The new AI model, detailed in the recent study published in Science Advances, analyzes 22 key steroid hormones and their interactions
In a groundbreaking study, scientists at Osaka University in Japan have developed an artificial intelligence (AI) model that can accurately estimate a person’s biological age using just five drops of blood. Unlike chronological age, which simply measures the time since birth, biological age reflects how well the body has aged and is influenced by a variety of factors, including genetics and lifestyle.
The new AI model, detailed in the recent study published in Science Advances, analyzes 22 key steroid hormones and their interactions. These hormones are critical for regulating metabolism, immune function, and the body’s response to stress. By studying how these hormones interact and change over time, the AI system can offer a much more precise health assessment, offering a clearer picture of an individual’s overall health and well-being.
“Our bodies rely on hormones to maintain homeostasis, so we thought, why not use these as key indicators of aging?” explained Dr. Qiuyi Wang, co-first author of the study. The team focused specifically on steroid hormones, which are essential for various bodily functions and are considered reliable markers for aging processes. To create the AI model, the research team developed a deep neural network (DNN) that incorporates steroid metabolism pathways. This innovation makes the model the first of its kind to explicitly account for how different steroid molecules interact with each other. This approach provides more accurate predictions of biological age compared to previous methods.
One of the most striking findings of the study involves cortisol, the steroid hormone often associated with stress. The researchers discovered that when cortisol levels doubled, biological age increased by approximately 1.5 times. This finding underscores the link between chronic stress and accelerated aging at a biochemical level. “Stress is often discussed in general terms, but our findings provide concrete evidence that it has a measurable impact on biological aging,” said Professor Toshifumi Takao, the study’s corresponding author.
The implications of this AI-powered biological age model are significant. It could pave the way for personalized health monitoring, allowing for the early detection of age-related health risks, as well as tailored wellness programs and lifestyle recommendations to slow down the aging process.
With this breakthrough, the future of personalized healthcare may become more precise, helping individuals manage their health with a level of specificity never before possible.
