Fight Aging!

The original epigenetic clock is a measure of age, a weighted algorithmic combination of specific DNA methylation sites on the genome. Numerous variations on this theme are being produced, and here I'll point out news on the latest, a metric called GrimAge. DNA methylation is an epigenetic mechanism that steers protein production and thus cell behavior. Epigenetic clocks correlate well with chronological age, and it has been shown that populations of older individuals with pronounced age-related disease or otherwise exhibiting higher mortality rates tend to have higher epigenetic ages.

There are some problematic exceptions, groups expected to show higher epigenetic age, but who do not, but researchers are nonetheless forging ahead to try to turn this tool into a robust method of assessing the burden of cell and tissue damage that causes aging. If one or more clock variants can be made robust enough, the variations understood and linked to specific causes and dysfunctions of aging, then these epigenetic clocks offer the possibility of greatly accelerating the development of rejuvenation therapies.

At present the only robust way of demonstrating that a therapy does in fact turn back aging, and measuring the degree to which it does so, is to run life span studies. When using mice, this is a greater expense in time and funds than most research groups can stomach, and carrying out such studies in humans is just out of the question. What is needed is a way to quickly assess how greatly a therapy reduces the burden of aging, a test that can be applied beforehand, and a month or two after treatment, and the result compared. Even as a way to cull the useless and marginal work in the field of human aging, work that consumes far too much attention and funding, this would be very valuable. More importantly, it would allow researchers to more cost-effectively assess scores of promising approaches that are presently lacking in the funds and support to prove their worth.

The epigenetic clock: a molecular crystal ball for human aging?

A hat trick of new epigenetic clocks has recently been published: The Skin and Blood clock provides a more precise estimation of chronological age in tissues and cell types frequently used in research and forensics, while PhenoAge and GrimAge aim to capture biological aging and derive an improved prediction of mortality and morbidity risks. Together, these new epigenetic clocks present valuable tools to investigate human aging, shed light on the question of why we all age differently, and develop strategies to extend human lifespan and healthspan.

Horvath's multi-tissue clock is based on DNA methylation data. DNA methylation, the addition of methyl groups to cytosine bases of the DNA, is the most widely studied epigenetic modification so far. It plays an important role in the regulation of gene expression, altering the phenotype without changing the genotype. A particular locus in the genome can either be methylated or unmethylated. But as DNA methylation measurements are usually obtained from a pool of tens of thousands of cells, what is measured is the proportion of the cells in which a locus is methylated. In many positions of the human genome, this methylation heterogeneity changes with age. These usually small but consistent age-associated changes in DNA methylation are what make the epigenetic clock work. And it works very precisely, with a median absolute error (MAE) of only 3.6 years, clearly outperforming previously used molecular biomarkers of age.

DNA methylation GrimAge strongly predicts lifespan and healthspan

DNA methylation (DNAm) levels have been used to build accurate composite biomarkers of chronological age. DNAm-based age (epigenetic age) estimators predict lifespan after adjusting for chronological age and other risk factors. Moreover, they are also associated with a large host of age-related conditions. Recently, DNAm-based biomarkers for lifespan (time-to-death due to all-cause mortality) have been developed.

Many analytical strategies are available for developing lifespan predictors from DNAm data. The single stage approach involves the direct regression of time-to-death (due to all-cause mortality) on DNAm levels. By contrast, the current study employed a novel two-stage procedure: In stage 1, we defined DNAm-based surrogate biomarkers of smoking pack-years and a selection of plasma proteins that have previously been associated with mortality or morbidity. In stage 2, we regressed time-to-death on these DNAm-based surrogate biomarkers. The resulting mortality risk estimate of the regression model is then linearly transformed into an age estimate (in units of years). We coin this DNAm-based biomarker of mortality "DNAm GrimAge" because high values are grim news, with regards to mortality/morbidity risk.

Using large scale validation data from thousands of individuals, we demonstrate that DNAm GrimAge stands out among existing epigenetic clocks in terms of its predictive ability for time-to-death, time-to-coronary heart disease, time-to-cancer, its strong relationship with computed tomography data for fatty liver/excess visceral fat, and age-at-menopause. Adjusting DNAm GrimAge for chronological age generated novel measure of epigenetic age acceleration, AgeAccelGrim. AgeAccelGrim is strongly associated with a host of age-related conditions including comorbidity count. Overall, these epigenetic biomarkers are expected to find many applications including human anti-aging studies.