非向的代谢学可以确定缺血性中风的疾病特征
Haijun Zhang1, Zhiyuan Guo1, Tian Zhao2
1School of Public Health, Wannan Medical College, Wuhu, China.
Metabolomics : Official journal of the Metabolomic Society
|December 1, 2025
概括
这项研究使用代谢学和机器学习确定了缺血性中风 (IS) 的四个关键代谢生物标志物. 开发了一个预测模型,在区分IS患者和对照人群方面表现出高准确度.
科学领域:
- 生物化学和生物信息学
- 临床诊断 临床诊断 临床诊断
- 生物标志物发现发现
背景情况:
- 缺血性中风 (IS) 是全球死亡和残疾的主要原因.
- 代谢学和机器学习 (ML) 提供了识别IS生物标志物的潜力.
- 了解代谢变化对于IS研究至关重要.
研究的目的:
- 确定缺血性中风 (IS) 的新型代谢生物标志物.
- 为了阐明关键的代谢途径,涉及到IS的发病.
- 开发和验证基于机器学习的IS的预测模型.
主要方法:
- 从786名参与者 (IS患者和对照组) 的血样本使用非向液体染色学-并联质谱分析.
- 途径丰富分析确定了差异性代谢物和受影响的代谢途径.
- 使用XGBoost算法构建了一个预测模型,在一个独立的队列上进行验证.
主要成果:
- 在IS患者和对照人群之间确定了200种不同的代谢物.
- 阿拉基酸代谢和叶酸生物合成是显著改变的途径之一.
- 四种代谢物 (酸,迪瓜诺辛四酸,PC (P-18:0/P-18:1(9Z) 和脱氧醇酸) 有效地区分了IS与对照组.
- 预测模型实现了高准确性,在外部验证集中AUC为0.941.
结论:
- 代谢学分析显示,在缺血性中风中发生了显著的代谢变化.
- 四种特定的代谢物被确定为IS的潜在生物标志物.
- 开发了一个强大的基于ML的预测模型,显示IS的高诊断性能.
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