Association between gout and cancers: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate the association between gout and cancer risk. METHODS: This study was registered with the Prospective Registry for International Systematic Reviews (ID: CRD42023465587). We searched PubMed, Embase, Scopus, Cochrane, and Web of Science databases for studies related to gout and cancer risk, with a timeframe from the date the database was created to September 2023. We assessed the methodological quality of the included studies using the Newcastle-Ottawa scale and assessed heterogeneity between studies using the I2 statistic. Depending on the heterogeneity, we calculated pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) using fixed-effects or random-effects models. In addition, we performed sensitivity analyses and publication bias tests. RESULTS: In this study, we conducted a meta-analysis of 6 studies encompassing a total of 1279,804 participants. Our analysis revealed that individuals with gout are at a heightened risk of developing cancer in general (HR = 1.18, 95% CI = 1.04-1.34, P < .001). Moreover, specific types of cancer displayed a significant correlation with gout, including gastric cancer (HR = 1.31, 95% CI = 1.07-1.62, P = .012), liver cancer (HR = 1.24, 95% CI = 1.01-1.52, P < .001), lung cancer (HR = 1.26, 95% CI = 1.03-1.53, P = .001), and bladder cancer (HR = 1.57, 95% CI = 1.02-2.41, P < .001). Furthermore, gout exhibited a marginally increased risk for other cancer types, such as head and neck cancer and esophageal cancer, although these associations did not attain statistical significance. CONCLUSION: Our study suggests that gout is a risk factor for cancer, especially for stomach, liver, lung, and bladder cancers. Patients with gout have an increased risk of developing overall cancers, lung cancer, liver cancer, stomach cancer, and bladder cancer. However, more high-quality epidemiologic studies are needed to explore the association between gout and individual cancers more accurately.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".