Predicting a Species’ Longevity Through Epigenetics

This work may pave the way to fundamental interventions.


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A preprint study published on bioRxiv discusses how a species’ maximum lifespan can be predicted by its epigenetics, showing that these differences are largely unaffected by interventions.

Differences and similarities in mammals

The researchers begin their paper with a note that bowhead whales live over a hundred times as long as shrews, even though these are both mammalian species. The reasons behind this have been previously investigated by expert researchers [1]. Various studies have focused on various aspects of aging, including telomeres [2], genomic stability [3], and epigenetics [4], but these researchers note that epigenetic studies have generally suffered from small sample sizes with inconsistent data gathering.

To solve this problem, this study was built using the Mammalian Methylation Consortium, which uses epigenetics from 348 mammalian species in order to determine why some species live much longer than others [5]. This database focuses on 36,000 separate epigenetic sites that are surrounded by similar base pairs across mammals.

A robust dataset, with caveats

Before using this dataset to work on aging, they tested its capability for simple identification. The species of an animal, its sex, and the tissue it came from were nearly universally correctly identified solely through epigenetics, with the sole exception being marmosets, which can transfer cells to each other before birth.

The researchers then put their data to work. Averaging out sex and tissue types, they mapped the epigenetic differences in these species to gestation time, age at sexual maturity, and lifespan. Even when some animals were randomly excluded to test robustness, this model was able to accurately predict these three qualities.


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The data remained accurate even when data from very young animals was used. It is possible to take any young mammal, analyze its epigenetics, and have an fairly accurate estimate of how long it will naturally live. This epigenetic correlation was stronger than body weight and the lifespans of related mammalian orders.

Tissue type was found to play a strong role in the model’s estimates. While the average tissue was accurate, certain tissues, such as the blood and outer skin, give inaccurately long estimates. Meanwhile, samples taken from stem cells usually give inaccurately short ones. The researchers built further models to mitigate these confounding factors and create more accurate estimates on a per-tissue basis.

Some things were unpredicted

This model did not predict cancer: no correlation was found between these epigenetic markers of lfiespan and cancer-related mortality risk. While there was an epigenetic influence of high-fat diets, there was little evidence for the influence of caloric restriction. While full epigenetic reprogramming had a small but significant effect, partial reprogramming was not found to have any significant effect on this model’s predictions.

Most critically, these predictions were completely unaffected by factors known to affect lifespan. Taking epigenetic data from the Framingham Heart Study, these researchers found that diet, smoking, fat, and physical abilities had no significant effect on their predictor. Their predictor was even unaffected by dog breeds, even though some dog breeds live considerably longer than others.

The researchers believe that their predictive model is utilizing very basic longevity-related factors that go deeper than the modest changes brought about by lifestyle interventions. They hypothesize that their work could potentially be used to develop substantially stronger interventions that “affect epigenetic maximum lifespan as they may be the key to achieving large lifespan differences observed between species.”


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[1] Magalhães, J. P. D., Costa, J., & Church, G. M. (2007). An analysis of the relationship between metabolism, developmental schedules, and longevity using phylogenetic independent contrasts. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 62(2), 149-160.

[2] Gorbunova, V., & Seluanov, A. (2009). Coevolution of telomerase activity and body mass in mammals: from mice to beavers. Mechanisms of ageing and development, 130(1-2), 3-9.

[3] Tian, X., Firsanov, D., Zhang, Z., Cheng, Y., Luo, L., Tombline, G., … & Gorbunova, V. (2019). SIRT6 is responsible for more efficient DNA double-strand break repair in long-lived species. Cell, 177(3), 622-638.

[4] Sen, P., Shah, P. P., Nativio, R., & Berger, S. L. (2016). Epigenetic mechanisms of longevity and aging. Cell, 166(4), 822-839.

[5] Arneson, A., Haghani, A., Thompson, M. J., Pellegrini, M., Kwon, S. B., Vu, H., … & Horvath, S. (2022). A mammalian methylation array for profiling methylation levels at conserved sequences. Nature communications, 13(1), 783.

About the author
Josh Conway

Josh Conway

Josh is a professional editor and is responsible for editing our articles before they become available to the public as well as moderating our Discord server. He is also a programmer, long-time supporter of anti-aging medicine, and avid player of the strange game called “real life.” Living in the center of the northern prairie, Josh enjoys long bike rides before the blizzards hit.