Serum uric acid levels in patients with Parkinson’s disease: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Lower serum uric acid (UA) levels have been reported as a risk factor in Parkinson's disease (PD). However, the results have been inconsistent so far. OBJECTIVES: The aim of the present study was to clarify the potential relationship of uric acid with PD. METHODS: Comprehensive electronic search in pubmed, web of science, and the Cochrane Library database to find original articles about the association between PD and serum uric acid levels published before Dec 2015. Literature quality assessment was performed with the Newcastle-Ottawa Scale. Random-effects model was used to estimate the standardized mean differences (SMDs) with 95% confidence intervals (CIs). Heterogeneity across studies was assessed using I2 and H2 statistics. Sensitivity analyses to assess the influence of individual studies on the pooled estimate. Publication bias was investigated using funnel plots and Egger's regression test. Analyses were performed by using Review Manager 5.3 and Stata 11.0. RESULTS: Thirteen studies with a total of 4646 participants (2379 PD patients and 2267 controls) were included in this meta-analysis. The current results showed that the serum UA levels in PD patients were significantly lower compared to sex and age-matched healthy controls (SMD: -0.49, 95% CI: [-0.67, -0.30], Z = 5.20, P < 0.001) and these results showed no geographic regional (Asia: SMD = -0.65, 95% CI [-0.84, -0.46], Z = 6.75, p <0.001; Non-Asia: SMD = -0.25, 95% CI [-0.43, -0.07], Z = 2.70, p = 0.007) and sex differences (women: SMD = -0.53, 95% CI [-0.70, -0.35], z = 5.98, p <0.001; men: SMD = -0.66, 95% CI [-0.87, -0.44], z = 6.03, p <0.001). Serum UA levels in middle-late stage PD patients with higher H&Y scales were significantly lower than early stage PD patients with lower H&Y scales (SMD = 0.63, 95% CI [0.36,0.89], z = 4.64, p <0.001). CONCLUSIONS: Our study showed that the serum UA levels are significantly lower in PD and the level is further decreased as the disease progresses. Thus it might be a potential biomarker to indicate the risk and progression of PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".