There has long been a great interest in human aging processes and the desire to understand the underlying mechanisms. A significant breakthrough has been made in recent years when aging has been linked with global changes in DNA methylation. Since then dozens of differentially methylated regions (DMRs) in the genome have been identified to be correlated with age and several age prediction models have been developed. Various systems rely on analysis of several to hundreds of CpG sites to predict age. A method that allows age prediction in blood that is popular in forensics includes analysis of 5 markers suggested by Zbieć-Piekarska et al. (2015)
and studied with pyrosequencing. The developed model was implemented in a simple age calculator
and markers included have been validated in many following studies. Subsequently, first age predictive models for saliva and semen
have also been proposed by a group led by H.Y Lee.
Although DNA methylation has been shown to be highly predictive for individual's age with far greater accuracy when comparing to other methods previously used for age prediction, the role of DNA methylation in aging is still poorly understood and need to be better studied before the potential of DNA methylation can be fully used. We would like to know why some people get older faster and therefore are more prone to age-related diseases. Indeed, individuals of the same chronological age may exhibit greatly different susceptibilities to age-related diseases. Consequently, chronological age is believed to constitute only a very rough measure of aging processes and therefore there has been a strong demand to identify reliable biomarkers of aging that would properly capture the differences in aging rates and susceptibilities to age-related diseases. Increasing the knowledge on aging may have significant meaning to medicine by developing the early diagnostics tools useful for prediction of a risk of age-related diseases and increased mortality risk. Moreover, the results may help to identify promising molecular targets of interventions that extend the human health span and lifespan. Increase in the knowledge on aging may also have practical application in cosmetic industry by developing products to prevent, slow down or reverse the clinical signs of phenotypic aging. Finally, these studies may find practical applications in forensics and anthropology. Accurate prediction of chronological age of a person based on analysis of biological traces left at the crime scene or human remains may speed up the process of investigation and personal identification. Our chronological age is recorded in various databases, and thus obtaining such information gives the police an easy opportunity to select potential suspects. Because aging is inherently connected with changes in human appearance (e.g. hair loss, hair greying, skin wrinkles, changes in pigmentation), knowledge about biological age (instead of chronological age) allows us to more accurately predict our physical appearance.
As of yet it is unclear to what extent particular DNA methylation markers are associated with chronological age or are they rather a measure of biological age of the organism. Age-correlated DMRs are often classified in one of two groups. The epigenetic clock sites are correlated with age in a consistent way across different individuals and can be used to predict chronological age. The second group of DMRs includes markers affected by higher inter-individual variability that are more prone to the influence of intrinsic (genetic) or extrinsic (environmental) factors and measure real biological age. CpG methylation markers which methylation level reflects biological age of the organism can be used to assess age acceleration rate. Age acceleration rate is defined as the discrepancy between DNA methylation age and chronological age with positive value suggesting that a person is biologically older than chronological age and negative value suggesting that individual is biologically younger than chronological age indicates. Age acceleration is a biologically meaningful biomarker and in several studies that have been conducted up to date it has been linked with aging-related diseases, stress and lifestyle and particularly can predict cancer, cardiovascular diseases and mortality. Although it has been postulated that the rate of epigenetic aging is in ~40% controlled by inherited DNA sequence, so far only few genes (e.g. TERT) have been associated with age acceleration and further studies are needed. The remaining proportion of variation (~60%) is suggested to be attributed to the environmental factors but it needs to be elucidated which age-correlated CpG sites are sensitive to the influence of genes and/or environment. Therefore, although the picture arising from multiple studies indicates that there is a great potential in DNA methylation analysis, many questions need to first be addressed.