Serum Urate and the Risk of Parkinson's Disease: Results From a Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Serum urate may exert protective effects against Parkinson's disease (PD) through its antioxidant capacities. In this article, we examine the hypothesis that high serum urate levels are associated with lower risk of PD. METHODS: We searched NCBI (PubMed), ISI Web of Science and EMBASE for studies that reported the risk of PD associated with serum urate. Fixed or random effects meta-analysis was used to pool results across studies, and further analysis was used to assess the effects by gender. RESULTS: Six studies met the inclusion criteria involving a total of 33 185 participants. Overall, we found a 33% reduction in PD incidence among persons with high serum urate level (relative risk [RR]=0.67; 95% confidence interval [CI], 0.50-0.91). Subgroup analysis was performed with 20 641 men and 12 544 women included, indicating statistically significant protective effects of serum urate in men (RR=0.60; 95% CI, 0.40-0.90) but not in women. A dose-response trend of serum urate to reduce PD risk was also observed involving 11 795 participants (RR=0.77; 95% CI, 0.68-0.88). Additionally, high serum urate levels seemed to slow the clinical decline of PD patients (RR=0.56; 95% CI, 0.43-0.72). CONCLUSIONS: In light of these findings, our study confirms previous findings of a robust association between high serum urate level and PD risk, especially in men. It also suggests that long-term exposure to high serum urate may be linked to the delay of PD progression, however more well-designed investigations are needed.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.054 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".