DeepMAge: most accurate aging clock
By: Fedor Galkin
Tags: Biology, Aging, Science, Deep Learning, Neural Networks

DeepMAge: most accurate aging clock

[DNA methylation marks affect gene regulation and accumulate with age. We can study this process to come up with new ways to fight aging and develop geroportectors.]

Aging leaves many footprints: skin pigmentation, grey hair, wrinkles… But most aging-related changes are more than skin-deep and touch the very foundation of the human biology — genome.

Every human cell has the same genetic information encoded in the DNA but each cell manages this information differently. Bones need to be dense and solid while brains need to be soft and squishy. Stomach needs to secrete digestive juices when there is food and remain inactive when it is empty. Epigenetic (“above genetic”) regulation lets cells maintain their tissue and functional identity despite being genetically equal to any other cell.

In other words, genetics are cellular kitchens equipped with every kind of utensil and ingredient. And in this kitchen, epigenetic is a recipe book. Epigenetics determine what products are mixed, the order in which it is done, how hot the oven is and how many people need be served. Epigenetics strictly control gene expression to avoid cell death or malignant transformation.

DNA methylation (DNAm) is one of the epigenetic regulatory mechanisms. Marking genes with methyl moieties at locations called CpG islands deactivates them.

[DNAm is an epigenetic mark that regulates gene expression. This mechanism ensures the tissue and functional identity of cells][1]

There are 28 million CpG sites in the human genome and apparently some of them change with age [2]. And since 2013 CpG sites have been actively studied as biomarkers of aging. As organisms become older and age-related damage accumulates in the process, DNAm placement loosens and cellular processes of damage repair become less organized. Some genes that should remain silent become active, and vice versa. This exacerbates the harmful effects of aging.

Using microarray technology it is possible to make a snapshot of thousands of CpG sites to see if they are marked — obtain a DNAm profile of the person. The intensity and position of the methylation marks are expressed as a numeric vector, which is fed into a statistical model to determine age-related patterns.

Such statistical models are called “aging clocks” and can be used to measure the pace of aging in humans. Most commonly, such clocks try to estimate your age and the difference between this estimation and your actual, chronological age tells if you are an overager, and underager or a normal ager. Being an underager means you are aging at a slower pace and most likely have higher life expectancy.

[DNAm aging clocks are statistical models that learn to tell young and old people apart based on their DNAm levels at specific CpG sites]

DeepMAge [3] was trained on a collection of methylation profiles from >6,000 people in 32 studies. Ultimately, it learnt to determine the age of a person with 2.77 years of error in healthy people. Interestingly, people with diseases, such as cancer, multiple sclerosis or dementia were predicted significantly higher than healthy people. These results may indicate a faster pace of aging in people with these pathologies.

This sensitivity to diseases means that DeepMAge could be used as an early diagnostics tool in clinical practice or in insurance industry. It could also be employed to guide longevity focused therapies that aim to affect the source of many chronic diseases — aging itself. Longevity medicine focuses on preventing and postponing a disease rather than dealing with it post factum. In this young branch of medicine aging clocks are truly irreplaceable since they allow doctors to verify whether their decisions actually decrease the many harms aging causes on a molecular level.

[DeepMAge was trained in 4930 blood samples and verified in 1293 samples from 15 independent studies. The median absolute error achieved by DeepMAge is lower than in any other comparable aging clock][4]

Deep Longevity CEO Alex Zhavoronkov claims: “We have created DeepMAge to help researchers, health professionals and consumers track aging processes in the organism. As the global population grows older with each generation, the problem of extending the productive period of human lives is getting increasingly important. Solving it is impossible without a way to quantify aging and digitally test our ideas on how to affect it with medicine”.

Global median age was 24 years in 1950, it is 30 years now and it is estimated to reach 36 years by 2050 [5]. Aging is not yet recognized as a disease, but one day it may be. When it comes, the aging world will desperately need a way to test geroprotective drugs and therapies faster than it takes to measure human lifespan with a stopwatch. Aging clocks are created for this task.

On top of being a biological age predictor, DeepMAge is also a neural network — a statistical model famous for its flexibility. Neural networks have been shown to be extremely effective in such tasks as image recognition, text generation, autonomous driving etc. We believe that DeepMAge can also be repurposed for more complex tasks than age prediction. For example, it could be used to simulate aging on a molecular level and fast-track the drug target discovery process by reducing the need for live test subjects.

[1] Functions of DNA methylation: islands, start sites, gene bodies and beyond (2012)

[2] DNA methylation in human epigenomes depends on local topology of CpG sites (2016)

[3] DeepMAge: A Methylation Aging Clock Developed with Deep Learning (2020)

[4] Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities (2020)

[5] Ritchie H (2019) Age Structure. Our World Data

Deep Longevity is currently the subject to a conditional acquisition by Regent Pacific Group Limited (SEHK:0575.HK), a public company whose securities are listed in Hong Kong

Stay tuned for more short articles on aging clocks soon!

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