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  • Writer's pictureMatilde Mantovani

Methylation-based aging clocks

The role of this specific epigenetic modification in aging



At Maximon, we are on a mission to constantly deliver the latest scientific advancements in Longevity and Aging. We carefully read and select the most relevant and impactful scientific publications to keep you informed about the rapidly evolving developments in this field. In the present article, we are providing insights about epigenetics and the role of epigenetic modifications in the aging process.



What is epigenetics?


Epigenetics is defined as the study of how the environment influences the functioning of our genes, without altering the DNA sequence. Beyond "traditional" genetics, we now know that external factors, such as DNA-methylation and Histone modifications, can regulate gene expression, switching genes on or off. Epigenetic changes are typically mediated by chemical modifications to DNA or the proteins associated with DNA, such as histones. These modifications can influence the accessibility of genes to the cellular machinery responsible for gene expression.


Have you ever wondered why our neurons differ from our skin cells, or why muscle cells differ from liver cells, despite all our cells sharing the same genome? The answer lies in epigenetic modifications, which lead cells to express specific genes, thus defining their functions and phenotypic traits. Epigenetic changes can be influenced by various factors, such as environmental factors and lifestyle. They can play a crucial role in various biological processes, including development, aging, and the onset of diseases, including cancer.


DNA-Methylation


As mentioned in the previous chapter, DNA-methylation is one of the epigenetic markers that influence gene expression. Biochemically speaking, DNA-methylation is the addition of a methyl group (CH3) to the DNA molecule. This modification primarily occurs at specific sites within the DNA sequence where a cytosine base (one of the four nitrogenous bases in DNA) is followed by a guanine base, forming what is known as a CpG site.




Methylation levels of DNA are correlated with aging


Aging is associated with epigenetic dysregulation. More specifically, as an individual ages, it goes through natural and stochastic changes in epigenetic marks, particularly DNA methylation patterns, known as epigenetic drift.

Epigenetic drift is not uniform across all individuals. Different people may experience different patterns of epigenetic changes as they age. However, one of the key observations in the field of epigenetics and aging is that there are specific patterns of DNA methylation changes that are associated with age. These epigenetic changes can be measured to predict an individual’s biological age via specific mathematical tools called "epigenetic clocks". Epigenetic clocks refer to a set of molecular markers or patterns in an individual's epigenome (the chemical modifications to DNA and associated proteins) that change over time in a predictable manner. In simple words, by analysing the epigenetic changes in an individual's DNA, it is possible to estimate how biologically "old" or "young" a person's cells or tissues are compared to their actual chronological age.



Methylation-based clocks


For this reason, epigenomics can be used to identify and quantify changes in the epigenome. Aging clocks are computational models employing mathematical formulas to determine a person's age by analysing characteristics that changes with time across lifespan, such as DNA methylation patterns. These clocks use the concept of epigenetic drift to predict how quickly or slowly a person is aging at the molecular level, which may differ from their chronological age.



Rutledge J, Oh H, Wyss-Coray T., Nat Rev Genet. 2022 Dec;23(12):715-727.

There are different types of methylation-based clocks:


- First generation clocks: These clocks were groundbreaking in demonstrating the relationship between epigenetic changes and the aging process, and they provided a way to estimate biological age, which could sometimes differ from chronological age. Recently, they have been used to estimate mammalian tissue age with high accuracy, suggesting that aging is evolutionary conserved across all mammals.


- Second generation clocks: These clocks are an improvement over first-generation ones, as they are more accurate predictors of morbidity and mortality. They are trained on phenotypic traits, reflecting functional biology rather than just chronological age.


- Third generation clocks (page of aging clocks): These clocks are unique because they are trained on collecting data at several time points from the same individual (longitudinal data), rather than collecting one time point from different age-groups (cross-sectional data).


Variations exist among epigenetic clocks. The most effective ones today are those trained on functional biomarkers (like lung function, blood pressure, grip strength, and brain size) as well as indirect markers (such as blood biomarkers like cholesterol) alongside chronological age. The second and third generations clocks are the only ones extensively trained in this manner.

Current challenges


However, there are current challenges that limit the use of these clocks. Firstly, the absence of a mechanistic understanding of how the selected methylation sites relate to functional biology. In other words, it's unclear whether these sites have a causal relationship with aging biology or simply correlate with the aging process.

In addition, individuals and individual's organs are aging at different rates, creating a large inter and intra-individual variability.


For all these reasons, different clocks are sensitive to different factors, raising the question of which one would work best and whether they can be applied universally.


To conclude, DNA-methylation is closely connected with aging through its role in regulating gene expression and its association with age-related changes in the genome. Epigenetic clocks and studies on age-related DNA methylation changes provide valuable insights into the molecular mechanisms of aging and age-related diseases. Future challenges include further understanding these connections and the implications they might have for developing interventions to promote healthier aging and prevent age-related illnesses.



References:




Measuring biological age using omics data, Rutledge et al., Nat Rev Genet 2022










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