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The GrimAge Epigenetic Clock Reflects Mortality Risk Differences Between Twins

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Posted 30 June 2021 - 10:11 AM

Epigenetic clocks are correlations identified between physiological age and algorithmic combinations of DNA methylation status at various CpG sites on the genome. Cells constantly change their epigenetic marks, such as DNA methylation, in response to circumstances. Some of those circumstances involve characteristic damage and responses to damage that occur with age, and that are broadly similar between individuals in later life. The clocks thus reflect, to some degree, biological rather than chronological age, the progression of processes of damage rather than time.

It is entirely unclear, and will remain so for some time, as to what exactly is measured by these clocks, however. Which processes of aging drive these epigenetic changes? Without knowing the answer to that question, it is hard to use the clocks to test the efficacy of a potential rejuvenation therapy. Perhaps a clock entirely fails to consider the specific form of damage repaired in a study. There is no practical way to find out other than to run a lot of studies with a lot of different clocks and different potential rejuvenation therapies. Early clocks have interesting and potentially problematic blind spots: the Horvath clock is insensitive to fitness, for example, as demonstrated in twin studies with fit and unfit twin pairs. This is known, and improvements were made. The study noted here demonstrates that the later GrimAge clock is a clear improvement, as it does identify differences in mortality risk between genetically identical twins.

Novel measures of biological aging known as "epigenetic clocks" have been used to assess biological aging process and mortality risk. The major advantage of epigenetic clocks is that they can be utilized to estimate the progress of aging over the life course. Horvath's algorithm was the first widely used epigenetic clock. It was trained against chronological age, and therefore it has been argued that Horvath's DNAmAge estimates may exclude CpGs whose methylation patterns may reflect a deviation of biological age from chronological age. DNAm GrimAge was subsequently developed to predict mortality. It is a combination of DNAm-based surrogate biomarkers for health-related plasma proteins and smoking pack-years as well as sex and chronological age. It is associated with the key "hallmarks of aging," such as mitochondrial dysfunction and cellular senescence.

So far, multiple studies with varying study designs and outcomes have found epigenetic age acceleration - an older DNAm age estimated by epigenetic clocks compared to chronological age - to be associated with increased mortality risk. It has been suggested that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors.

We examined the association of epigenetic age acceleration, defined by Horvath's DNAmAge and DNAm GrimAge, with all-cause mortality within a population-based cohort of 413 Finnish twin sisters. The female participants are twin pairs who share sex, age, and all (monozygotic pairs) or half (dizygotic pairs) of their genetic polymorphisms and most of the intrauterine and childhood environment. This allows us to distinguish the effect of lifestyle and genetic factors on the association of epigenetic aging and mortality.

Our results suggest that DNAm GrimAge outperforms Horvath's DNAmAge in mortality risk prediction. We performed pairwise analysis in which risk for survival as a function of an epigenetic age acceleration was conducted to minimize potential pleiotropic genetic and familial influences on the association between epigenetic aging and mortality. Our genetically controlled analysis suggest that faster epigenetic aging is associated with a higher risk of mortality irrespective of genetic influences. Further, the results indicate that smoking plays an important role in the association between epigenetic aging and mortality. In conclusion, the findings suggest that DNAm GrimAge is a strong predictor of mortality independent of genetic influences.

Link: https://doi.org/10.1...148-021-01112-7

View the full article at FightAging

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