Why do people age, and more importantly, can we reverse it? Whether you’re a biologist investigating longevity or a layperson in a reflective mood, these are profound, complicated questions. Since antiquity, mankind has been imagining or searching for ways to usurp death through mythology, the protoscience of alchemy, and now, modern science.

But, before we all finally get to wet our lips on the fountain of youth or wrap our mitts around the philosopher’s stone, we need to ask ourselves a question. How should we measure the process of ageing?

Chronological Age, Biological Age & Epigenetic Processes as Biomarkers

The problem with chronological age, defined as time since birth, is that it’s an imperfect measure of the ageing process (Baker & Sprott, 1988; Warner, 2004). While it serves as a risk indicator for age-related illness, it doesn’t tell us anything about the underlying molecular mechanisms of ageing.

So how else are we supposed to measure ageing?

Over the last thirty years, researchers have set out to investigate whether they could identify biomarkers that would serve as more robust indicators. Since then, epigenetic biomarkers, specifically direct DNA methylation modifications, have proven promising (Horvath, 2013; Quach, 2017).

DNA methylation is an epigenetic process whereby a methyl group (CH3) is added to a cytosine residue belonging to a CpG dinucleotide. This molecular mechanism is intended to regulate gene expression, giving it an essential role in cellular development, proliferation, and maintenance. There are 28 million of these CpG dinucleotides in the human genome, and as we age, alterations to our DNA methylation profiles occur (Thomas, 2014).

The epigenetic clock theory of ageing devised by Horvath and Raj hypothesizes that these epigenetic developmental and maintenance programmes have an unintended consequence; they drive ageing. These programmes leave methylation footprints across the genome, which epigenetic age estimators analyse to determine the epigenetic age or DNA methylation age (DNAm age) of a cell, tissue or organ (Horvath & Raj, 2018).

These age estimators – powerful, trained mathematical algorithms that trawl massive genomic data sets – determine a person’s biological age rather than their chronological age. The definition of biological age is still somewhat ambiguous, but it's often described as an organism’s phenotypic or physiological age. The critical point here is that biological and chronological age are not necessarily the same in every individual (Horvath & Raj, 2018).

With an accurate biological age estimator, examining the differences between biological age in individuals of the same chronological age will help us glean valuable insight into how ageing occurs, what drives it, what accelerates it, and what might reverse it.

The Horvath Epigenetic Clock

The Horvath clock, developed by Professor Steve Horvath in 2013, is the most accurate biological estimator available to date. It looks at the methylation states of 353 different CpG dinucleotide sites to determine the biological age of a cell, tissue or organ (Horvath, 2013; Horvath & Raj, 2018).

While there are a few different epigenetic age estimators available, Horvath's clock is unique because it's the first multi-tissue age estimator, meaning it can accurately determine biological age from all tissue and cell types within an individual across an entire lifespan (excluding sperm) (Horvath & Raj, 2018). This previously elusive multi-tissue quality has led to the widespread use of Horvath's clock in the anti-ageing field.

Epigenetic Age Acceleration

From birth to death, our epigenetic age increases alongside our chronological age. Over time, epigenetic age can diverge from chronological age. This divergence can be described in terms of acceleration.

A positive epigenetic acceleration indicates that a person’s underlying tissues age at a rate faster than expected when their chronological age is taken into consideration. A negative value means the opposite - that your tissues are ageing at a slower than expected rate (Horvath & Raj, 2018).

Interestingly, the use of Horvath’s clock found that the rate of this acceleration is not linear and constant throughout life. In fact, the first year of life sees a marked rapid increase in epigenetic age, followed by a nonlinear deceleration until approximately 20 years of age. Only after 20 does the acceleration settle to a slower, constant rate (Horvath & Raj, 2018).

Studies utilising Horvath's clock have associated a panoply of age-related diseases to positive epigenetic acceleration. This includes Alzheimer’s disease, cancer, Down's syndrome, and osteoarthritis to name a few (Horvath, 2015; Zheng, 2016; Levine, 2015; Vidal, 2016).

Anti-ageing Interventions

Now that a suitable “measuring stick” has been developed, the underlying promise of utilising Horvath's clock is the identification of anti-ageing interventions in humans. It's important to note that DNA methylation is a reversible process. So, in theory, we should be able to turn back the epigenetic clock, and by extension, ageing.

Currently, Yamanaka factors are showing promise in vitro. When expressed, this famous group of transcription factors is able to induce reversion of somatic cells to pluripotent stem cells, essentially reversing the epigenetic clock (Horvath, 2013).

In vivo, haematopoietic stem cell transplantation from a young donor to an old recipient has been shown to reset the epigenetic age of the recipient's blood to that of the donor, albeit temporarily (Weidner, 2015). While the rejuvenation effect is short term, it still nonetheless provides an exciting insight into epigenetic ageing and how it might be addressed clinically in the future.

Horvath's clock is an important and promising development in the longevity field. It has moved the discussion away from asking whether reliable biomarkers of ageing can be developed, to why DNA methylation holds potential, and what it can tell us about the biology of ageing. With a validated form of age measurement available to researchers, perhaps the fountain of youth will soon turn myth into reality.

References

Baker, G. T., & Sprott, R. (1988). Biomarkers of aging. Experimental Gerontology, 223-229.

Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology, 14.

Horvath, S. (2015). Accelerated epigenetic aging in Down syndrome. Aging Cell, 491–495.

Horvath, S., & Raj, K. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics, 371–384.

Levine, M. e. (2015). Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging, 1198—1211.

Quach, A. e. (2017). Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging, 419–446.

Thomas, M. e. (2014). DNA Methylation Biomarkers: Cancer and Beyond. Genes, 821–864.

Vidal, L. e. (2016). Specific increase of methylation age in osteoarthritis cartilage. Osteoarthritis and Cartilage, S63.

Warner, H. R. (2004). The Future of Aging Interventions: Current Status of Efforts to Measure and Modulate the Biological Rate of Aging. The Journals of Gerontology, B692–B696.

Weidner, C. I. (2015). Epigenetic aging upon allogeneic transplantation: the hematopoietic niche does not affect age-associated DNA methylation. Leukemia, 985–988.

Zheng, Y. e. (2016). Blood Epigenetic Age may Predict Cancer Incidence and Mortality. EBioMedicine, 68-73.