Persistent Sexual Dysfunction and Suicidal Ideation in Young Men Treated with Low‐Dose Finasteride: A Pharmacovigilance Study
Bibliographic record
Abstract
STUDY OBJECTIVE: Finasteride, a 5α-reductase inhibitor, is marketed in a low dose (1 mg) as a popular therapy for androgenic alopecia in young men. As case reports and small surveys have suggested a link between persistent sexual dysfunction (SD) and suicidal ideation (SI) with low-dose finasteride, the aim of this study was to detect signals of SD and SI secondary to low-dose finasteride use in young men. DESIGN: Retrospective pharmacovigilance disproportionality analysis. DATA SOURCE: United States Food and Drug Administration Adverse Event Reporting System (FAERS) database. MEASUREMENTS AND MAIN RESULTS: Low-dose finasteride-related adverse event reports for men aged 18-45 years that were submitted to the FAERS between 1998 and 2013 were retrieved. Multi-item Gamma Poisson Shrinker disproportionality analysis was applied to calculate the empirical Bayes geometric mean (EBGM) and corresponding 95% confidence interval (CI) as an association metric between low-dose finasteride and the events of interest. Signals were defined as associations with thresholds of a CI lower limit of 2.0 or greater. Medical Dictionary for Regulatory Activities Preferred Terms denoting to SD and SI were identified to reflect the outcome of interest. In total, of 4910 reports, 577 persistent SD and 39 SI adverse event reports (11.8% and 7.9%, respectively) were identified for young men using low-dose finasteride; 34 (87.2%) of the 39 men with SI also experienced SD. The majority of these events were serious (e.g., contributed to the patient's death, hospitalization, or disability). Low-dose finasteride was associated with more than expected reporting of SD in young men compared with reporting of these events with all other drugs within the database (EBGM 28.0, 95% CI 26.1-30.0). Disproportional reporting in SI events was noted, although it did not reach signal threshold (EBGM 1.72; 95% CI 1.31-2.23). Among serious SD events, 43% led to disability; 28% required medical intervention, including hospitalization; and 5% were life-threatening. Six fatal SD reports were identified. CONCLUSION: Persistent SD might be a potential risk of low-dose finasteride for androgenic alopecia therapy in young men, and this risk might contribute to SI. Our findings provide a strong hypothesis for pharmacoepidemiologic studies to further examine this association.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".