Leveraging epigenetic age testing: A roadmap to clinical practice

Cancer, heart disease, and neurodegenerative disease are linked by age. Advanced age serves as the primary risk factor for these classes of disease, although the molecular basis of age still needs further elucidation. To date, advances in molecular aging research have been sluggish, and tools to predict whether an individual ages slowly or quickly have been unpredictable.

The key to shedding light on age as a disease risk factor and the development of interventions requires a clock for biological aging. To this end, various age predictors using DNA methylation (ie, epigenetic clocks) have been developed, according to the authors of a review in Aging Cell.^[]

DNA methylation (DNAm) is an essential epigenetic mechanism that plays a crucial role in gene regulation and cellular differentiation. Unlike changes in the DNA sequence (mutations), epigenetic modifications, such as DNAm, do not alter the genetic code itself but influence how genes are expressed and function.

At a basic level, DNAm involves the addition of a methyl group (CH3) to the cytosine base of DNA, typically occurring at specific cytosine-guanine dinucleotide (CpG) sites. These CpG sites are often clustered in regions known as CpG islands, which are found in or near the regulatory regions of many genes.

DNAm patterns are not static. They can change throughout an individual's life in response to various environmental factors and developmental stages. For example, during early development, the DNA of germ cells undergoes extensive demethylation and subsequent remethylation as cells differentiate into different tissue types. In addition, environmental factors like diet, stress, and exposure to toxins can influence DNAm patterns, impacting gene expression and potentially contributing to disease susceptibility.

Questions remain, however, regarding whether methylation changes used to ‘train’ epigenetic clocks represent other basic cellular or molecular processes, or whether methylation plays a role in the aging process. Moreover, even if these clocks are associated with chronologic age, they may not yield meaningful and actionable results with respect to interventions aimed at underlying biology. Despite this debate, the use of epigenetic clocks with relation to disease is an emerging and exciting field of study.

Epigenetic clocks explained

According to authors writing in the journal eLife, biomarkers based on methylation have shown potential in the field of “geroscience,” which targets age to enhance health outcomes.^[] Methylation biomarkers quantify the number of cells in which a gene locus is methylated. Small but consistent alterations in the methylation of some loci occur with aging, and age can be estimated based on such changes, as noted by the eLife authors.

Epigenetic clocks predict epigenetic age or DNAm age, which correlates with chronological—or calendar—age, according to a review in Genome Medicine.^[] Biological age recapitulates age-related health status. When the epigenetic age is higher than the chronological age, it is referred to as "age acceleration," indicating that the individual's biological age appears older than their actual age.

“DNAm is dynamic, tissue-specific, and is influenced by both genetic and environmental factors. DNAm can precisely track aging through predictors termed ‘epigenetic clocks.’ DNAm has also been found to capture other components of health, such as smoking status, alcohol consumption, obesity, and protein levels,” according to the Genome Medicine authors. 

DNAm of 5‐methyl‐cytosine (5mC) is one of the best predictors of biological age and has been used to design epigenetic clocks. These clocks use CpGs that exhibit delineated changes with age. 

“Key CpGs whose age‐related hyper‐ and hypomethylation correlate with age are selected and weighted in a linear model. The result is an equation, whereby chronological age can be estimated based on the percentage methylation at these key CpG sites in a given sample,” according to the Aging Cell authors.

Epigenetic clocks over time

Early epigenetic clocks included few CpG sites and samples in associated training data sets, thus resulting in lower accuracy. More recently, epigenetic clocks have grown to include more CpGs, samples, and tissues. 

Ideally, the epigenetic clock predicts age in multiple tissue types based on a small number of CpGs. It should be noted that tissues age at different rates. The first multi-tissue age predictor was known as the Horvath or Pan‐Tissue clock developed by Steve Horvath in 2013. It relied on 353 CpGs and exhibited an error of 3.6 years, which was the lowest at the time.^[] 

Because the Horvath and other first-generation epigenetic clocks were trained on chronological age, it was thought that with increasing sample sizes these clocks could “tell” chronological age almost perfectly. 

The issue, however, was that they couldn’t track or quantify biological age. Consequently, second-generation clocks have built upon the foundation laid by the Horvath clock and have been trained on other variables including a phenotypic biomarker of morbidity (ie, PhenoAge); rate of aging (DunedinPACE); and time to all-cause mortality (ie, GrimAge), as noted in Genome Medicine. 

Epigenetics in clinical practice

Although various epigenetic biomarkers have been identified, few have translated into clinical practice—and almost all of them have been in the field of oncology. According to the author of an article published in Frontiers in Cell and Developmental Biology, this low level of crossover into clinical practice “might be attributed to the different demands of either publishing a new finding or establishing a standardized and approved diagnostic procedure.”^[]

“Development of an epigenetic biomarker for clinical application is a long and cumbersome process that is only initiated with the identification of an epigenetic signature,” the author added. “A biomarker for biological age has great potential for individualized medicine to evaluate therapeutic options. In addition, such studies can help to uncover factors that influence aging to adjust lifestyle for healthy aging.”

Currently, there are approved in vitro diagnostic tests for methylation only in the field of oncology. Epigenetic aberrations can look like genomic mutations (eg, DNAm aberrations in DNMT3A in acute myeloid leukemia). 

Behind driver mutations, epigenetic mutations (ie, epimutations) appear to be a leading cause of malignant transformation. Epimutations and episignatures, or epigenetic clocks, can illuminate the malignant clone and be used for initial diagnostics and disease stratification. DNAm patterns can also be used to predict responses to therapeutic regimens and assess minimal residual disease.^[]

An emerging application was highlighted in a study published in Aging. US researchers conducted a 15-year longitudinal study involving 1,682 healthy participants with an average age of 40 years. The aim was to explore the connections between cognitive function, blood-based epigenetic aging, and neuroimaging-based brain aging. The findings revealed that accelerated epigenetic aging and brain imaging changes were both linked to declined cognitive outcomes in the participants.^[]

The authors stated that although epigenetic aging serves as a stable biomarker with robust long-term predictive capabilities for cognitive function, brain aging biomarkers may exhibit more dynamic changes that correlate with the temporal progression of cognitive decline.

“Overall, the results showcase the prognostic significance of biological aging markers for cognitive health. With further validation, epigenetic and brain aging markers may help aid timely identification of individuals at risk for accelerated cognitive decline and promote the development of interventions to preserve optimal functioning across the lifespan,” they continued.

In an associated press release, senior author Lifang Hou, MD, PhD, of Northwestern Medicine, stated, “This could help individuals understand [that] they need to modify their lifestyle, and bring awareness to people with accelerated aging.”^[]

In addition to neurodegenerative disease, age acceleration is implicated in various other conditions and environmental factors, such as Down syndrome, HIV, smoking, vitamin D deficiency, and obesity, as noted in Aging Cell. This suggests that epigenetic age testing may have broad applications in understanding and managing diverse pathologies.