Correlation Between Metabolic Syndrome and Hyperuricemia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The main goal of this study was to conduct a meta-analysis and systematic review to examine the correlation between metabolic syndrome (MetS) and hyperuricemia. METHODS: All studies available in PubMed, Cochrane Library, Embase, and Web of Science were obtained within the retrieval timeframe ending on 9 December 2023. Utilizing the Agency for Healthcare Research and Quality (AHRQ) and the Newcastle-Ottawa Scale (NOS), the included studies underwent quality appraisal, and Stata v14 software was employed for the subsequent data analysis. RESULTS: A total of 40 studies, covering 214,091 patients, were selected based on specified inclusion and exclusion criteria. The analysis revealed a substantial association between MetS and hyperuricemia (odds ratio (OR) = 2.25, 95% confidence interval (CI) 1.19-4.26, P < 0.001). The metabolically abnormal overweight/obese group (MUHOWO) exhibited a heightened risk of hyperuricemia (OR = 3.54, 95% CI 2.66-4.71, P = 0.002). Additionally, hyperuricemia increased the likelihood of developing MetS (OR = 2.13, 95% CI 1.63-2.79, P < 0.001). Stratified by gender, hyperuricemia elevated the risk of MetS in both men (OR = 1.92, 95% CI 1.43-2.58, P < 0.001) and women (OR = 2.13, 95% CI 1.62-2.8, P < 0.001). CONCLUSIONS: This meta-analysis and systematic review robustly affirm a significant bidirectional association between MetS and hyperuricemia. The increased risk observed, especially in MUHOWO and across gender lines, underscores the clinical relevance. Addressing MetS emerges as crucial in preventing and managing hyperuricemia, and vice versa. These findings offer valuable insights, urging further research into underlying mechanisms for more targeted interventions and personalized treatments in clinical practice.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".