The test predicts aging and mortality risk

In summary, using cheek cell samples, researchers have created a second-generation epigenetic clock called CheekAge that accurately predicts mortality risk. CheekAge is non-invasive and records methylation patterns associated with aging and lifespan, in contrast to previous clocks that were based on blood samples.

Every standard deviation increase in the clock’s age prediction resulted in a 21% increase in the mortality risk, according to a recent study by CheekAge involving over 1,500 participants. With the potential to track age-related diseases, this new tool could offer a straightforward and efficient method of monitoring aging.

Key Facts:.

With a 21 percent increased hazard ratio, CheekAge predicts mortality risk using cheek cells.

Compared to blood-based epigenetic clocks, it provides a non-invasive option.

CheekAge pinpoints important genes associated with longevity and age-related illnesses.

Frontiers as a source.

Not everyone ages at the same pace. Although some supercentenarians may age remarkably slowly as a result of winning the lottery of genetics, many behavioral and lifestyle factors, such as stress, poor sleep, poor nutrition, smoking, and alcohol consumption, are known to accelerate the aging process.

Characterizing the epigenome at prognostic genomic sites allows one to quantify molecular aging because such environmental effects become ingrained in our genome as epigenetic marks.

In the last ten years, researchers have created a number of these “epigenetic clocks,” which are adjusted for chronological age and other lifestyle variables in a sizable population.

The majority of these examined DNA methylation in blood cells, which makes sample collection difficult and stressful for the patient. However, US researchers earlier this year created CheekAge, a second-generation clock based on methylation information in readily collected cells from within the cheeks.

The researchers have now demonstrated for the first time in Frontiers in Aging that CheekAge can correctly predict mortality risk, even when input is provided by epigenetic data from a different tissue.

The study’s first author, Dr. Maxim Shokhirev, who is Head of Computational Biology and Data Science at Tally Health in New York, added, “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.”.

In order to develop or “train” CheekAge, the percentage of methylation at roughly 200,000 sites was correlated with an overall health and lifestyle score, which was thought to reflect variations in physiological aging.

There is a biological clock in place.

The current study by Shokhirev et al. examined the predictive power of statistical programming for mortality from all causes in 1,513 women and men born in 1936 and 1921, respectively, who were tracked throughout their lives by the University of Edinburgh’s Lothian Birth Cohorts (LBC) program.

Linking variations in cognitive aging to lifestyle, psychosocial, genetic, epigenetic, and brain imaging data was one of the objectives of the LBC. The volunteers’ blood cells’ methylome was measured at about 450,000 DNA methylation sites every three years.

The last available methylation time point was used along with the mortality status to calculate CheekAge and its association with mortality risk. The Central Register of the Scottish National Health Service provided the mortality data.

The authors wrote, “[Our results] demonstrate that CheekAge outperforms first-generation clocks trained in datasets containing blood data and is significantly associated with mortality in a longitudinal dataset.”.

Specifically, the hazard ratio of all-cause mortality increased by 21% for every standard deviation increase in cheek age. Accordingly, there is a strong correlation between older adults’ CheekAge and their risk of dying.

“There may be universal death signals throughout tissues because our epigenetic clock trained on cheek cells can predict mortality when measuring the methylome in blood cells,” stated Shokhirev.

This suggests that a straightforward, non-invasive cheek swab may be a useful substitute for researching and monitoring the biology of aging. “.

greatest forecasters.

With more attention, the researchers examined the methylation sites that showed the strongest correlation with death. Genes around or close to these locations may have an effect on longevity or the likelihood of developing age-related illnesses.

For instance, the gene ALPK2, which has been linked to heart health and cancer in animal models, and the gene PDZRN4, which may be a tumor suppressor. A few other notable genes were previously linked to the onset of metabolic syndrome, osteoporosis, cancer, and inflammation.

The last author of the study and Head of Scientific Affairs and Education at Tally Health, Dr. Adiv Johnson, stated that it would be interesting to find out if genes like ALPK2 affect lifespan or health in animal models.

Future research is also required to determine whether CheekAge can be used to capture associations other than all-cause mortality.

Other potential correlations could be the frequency of different age-related illnesses or the length of a person’s “healthspan,” or the time they spend healthy and free from age-related chronic illness and disability. “.

About this news of aging and epigenetics research.

Mischa Dijkstra wrote this.

Frontiers is the source.

Get in touch with Frontiers’ Mischa Dijkstra.

Photo credit: Neuroscience News is acknowledged for this image.

Original Study: Disclosed under open license.

According to Maxim Shokhirev et al., “CheekAge, a next-generation epigenetic buccal clock, is predictive of mortality in human blood.”. Ageing’s Frontiers.

Inabst.

In human blood, CheekAge—a next-generation epigenetic buccal clock—predicts mortality.

More current next-generation epigenetic aging clocks incorporate DNA methylation information more relevant to health, lifestyle, and/or outcomes, whereas earlier first-generation clocks were trained to estimate chronological age as accurately as possible.

Using Infinium MethylationEPIC data from over 8,000 different adult buccal samples, we recently created a non-invasive next-generation epigenetic clock.

We did not evaluate this clock’s capacity to capture mortality, even though it correlated with a number of lifestyle, health, and disease-related variables.

We used CheekAge to analyze the longitudinal Lothian Birth Cohorts of 1921 and 1936 in order to close this gap. In this longitudinal blood dataset, CheekAge was significantly associated with mortality despite missing nearly half of its CpG inputs.

To be more precise, a one standard deviation change was associated with a hazard ratio (HR) of 1.21 (FDR q = 1.66e-6). Compared to the next-generation, blood-trained DNAm PhenoAge clock (HR = 1.23, q = 2.45e-9), CheekAge outperformed all first-generation clocks tested.

We repeatedly eliminated each clock CpG and recalculated the overall mortality association in order to gain a better understanding of the relative significance of each CheekAge input in blood.

Slashing out the CpG cg14386193, annotated to the gene ALPK2, had the largest impact. This DNA methylation site was excluded, which nearly tripled the FDR value (to 4.92e-06).

To gain a deeper understanding of the biology associated with the top annotated CpGs that impact mortality, we also conducted enrichment analyses of these genes.

When combined, we offer significant support for CheekAge and shed light on novel CpGs that are linked to a recently discovered mortality association.