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Related Concept Videos

Aging01:26

Aging

Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
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Related Experiment Video

Updated: Jun 20, 2026

High-Throughput Behavioral Aging and Lifespan Assays Using the Lifespan Machine
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Integrating chronological aging and asynchronous aging for enhanced biological age prediction using artificial

Qingfeng Tang, Pengcheng Ding, Baoqian Wang

    IEEE Journal of Biomedical and Health Informatics
    |June 18, 2026
    PubMed
    Summary

    This study introduces a novel AI framework to predict biological age by integrating chronological age with an asynchronous aging index. This approach significantly improves prediction accuracy and health risk classification, offering a more precise measure of aging.

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    Area of Science:

    • Biogerontology
    • Artificial Intelligence
    • Biostatistics

    Background:

    • Accurate biological age (BA) estimation is crucial but limited by chronological age (CA) and individual aging variations.
    • Existing methods do not fully capture the heterogeneity in aging trajectories.

    Purpose of the Study:

    • To develop a unified AI framework for BA prediction that integrates CA with a quantifiable measure of asynchronous aging.
    • To evaluate strategies for combining CA and asynchronous aging index (AAI) for enhanced BA estimation.

    Main Methods:

    • Quantified asynchronous aging index (AAI) using a pre-training framework, defined as deviation from a healthy reference population's predicted age.
    • Proposed and evaluated three strategies: AAI-score, Loss(AAI,MSE) hybrid loss function, and AAI-driven data cleaning.
    • Applied the framework to arterial stiffness data from over 36,000 individuals.

    Main Results:

    • Integrated approaches uniformly enhanced predictive accuracy compared to traditional frameworks.
    • The AAI-score strategy significantly reduced Mean Absolute Error (MAE) in both males and females.
    • Area Under the Curve (AUC) for health risk classification substantially improved across both sexes.

    Conclusions:

    • Asynchronous aging is a fundamental component of biological age.
    • Integrating AAI with CA provides a more accurate, interpretable, and clinically promising method for BA estimation.
    • The proposed AI framework offers a significant advancement in understanding and predicting individual aging processes.