Forget to calculate candles on birthday cakes – A new artificial intelligence (AI) model developed by scientists at Osaka University in Japan can use five drops of blood to estimate your biological age. By analyzing 22 key steroids, AI provides a personalized measure to achieve the state of your body’s aging, providing potential insights into health management and age-related illnesses.
Aging is not just the passage of time; it is a complex process influenced by genetics, lifestyles, and the environment. Traditional methods for evaluating biological age often rely on a wide range of biomarkers such as DNA methylation or protein levels. However, these methods may miss the complex hormone network that regulates our bodies. The Osaka University team focused on steroid hormones, compounds that are key in metabolism, immune function and stress response.
“Our body relies on hormones to maintain homeostasis, so we think, why not use it as a key indicator of aging?” said Dr. Qiuyi Wang, co-first author of the study. Science Advances. The study introduces a deep neural network (DNN) model that incorporates steroid metabolic pathways, a new approach to enhance the biological explanatory nature of the model.
Instead of examining the absolute steroid levels that may vary between individuals, the model focuses on the ratios between steroids. “Our approach reduces noise caused by differences in individual steroid levels and allows the model to focus on meaningful patterns,” explains the work’s joint first and corresponding author Dr. Zi Wang.
The team analyzed 22 steroids in blood samples from 148 individuals aged 20 to 73 years old, and used 98 samples for model training and 50 samples for validation. AI models capture the complex interaction between steroids and chronological age, suggesting that differences between organisms and chronological age tend to expand over time. The researchers liken this effect to the expansion of rivers as they flow downstream, which illustrates how the trajectory of individual aging diverges over the years.
One of the most striking findings is cortisol, a stress-related hormone. The study found that when cortisol levels doubled, biological age increased by about 1.5 times. “Stress is usually discussed in general terms, but our findings provide concrete evidence that it has a measurable impact on biological aging,” said Prof. Toshifumi Takao, corresponding author and expert in analytical chemistry and mass spectrometry.
The model also found gender-specific changes in steroid metabolism. Different patterns emerge in men and women, reflecting inherent biological differences. For example, the effects of estrogen-related steroids in female models are enhanced, while androgen-related steroids are more pronounced in male models. This highlights the importance of considering gender-specific hormone characteristics in aging studies.
Interestingly, the study also explores the effects of lifestyle factors such as smoking. The results show that male smokers showed statistically significant acceleration in biological aging compared with nonsmokers. This suggests that lifestyle choices can have a measurable impact on the aging process, although the study acknowledges that more comprehensive data on other lifestyle factors are needed to gain a more comprehensive understanding.
Although the study provides promising insights, the authors note some limitations. A relatively small sample size and a lack of detailed lifestyle data may limit the generality of the findings. Furthermore, the model treats steroids in a static way, rather than taking into account circadian fluctuations. Future research and longitudinal data research may help further refine the model.
“This is just the beginning,” said Dr. Z. Wang. “By expanding our dataset and combining other biomarkers, we hope to further refine the model and gain a deeper understanding of the aging mechanism.”
The potential applications of this AI-driven model are broad. It may pave the way for more personalized health monitoring, early disease detection and customized health plans. The ability to assess a person’s “rate of aging” through a simple blood test can mark a significant development in preventive healthcare.
As AI and biomedical research continues to advance, precise measurements and even slowing biological aging are becoming increasingly feasible. For now, the study highlights that hormones, especially those associated with stress, such as cortisol, can have a profound impact on our age.
The study, titled “Bio-age prediction of DNN models based on steroid generation pathways”, was published in Science Advances.
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