The development of rejuvenation therapies is haphazard and inefficient in part because measuring rejuvenation is costly, uncertain, and slow. On the one hand, rigorous and convincing data is needed to persuade conservative, risk-averse regulators and sources of funding to support work on rejuvenation at all. Further, cost-effective early guidance on whether one approach is better or worse than another is needed in to order to avoid a great deal of effort directed towards programs that cannot produce sizable outcomes for health.
With this in mind, the research community is in search of a way to rapidly assess biological age before and after potential therapies. Epigenetic clocks are one possible path towards this capability, but as today's popular science article notes, there are sizable hurdles yet to overcome.
It is now straightforward to generate clocks that reflect age and disease risk from near any sizable set of biological data. Many characteristic changes take place with age in the epigenome, proteome, transcriptome, and so forth. Modern machine learning makes it practical to identify such changes in large datasets. The problem is that researchers don't yet know how these changes connect to the underlying processes of aging. Thus no-one knows whether any given clock will accurately reflect the results of adjusting just one or a few of those processes.
So it is simple enough to run clocks on blood samples and produce numbers - but can those numbers be trusted? At the moment, no. Clocks must be calibrated against any potential therapy they are to be used with, via life span studies and other lengthy and costly exercises. That defeats the point of the exercise, to find a faster way forward. The alternative is a great deal more work aimed at understanding exactly how clocks as a category respond to mechanisms of aging.
Biological age is an important concept, albeit a slippery one. Everyone's physical and mental functioning gradually declines from early adulthood onwards, but this occurs at different rates in different people. A technique for measuring biological age detects a signal that is a better guide to a person's functional capacity than their actual, chronological age. As more and more scientists seek to slow, halt or rewind ageing, such methods will be needed to assess whether the new manipulations achieve these goals.
Epigenetic clocks use algorithms to calculate biological age on the basis of a read-out of the extent to which dozens or even hundreds of sites across an individual's genome are bound by methyl groups - a form of epigenetic modification. In 2019, a small study raised the tantalizing prospect that ageing could be reversed. Scientists gave 9 men aged 51 to 65 a growth hormone and two diabetes medications for a year. The drugs seemed to rejuvenate the men's thymus glands and immune function. They also shaved 2.5 years off the men's biological age, as measured by epigenetic clocks.
The study is one of many, in humans and in animals, that seek ways to reduce epigenetic clock scores - and thereby develop new anti-ageing interventions. But some experts are concerned by the unknowns that still surround this technology. "It's become a sort of dogma in the field - and in the popular perception - that these things are really measuring biological ageing. We really need to understand how these things are working. That's the weakness of these biomarkers. They come out of a machine-learning algorithm. They work beautifully in a mathematical sense, but biologists want more."
The US Food and Drug Administration does not currently recognize epigenetic clock scores as surrogate end points for clinical trials. It wants their mechanistic basis to be better defined. And it wants an answer to the crucial question of whether a short-term decrease in someone's epigenetic clock score definitively lowers their chances of developing age-related ill health.