CheekAge Can Accurately Predict Mortality Risk, Study Shows

We don’t all age at the same rate. But while some supercentenarians may age exceptionally slowly due to hitting the genetics jackpot, a multitude of behavioral and lifestyle factors are known to accelerate aging, including stress, lack of sleep, poor nutrition, smoking and alcohol. Since such environmental effects are imprinted on our genome in the form of epigenetic marks, it is possible to quantify molecular aging by characterizing the epigenome at prognostic genomic locations.

Over the past decade, scientists have developed several such “epigenetic clocks,” calibrated to the chronological age and various lifestyle factors of large numbers of people. Most of them focused on DNA methylation in blood cells, which makes sample collection costly and stressful for the patient. But earlier this year, US scientists developed a second-generation clock, called CheekAge, that relies on methylation data in easy-to-collect cells from the inside of the cheeks.

Now, in Frontiers in Agingthe team showed for the first time that CheekAge can accurately predict mortality risk – and even if epigenetic data from another tissue is used as input.

We also demonstrate that specific methylation sites are especially important for this correlation, revealing potential links between specific genes and processes and human mortality captured by our clock.”

Dr. Maxim Shokhirev, first author of the study and head of Computational Biology and Data Science at Tally Health, New York

CheekAge was developed or “trained” by correlating fractional methylation at approximately 200,000 sites with an overall health and lifestyle score, reflecting putative differences in physiological aging.

The biological clock is ticking

In the current study, Shokhirev and colleagues used statistical programming to see how well it predicted all-cause mortality in 1,513 women and men, born in 1921 and 1936 and followed throughout their lives by the University of Edinburgh’s Lothian Birth Cohorts (LBC) program. One of the goals of LBC was to link differences in cognitive aging to lifestyle and psychosocial factors and to biomedical, genetic, epigenetic, and brain imaging data. Every three years, volunteers had their blood cell methylome measured at approximately 450,000 DNA methylation sites. The last available methylation time point was used along with mortality status to calculate CheekAge and its association with mortality risk. Mortality data were obtained from the Scottish National Health Service Central Register.

“(Our results show that) CheekAge is significantly associated with mortality in a longitudinal dataset and outperforms first-generation clocks trained on datasets containing blood data,” the authors concluded.

Specifically, for every single standard deviation increase in CheekAge, the all-cause mortality risk rate increased by 21%. This means that CheekAge is strongly associated with mortality risk in older adults.

“The fact that our epigenetic clock trained in cheek cells predicts mortality by measuring the methylome in blood cells suggests that there are common mortality signals in tissues,” Shokhirev said.

“This implies that a simple, non-invasive cheek swab could be a valuable alternative for studying and tracking the biology of aging.”

Strongest Predictors

The researchers looked in more detail at the methylation sites that were most strongly associated with mortality. Genes located around or near these sites are potential candidates for impacting lifespan or risk of age-related diseases. For example, the PDZRN4 gene, a possible tumor suppressor, and ALPK2, a gene implicated in cancer and heart health in animal models. Other genes that stood out had already been implicated in the development of cancer, osteoporosis, inflammation and metabolic syndrome.

“It would be intriguing to determine whether genes like ALPK2 impact lifespan or health in animal models,” said Dr. Adiv Johnson, senior author of the study and head of scientific affairs and education at Tally Health.

“Future studies are also needed to identify what associations other than all-cause mortality can be captured with CheekAge. For example, other possible associations could include the incidence of various age-related diseases or the length of the ‘health span,’ the period of healthy life a life free from chronic diseases and age-related disabilities.”