在大规模人口中基于大型语言模型的生物年龄预测
Yanjun Li1, Qi Huang1, Jin Jiang2
1Vanke School of Public Health, Tsinghua University, Beijing, China.
Nature medicine
|July 23, 2025
概括
大型语言模型 (LLM) 提供了一种新且准确的方法,可以使用健康报告来评估个体的衰老. 这种方法在预测死亡率和疾病风险方面优于现有的代理,使得个性化健康管理成为可能.
科学领域:
- 生物医学信息学 生物医学信息学
- 老年学是一门学科.
- 医疗保健中的人工智能
背景情况:
- 准确的个人衰老评估对于积极的健康管理和疾病预防至关重要.
- 现有的老化代理具有局限性,包括方法上的约束,弱的结果关联和糟糕的概括性.
研究的目的:
- 引入一种使用大型语言模型 (LLM) 来估计整体和器官特定个体衰老的新框架.
- 在多样化,大规模的人口队伍中验证基于LLM的衰老评估框架.
主要方法:
- 开发并验证了一个基于LLM的框架,使用六个队伍中超过1000万参与者的健康检查报告.
- 将LLM预测的衰老指标与已建立的衰老代理和机器学习模型进行比较,以预测死亡率和疾病结果.
主要成果:
- 与端粒长度,脆弱性指数,表观遗传年龄和ML模型相比,LLM预测的整体年龄显示出所有原因死亡率 (C指数0.757) 的优异预测能力.
- 基于LLM的方法显示,年龄差距和不良健康结果之间存在很强的关联 (死亡率为HR 1.055).
- 在预测特定器官疾病方面,LLM预测的特定器官年龄和年龄差距超过了ML模型.
结论:
- 基于LLM的衰老评估框架为估计整体和器官特定衰老提供了精确,可靠和具有成本效益的方法.
- 这一框架在大量人群中具有个性化健康评估和管理的巨大潜力.
- 法律学可以识别衰老的生物标志物,并开发疾病风险预测模型,增强我们对衰老过程的理解.
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