纵向机器学习将健康的衰老因素与慢性疾病风险分开
Netta Mendelson Cohen1,2, Aviezer Lifshitz1,2, Rami Jaschek1,2
1Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel.
Nature aging
|December 7, 2023
概括
机器学习模型可以通过分析电子健康记录来预测健康的衰老和长寿潜力. 这种方法识别了健康衰老的早期指标,独立于慢性疾病风险,并显示了与更长寿命的遗传联系.
科学领域:
- 老年学是一门学科.
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 区分固有的衰老和与年龄有关的疾病对于理解人类寿命至关重要.
- 纵向追踪个人一生是具有挑战性的,但对于这种区别是必要的.
研究的目的:
- 开发一种机器学习模型,以推断成人的健康轨迹和长寿潜力.
- 为了确定健康衰老的早期生物标志物,独立于慢性疾病风险.
主要方法:
- 利用机器学习从电子医疗记录中推断出健康轨迹,其中包含部分纵向数据.
- 开发了一个多变量得分,以根据长寿潜力区分个人.
- 在不同的人群中 (以色列,英国,美国) 验证了模型及其标记.
主要成果:
- 该模型成功跟踪了健康个体,并预测了长寿潜力.
- 轻微低的中性粒细胞计数和性酸酶水平被确定为健康衰老的早期指标.
- 长寿得分表明了遗传性和遗传关联,高得分的个体的父母显示了延长寿命.
结论:
- 健康个体的纵向建模是研究健康衰老和寿命的可行工具.
- 鉴定的生物标志物为健康衰老的生物基础提供了洞察力.
- 这种方法可以促进我们对促进更长,更健康的生活因素的理解.
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