Risk of dementia in gout and hyperuricaemia: a meta-analysis of cohort studies
Bibliographic record
Abstract
OBJECTIVES: Gout, characterised by hyperuricaemia with monosodium urate crystal formation and inflammation, is the most common inflammatory arthritis in adults. Recent studies have found that elevated uric acid levels are related to the occurrence of dementia. We conducted a study to investigate the association between dementia and gout or hyperuricaemia. DESIGN: Systematic review and meta-analysis of cohort studies. DATA SOURCES: Studies were screened from inception to 28 June 2019 by searching Medline, Embase and the Cochrane Library databases. ELIGIBILITY CRITERIA: Cohort studies comparing the risk of dementia in patients with gout and hyperuricaemia versus non-gout and non-hyperuricaemia controls were enrolled. DATA EXTRACTION AND ANALYSIS: Two reviewers separately selected studies and extracted data using the Medical Subject Headings without restriction on languages or countries. The adjusted HRs were pooled using the DerSimonian and Laird random effects model. Sensitivity analyses were conducted to evaluate the stability of the results. Publication bias was evaluated using Egger's and Begg's tests. Quality assessment was performed according to the Newcastle-Ottawa Scale. RESULTS: Four cohort studies that met the inclusion criteria were included in our meta-analysis. We found that gout and hyperuricaemia did not increase the risk of dementia, with a pooled HR of 0.94 (95% CI 0.69 to 1.28), but might decrease the risk of Alzheimer's disease (AD), with a pooled HR of 0.78 (95% CI 0.64 to 0.95). There was little evidence of publication bias. Quality assessment of the included studies was high (range: 6-8 points). CONCLUSIONS: Our study shows that gout and hyperuricaemia do not increase the risk of dementia. However, gout and hyperuricaemia might have a protective effect against AD. Due to the limited number of research articles, more investigations are needed to demonstrate the potential relationship between dementia and gout or hyperuricaemia.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.061 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".