Hyperuricemia and coronary heart disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: The role of serum uric acid as an independent risk factor for cardiovascular disease remains unclear, although hyperuricemia is associated with cardiovascular disease such as coronary heart disease (CHD), stroke, and hypertension. METHODS: A systematic review and meta-analysis using a random-effects model was conducted to determine the risk of CHD associated with hyperuricemia in adults. Studies of hyperuricemia and CHD were identified by searching major electronic databases using the medical subject headings and keywords without language restriction (through February 2009). Only prospective cohort studies were included if they had data on CHD incidences or mortalities related to serum uric acid levels in adults. RESULTS: Twenty-six eligible studies of 402,997 adults were identified. Hyperuricemia was associated with an increased risk of CHD incidence (unadjusted risk ratio [RR] 1.34, 95% confidence interval [95% CI] 1.19-1.49) and mortality (unadjusted RR 1.46, 95% CI 1.20-1.73). When adjusted for potential confounding, the pooled RR was 1.09 (95% CI 1.03-1.16) for CHD incidence and 1.16 (95% CI 1.01-1.30) for CHD mortality. For each increase of 1 mg/dl in uric acid level, the pooled multivariate RR for CHD mortality was 1.12 (95% CI 1.05-1.19). Subgroup analyses showed no significant association between hyperuricemia and CHD incidence/mortality in men, but an increased risk for CHD mortality in women (RR 1.67, 95% CI 1.30-2.04). CONCLUSION: Hyperuricemia may marginally increase the risk of CHD events, independently of traditional CHD risk factors. A more pronounced increased risk for CHD mortality in women should be investigated in future research.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".