.A., 2021. Reliability of DNA methylation measures using Illumina methylation BeadChip. Epigenetics 16, 495–502. https://doi.org/10.1080/15592294.2020.1805692
