Rather than measuring absolute steroid levels, the model evaluates steroid ratios, allowing for more personalized assessments of biological age.

Scientists at Osaka University have developed an innovative AI model that estimates biological age through hormone analysis, revealing a significant link between stress and accelerated aging.

Published in Science Advances on March 14, 2025, the study aims to enhance personalized health management and facilitate early detection of age-related risks.

Utilizing a deep neural network, the AI model assesses biological age by considering steroid metabolism pathways and the interactions between steroid molecules.

The research team acknowledges the complexity of biological aging and plans to refine the model by incorporating additional biological markers in the future.

The AI-driven model has the potential to lead to personalized health monitoring, early disease detection, and tailored wellness programs aimed at slowing the aging process.

The research findings indicate that doubling cortisol levels can increase biological age by approximately 1.5 times, underscoring the impact of chronic stress on the aging process.

Professor Toshifumi Takao points out that this study provides concrete evidence of the measurable effects of stress on biological aging.

This groundbreaking model requires only five drops of blood to analyze 22 key steroids, offering a more precise health assessment compared to traditional methods.

The research highlights that aging is influenced by genetics, lifestyle, and environmental factors, with the new method focusing specifically on hormonal networks rather than broad biomarkers.

Dr. Qiuyi Wang, a co-author of the study, emphasizes the importance of hormones in maintaining body balance, which serve as key indicators of aging.