Epigenetic Chronometer

The most accurate epigenetic clock has been developed

Georgy Golovanov, Hi-tech+

A startup from Hong Kong, DeepLongivity, published a study in the field of aging epigenetics in the journal Aging Research Reviews, in which he described a new neural network that determines a person's age based on more than 6,000 DNA tags (Galkin et al., Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities). Analysis of the methylation level allows you to determine the biological age with an accuracy of plus or minus 3 years, and this is the highest accuracy of all available methods.

The boom of the epigenetic clock – a method of determining the biological age by the level of DNA methylation – began in 2013, when the first versions of Steve Howarth, Gregory Hannum and other scientists were published. They proved to be an indispensable tool in the research of aging and made it possible to better understand the mechanism of this process, as well as to develop methods for prolonging life.

Unlike its predecessors, the DeepMAge neural network can be useful not only for accurate age determination, although in this it has reached record levels – the margin of error was 2.77 years. For example, it can be used as a diagnostic tool, writes EurekAlert.

The researchers noted that in patients with certain diseases, the neural network found signs of premature aging. Thus, in women with ovarian cancer, the epigenetic age is on average 1.7 years older than the chronological age, and in patients with multiple sclerosis – 2.1 years.

The authors of the study are not yet ready to answer the question of what is primary – the appearance of the disease or accelerated aging of cells, tissues and organs. They plan to continue their research to establish a link between epigenetics and life expectancy.

The architecture of the DeepMAge neural network can also be modified in such a way as to simulate the effect of food abstinence, exercise, medication and other lifestyle changes on longevity.

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