多发性硬化症中的肠道微生物组特征:用机器学习和全球数据集成的病例对照研究
Margarita V Neklesova1,2, Karine S Sogomonyan2, Ivan A Golovkin2,3
1Institute of Cytology of the Russian Academy of Sciences, 194064 St. Petersburg, Russia.
Biomedicines
|August 28, 2025
概括
肠道微生物组的改变与多发性硬化症 (MS) 有关. 机器学习模型有效地识别了这些微生物特征,显示出基于肠道细菌概况的多发性硬化症诊断潜力.
科学领域:
- 微生物组研究
- 计算生物学
- 神经免疫学
背景情况:
- 肠道失调与多发性硬化 (MS) 有关,但缺乏一致的微生物生物标志物.
- 机器学习 (ML) 可以整合微生物组数据以识别与疾病相关的生物标志物并了解多发性硬化病变的发生.
- 之前的研究显示MS患者的肠道微生物特征不一致.
研究的目的:
- 确定与多发性硬化相关的特定肠道微生物特征.
- 评估使用微生物组数据检测多发性硬化病的诊断潜力.
- 将发现与现有文献进行整合,以获得全面的理解.
主要方法:
- 来自29名多发性硬化患者和27名健康对照者的便样本的16S rRNA基因测序.
- 对29项已发表的研究进行差异化丰度分析和数据整合.
- 开发和评估四种ML模型,以区分MS相关的微生物群.
主要成果:
- 患有多发性硬化症的患者表现出Eubacteriales,Lachnospirales,Oscillospiraceae,Lachnospiraceae,Parasutterella和Faecalibacterium的数量有所减少.
- 在MS患者中观察到LachnospiraceaeUCG- 008的丰度增加.
- 在区分多发性硬化症患者中,光梯度提升机分类器实现了高性能 (精度:0. 88,AUC-ROC:0. 95).
结论:
- 这项研究在多发性硬化症患者中发现了特定的肠道微生物组失调.
- 基于微生物组概况的ML模型显示出对MS的诊断具有重大潜力.
- 需要进一步的研究来探索MS中这些微生物变化的机制作用和治疗影响.
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