Unraveling the impact of Lactobacillus spp. and other urinary microorganisms on the efficacy of mirabegron in female patients with overactive bladder.

Abstract

Overactive bladder (OAB) is a disease that seriously affects patients' quality of life and mental health. To address this issue, more and more researchers are examining the relationship between OAB treatment and urinary microecology. In this study, we sought to determine whether differences in treatment efficacy were related to microbiome diversity and composition as well as the abundance of specific genera. Machine learning algorithms were used to construct predictive models for urine microbiota-based treatment of OAB. Urine samples were obtained from 64 adult female OAB patients for 16S rRNA gene sequencing. Patients' overactive bladder symptom scores (OABSS) were collected before and after mirabegron treatment and patients were divided into effective and ineffective groups. The relationship between the relative abundance of certain genera and OABSS were analyzed. Three machine learning algorithms, including random forest (RF), supporting vector machine (SVM) and eXtreme gradient boosting (XGBoost) were utilized to predict the therapeutic effect of mirabegron based on the relative abundance of certain genera in OAB patients' urine microbiome. The species composition of the two groups differed. For one, the relative abundance of Lactobacillus was significantly higher in the effective group than in the ineffective group. In addition, the relative abundance of Gardnerella and Prevotella in the effective group was significantly lower than in the ineffective group. Alpha-diversity and beta-diversity differed significantly between the two groups. LEfSe analysis revealed that Lactobacillus abundance increased while Prevotella and Gardnerella abundance decreased in the effective group. The Lactobacillus abundance ROC curve had high predictive accuracy. The OABSS after treatment was negatively correlated with the abundance of Lactobacillus, whereas the relationship between OABSS and Prevotella and Gardnerella showed the opposite trend. In addition, RF, SVM and XGBoost models demonstrated high predictive ability to assess the effect of mirabegron in OAB patients in the test cohort. The results of this study indicate that urinary microbiota might influence the efficacy of mirabegron, and that Lactobacillus might be a potential marker for evaluating the therapeutic efficacy of mirabegron in OAB patients.