Epidemiology of Depression and Anxiety in Gout: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review of depression and anxiety among patients with gout that specifically evaluates the prevalence, incidence, determinants, and effects of these mental health comorbidities. METHODS: We conducted a literature search in Medline, Embase, Cochrane Database of Systematic Reviews, CINAHL, and PsycINFO using indexed terms and key words to identify studies reporting on depression/anxiety in patients with gout. This review included full-text articles published in English that reported on patients with gout, evaluated depression/anxiety using a routinely reported measure, and provided estimates or sufficient data on the prevalence, incidence, determinants, or effects of depression/anxiety. Metaanalyses were conducted using random effects models. RESULTS: Twenty of 901 articles identified through the search strategy met our inclusion criteria. All 20 studies evaluated depression, while only 10 assessed anxiety (50%). Metaanalyses suggest a positive association between mental health disorders and gout, as resultant pooled OR were 1.29 (95% CI 1.07-1.56) for depression and 1.29 (95% CI 0.96-1.73) for anxiety. Findings from four studies reporting on the incidence of depression in patients with gout resulted in a pooled HR of 1.17 (95% CI 1.01-1.36). Significant determinants of depression included number of tophi, frequency of flares, and oligo/polyarticular gout. CONCLUSION: Our systematic review suggests that depression and anxiety are significantly associated with gout, highlighting the need for future research to focus on the onset of mental disorders after gout diagnosis. We also identify potential targets for intervention.
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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 | 0.024 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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, 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".