Science of Epigenetic Testing

What are epigenetics?

All cells in your body have the same DNA. Still, a skin cell knows it needs to do skin cell stuff and not muscle cell stuff. Our cells get their specific function or identity by turning on different genes. Turning on genes is done by DNA methylation and your DNA methylation pattern is your epigenome.

History of biological age tests («age clocks»)

1st generation

In 2013 some researches realized that our epigenome is constantly changing and that this change correlates with age. By using machine learning on many epigenetic samples to predict chronological age, the first generation of biological age tests were born.[1] With these clocks, a result, e.g., 40, means you are epigenetically similar to an average person of age 40.

2nd generation

However, chronological age does not account for the biochemistry of our bodies. This is why some 50 year old people look like they're 30 and vice versa. To account for these differences, i.e., your underlying health, second generation clocks were developed.[2] With these clocks, a result, e.g., 40, means you have a similar disease risk to an average person of age 40.

3rd generation

Despite the improvement, second generation clocks still had shortcomings. These clocks still cannot tell you if you are currently living a healthy life. A historically unhealthy person that has recently started with healthy habits and a historically healthy person that recently let themself go could have the same biological age signature. This is why the third generation of clocks, the pace of aging, was developed. There is currently just one pace of aging clock, called DunedinPACE.[3] It was developed by following the population cohort of Dunedin born in 1972/73 since their birth until now. DunedinPACE is the most precise clock with the best risk prediction profile currently available. With these clocks, a result, e.g. 0.8, means you currently age 0.8 biological years per calendar year.

The science behind our test

Our epigenetic age test will give you a result for clocks from all generations.

Pace of aging

Our 3rd generation clock, the DunedinPACE, is the most predictive currently available. You will get a result between 0.6 and 1.4 that can roughly be interpreted as the years you age in one calendar year. With each 0.1 reduction of your pace, your mortality risk decreases by 26-65%, and your risk of morbidity such as cardiovascular disease or stroke decreases by 23-39%.[3]

Telomere length

This 2nd generation clock is the result of an epigenetics-based telomere length estimator. Telomers are little DNA sequence tails at the end of chromosomes that get shorter with each cell division. Longer telomeres have been associated with younger age and better health. Interestingly, the estimated telomere length is an even better predictor of disease risk than actual telomere length. With each kilobase increase of estimated telomere length your mortality risk is reduced by 63%, the risk of coronary heart disease decreases by 49%, and the risk of congestive heart failure lowers by 73%.[4]

Biological age

This is a 1st generation clock and only shows which age group is most similar to you epigenetically. There is no validated associated risk and the result only shows the state of the art a decade ago. This result is also less precise and accurate than later generation clocks and can result in inconsistencies. Just remember, the newer the generation, the more predictive the result.

Sign up and find out your biological age
  1. DNA methylation age of human tissues and cell types
    Steve Horvath
    Genome Biol. 2013;14(10):R115. doi: 10.1186/gb-2013-14-10-r115.
  2. DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan
    Tessa Bergsma and Ekaterina Rogaeva
    Neurosci Insights. 2020; 15: 2633105520942221.
  3. DunedinPACE, a DNA methylation biomarker of the pace of aging
    Daniel W Belsky, Avshalom Caspi, David L Corcoran, Karen Sugden, Richie Poulton, Louise Arseneault, Andrea Baccarelli, Kartik Chamarti, Xu Gao, Eilis Hannon, Hona Lee Harrington, Renate Houts, Meeraj Kothari, Dayoon Kwon, Jonathan Mill, Joel Schwartz, Pantel Vokonas, Cuicui Wang, Benjamin S Williams, and Terrie E Moffitt
    eLife. 2022; 11: e73420.
  4. DNA methylation-based estimator of telomere length
    Ake T. Lu, Anne Seeboth, Pei-Chien Tsai, Dianjianyi Sun, Austin Quach, Alex P. Reiner, Charles Kooperberg, Luigi Ferrucci, Lifang Hou, Andrea A. Baccarelli, Yun Li, Sarah E. Harris, Janie Corley, Adele Taylor, Ian J. Deary, James D. Stewart, Eric A. Whitsel, Themistocles L. Assimes, Wei Chen, Shengxu Li, Massimo Mangino, Jordana T. Bell, James G. Wilson, Abraham Aviv, Riccardo E. Marioni, Kenneth Raj, and Steve Horvath
    Aging (Albany NY). 2019 Aug 31; 11(16): 5895–5923.