一个机器学习支持的血液转录学签名用于数字诊断和阿尔茨海默病的亚型
Shuo Ma1,2, Dawen Chen1,2, Yanzhi Li3
1Center of Clinical Laboratory Medicine, Zhongda Hospital, Medical School of Southeast University, Nanjing, Jiangsu, China.
NPJ digital medicine
|January 6, 2026
概括
一种使用机器学习模型的新型血液检测,称为乳酸衍生分数 (LDS),对早期阿尔茨海默氏病的检测有希望. 这种转录组模型补充了现有的生物标志物,用于改进诊断和精确医学.
科学领域:
- 生物标志物 生物标志物
- 基因组学就是基因组学.
- 神经科学是一个神经科学.
背景情况:
- 早期和可访问地检测阿尔茨海默病 (AD) 是一个重大的临床挑战.
- 目前的诊断方法往往缺乏可访问性或是侵入性的.
- 需要用于AD诊断的可扩展和可靠的生物标志物.
研究的目的:
- 开发和验证基于机器学习的血液转录组模型,用于阿尔茨海默病的检测.
- 为了评估单独和与血tau生物标志物结合的乳酸衍生得分 (LDS) 的诊断性能.
- 探索LDS在识别与AD相关的神经炎症和代谢压力特征方面的实用性.
主要方法:
- 开发了一种机器学习模型 (LDS),使用来自9个AD队伍的乳化相关基因.
- 采用标准化管道,包括z-score规范化,随机森林特征选和plsRglm建模与交叉验证.
- 在独立的大脑转录和血队列中验证了LDS,使用逻辑回归将其与p-tau181和p-tau217集成在一起.
主要成果:
- 在训练队列中,LDS的AUC为0.897,在血验证队列中为0.772.
- 一个三标记模型 (LDS + p-tau181 + p-tau217) 显示了最高的诊断性能 (AUC 0.859).
- LDS有效地识别了粉样蛋白阳性个体 (AUC 0.861) 和分层轻度认知障碍 (AUC 0.809).
结论:
- 乳化衍生分数 (LDS) 是一种可扩展和可解释的血液转录组模型,用于阿尔茨海默病的检测.
- LDS补充了血中的tau生物标志物,提高了诊断的准确性.
- 该模型支持用于AD管理的精密数字医学的进步.
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