A new ‘clock’ may provide an answer to the question: What makes us age?
Researchers at Harvard-affiliated Brigham and Women’s Hospital have developed a new epigenetic clock, a machine learning model designed to predict biological age from DNA structure. The clock can distinguish between genetic differences that slow or accelerate aging, predict biological age, and more accurately assess anti-aging interventions. The results are published in Nature Aging.
“Previous clocks took into account the relationship between methylation patterns and traits known to correlate with aging, but they did not tell us which factors cause a person’s body to age faster or slower. ” said corresponding author Vadim Gradyshev. Genetics Department of BWH. “Our clock distinguishes between age-promoting and age-defying changes to predict biological age and assess the effectiveness of aging interventions.”
Aging researchers have long recognized the impact of DNA methylation (changes in genetic structure that shape gene function) on the aging process. In particular, certain regions of DNA known as CpG sites are strongly associated with aging. Lifestyle choices such as smoking and diet affect DNA methylation, but they also affect genetic inheritance, which explains why people with similar lifestyles age at different rates.
“Our clock distinguishes between age-promoting and age-defying changes to predict biological age and assess the effectiveness of aging interventions.”
Vadim Gradyshev, Principal Researcher
Existing epigenetic clocks use DNA methylation patterns to predict biological age (the actual age of a cell, not chronological). But until now, no one had distinguished between methylation differences that cause biological aging and those that simply correlate with the process.
First author Kejun (Albert) Yin, a graduate student in Gradyshev’s lab, uses large genetic datasets to randomize data and establish causal relationships between DNA structure and observable traits. We performed epigenome-wide Mendelian randomization (EWMR), a technique used for 20,509 CpG sites are responsible for eight aging-related properties.
The eight characteristics associated with aging include longevity, extreme longevity (defined as surviving above the 90th percentile), healthspan (age at onset of major age-related diseases), and frailty index (a person’s average age based on their accumulated health). scale of frailty). His three broad age-related measures incorporate lifelong disability), self-rated health, family history, socioeconomic status, and other health factors.
With these traits and their associated DNA sites in mind, Ying created three models. One is CausAge, a general clock that predicts biological age based on causal DNA factors, and the other is DamAge and AdaptAge, which include only damaging or protective changes. The researchers then analyzed blood samples from 7,036 people in the Scottish Generation Cohort, from age 18 to age 93, and ultimately trained a model on data from 2,664 people in the cohort. Did.
Armed with these data, the researchers developed a map that identifies CpG sites in humans that are responsible for biological aging. This map allows researchers to identify biomarkers that cause aging and assess how different interventions promote longevity or accelerate aging.
Scientists tested the clock’s effectiveness on data collected from 4,651 people in the Framingham Heart Study and the Standard Aging Study. They found that DamAge correlated with adverse outcomes such as mortality, and AdaptAge correlated with longevity, suggesting that age-related damage contributes to mortality risk, while protective changes on DNA methylation. This suggests that it may contribute to extending lifespan.
Next, we tested the clock’s ability to assess biological age by reprogramming stem cells. This returns specialized cells, such as skin cells, to a younger, less defined state where they can develop into different types of cells in the body. When the clock was applied to newly transformed cells, DamAge decreased and showed reduced age-related damage during reprogramming, whereas AdaptAge did not show any specific pattern.
Finally, the team tested the clock’s performance on biological samples from patients with various chronic diseases such as cancer and high blood pressure, as well as samples that had been damaged by lifestyle choices such as smoking. While DamAge consistently increases in age-related injury-related symptoms, AdaptAge decreases, effectively capturing protective adaptations.
“Aging is a complex process, and we still don’t know what interventions against aging actually work,” Gradyshev says. “Our findings provide a step forward in aging research, allowing us to more accurately quantify biological age and evaluate the ability of new aging interventions to extend lifespan.”
Disclosure: Kejun Ying and Vadim Gladyshev are inventors on patent applications related to the reported work.
This research was supported by the National Institute on Aging, an Impetus grant, and the Michael Antonov Foundation.
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