We know only too well that old age is the main risk factor for cancer, cardiovascular disease, and neurodegeneration. Frustratingly, advances in aging research were delayed for many years due to the poor reliability of tools used in predicting the rate of patients’ biological aging. To better understand the aging process and to develop interventions, the anti-aging field needed access to a more effective system for measuring biological age. 

Enter epigenetic clocks. These age predictors, based on DNA methylation (DNAm), have come to prominence over the last decade or so, paving the way for more quantitative studies. New clocks and applications, including forensics, are announced frequently. They represent a genuine breakthrough, even if the precise aspects of aging captured by epigenetic clocks remain unclear. Let’s look into a few of the epigenetic clocks available today, and summarize their strengths and weaknesses.

So, DNAm has emerged as one of the most efficient biomarkers to predict biological age. Epigenetic clocks (also known as DNAm age predictors) are developed using CpGs (DNA regions) that change with age. Most clocks are built using something called a penalized regression model, which helps researchers to select relevant groups of CpGs. The clocks are then used to estimate chronological age based on the percentage methylation at key CpG sites. Improvements and new discoveries are coming thick and fast.

Age Acceleration

Let’s start by looking at age acceleration, which refers to the difference between epigenetic age (eAge) and chronological age (chAge). This is associated with several age-related conditions. For example, patients with obesity, Down's syndrome, Huntington's disease, Sotos syndrome, and Werner syndrome tend to show increased age acceleration. eAge acceleration is also linked to physical and cognitive fitness. Variation in epigenetic aging rates varies greatly depending on sex and ethnic background. 

People who are vitamin D-sufficient have a lower eAge acceleration and lengthier leukocyte telomeres (LTL). Smoking has been connected to an elevated eAge in airway cells and lung tissue (by 4.9 and 4.3 years respectively). In addition, researchers have established that smoking during pregnancy might have a detrimental effect on eAge in offspring. New findings are revealed all the time, but it’s clear that epigenetic clocks have proven themselves to be accurate at predicting biological age. 

The Early Days of Clock Design

The first epigenetic clocks included relatively few CpG sites and samples in their training data sets, compared to later versions. Early researchers created a clock from 68 samples (34 twin pairs) that predicted age in saliva with an average accuracy of 5.2 years. After the initial studies, epigenetic clocks grew in complexity in terms of the number of samples, tissues, and CpGs implemented.  

The first multi-tissue age predictor — the Horvath or Pan-Tissue clock — used 353 CpGs and had a mean error of 3.6 years, unprecedented at the time. The clock was developed using 8000 samples from 82 studies, including more than 50 healthy tissues. The impressive size of the training data represented a new benchmark in clock design. The Horvath clock quickly gained a large fanbase in the scientific community due to its capacity to predict age in multiple tissues using minimal CpGs. 

Design Evolution

The Horvath clock was also used to establish that tissues may age at different rates. For example, it seems that brain tissue ages slower relative to other tissues in the body. However, the clock did not work consistently on cultured cells, particularly fibroblasts. As a result, Horvath set out to develop an epigenetic clock that predicted the age of human fibroblasts, buccal cells, endothelial cells, keratinocytes, lymphoblastoid cells, blood, skin, and saliva samples. This new clock, called the skin and blood (S&B) clock, can predict both in vivo and in vitro tissues with great accuracy.

Other researchers later developed an accurate skin age predictor. Meanwhile, the Zhang clock, while primarily trained to work on blood, is capable of predicting the ages of breast, liver, adipose, and muscle tissue to the same degree of accuracy as the Horvath clock. This clock also outdoes both the Horvath and Hannum clocks when it comes to predicting blood age. It is distinguished by the size of its training data, with over 13,000 samples. 

Limitations and Inaccuracies

Some inaccuracies in epigenetic clocks became evident when predicting the age of younger people (under 20 years old), and the Pediatric-Buccal-Epigenetic (PedBE) clock was created to address this issue. It was aimed specifically for use in newborns to 20-year-olds. This provides a good example of how the accuracy of epigenetic clocks can be boosted — not only by targeting certain tissues, but also specific age groups. However, despite their promise, epigenetic clocks still suffer some limitations at present.

Most epigenetic clocks depend on an expensive Illumina Infinium methylation array, which makes the widespread application of eAge technology impractical in the field of new drug discovery. The Qiagen sequencing platform allows for a more cost-effective approach, but it has its own drawbacks. The use of minimized clocks in forensics is still evolving and cross-validation is missing for most clocks. Researchers have shown that both the Horvath and Hannum clocks routinely underestimate the age of older people.

Promise For The Future

In summary, eAge prediction is an exciting and rapidly growing new field that has already radically transformed the world of experimental gerontology. As the number and variety of epigenetic clocks increases, so too does humanity’s understanding of biological age. It is still early days, however. Although linear models are useful in predicting the eAge of individuals between the ages of 20 and 70, there is weaker accuracy outside of these ages.

Scientists are also experimenting with a range of other techniques that do not rely exclusively on DNAm data. Composite clocks such as PhenoAge and GrimAge are the first steps in that direction. 

References:

1. Baker, G. T., & Sprott, R. L. (1988). Biomarkers of aging. Experimental Gerontology, 23(4-5), 223– 239

2. Bacalini, M. G., Deelen, J., Pirazzini, C., De Cecco, M., Giuliani, C., Lanzarini, C., Ra-vaioli, F., Marasco, E., Van Heemst, D., Suchiman, H. E. D., Slieker, R., Giampieri, E., Recchioni, R., Marcheselli, F., Salvioli, S., Vitale, G., Olivieri, F., Spijkerman, A. M., DollCrossed, M. E., … Garagnani, P. (2017). Systemic age-associated DNA hypermethylation of ELOVL2 Gene. In vivo and in vitro evidences of a cell replication process. Journals of Gerontology - Series A Biological Sciences and Medical Sciences, 72(8), 1015– 1023.

3. Arneson, A., Haghani, A., Thompson, M. J., Pellegrini, M., Kwon, S. B., Vu, H., Yao, M., Li, C. Z., Lu, A. T., Barnes, B., Hansen, K. D., Zhou, W., Breeze, C. E., Ernst, J., & Horvath, S. (2021). A mammalian methylation array for profiling methylation levels at conserved sequences. bioRxiv, 2021.01.07.425637.

4. Aliferi, A., Ballard, D., Gallidabino, M. D., Thurtle, H., Barron, L., & Syndercombe Court, D. (2018). DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models. Forensic Science International: Genetics, 37, 215– 226.

5. Al Muftah, W. A., Al-Shafai, M., Zaghlool, S. B., Visconti, A., Tsai, P.-C., Kumar, P., Spector, T., Bell, J., Falchi, M., & Suhre, K. (2016). Epigenetic associations of type 2 diabetes and BMI in an Arab population. Clinical Epigenetics, 8(1).

6. Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., Harrington, H., Israel, S., Levine, M. E., Schaefer, J. D., Sugden, K., Williams, B., Yashin, A. I., Poulton, R., & Moffitt, T. E. (2015). Quantification of biological aging in young adults. Proceedings of the National Academy of Sciences of the United States of America, 112(30), E4104– E4110.

7. Bergsma, T., & Rogaeva, E. (2020). DNA methylation clocks and their predictive capacity for aging phenotypes and healthspan. Neuroscience Insights, 15, 263310552094222.

8. Binder, A. M., Corvalan, C., Mericq, V., Pereira, A., Santos, J. L., Horvath, S., Shepherd, J., & Michels, K. B. (2018). Faster ticking rate of the epigenetic clock is associated with faster pubertal development in girls. Epigenetics, 13(1), 85– 94. 

9. Bocklandt, S., Lin, W., Sehl, M. E., Sánchez, F. J., Sinsheimer, J. S., Horvath, S., & Vilain, E. (2011). Epigenetic predictor of age. PLoS One, 6(6), e14821.

10. Breitling, L. P., Saum, K.-U., Perna, L., Schöttker, B., Holleczek, B., & Brenner, H. (2016). Frailty is associated with the epigenetic clock but not with telomere length in a German cohort. Clinical Epigenetics, 8(1), 1– 8.