A team of researchers, including professor Steve Horvath, the pioneer of the epigenetic clock, report in this new paper about an improved version of that clock . His original epigenetic clock measures the age of a person by looking at DNA methylation patterns; these patterns correlate closely with the actual age of a person, with a margin of error of around two years or so.
Since the original clock was first created, work has continued on refining the process and how aging is measured. In terms of aging biomarkers, it is generally considered the gold standard, given how reliable it is as a way to determine biological age.
While chronological age is linked to the likelihood of us developing age-related diseases and dying, it is important to distinguish the difference between chronological age and biological age. Individuals of the same chronological age may not age in quite the same way or even at the same rate, showing differences in their susceptibility to different age-related diseases.
This reflects the differences in individual biological aging processes, and, as such, finding better ways to determine someone’s biological age will aid the development of individual healthcare strategies, potentially allowing focus on areas of concern before diseases develop.
The other benefit of developing better aging biomarkers is that they can help confirm the efficacy of therapies that target the aging processes, offering another data point alongside these therapies’ effects on their actual targeted diseases.
Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer’s disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.
The more reliable ways of measuring aging we have, the better; there is an urgent need for research to have such quality biomarkers, so this publication is positive news for the field.
 Levine, M. E., Lu, A. T., Quach, A., Chen, B., Assimes, T. L., Bandinelli, S., … & Whitsel, E. A. (2018). An epigenetic biomarker of aging for lifespan and healthspan. bioRxiv, 276162.