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ORIGINAL RESEARCH article

Front. Aging
Sec. Genetics, Genomics and Epigenomics of Aging
Volume 5 - 2024 | doi: 10.3389/fragi.2024.1526146
This article is part of the Research Topic Aging Epigenome and Longevity View all 3 articles

Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease

Provisionally accepted
David Martínez-Enguita David Martínez-Enguita 1Thomas Hillerton Thomas Hillerton 1Julia Åkesson Julia Åkesson 1Daniel Kling Daniel Kling 2Maria Lerm Maria Lerm 3Mika Gustafsson Mika Gustafsson 1*
  • 1 Department of Physics, Chemistry and Biology, Linköping University, Linköping, Östergötland, Sweden
  • 2 Department of Forensic Genetics and Toxicology, Swedish National Board of Forensic Medicine, Linköping, Sweden
  • 3 Division of Inflammation and Infection (II), Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Östergötland, Sweden

The final, formatted version of the article will be published soon.

    DNA methylation (DNAm) age clocks are powerful tools for measuring biological age, providing insights into aging risks and outcomes beyond chronological age. While traditional models are effective, their interpretability is limited by their dependence on small and potentially stochastic sets of CpG sites. Here, we propose that the reliability of DNAm age clocks should stem from their capacity to detect comprehensive and targeted aging signatures. We introduce NCAE-CombClock, a novel highly precise (R2 = 0.978, mean absolute error = 1.96 years) deep neural network age clock integrating data-driven DNAm embeddings and established CpG age markers from 17,726 whole blood samples comprising the entire human lifespan (0 to 112 years). Additionally, we developed a suite of interpretable NCAE-Age neural network classifiers tailored for adolescence and young adulthood. These clocks can accurately classify individuals at key milestone ages (AUROC = 0.953, 0.972, and 0.927 for 15, 18, and 21 years) and capture fine-grained, single-year DNAm signatures of aging enriched in biological processes associated with anatomic and neuronal development, immunoregulation, and metabolism. We showcased the practical applicability of this approach by identifying candidate mechanisms underlying the altered pace of aging observed in pediatric Crohn's disease. Our models offer broad applications in personalized medicine and aging research, providing a valuable resource for interpreting aging trajectories in health and disease.

    Keywords: DNA Methylation, neural networks, age clock, Epigenetic age, Youth

    Received: 11 Nov 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Martínez-Enguita, Hillerton, Åkesson, Kling, Lerm and Gustafsson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mika Gustafsson, Department of Physics, Chemistry and Biology, Linköping University, Linköping, 581 83, Östergötland, Sweden

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.