EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning

Kalyakulina, Alena; Yusipov, Igor; Trukhanov, Arseniy; Franceschi, Claudio; Moskalev, Alexey; Ivanchenko, Mikhail

doi:10.3390/ijms26136284

Open AccessArticle

EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning

by

Alena Kalyakulina

^1,2,*,†,

Igor Yusipov

^1,2,†

,

Arseniy Trukhanov

³

,

Claudio Franceschi

²

,

Alexey Moskalev

²

and

Mikhail Ivanchenko

^1,2

¹

Artificial Intelligence Research Center, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia

²

Institute of Biogerontology, Lobachevsky State University, Nizhny Novgorod 603022, Russia

³

Mriya Life Institute, National Academy of Active Longevity, Moscow 124489, Russia

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Int. J. Mol. Sci. 2025, 26(13), 6284; https://doi.org/10.3390/ijms26136284

Submission received: 27 May 2025 / Revised: 23 June 2025 / Accepted: 27 June 2025 / Published: 29 June 2025

(This article belongs to the Section Molecular Biology)

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Abstract

We present EpInflammAge, an explainable deep learning tool that integrates epigenetic and inflammatory markers to create a highly accurate, disease-sensitive biological age predictor. This novel approach bridges two key hallmarks of aging—epigenetic alterations and immunosenescence. First, epigenetic and inflammatory data from the same participants was used for AI models predicting levels of 24 cytokines from blood DNA methylation. Second, open-source epigenetic data (25 thousand samples) was used for generating synthetic inflammatory biomarkers and training an age estimation model. Using state-of-the-art deep neural networks optimized for tabular data analysis, EpInflammAge achieves competitive performance metrics against 34 epigenetic clock models, including an overall mean absolute error of 7 years and a Pearson correlation coefficient of 0.85 in healthy controls, while demonstrating robust sensitivity across multiple disease categories. Explainable AI revealed the contribution of each feature to the age prediction. The sensitivity to multiple diseases due to combining inflammatory and epigenetic profiles is promising for both research and clinical applications. EpInflammAge is released as an easy-to-use web tool that generates the age estimates and levels of inflammatory parameters for methylation data, with the detailed report on the contribution of input variables to the model output for each sample.

Keywords: aging; biological clock; inflammaging; DNA methylation; inflammatory profile; deep neural network; explainable artificial intelligence

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MDPI and ACS Style

Kalyakulina, A.; Yusipov, I.; Trukhanov, A.; Franceschi, C.; Moskalev, A.; Ivanchenko, M. EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning. Int. J. Mol. Sci. 2025, 26, 6284. https://doi.org/10.3390/ijms26136284

AMA Style

Kalyakulina A, Yusipov I, Trukhanov A, Franceschi C, Moskalev A, Ivanchenko M. EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning. International Journal of Molecular Sciences. 2025; 26(13):6284. https://doi.org/10.3390/ijms26136284

Chicago/Turabian Style

Kalyakulina, Alena, Igor Yusipov, Arseniy Trukhanov, Claudio Franceschi, Alexey Moskalev, and Mikhail Ivanchenko. 2025. "EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning" International Journal of Molecular Sciences 26, no. 13: 6284. https://doi.org/10.3390/ijms26136284

APA Style

Kalyakulina, A., Yusipov, I., Trukhanov, A., Franceschi, C., Moskalev, A., & Ivanchenko, M. (2025). EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning. International Journal of Molecular Sciences, 26(13), 6284. https://doi.org/10.3390/ijms26136284

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EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning

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