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Updated: Sep 16, 2025

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

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Published on: January 26, 2024

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

Alena Kalyakulina^1,2, Igor Yusipov^1,2, Arseniy Trukhanov³

¹Artificial Intelligence Research Center, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.

International Journal of Molecular Sciences

|July 12, 2025

Summary

EpInflammAge integrates epigenetic and inflammatory markers to predict biological age accurately. This explainable AI tool shows promise for research and clinical applications in aging and disease.

Keywords:

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

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Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Published on: November 11, 2016

Area of Science:

Aging research
Computational biology
Immunology

Background:

Aging is characterized by epigenetic alterations and immunosenescence.
Predicting biological age accurately is crucial for understanding health and disease.
Existing epigenetic clocks have limitations in disease sensitivity.

Purpose of the Study:

To develop EpInflammAge, an explainable deep learning tool for biological age prediction.
To integrate epigenetic and inflammatory markers for enhanced accuracy and disease sensitivity.
To bridge the hallmarks of aging: epigenetic changes and immunosenescence.

Main Methods:

Utilized deep neural networks for tabular data analysis.
Integrated epigenetic (DNA methylation) and inflammatory (cytokine) data from participants.
Employed open-source epigenetic data to generate synthetic inflammatory biomarkers.
Trained an age estimation model using combined datasets.

Main Results:

EpInflammAge achieved competitive performance against 34 epigenetic clock models.
Demonstrated a mean absolute error of 7 years and a Pearson correlation of 0.85 in healthy controls.
Showcased robust sensitivity across multiple disease categories.
Explainable AI identified feature contributions to age prediction.

Conclusions:

Combining epigenetic and inflammatory profiles enhances biological age prediction sensitivity to disease.
EpInflammAge offers a promising tool for both research and clinical applications.
The explainable nature of the tool aids in understanding predictive factors.