检查D-氨基酸配置和轻度认知障碍患者的认知功能之间的关系:一种机器学习方法
Sou Sugiki1, Shigeki Tsuchiya2, Ren Kimura2
1Human Health Care Products Research, Kao Corporation, Tokyo, Japan.
The international journal of neuropsychopharmacology
|March 15, 2025
概括
早期发现轻度认知障碍 (MCI) 是至关重要的. 使用机器学习将血液D-氨基酸概况与受试者数据相结合,为MCI提供了一个有希望的,高精度的查工具.
科学领域:
- 生物标志物 生物标志物
- 神经科学是一个神经科学.
- 医疗保健中的机器学习
背景情况:
- 全球痴呆症患病率正在上升,需要对轻度认知障碍 (MCI) 进行早期检测和预防策略.
- 目前的MCI检测受到缺乏确定的生物标志物的限制.
- 这项研究旨在开发一种精确的MCI查方法,使用外周血液D-氨基酸概况和非侵入性受试者数据.
研究的目的:
- 开发一种高精度的MCI选方法.
- 将外围血液中的D-氨基酸概况与非侵入性受试者信息相结合.
- 使用非线性机器学习 (ML) 算法来增强MCI检测.
主要方法:
- 一项横截面研究涉及200名参与者 (50-89岁),分类为认知正常或疑似MCI.
- 在外周血液中对D-alanine和D-proline百分比的高通量分析.
- 机器学习模型的比较,包括后勤回归,随机森林,内核SVM和ANN用于预测性能.
主要成果:
- 非线性ML模型 (核SVM和ANN) 整合D-氨基酸配置文件和受试者数据,产生高AUC值 (0.78和0.79).
- 这些结果证实了将D-氨基酸配置文件与MCI检测的非侵入性受试者信息相结合的有效性.
- 这项研究强调了D-氨基酸作为认知障碍生物标志物的潜力.
结论:
- 非线性ML模型,特别是内核SVM和ANN,结合D-氨基酸概况和非侵入性数据,显示出对高精度MCI查的重大前景.
- 这种方法提供了一个具有成本效益的初步查工具,可能会先于更具侵入性的诊断方法.
- 这些发现可以极大地帮助早期发现和发展痴呆症的干预措施.
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