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Research Paper|Volume 17, Issue 1|pp 131—160

EpiAge: a next-generation sequencing-based ELOVL2 epigenetic clock for biological age assessment in saliva and blood across health and disease

David Cheishvili1,2,3, Sonia Do Carmo4, Filippo Caraci6,7, Margherita Grasso7, A Claudio Cuello4,5, Moshe Szyf1,2
  • 1EpiMedTech Global, Singapore 409051, Singapore
  • 2HKG Epitherapeutics Ltd., Hong Kong SAR, China
  • 3Gerald Bronfman Department of Oncology, McGill University, Montreal H4A 3T2, Canada
  • 4Department of Pharmacology & Therapeutics, McGill University, Montreal H3G 1Y6, Canada
  • 5Visiting Professor, Department of Pharmacology, Oxford University, Oxford OX13QT, UK
  • 6Department of Drug and Health Sciences, University of Catania, Catania 95125, Italy
  • 7Neuropharmacology and Translational Neurosciences Research Unit, Oasi Research Institute-IRCCS, Troina 94018, Italy
* Equal contribution
Received: May 8, 2024Accepted: January 6, 2025Published: January 22, 2025
https://doi.org/10.18632/aging.206188

Copyright: © 2025 Cheishvili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This study introduces EpiAgePublic, a new method to estimate biological age using only three specific sites on the gene ELOVL2, known for its connection to aging. Unlike traditional methods that require complex and extensive data, our model uses a simpler approach that is well-suited for next-generation sequencing technology, which is a more advanced method of analyzing DNA methylation. This new model overcomes some of the common challenges found in older methods, such as errors due to sample quality and processing variations.

We tested EpiAgePublic with a large and varied group of over 4,600 people to ensure its accuracy. It performed on par with, and sometimes better than, more complicated models that use much more data for age estimation. We examined its effectiveness in understanding how factors like HIV infection and stress affect aging, confirming its usefulness in real-world clinical settings.

Our results prove that our simple yet effective model, EpiAgePublic, can capture the subtle signs of aging with high accuracy. We also used this model in a study involving patients with Alzheimer’s Disease, demonstrating the practical benefits of next-generation sequencing in making precise age-related assessments.

This study lays the groundwork for future research on aging mechanisms and assessing how different interventions might impact the aging process using this clock.