新的AI模型來估計一個人的生物年齡

這種新方法僅使用五滴血液，分析22種關鍵類固醇及其相互作用，以提供更精確的健康評估。

Scientists at Osaka University in Japan have devised a new AI model to estimate a person’s biological age — a measure of how well their body has aged, rather than just counting the years since birth.

日本大阪大學的科學家設計了一種新的AI模型來估計一個人的生物年齡，這是對自己的身體老化的衡量標準，而不僅僅是算出出生以來的幾年。

This new method, which uses just five drops of blood to analyse 22 key steroids and their interactions, provides a more precise health assessment. The team’s breakthrough study, published in Science Advances, offers a potential step forward in personalised health management, allowing for earlier detection of age-related health risks and tailored interventions.

這種新方法僅使用5滴血液來分析22種關鍵類固醇及其相互作用，可提供更精確的健康評估。該團隊的突破性研究在《科學進步》上發表，在個性化的健康管理方面邁出了潛在的一步，可以早期發現與年齡相關的健康風險和量身定制的干預措施。

“Our bodies rely on hormones to maintain homeostasis, so we thought, why not use these as key indicators of aging?” said Dr Qiuyi Wang, co-first author of the study.

“我們的身體依靠激素來維持體內平衡，所以我們認為，為什麼不將其用作衰老的關鍵指標？”該研究的聯合第一作者Qiuyi Wang博士說。

To test this idea, the research team focused on steroid hormones, which play a crucial role in metabolism, immune function, and stress response. They collected blood samples from 130 individuals ranging in chronological age from 20 to 79 years old.

為了檢驗這一想法，研究小組專注於類固醇激素，這些激素在代謝，免疫功能和壓力反應中起著至關重要的作用。他們收集了從20至79歲的年代年齡段的130名個人中收集的血液樣本。

The team developed a deep neural network (DNN) model that incorporates steroid metabolism pathways, making it the first AI model to explicitly account for the interactions between different steroid molecules. This approach enabled the DNN to learn complex relationships between steroids and biological age.

該團隊開發了一個深神網絡（DNN）模型，該模型結合了類固醇代謝途徑，使其成為第一個明確說明不同類固醇分子之間相互作用的AI模型。這種方法使DNN能夠學習類固醇與生物年齡之間的複雜關係。

One of the study’s most striking findings involves cortisol, a steroid hormone commonly associated with stress. The researchers found that when cortisol levels doubled, biological age increased by approximately 1.5 times.

該研究最引人注目的發現之一是皮質醇，皮質醇是一種通常與壓力相關的類固醇激素。研究人員發現，當皮質醇水平翻了一番時，生物年齡增加了約1.5倍。

This observation, supported by further analysis of steroid ratios and interaction terms, suggests that chronic stress could accelerate aging at a biochemical level, reinforcing the importance of stress management in maintaining long-term health.

這一觀察結果得到了對類固醇比和相互作用術語的進一步分析的支持，這表明慢性壓力可以在生化水平上加速衰老，從而增強了壓力管理在維持長期健康方面的重要性。

“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, a corresponding author and an expert in analytical chemistry and mass spectrometry.

“壓力通常是一般性的，但是我們的發現提供了具體的證據，表明它對生物衰老產生了可衡量的影響，”分析化學和質譜法專家Toshifumi Takao教授說。

The researchers are optimistic that this AI-powered biological age model could pave the way for more personalised health monitoring.

研究人員樂觀地認為，這種AI驅動的生物年齡模型可以為更個性化的健康監測鋪平道路。

Future applications may include earlier disease detection, customised wellness programmes, and even lifestyle recommendations tailored to slow down aging.

未來的應用可能包括較早的疾病檢測，定制的健康計劃，甚至是針對減慢衰老的量身定制的生活方式建議。