基于机器学习的双极性障碍的诊断潜力,使用肠道微生物群签名
Hang Li1,2, Yan-Ting Jin3, Dong-Xin Ye4
1Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China.
IET systems biology
|January 14, 2026
概括
双极性障碍 (BD) 与肠道微生物组的变化有关. 使用这些肠道细菌生物标志物的机器学习模型显示出对非侵入性BD诊断和理解微生物群-肠道-大脑轴的前景.
科学领域:
- 微生物组研究的研究.
- 精神疾病 精神疾病
- 计算生物学是一种计算生物学.
背景情况:
- 双极性障碍 (BD) 是一种具有显著认知和社会障碍的慢性疾病.
- 肠道微生物群-肠道-大脑轴越来越多地与BD病理生理学有关.
- 对于BD的诊断和治疗策略需要进一步发展.
研究的目的:
- 在BD患者中调查肠道微生物的变化.
- 使用机器学习评估肠道微生物群的诊断潜力.
- 确定用于BD非侵入性诊断的微生物生物标志物.
主要方法:
- 16S rRNA测序用于分析肠道微生物的组成和多样性.
- 机器学习算法,包括随机森林 (RF),用于分类.
- PICRUSt2用于微生物通路的功能预测.
- 特性选择方法,以确定最佳的微生物生物标志物.
主要成果:
- 与健康对照组 (HC) 相比,在BD患者中观察到减少的α-多样性和改变的β-多样性.
- 射频分类器使用35种微生物生物标志物实现了高诊断性能 (AUC=0.9316).
- 功能分析揭示了神经退行,脂质代谢和血红素生物合成中的改变途径.
- 组合的组成和功能特征进一步提高了诊断准确度 (AUC=0.9499).
结论:
- 在BD中存在显著的组成和功能性肠道微生物乱.
- 基于机器学习的微生物组分析为非侵入性BD诊断提供了一个有希望的方法.
- 已识别的标记物为微生物群-肠-大脑轴提供了洞察力,支持精准精神病学和针对微生物的干预措施.
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