在代谢学中轻度认知障碍的多标志物发现,使用机器学习与通过部分最小方程的全球替代模型进行机器学习
Yota Tatara1, Hiroyuki Yamamoto2,3, Kozue Terai2
1Department of Stress Response Science, Biomedical Research Center, Graduate School of Medicine, Hirosaki University, 5 Zaifu-cho, Hirosaki, Aomori, 036-8562, Japan.
Metabolomics : Official journal of the Metabolomic Society
|November 15, 2025
概括
轻度认知障碍 (MCI) 的早期诊断对于预防痴呆症至关重要. 这项研究开发了一个与能量代谢和神经传递相关的五种代谢物的血面板,以帮助诊断MCI.
科学领域:
- 生物化学 生物化学
- 神经科学是一个神经科学.
- 生物标志物发现发现
背景情况:
- 预防痴呆症需要早期干预.
- 轻度认知障碍 (MCI) 诊断需要可靠的生物标志物.
- 血代谢产物为MCI提供了潜在的诊断标志物.
研究的目的:
- 开发用于MCI诊断的血代谢物的多标志物小组.
- 确定MCI的关键代谢物.
- 阐明与MCI相关的生物途径.
主要方法:
- 利用随机森林算法与可变选择用于代谢物板构建.
- 采用主要组件分析 (PCA) 和部分最小方程 (PLS) 来进行全球替代模型.
- 分析了血代谢物概况,以确定与MCI相关的标记物.
主要成果:
- 一个由五种代谢物组成的小组 (甲氨酸,酸,低氨酸,O-乙甲氨酸,2-酸) 实现了0.85的AUC,用于MCI预测.
- PLS分析显示了14种代谢物与神经元能量代谢/神经传递之间的关联.
- 该小组建议MCI与能量代谢和神经传递受损有关.
结论:
- 开发的方法适用于多标记物代谢物面板分析和生物解释.
- 这些发现突出了能量代谢和神经传递在MCI中的作用.
- 建议在更大的队列中进一步验证以确认可概括性.
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