一个基于微生物群的视角尿路结石疾病:从16SrRNA测序和机器学习模型的见解
Yufeng Liu1, Aoyu Yang1, Ziyi Zhang1
1Department of Urology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
Frontiers in cellular and infection microbiology
|November 10, 2025
概括
不同的肠道和尿道微生物形状与不同类型的尿路结石有关. 结合微生物群和临床数据的机器学习模型可以准确地预测非侵入性的石头类型,提供新的诊断方法.
科学领域:
- 微生物学 微生物学
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 生物信息学是一种生物信息学.
背景情况:
- 尿 पथ是多因素的,人们对微生物的作用越来越感兴趣.
- 目前的石材分类缺乏关于微生物关联的严格性.
- 肠道和尿路微生物群可能会影响各种尿路结石类型的发病.
研究的目的:
- 在患有氧化 (CaOx),尿酸 (UA) 和感染 (Inf) 石头的患者中分析肠道和尿道微生物群组成.
- 整合微生物群数据与临床信息,用于机器学习模型开发.
- 评估这些模型在区分尿路结石类型的诊断价值.
主要方法:
- 来自81名石头患者和26名健康对照者的便和尿液样本的16S rRNA测序.
- 对于不同类型的石头,肠道和尿道微生物群的特征描述.
- 微生物群和临床数据 (年龄,性别,BMI) 与机器学习算法 (SVM,随机森林,XGBoost) 的整合,用于预测模型的创建.
主要成果:
- 特定的肠道细菌 (例如Paramuribaculum,Muribaculum) 和尿道细菌 (例如Enterococcus) 在CaOx石患者中得到丰富.
- 在UA石患者中,肠道Massilioclostridium和尿道Fenollaria,Anaerococcus,Enterococcus和Escherichia的增加.
- 输卵石患者没有明显的肠道微生物群,但显示出尿道埃舍里基亚丰富.
- 基于尿路微生物群和临床数据的机器学习模型实现了高预测性能 (AUC 0.8660.922).
结论:
- 不同的尿路结石类型与肠道和尿液中独特的微生物组成有关.
- 使用微生物群和临床数据的机器学习模型为预测尿路结石类型提供了一种非侵入性方法.
- 这项研究为尿路结石形成的微生态机制和潜在的诊断策略提供了新的见解.
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