Association between gout and the development of Parkinson’s disease: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: As a natural antioxidant, uric acid plays a protective role against neurodegenerative disorders, including Parkinson's disease (PD). Therefore, the risk of PD has been found to be lower in people with hyperuricemia. In this article, we conducted a systematic review and meta-analysis to investigate whether gout affects the future risk of developing PD. METHODS: We searched PubMed, Scopus, the Web of Science, and Google Scholar to find relevant studies, up to March 16, 2022. Studies investigating the risk of PD, following a gout diagnosis, were included if they were cross-sectional, case-control or cohort studies. The Newcastle Ottawa Scale (NOS) checklist was used to assess the quality of all included studies. The meta-analysis was performed using STATA 17.0. RESULTS: Ten studies were included, which were comprised of three case-controls, six cohort studies and one nested case-control study. We found no significant association between gout and the risk of PD among both sexes (RR = 0.94, 95% CI: 0.86-1.04), although the association was significant for females (RR = 1.09; 95% CI: 1.02-1.17). Subgroup analysis also showed no significant findings by age group, whether they were receiving treatment for gout, study design, quality assessment score, and method of gout ascertainment. In contrast, the studies that defined PD according to the use of drugs showed significant results (RR = 0.82; 95% CI: 0.76-0.89). There was a significant publication bias on the association between gout and PD. CONCLUSIONS: The presence of gout had no significant effect on the risk of subsequently developing PD. Further analyses are recommended to investigate the effects of demographic and behavioral risk factors.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".