Strong evidence exists linking methylation changes in the DNA with aging and many diseases related to it. Aging causes organ systems to falter in efficacy, resulting in greater chances of acquiring diseases (M & GP, 2015). Genes play an important role in the process of aging and scientists are studying how changes in the expression of certain genes lead to detrimental effects in the human body. One of the many changes seen in DNA expression is epigenetic change. Several types of epigenetic changes exist, like alterations in histones, RNAs which are non-coding, DNA methylation, acetylation, etc (BA et al., 2015). DNA methylation is the process by which a methyl compound is added to the structure of DNA, making the genes in that region increase or decrease in activity. Studies have shown that DNA methylation affects aging and may even regulate the aging process (Li et al., 2020).

DNA methylation process includes the addition of methyl groups either from cytosine from the fifth (5mC) or adenine from the sixth position(6mA). 5mC mostly occurs on cytosine which comes before a guanine, known as CpG sites (Dor & Cedar, 2018). Various enzymes are associated with DNA methylation, the expression of which varies aging and conditions related to old age. Although DNA methylation is supposed to continuously change according to the status of an individual, these changes in methylation begin to become unreliable, leading to aging-related degeneration in the body. These “changes” can be put to use to develop an aging clock, which nowadays are called the epigenetic aging clocks (C. G. Bell et al., 2019).

Chronological age is not as reliable as biological age in predicting long-term health outcomes. Biological age functions better as a measure of the body’s aging status (Bai, 2018). Many markers have been developed to accurately measure biological age, out of which DNA methylation is by far the one with the most chances of success (Horvath & Raj, 2018). There are certain particular changes in DNA methylation, which only take place in association with aging, making DNA methylation based clocks an accurate measure of biological age (J. T. Bell et al., 2012). The level of DNA methylation is used to calculate chronological age, while methylation based CpGs along with complex mathematical formulas is used to calculate the biological age (Maegawa et al., 2010).

The methylation of DNA affects the aging process in multiple ways. A decrease in protein balance is related to DNA methylation. Increased protein degradation and decreased production translates into aging. DNA methylation inhibits autophagy, which causes aging and hence substances restricting methylation have an anti-aging effect on the body (Zhou et al., 2019). Mitochondrial dysfunction is caused by enhanced DNA methylation, especially of Elovl2 (Elongation of very long chain fatty acids protein 2), by causing increased levels of stress in the endoplasmic reticulum (X et al., 2020). Stem cell inactivation has been seen to be caused by DNA methylation. Hematopoietic stem cells (HSCs) undergo changes in renewal according to DNA methylation levels in the body (Adelman et al., 2019). Any abnormalities in the DNA methylation process can adversely affect the immune system leading to immunosenescence, so much so that methylation is hypothesized to be a contributing factor for the incidence of inflammation in several conditions like cancer and neurodegeneration (Castellano-Castillo et al., 2018; Neal & Richardson, 2018).

Many epigenetic clocks have been devised over the last few years to enable individuals to measure their biological age and work towards finding effective ways to prevent further aging. Hannum in 2013 developed a clock that included 71 CpG sites and also involved other factors like BMI, gender, ethnicity and diabetes (Hannum et al., 2013). Horvath increased the number of CpG sites used to 353 and derived these from different tissue samples, using only methylation as the sole component for his aging clock (Horvath, 2013). Levine devised a clock in the year 2018, using only one dataset, known as the PhenoAge clock. He used variables like albumin, blood glucose, white blood cell count, serum creatinine, alkaline phosphatase and also age, to calculate a composite (Levine et al., 2018). The result the clock gives is related to age but is not age itself. The clock utilized 513 CpGs and used a linear function model to calculate the age from the composite result obtained earlier. This calculated age would then be compared with chronological age and the difference in these two ages was used to predict risk of mortality. GrimAge was another clock devised in 2019 which used the data from the Framingham Heart Study and a grand total of 1030 CpGs (Lu et al., 2019).

The Horvath and Hannum clocks were earlier models which are less accurate than the clocks devised later on. These two clocks relied on cross-sectional data, collected all at once from people of different ages, and devised a model based on that data. While the clocks introduced afterward, like PhenoAge and GrimAge utilized data collected over a longer period of time from the same group of individuals (Levine, 2020). Characteristics of these clocks have been measured and compared in the past to deduce which clock is more accurate and predictive. Four clocks, including three discussed here: Horvath, Hannum and Levine were compared with each other and a newer clock known as the Cortical clock in the Religious Orders Study and the Rush Memory and Aging Project (Bennett et al., 2018). Their effectiveness was measured across three specimens such as CD4+ cells, cells from dorsolateral prefrontal cortex (DLPFC) and cells from posterior cingulate gyrus (PCC) (Grodstein et al., 2020). Pearson correlation was measured for each clock across these three different samples and then analyzed. Correlations for the Hannum, Horvath and PhenoAge clocks ranged from 0.5 to 0.7 with a statistically significant p value, while the newer Cortical clock reported the highest correlation of 0.83, especially in the DLPFC tissue samples. It was seen that the epigenetic age calculated by all four clocks was lower when compared to chronological age, with the gap being the least for the Cortical clock. Pearson correlations decreased for all four clocks when measured in paired samples from blood and brain tissue. Future clocks may consider using CpG sites that are consistent in both blood and the brain to better correlate with neurodegenerative diseases. The biomarker provided by the GrimAge clock, known as AgeAccelGrim, by far predicts the best difference in ratios between hazard ratios of the top and bottom 5% of individuals.

Measuring even a few CpG sites provides information that can be used to track one’s health and aging speed. Evidence exists stating that small fragments of these epigenomes may be the cause behind aging, rather than just being an after-effect of the aging process. Further investigations and research into this realm may even see epigenetics becoming the prime aspect that drugs and therapeutics in the future target.