Forget counting candles on your birthday cake—a new artificial intelligence (AI) model developed by scientists at Osaka University in Japan can estimate your biological age using just five drops of blood. By analyzing 22 key steroids, the AI provides a personalized measure of how well your body has aged, offering potential insights into health management and age-related diseases.
Aging is more than just the passage of time; it’s a complex process influenced by genetics, lifestyle, and environment. Traditional methods of assessing biological age often rely on broad biomarkers like DNA methylation or protein levels. However, these approaches can miss the intricate hormonal networks that regulate our bodies. The Osaka University team focused on steroid hormones, compounds pivotal in metabolism, immune function, and stress response.
“Our bodies rely on hormones to maintain homeostasis, so we thought, why not use these as key indicators of aging?” says Dr. Qiuyi Wang, co-first author of the study published in Science Advances. The research introduces a deep neural network (DNN) model that incorporates steroid metabolism pathways, a novel approach that enhances the model’s biological interpretability.
Instead of examining absolute steroid levels, which can vary widely among individuals, the model looks at the ratios between steroids. “Our approach reduces the noise caused by individual steroid level differences and allows the model to focus on meaningful patterns,” explains Dr. Zi Wang, co-first and corresponding author of the work.
The team analyzed 22 steroids from blood samples of 148 individuals aged 20 to 73, using 98 samples for model training and 50 for validation. The AI model captured the complex interactions between steroids and chronological age, revealing that discrepancies between biological and chronological age tend to widen over time. The researchers liken this effect to a river widening as it flows downstream, illustrating how individual aging trajectories can diverge over the years.
One of the most striking findings involves cortisol, a hormone associated with stress. The study found that when cortisol levels doubled, biological age increased by approximately 1.5 times. “Stress is often discussed in general terms, but our findings provide concrete evidence that it has a measurable impact on biological aging,” says Professor Toshifumi Takao, a corresponding author and expert in analytical chemistry and mass spectrometry.
The model also uncovered sex-specific variations in steroid metabolism. Distinct patterns emerged for males and females, reflecting inherent biological differences. For example, estrogen-related steroids had a heightened influence in the female model, while androgen-related steroids were more pronounced in the male model. This highlights the importance of considering sex-specific hormonal profiles in aging research.
Interestingly, the study also explored the impact of lifestyle factors such as smoking. The results showed that male smokers exhibited a statistically significant acceleration in biological aging compared to nonsmokers. This suggests that lifestyle choices can have a measurable effect on the aging process, although the study acknowledges that more comprehensive data on other lifestyle factors would be needed for a fuller understanding.
While the research offers promising insights, the authors note several limitations. The relatively small sample size and the lack of detailed lifestyle data may limit the generalizability of the findings. Additionally, the model treats steroids in a static manner, not fully accounting for circadian fluctuations. Future studies with larger cohorts and longitudinal data could help refine the model further.
“This is just the beginning,” says Dr. Z. Wang. “By expanding our dataset and incorporating additional biological markers, we hope to refine the model further and unlock deeper insights into the mechanisms of aging.”
The potential applications of this AI-powered model are vast. It could pave the way for more personalized health monitoring, early disease detection, and customized wellness programs. The ability to assess one’s “aging speed” with a simple blood test could mark a significant development in preventive healthcare.
With ongoing advancements in AI and biomedical research, accurately measuring—and even slowing—biological aging is becoming increasingly feasible. For now, the study underscores the profound impact that hormones, especially stress-related ones like cortisol, can have on how we age.
The study, titled “Biological age prediction using a DNN model based on pathways of steroidogenesis,” was published in Science Advances.
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