Independent Impact of Gout on Mortality and Risk for Coronary Heart Disease
Bibliographic record
Abstract
BACKGROUND: Although gout and hyperuricemia are related to several conditions that are associated with reduced survival, no prospective data are available on the independent impact of gout on mortality. Furthermore, although many studies have suggested that hyperuricemia is associated with cardiovascular disease (CVD), limited data are available on the impact of gout on CVD. METHODS AND RESULTS: Over a 12-year period, we prospectively examined the relation between a history of gout and the risk of death and myocardial infarction in 51,297 male participants of the Health Professionals Follow-Up Study. During the 12 years of follow-up, we documented 5825 deaths from all causes, which included 2132 deaths from CVD and 1576 deaths from coronary heart disease (CHD). Compared with men without history of gout and CHD at baseline, the multivariate relative risks among men with history of gout were 1.28 (95% confidence interval [CI], 1.15 to 1.41) for total mortality, 1.38 (95% CI, 1.15 to 1.66) for CVD deaths, and 1.55 (95% CI, 1.24 to 1.93) for fatal CHD. The corresponding relative risks among men with preexisting CHD were 1.25 (95% CI, 1.09 to 1.45), 1.26 (95% CI, 1.07 to 1.50), and 1.24 (95% CI, 1.04 to 1.49), respectively. In addition, men with gout had a higher risk of nonfatal myocardial infarction than men without gout (multivariate relative risk, 1.59; 95% CI, 1.04 to 2.41). CONCLUSIONS: These prospective data indicate that men with gout have a higher risk of death from all causes. Among men without preexisting CHD, the increased mortality risk is primarily a result of an elevated risk of CVD death, particularly from CHD.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".