Aging clocks can be built from any sufficiently complex set of biological data measured in a sufficiently large number of people across a sufficiently large range of different ages. Machine learning techniques are used to find algorithmic combinations of data points that predict age to some sufficient threshold of accuracy. The algorithm is then applied to people who were not in the original sample populations, and most such clock algorithms do an acceptably good job of hitting the mark when considered over groups of people. Unfortunately they are not all that useful for an individual; in part the variance is a problem, but the main challenge is that it is entirely unclear in most clocks as to what the results actually mean. It is also unclear as to how we should expect any given clock to react to any given intervention used to treat aging.
The best path forward to making aging clocks useful for individuals, and for the assessment of novel therapies to treat aging, is probably to collect as much data as possible and observe the emerging patterns. Classes of therapy will have to be assessed in parallel with clocks. Different populations and different strategies for clock use will have to be assessed against actual outcomes, such as mortality rate and disease incidence years later. This won't be a fast process.
Nonetheless, interesting new findings emerge on a fairly regular basis as the use of clocks spreads. In today's open access paper, for example, research demonstrate that change over time in clock assessments is a useful piece of information, perhaps much more useful than single measures. This is particularly relevant to the use of clocks by an individual rather than in a population study, as many of the unknowns become irrelevant when one person uses the same clock repeatedly over a period of years to measure something that may be closely related to the pace of biological aging.
Longitudinal changes in epigenetic clocks predict survival in the InCHIANTI cohort
Over the past years, several proxy biomarkers of biological aging have been developed and validated, with the most advanced using data from DNA methylation. Broadly termed 'epigenetic clocks,' these methylation-based markers of aging have been shown to predict several adverse health outcomes, including mortality, independently of chronological age.
However, whether longitudinal changes in these phenotypes provide additional information on health outcome prediction over and beyond one single measure has not been demonstrated. Based on cross-sectional studies, we cannot definitively exclude that deviations of DNA methylation age from chronological age are determined early in life and are not modulated by behavioral, environmental exposures or changes in health status. In addition, if biological aging clocks are to be used to track the effectiveness of intervention over time, it is important to demonstrate that deviations of epigenetic clock trajectories reflect meaningful changes in health status.
In this longitudinal study of 699 adults from the InCHIANTI cohort followed for up to 24 years, we evaluated whether temporal acceleration of several epigenetic clocks-including first-, second- and third-generation epigenetic clocks-was associated with mortality. We found that faster increases in several clocks were linked robustly to higher risk of death, independent of baseline epigenetic age and other confounders. These findings suggest that dynamic changes in epigenetic aging reflect evolving health status and may serve as sensitive indicators for interventions aimed at extending healthspan and longevity.
View the full article at FightAging
