Fight Aging!

There is at present a diverse exploration of clocks that assess biological age, these clocks constructed as weighted combinations of data picked from the epigenome, transcriptome, or proteome, all of which change in characteristic ways with age. Many different clocks are at various stages of development and refinement. The goal is the production of a robust, low-cost, rapid way to assess the efficacy of potential rejuvenation therapies: if one can use a blood test ten days before and ten days after a treatment, that would be a great deal easier than having to wait and see over the course of a life span.

Unfortunately, this goal remains a future phase of development for this class of technology. Given that there is no good understanding of exactly which processes of molecular damage cause specific changes in the epigenome, transcriptome, and proteome, every algorithm must be calibrated against a potential treatment before it can be used to assess that treatment. Which somewhat defeats the point, as the only way to calibrate it is to run the slow, expensive life span studies that we'd all like to avoid. Still, the research community is presently energetically engaged in improving on present approaches to the production of clock algorithms, as illustrated by the example here.

Researchers have produced DeepMAge, a novel aging clock that was trained to predict human age on more than 6000 DNA methylation profiles. By analyzing the methylation patterns it can estimate human age within a 3-year error margin, which is more accurate than any other human aging clock. Aging clocks boom started in 2013 when the first DNA methylation aging clocks by Horvath and Hannum were published. They have proven to be an indispensable tool in aging research, letting scientists understand its mechanisms and develop longevity interventions.

Unlike its predecessors, DeepMAge is a neural network that may prove to be more efficient in some other ways apart from prediction accuracy. In the original paper, DeepMAge deems people with certain conditions to be older, which may be useful for the development of early diagnostics tools. For example, women with ovarian cancer are on average predicted 1.7 years older than healthy women of the same chronological age, and likewise, multiple sclerosis patients are predicted 2.1 years older. Similar results have been obtained for several other conditions: irritable bowel diseases, dementia, obesity.

Higher age predictions indicate a faster pace of aging in these conditions, which begs the question: is a higher aging rate a precondition to them or is it just an epigenetic footprint of the harm they cause? The authors plan to further investigate the links between epigenetics and longevity using DeepMAge. "Aging clocks have come a long way since the first works by Horvath and Hannum in 2013. We are happy to contribute to this research field. Now, we are going to explore how epigenetic aging can be slowed down with the interventions available to consumers."

Link: https://www.eurekalert.org/pub_releases/2020-12/dll-dlp120720.php