We don’t all age at the same rate. But while some seniors can age incredibly slowly thanks to winning the big trophy of good genes, a multitude of behavioral and lifestyle factors are known to accelerate aging, including stress, insufficient sleep, poor diet, smoking and the alcohol.
Because such environmental effects are imprinted in our genome as epigenetic marks, it is possible to quantify molecular aging by characterizing the epigenome in prognostic genomic areas.
Earlier this year, scientists in the US developed a second-generation clock, called CheekAge, which relies on easy-to-collect methylation data from inside the cheeks.
CheekAge accurately predicts mortality risk
The team demonstrated for the first time that CheekAge can accurately predict – and even if epigenetic data from another tissue is used as a starting point for analyses.
“We also demonstrate that all specific methylation fields are particularly important for this correlation, revealing potential links between specific genes and processes and mortality,” said Dr. Maxim Shokhirev, Head of Computational Biology and Data Science at Tally Health from New York.
CheekAge was developed or “trained” by correlating methylation fraction at approximately 200,000 sites with an overall health and lifestyle score reflecting presumed differences in physiological aging, according to
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 between 1921 and 1936 and followed throughout their lives by the Lothian Birth Cohorts (LBC) programme. of the University of Edinburgh.
Strongly associated with mortality risk in older adults
One of the goals of the LBC was to associate differences in lifestyle and psychosocial factors with biomedical, genetic, epigenetic, and brain imaging data.
Every three years, the volunteers had their blood cell methylome measured at approximately 450,000 DNA methylation sites. The last available methylation time point was used together with mortality status to calculate CheekAge and its association with mortality risk.
“(Our results show that) CheekAge is significantly associated with mortality in a longitudinal dataset and outperforms first-generation clocks trained in datasets containing blood data,” the authors concluded.
Specifically, for every one standard deviation increase in CheekAge, the hazard ratio of all-cause mortality increased by 21%. This means that CheekAge is strongly associated with mortality risk in older adults.
