Neurotrophins and Proneurotrophins as Biomarkers for Overactive Bladder Syndrome in Aging Females
Bibliographic record
Abstract
Background/Objectives: Overactive bladder (OAB), common in elderly women, involves urgency, frequency, and nocturia, with complex phenotypes. The use of neurotrophins as non-invasive urinary biomarkers has been previously explored. The objective of this study was to assess the diagnostic and therapeutic utility of urinary biomarkers in a Canadian population of aging female OAB patients. Methods: We conducted a single-center prospective study of aging female patients diagnosed with OAB and age-matched healthy controls, where we conducted pre- and post-treatment assessments using a combination of clinical questionnaires, voiding diaries, and urinary biomarkers nerve growth factor (NGF), proform of NGF (proNGF), brain-derived neurotrophic factor (BDNF), proform of BDNF (proBDNF), and neurotrophin receptor p75 extracellular domain (p75ECD)) quantified using ELISA. Baseline and post-treatment urinary biomarker levels in OAB patients were compared with those of controls. Results: OAB patients and controls at baseline displayed significant differences in neurotrophin levels and in their ratios of mature/precursors. In the post-treatment OAB cohort, only NGF and proNGF exhibited significant improvement correlating with clinical symptom relief. Biomarkers in non-responders remained unchanged, suggesting heterogeneity in therapeutic response. Conclusions: Urinary neurotrophins show promise as non-invasive diagnostic markers of OAB and monitoring treatment response in aging female patients. While this study focused on patients broadly diagnosed with OAB, future research should aim to classify OAB subtypes—such as those based on urodynamic studies or underlying pathophysiology—to better understand how urinary neurotrophins can differentiate between mechanisms like detrusor overactivity, detrusor underactivity, or bladder outlet obstruction. This will enhance their relevance in guiding personalized treatment strategies and predicting outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".