A microbiota-based perspective on urinary stone disease: insights from 16S rRNA sequencing and machine learning models

Background Urinary 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. Methods A 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. Results Results 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. Conclusion This 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.