Fight Aging!

Over the past few years, researchers have been working to construct and validate a biomarker of aging based on changes in DNA methylation patterns that occur over a lifetime. DNA methylation is constantly in flux, part of the complex system of epigenetic regulation that alters the output of proteins in response to circumstances in order to regulate cellular behavior. Some of these changes are driven by the accumulation of forms of cell and tissue damage that cause aging, and measurements of those should correlate well with biological age: how damaged you are, and thus how aged you are, and further how high your mortality risk is as a result.

A low-cost, quick, and reliable measure of the degree to which an individual is are damaged and aged will be a necessary tool for future research into aging and longevity. Currently the only way to assess whether or not a putative rejuvenation therapy works as intended to extend healthy life is to wait and see. That is very costly, even in studies that use short-lived species, and a therapy has to be tested in longer-lived mammals at some point on the road to clinical application. Given a reliable biomarker that measures biological age, it will be possible to rapidly assess many more potential therapies that treat the causes of aging, steering the community towards the most effective approaches, and reducing the time spent on dead ends or less effective research programs.

Several recent studies have made use of the age-related changes in methylation profiles to construct DNA methylation signatures, a DNA methylation age (DNAm age) or 'epigenetic clock', with impressively high correlations with chronological age, of about 0.7 or greater. Considering that methylation profiles are modifiable by lifestyle and other environmental influences, it has been proposed that DNAm age is a biomarker of aging, that is, that DNAm age provides a better estimate of biological age than chronological age and is associated with current and future health and mortality.

In this study, we estimated DNAm age using the frequently applied Horvath prediction model and confirmed it using the Hannum prediction model. The study sample consisted of 378 twins aged 30-82 years from the Danish Twin Registry. The oldest 86 twins (mean age 76.2 years at intake) were resampled in a 10-year follow-up study and had methylation age determined again at mean age 86.1 years. The mortality in this sample was subsequently followed for 8 years. The twin design enabled us to control partly for genetic and rearing environment in the mortality study.

We found that the DNAm age is highly correlated with chronological age across all age groups, but that the rate of change of DNAm age decreases with age. The results may in part be explained by selective mortality of those with a high DNAm age. This hypothesis was supported by a classical survival analysis showing a 35% (4-77%) increased mortality risk for each 5-year increase in the DNAm age vs. chronological age. Furthermore, the intrapair twin analysis revealed a more-than-double mortality risk for the DNAm oldest twin compared to the co-twin and a 'dose-response pattern' with the odds of dying first increasing 3.2 (1.05-10.1) times per 5-year DNAm age difference within twin pairs, thus showing a stronger association of DNAm age with mortality in the oldest-old when controlling for familial factors. In conclusion, our results support that DNAm age qualifies as a biomarker of aging.

Link: http://onlinelibrary.wiley.com/enhanced/doi/10.1111/acel.12421/