Assessment of biological age via patterns of DNA methylation is an active area of development. Methylation of CpG sites on the genome is a form of epigenetic mark that regulates the expression of specific proteins. Methylation status of these sites changes constantly, cell by cell, in response to environmental circumstances. Some of these changes are characteristic of aging, and the ability to assess DNA methylation across the whole genome thus led to the discovery of weighted combinations of CpG site methylation status that strongly correlate with age and disease status. The process of understanding what these combinations actually represent, in terms of underlying processes of damage and reaction to damage, has only barely started, however.
In contrast to the steady pace of chronological age, the pace of biological age varies among individuals and may predict distinct aspects of aging at different life stages. As chronological age does not sufficiently represent fundamental aging processes, methods to measure biological aging have been developed, which is important for assessing strategies to slow down biological aging and extend healthspan. Technical breakthroughs have led to the discovery of several molecular markers of aging, including epigenetic biomarkers.
Among biomarkers of aging, such as telomere length (TL), metabolomic, transcriptomic, and proteomic variations, the most promising are based on the DNA methylation (DNAm) of cytosines at CpG dinucleotides, representing one of the key epigenetic mechanisms altering gene expression or splicing. The cumulative assessment of DNAm levels at age-related CpGs could be used as a DNAm clock, which may mirror biological aging. Although some clinical biomarkers outperform DNAm clocks in reflecting morbidity and mortality, the advantage of DNAm clocks is their ability to measure either multitissue or cell-/tissue-specific aging. DNAm clocks could help explain why some individuals stay healthy, whereas others develop age-related neurodegenerative diseases.
Several studies support the link between DNAm clocks and biological age. DNAm-age acceleration (difference between DNAm-age and chronological age) was associated with major neurodegenerative diseases. Similarly, HIV-infected individuals exhibit premature aging based on methylome-wide changes. Furthermore, individuals with Werner syndrome or Down syndrome also display accelerated DNAm clocks. In contrast, DNAm-age in centenarians is on average 9 years younger than their chronological age. However, it is mostly unclear what the underlying molecular mechanisms of DNAm clocks are. Do they reflect similar aspects of the aging process? What is their capacity to predict risk of decline before disease onset and therapeutic effectiveness aiming to extend healthspan? Various confounders may influence the outcome of these age predictors, including genetic and environmental factors, as well as technical differences in the selected DNAm arrays. These factors should be taken into consideration when interpreting DNAm clock predictions.