Scientists at Osaka University in Japan have developed an advanced AI model capable of estimating a person's biological age — a more accurate measure of how well the body has aged compared to their actual chronological age. This innovative method is set to revolutionize personalized health management by providing insights into age-related risks and early intervention strategies. The breakthrough, published in the journal Science Advances, reveals that the model requires just five drops of blood to perform a detailed analysis of 22 key steroid hormones and their interactions. By assessing these biochemical markers, the model offers a more precise evaluation of an individual's internal aging process. According to Dr. Qiuyi Wang, co-first author of the study, the researchers hypothesized that because hormones play a vital role in maintaining homeostasis, they could serve as effective indicators of aging. This innovative approach led the team to develop a deep neural network (DNN) model designed to account for the complex interactions between various steroid molecules — a first in the field of AI-driven aging research.
 

One of the most notable discoveries from the study relates to cortisol, a steroid hormone commonly linked to stress. The researchers found that when cortisol levels doubled, a person’s biological age increased by approximately 1.5 times. This finding underscores the significant impact that chronic stress can have on the body’s aging process, providing new evidence that effective stress management may be crucial for maintaining long-term health. Professor Toshifumi Takao, a corresponding author and expert in analytical chemistry and mass spectrometry, highlighted the importance of the discovery. “Stress is often discussed in general terms, but our findings provide concrete evidence that it has a measurable impact on biological aging,” he explained.
 

The Osaka University research team believes this AI-powered biological age model holds immense potential for improving personalised health monitoring. The ability to detect aging-related changes early could pave the way for customized wellness programs, lifestyle modifications, and early disease detection strategies. By leveraging this technology, healthcare providers may soon be able to recommend targeted interventions that slow down the aging process and improve overall well-being. Future applications of this model could include identifying individuals at risk for age-related conditions such as cardiovascular disease, diabetes, and neurodegenerative disorders, enabling earlier and more effective preventive care. With the growing emphasis on precision medicine and personalized healthcare, this AI-based approach represents a significant step forward in understanding how biological aging is influenced by hormonal changes and stress levels. The study’s findings reinforce the need for holistic health strategies that prioritize both physical and mental well-being. As research continues to advance, this innovative model may soon become an essential tool for individuals seeking proactive ways to manage their health and extend their lifespan.