The relationship between hyperuricemia and erectile dysfunction: a scoping review
Bibliographic record
Abstract
Recent studies suggest an association between hyperuricemia (HUA) and erectile dysfunction (ED), yet controversy remains regarding whether HUA is an independent risk factor. The proposed mechanisms include HUA-induced oxidative stress, inflammation, and metabolic syndrome, leading to pathophysiological changes. To explore this, we conducted a scoping review following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews) guidelines. We performed literature searches in PubMed, Web of Science, and other databases, using the Newcastle-Ottawa Scale (NOS) and Appraisal tool for Cross-Sectional Studies (AXIS) for quality assessment. We included 16 clinical studies (n = 295,705) published between 2014 and 2025, comprising 13 cross-sectional and 3 cohort studies. A positive correlation between HUA and ED was supported by 15 of the 16 studies (93.8%). Among these, five demonstrated the association persisted after adjusting for confounding factors, while two identified HUA as an independent factor. The three cohort studies were all rated as high quality (NOS score 9/9). An exploratory pooled analysis of these high-quality studies revealed that patients with gout had a 16% increased risk of developing ED (HR (Hazard Ratio) = 1.16, 95% CI (confidence interval) 1.104–1.219, p < 0.001). This estimate should be interpreted cautiously due to the limited number of studies and heterogeneity. In conclusion, a significant association exists between HUA and erectile function, though the relationship remains complex. The pathophysiology may involve an “oxidative stress-inflammation-metabolism” triad. While controlling uric acid levels shows potential for the prevention and treatment of ED, further research is required before this approach can be supported for routine clinical application.
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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 | 0.019 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".