“Previous clocks considered the relationship between methylation patterns and features we know are correlated with aging, but they don’t tell us which factors cause one’s body to age faster or slower. We have created the first clock to distinguish between cause and effect,” said corresponding author Vadim Gladyshev, Ph.D., a principal investigator in the Division of Genetics at BWH. “Our clocks distinguish between changes that accelerate and counteract aging to predict biological age and assess the efficacy of aging interventions.”
There is a link between DNA methylation, and its influence on the aging process, notably, specific regions known as CpG sites that are more strongly associated with aging. Lifestyle choices may influence DNA methylations, but so does our genetic inheritance, which may help to explain why people with similar lifestyles can age at different rates.
Existing epigenetic clocks predict biological age using DNA methylation patterns, but there are no existing clocks that distinguish between methylation differences that cause biological aging and those that correlate with the aging process, until now.
Using a large genetic data set an epigenome-wide Mendelian Randomization technique was used to randomize the data and establish causation between DNA structure and observable traits on 20,509 CpG sites causal to 8 aging-related characteristics: extreme longevity (survival beyond the 90th percentile), health span (age of first major age-related diseases onset), frailty index (based on the accumulation of health deficits), self-rated health, and 3 broad ranging aging-related measurements that incorporated family history, socioeconomic status, and other health factors.
Using these traits and their associated DNA sites 3 models were created: CausAGe, which is a general clock that predicts biological age based on causal DNA factors, and DamAge and AdaptAge which include only damaging or protective changes. Next, blood samples were analyzed from 7,036 people between the ages of 18-93 years old, of which data from 2,664 was used to train the model.
This data was used to develop a map pinpointing human CpG sites that cause biological aging that allows researchers to identify biomarkers causative to aging and evaluate how different interventions could either promote longevity or accelerate aging.
The validity of the new clock was tested using data from 4,651 participants enrolled in the Framingham Heart Study and the Normative Aging Study. Results found that DamAge correlated with adverse outcomes including mortality, while AdaptAge correlated with longevity suggesting that age-related damage contributes to the risk of death and protective changes to DNA methylation may contribute to a longer lifespan.
The clock’s ability to assess biological age was then tested by reprogramming stem cells. Results showed that when applied to the newly transformed cells DamAge decreased indicating a reduction in age-related damage during reprogramming, but AdaptAge did not show a pattern.
Finally, the clock was tested using biological samples from people with various chronic conditions as well as samples from people with poor lifestyle choices. Results showed that DamAge increased in conditions associated with age-related damage, and AdaptAge decreased, capturing the protective adaptations.
“Aging is a complex process, and we still do not know what interventions against it actually work,” said Gladyshev. “Our findings present a step forward for aging research, allowing us to more accurately quantify biological age and evaluate the ability of novel aging interventions to increase longevity.”
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