Table 2_A microbiota-based perspective on urinary stone disease: insights from 16S rRNA sequencing and machine learning models.docx

BackgroundUrinary stones are a multifactorial disease. In recent years, the role of microorganisms in its pathogenesis has attracted considerable attention. Although studies have suggested that certain microbes present in the gut and urine are associated with the formation of urinary stones, the current criteria for stone classification are not rigorous enough. Therefore, this study aimed to analyze the gut and urinary microbiota composition via 16S rRNA sequencing in patients with pure CaOx, pure UA, and pure Inf stones. By integrating these microbiota data with clinical data, we constructed machine learning models and evaluated their diagnostic value in distinguishing stone types.
MethodsA total of 81 patients with urinary stones (including 30 with pure CaOx stones, 31 with pure UA stones, and 20 with pure Inf stones) and 26 healthy volunteers were enrolled. Stool and urine samples were collected from each participant and subjected to 16S rRNA sequencing to obtain microbiota data and characterize the gut and urinary microbiota profiles of patients with different stone types. We further integrated microbiota and clinical data, such as age, gender and BMI, using LASSO feature selection and six machine learning algorithms (e.g. SVM, Random Forest and XGBoost) to create prediction models for stone type. Model performance was evaluated through cross-validation.
ResultsResults showed enrichment of Paramuribaculum, Muribaculum, Mesorhizobium, and Acinetobacter in the gut of CaOx stone patients, with concurrent urinary enrichment of Enterococcus. Patients with UA stones demonstrated an increase in the abundance of Massilioclostridium in the gut and an increase in the abundance of Fenollaria, Anaerococcus, Enterococcus and Escherichia in the urine. Patients with Inf stones showed no differentially abundant gut taxa compared to healthy volunteers, but did exhibit urinary enrichment of Escherichia. The predictive model, which was based on urinary microbiota and clinical data, demonstrated excellent performance. The AUC was 0.922, 0.866 and 0.913 for the SVM, Random Forest and XGBoost models, respectively.
ConclusionThis study reveals that different types of stone are characterized by distinct compositions of microbiota. Machine learning models based on microbiota and clinical data can predict urinary stone types noninvasively. This provides novel insights into the microecological mechanisms of urinary stones and opens up new avenues for clinical diagnosis.

背景：尿路结石是一种多因素疾病。近年来，微生物在其发病机制中的作用受到广泛关注。尽管已有研究表明肠道与尿液中的特定微生物与尿路结石的形成相关，但当前的结石分类标准仍不够严谨。因此，本研究旨在通过16S rRNA测序，分析纯草酸钙（CaOx）结石、纯尿酸（UA）结石及纯感染性（Inf）结石患者的肠道与尿液微生物组组成，并整合这些微生物组数据与临床资料，构建机器学习模型，评估其在区分结石类型中的诊断价值。
方法：本研究共纳入81例尿路结石患者（其中纯草酸钙结石患者30例、纯尿酸结石患者31例、纯感染性结石患者20例）及26名健康志愿者。采集所有受试者的粪便与尿液样本，通过16S rRNA测序获取微生物组数据，以明确不同结石类型患者的肠道与尿液微生物组特征。本研究进一步整合微生物组数据与年龄、性别、体质量指数（BMI）等临床资料，采用LASSO特征筛选法及六种机器学习算法（如支持向量机（SVM）、随机森林（Random Forest）及XGBoost）构建结石类型预测模型，并通过交叉验证评估模型性能。
结果：结果显示，草酸钙结石患者肠道中Paramuribaculum、Muribaculum、Mesorhizobium及Acinetobacter丰度升高，同时其尿液中Enterococcus丰度亦升高。尿酸结石患者肠道中Massilioclostridium丰度升高，尿液中Fenollaria、Anaerococcus、Enterococcus及Escherichia丰度均升高。感染性结石患者与健康志愿者相比，肠道菌群分类群无显著差异，但尿液中Escherichia丰度升高。基于尿液微生物组与临床资料的预测模型表现优异，支持向量机（SVM）、随机森林（Random Forest）及XGBoost模型的受试者工作特征曲线下面积（AUC）分别为0.922、0.866及0.913。
结论：本研究表明，不同类型的尿路结石具有独特的微生物组组成特征。基于微生物组与临床资料的机器学习模型可无创预测尿路结石类型，为阐明尿路结石的微生态发病机制提供了新视角，也为临床诊断开辟了新途径。