Serum Uric Acid and the Risk of Incident and Recurrent Gout: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Lowering serum uric acid (SUA) levels can essentially cure gout; however, this is not widely practiced. To summarize epidemiologic evidence related to this causal link, we conducted a systematic review of the published literature reporting the association between SUA level and incident and recurrent gout (i.e., gout flares). METHODS: We systematically searched Medline, EMBASE, and the Cochrane Database of Systematic Reviews using separate search strategies for incident gout and recurrent gout. We screened 646 abstracts to identify 8 eligible articles reporting gout incidence and 913 abstracts to identify 18 articles reporting recurrent gout. RESULTS: For both gout incidence and recurrence, a graded trend was observed where the risk was increased with higher SUA levels. Gout incidence rates per 1000 person-years from population-based studies ranged from 0.8 (SUA ≤ 6 mg/dl) to 70.2 cases (SUA ≥ 10 mg/dl). Recurrent gout risk in clinical cohorts ranged from 12% (SUA ≤ 6 mg/dl) to 61% (SUA ≥ 9 mg/dl) among those receiving urate-lowering therapy (ULT), and 3.7% (SUA 6-7 mg/dl) to 61% (SUA > 9.3 mg/dl) after successful ULT. Retrospective database studies also showed a graded relationship, although the strength of the association was weaker. Studies reporting mean flares or time-to-flare according to SUA showed similar findings. CONCLUSION: This systematic review confirms that higher SUA levels are associated with increased risk of incident and recurrent gout in a graded manner. Although few prospective cohorts have evaluated incident and recurrent gout according to SUA, the existing evidence underscores the need to treat to SUA targets, as recommended by the American College of Rheumatology and the European League Against Rheumatism.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".