Prevalence of the metabolic syndrome in patients with gout: The Third National Health and Nutrition Examination Survey
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of metabolic syndrome among patients with gout and to examine the association between the 2 conditions in a nationally representative sample of US adults. METHODS: Using data from 8,807 participants age >or=20 years in the Third National Health and Nutrition Examination Survey (1988-1994), we determined the prevalence of metabolic syndrome among individuals with gout and quantified the magnitude of association between the 2 conditions. We used both the revised and original National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATP III) criteria to define metabolic syndrome. RESULTS: The prevalence (95% confidence interval [95% CI]) of metabolic syndrome according to revised NCEP/ATP III criteria was 62.8% (51.9-73.6) among individuals with gout and 25.4% (23.5-27.3) among individuals without gout. Using 2002 census data, approximately 3.5 million US adults with a history of gout have metabolic syndrome. The unadjusted and age- and sex-adjusted odds ratios (95% CI) of metabolic syndrome for individuals with gout were 4.96 (3.17-7.75) and 3.05 (2.01-4.61), respectively. With the original NCEP/ATP criteria, the corresponding prevalences were slightly lower, whereas the corresponding odds ratios were slightly higher. The stratified prevalences of metabolic syndrome by major associated factors of gout (i.e., body mass index, hypertension, and diabetes) remained substantially and significantly higher among those with gout than those without gout (all P values <0.05). CONCLUSION: These findings indicate that the prevalence of metabolic syndrome is remarkably high among individuals with gout. Given the serious complications associated with metabolic syndrome, this frequent comorbidity should be recognized and taken into account in long-term treatment and overall health of individuals with gout.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".