The Role of Urinary Microbiome Analysis in the Diagnostic Approach and Management of Urinary Incontinence: A Systematic Review
Bibliographic record
Abstract
Urinary incontinence (UI) is a significant global health issue that impacts mainly middle-aged women, severely affecting their quality of life. Emerging research highlights the urinary microbiome's complex role in the etiology and management of UI, with microbial dysbiosis potentially influencing symptom severity and treatment outcomes. This systematic review aimed to evaluate the current evidence on the urinary microbiome's role in diagnosing and managing UI, focusing on variations in microbial composition across UI subtypes. We identified 21 studies, mostly employing 16S rRNA sequencing to characterize urinary microbiota and their associations with various UI subtypes, including urgency urinary incontinence (UUI), overactive bladder (OAB), and stress urinary incontinence (SUI). The findings revealed distinct microbial patterns, such as reduced Lactobacillus levels and increased Gardnerella prevalence, particularly in UUI. Altered microbiome profiles correlated with symptom severity, with reduced Lactobacilli suggesting a protective role in maintaining urinary health. Specific microbial species, including Actinotignum schaalii and Aerococcus urinae, emerged as potential biomarkers for UI diagnosis. Despite promising findings, limitations such as small sample sizes, variability in microbiome profiling methods, and insufficient causal evidence underscore the need for further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".