Gout and risk of dementia, Alzheimer's disease or vascular dementia: a meta-epidemiology study
Bibliographic record
Abstract
Objectives The association between gout and dementia, Alzheimer's disease (AD), or vascular dementia (VD) is not fully understood. The aim of this meta-analysis was to evaluate the risk of all-cause dementia, AD, and VD in gout patients with or without medication. Methods Data sources were PubMed, Embase, the Cochrane Library, and reference lists of included studies. This meta-analysis included cohort studies assessing whether the risk of all-cause dementia, AD, and VD was associated with gout. The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used to access the overall certainty of evidence. Risk ratios (RR) with 95% confidence intervals (CI) were pooled using a random-effects model, and publication bias was assessed with funnel plots and Egger's test. Results A total of six cohort studies involving 2,349,605 individuals were included in this meta-analysis, which were published between 2015 and 2022. The pooling analysis shows that the risk of all-cause dementia was decreased in gout patients [RR = 0.67, 95% CI (0.51, 0.89), I2 = 99%, P = 0.005, very low quality], especially in gout patients with medication [RR = 0.50, 95% CI (0.31, 0.79), I2 = 93%, P = 0.003, low quality]. The risk of AD [RR = 0.70, 95% CI (0.63, 0.79), I2 = 57.2%, P = 0.000, very low quality] and VD [RR = 0.68, 95% CI (0.49, 0.95), I2 = 91.2%, P = 0.025, very low quality] was also decreased in gout patients. Despite the large heterogeneity, the sensitivity analysis indicated that the results were robust, and there was little evidence of publication bias. Conclusion The risk of all-cause dementia, AD, and VD is decreased in gout patients, but the quality of evidence is generally low. More studies are still needed to validate and explore the mechanisms of this association. Systematic review registration https://www.crd.york.ac.uk/prospero/#recordDetails , identifier: CRD42022353312.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| gpt | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.058 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".