Coffee, tea, and caffeine consumption and serum uric acid level: The third national health and nutrition examination survey
Bibliographic record
Abstract
OBJECTIVE: Coffee is one of the most widely consumed beverages in the world and may affect serum uric acid levels and risk of gout via various mechanisms. Our objective was to evaluate the relationship between coffee, tea, and caffeine intake and serum uric acid level in a nationally representative sample of men and women. METHODS: Using data from 14,758 participants ages >/=20 years in the Third National Health and Nutrition Examination Survey (1988-1994), we examined the relationship between coffee, tea, and caffeine intake and serum uric acid level using linear regression. Additionally, we examined the relationship with hyperuricemia (serum uric acid >7.0 mg/dl among men and >5.7 mg/dl among women) using logistic regression. Intake was assessed by a food frequency questionnaire. RESULTS: Serum uric acid level decreased with increasing coffee intake. After adjusting for age and sex, serum uric acid level associated with coffee intake of 4 to 5 and >/=6 cups daily was lower than that associated with no intake by 0.26 mg/dl (95% confidence interval [95% CI] 0.11, 0.41) and 0.43 mg/dl (95% CI 0.23, 0.65; P for trend < 0.001), respectively. After adjusting for other covariates, the differences remained significant (P for trend < 0.001). Similarly, there was a modest inverse association between decaffeinated coffee intake and serum uric acid levels (multivariate P for trend 0.035). Total caffeine from coffee and other beverages and tea intake were not associated with serum uric acid levels (multivariate P for trend 0.15). The multivariate odds ratio for hyperuricemia in individuals with coffee intake >/=6 cups daily compared with those with no coffee use was 0.57 (95% CI 0.35, 0.94; P for trend 0.001). CONCLUSION: These findings from a nationally representative sample of US adults suggest that coffee consumption is associated with lower serum uric acid level and hyperuricemia frequency, but tea consumption is not. The inverse association with coffee appears to be via components of coffee other than caffeine.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".