What is the cause of our age? New “clocks” developed by researchers may help point to answers. Investigators at Brigham & Women’s Hospital, a founding member of the Mass General Brigham Health Care System, unveil a new form of epigenetic clock — a machine learning model that uses DNA structure to predict biological age. is designed. The novel model distinguishes between genetic differences that slow and accelerate aging, predicts biological age and evaluates antiaging interventions with increased accuracy. The results are published in Nature Aging.

“Previous clocks understood the relationship between methylation patterns and traits we know are associated with aging, but they didn’t tell us what factors make someone’s body age faster or slower. We have created the first clock to distinguish between cause and effect,” said corresponding author Vadim Gladyshev, PhD, a principal investigator in the Division of Genetics at BWH. “Our clocks distinguish between changes that accelerate and counteract aging in order to predict biological aging and assess the efficacy of antiaging interventions.”

Aging researchers have long recognized the link between DNA methylation — changes in our genetic makeup that shape gene function — and its impact on the aging process. In particular, specific regions of our DNA, called CpG sites, are more strongly associated with aging. While lifestyle choices, such as smoking and diet, affect DNA methylation, so does our genetic inheritance, explaining why people with similar lifestyles can age at different rates.

Current epigenetic clocks use DNA methylation patterns to predict biological age (rather than the actual age history of our cells). However, until now, none of the current clocks have distinguished between methylation differences that cause biological aging and those that are simply related to the aging process.

Using a large genetic data set, first author Cajon (Albert) Young, a graduate student in the Gladiator lab, performed epigenome-wide Mendelian randomization (EWMR), which randomizes the data and DNA. technique used to establish causality between the structure and observable traits, eight characteristics associated with aging at 20,509 CpG sites. Eight aging-related traits include age, extreme longevity (defined as greater than 90 percent survival), health span (age at first occurrence of major age-related disease), frailty index (health A measure of one’s frailty based on the summation of (deficits during their aging), self-rated health, and three broad age-related measures including family history, socioeconomic status, and other health factors. .

With these traits and their associated DNA sites in mind, Young created three models, called CausAge, a general clock that predicts biological age based on causal DNA factors, and DamAge and AdaptAge. , which includes only deleterious or protective changes. The investigators then analyzed blood samples from 7,036 people aged 18 to 93 from the “Generation Scotland Cohort” and eventually trained their model on data from 2,664 people in the group.

With these data, the researchers created a map identifying human CpG sites that cause biological aging. This map allows researchers to identify biomarkers that cause aging and to assess how different interventions promote longevity or accelerate aging.

The scientists tested the accuracy of their watches on data collected from 4,651 people in the Framingham Heart Study and the Normative Aging Study. They found that DamAge is associated with adverse outcomes, including mortality, and AdaptAge is associated with longer life, suggesting that age-related damage contributes to mortality risk while protective changes in DNA methylation last longer. Can contribute to aging.

Next, they tested the clocks’ ability to predict biological age by reprogramming stem cells (turning specialized cells, such as skin cells, back into a smaller, less defined state where they can differentiate in the body). types of cells can be formed). When applying clocks to newly transformed cells, DamAge decreased, indicating a reduction in age-related damage during reprogramming, while AdaptAge showed no significant pattern.

Finally, the team tested the performance of their watches in biological samples from patients with various chronic conditions, including cancer and high blood pressure, as well as samples damaged by lifestyle choices such as cigarette smoking. DamAge increased consistently under conditions associated with age-related damage, whereas AdaptAge decreased, effectively eliciting protective adaptation.

“Aging is a complex process, and we don’t yet know what interventions work against it,” Gladyshev said. “Our findings represent a step forward for aging research, allowing us to more accurately quantify biological age and evaluate the potential of new antiaging interventions to increase longevity.”