A recent review discusses the development of biological aging clocks, their capabilities and limitations, the discoveries they enabled, and possible future developments in this area [1].
How do we measure aging?
Developments in science and technology have allowed researchers to move beyond speculation and philosophical debates about aging to study its molecular underpinnings and the processes that govern it, creating opportunities to reverse them.
However, to measure the potential of an age-reversing intervention, we need to identify specific molecular metrics that indicate whether the body’s molecular clocks have been reversed or slowed and to what extent.
In their recent review, Tony Wyss-Coray and Eric Topol discuss the tools that enable such measurements. They focus on biological aging clocks, computational models designed to track the pace of aging and assess the biological age of an organism, organs, or cells. Those clocks “capture individual physiological differences relative to a larger reference population.” The authors elaborate on the progress made in this area and how these clocks can aid researchers in understanding diseases and extending healthspan.
Tick-tock
Aging clocks are a diverse set of tools. The first generation of biological clocks was built on DNA methylation patterns; the next generation of clocks incorporated additional molecular data, such as plasma protein levels and other large-scale arrays, as well as information such as hand strength, cognitive function, locomotion, eye and hearing acuity, and psychological testing, creating a plethora of clocks that focus on different metrics and exhibit varying predictive capacities across measured endpoints.
The field went even further, drawing on information from organ- and cell-derived plasma proteins to construct more advanced organs and cell-based clocks. Studies that used organ clocks showed that the age of each organ, as assessed by the clock, was associated with organ-specific diseases, such as Alzheimer’s disease being associated with brain age [2]. Additionally, among all the organ clocks, the brain and immune system clocks showed the strongest association with survival. Interestingly, the aging of one organ didn’t show strong correlations with the pace of aging in other organs, suggesting that organs age at different rates within the same individual [3]. Even more detailed than organ-based clocks are cell-type-specific clocks, which have found associations between accelerated biological aging in specific cell types and the risk of various diseases.
Beyond those clocks, there are other aging clocks that measure the pace of aging using various types of biological -omics data, including gene expression (transcriptomics), sugars and carbohydrates (glycomics), metabolomics, lipidomics, and microbiomics. While -omics technologies are popular among the researchers who design aging clocks, there are also clocks that use electronic health records; lifestyle factors; telomere length; standard lab tests such as HbA1c, C-reactive protein (CRP), and hemoglobin; various types of medical images such as magnetic resonance images, bone imaging, CT scans and histology slides; and physical parameters such as handgrip strength, lung function, balance, gait speed, blood pressure, and waist circumference.
Not all types of data are created equal in aging clocks. Using certain types of data can make it much more difficult to build an aging clock; for example, the complexity of the immune system makes it challenging to use it as a basis.
Despite these difficulties, researchers continue to pursue different approaches. The authors present an example of a sperm aging clock that uses small noncoding RNA expression data and has the potential to be used in the future to assess “health risks in offspring in fathers of advanced age” [4].
Limitations
As with every tool, there are limitations to using aging clocks. For example, the training sets on which the clocks were built may have demographic biases if they were created from limited populations. Despite that, such clocks, while not perfect, have been successfully used in many studies to predict all-cause mortality, cause-specific mortality, healthspan, various health outcomes, and biological age.
While aging clocks have been shown to correlate with various health outcomes, they do not establish causality; further research is necessary to do so. Additionally, aging clocks data were obtained primarily from population-level studies, so we do not know how useful those tools are at the individual level, and, as of now, aging clocks haven’t been established for clinical use. While there are products based on those clocks that are sold to the public, there is no standardization in the field and no regulatory approval for these products.
Much work done, an exciting future ahead
Biological aging clocks have already provided researchers with valuable insights into aging processes. Recent studies combining these clocks with other tools have established links between markers identified by epigenetic and proteomic clocks and various diseases, aiding researchers in understanding the mechanisms underlying disease and aging [5, 6]. Those new protein biomarkers have the potential to become drug targets to prevent various diseases [7, 8].
Some studies have provided data that links aging to “specific medications, lifestyle behaviors, menopause, foods, and occupations” [9]. They also found that epigenetic aging can be slowed down by interventions such as exercise [10] or, to a lesser degree, by omega-3 and vitamin D supplementation, multivitamins, the shingles vaccine, and GLP-1 drugs [11-14].
The technology behind aging clocks has made great strides, and we can expect further developments in this area as biological clocks have many potential uses. One of them is measuring early endpoints of geroprotective interventions under investigation; however, their use in clinical trials as a substitute for long follow-up periods requires further validation. Nevertheless, the authors believe that using a refined version of a clock in mainstream clinical care is possible in the foreseeable future, especially if the latest AI technologies are effectively employed to refine the models.
Probably the most important use for aging clocks is as a tool to help prevent age-related diseases. As the authors wrote, the opportunity to develop and properly use aging clocks lies in developing drugs to prevent diseases rather than treat them.
Literature
[1] Wyss-Coray, T., & Topol, E. J. (2026). Biological aging clocks in health and disease. Nature medicine, 32(7), 2383–2394.
[2] Oh, H. S., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., Wang, L., Timsina, J., Western, D., Liu, M., Kohlfeld, P., Budde, J., Wilson, E. N., Guen, Y., Maurer, T. M., Haney, M., … Wyss-Coray, T. (2023). Organ aging signatures in the plasma proteome track health and disease. Nature, 624(7990), 164–172.
[3] Kivimäki, M., Frank, P., Pentti, J., Jokela, M., Nyberg, S. T., Blake, A., Lindbohm, J. V., Oh, H. S., Singh-Manoux, A., Wyss-Coray, T., & Partridge, L. (2025). Proteomic organ-specific ageing signatures and 20-year risk of age-related diseases: the Whitehall II observational cohort study. The Lancet. Digital health, 7(3), e195–e204.
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