Aging clocks can be readily produced by using what are by now well established machine learning techniques on any body of biological data that changes with age. Omics data is favored, as there are a great many databases of such data for large study populations, but clocks have been made using clinical chemistry results from simple blood tests, imaging data, and many other items. A clock is calibrated to predict age in the reference population, and when used on people outside that reference populations, most clocks tend to predict a higher age for people more greatly impacted by aging. Thus a higher clock age than chronological age tends to correlate with increased mortality risk, presence of age-related conditions, and so forth, at least over populations.
The most important goal for the development of aging clocks is to prove that one or more of them can be used to assess the quality of novel interventions in aging. Having a way to quickly assess whether or not a potential rejuvenation therapy is any good would transform the field, allowing researchers to rapidly focus on the best ways forward and optimize them. Unfortunately, there is no way to trust that a clock will be reliable in such circumstances other than to calibrate it against the intervention by running slow and expensive life span studies - which defeats the purpose. Unfortunately, there remains no well mapped direct connection between the data used to derive clocks and the underlying mechanisms of aging. Any given clock might underestimate or overestimate the effects of any given approach to treating aging, and whether that is the case or not is presently unknowable in advance.
