Impact of lifestyle factors and dietary patterns on serum uric acid levels and disease activity in gout: a systematic review
Bibliographic record
Abstract
BACKGROUND: Gout is a common type of inflammatory arthritis caused by monosodium urate (MSU) crystal deposition in the joints. This leads to pain, swelling, and restricted motion. Although pharmacological treatments are effective, lifestyle and dietary factors play crucial roles in managing gout and its flares. AIM: This systematic review aimed to assess the effect of lifestyle factors, physical activity, and dietary patterns on serum uric acid levels and gout activity. METHODS: A search of PubMed, BMJ journals, and Google Scholar identified eight studies (five prospective cohort studies, two case-cross-over studies, and one randomized controlled trial), involving 47,879 participants (predominantly males [78.5-95.3%], aged 55-66 years). Eligible studies focused on adults with gout and examined the lifestyle or dietary factors affecting uric acid levels or gout activity. The review followed a pre-specified protocol (PROSPERO registration CRD42024594359). To optimize the quality, the bias risk was assessed using the Newcastle-Ottawa scale for observational studies and the Cochrane Risk of Bias 1.0 tool for randomized controlled trials. RESULTS: The findings suggest that consuming polyunsaturated fatty acid-rich fish, regular physical activity, and increased vegetable intake may reduce gout flares. Conversely, high purine intake (especially animal sources), excessive alcohol consumption, and obesity are risk factors for gout exacerbation. Some studies have reported reduced serum uric acid levels with dietary changes, whereas others have found no significant effect. Despite the variability and recall bias, dietary and lifestyle modifications may help manage gout and reduce disease activity. CONCLUSION: The findings of this systematic review emphasize the importance of dietary and lifestyle factors in managing serum uric acid levels and reducing the risk of gout flares. Further research is required to establish clinical recommendations to improve patient outcomes.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.007 | 0.009 |
| 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".