Vitamin C intake and serum uric acid concentration in men.
Bibliographic record
Abstract
OBJECTIVE: We examined associations between vitamin C intake and serum uric acid in men in a population-based study. METHODS: We included 1387 men without hypertension and with body mass index (BMI) < 30 kg/m(2) in the Health Professional Follow-up Study. Dietary intake was assessed with a semiquantitative food frequency questionnaire validated for use in this population. Serum uric acid concentrations were measured. RESULTS: Greater intakes of total vitamin C were significantly associated with lower serum uric acid concentrations, after adjustment for smoking, BMI, ethnicity, blood pressure, presence of gout, use of aspirin, and intake of energy, alcohol, dairy protein, fructose, meat, seafood and coffee. An inverse dose-response association was observed through vitamin C intake of 400-500 mg/day, and then reached a plateau. Adjusted mean uric acid concentrations across total vitamin C intake categories (< 90, 90-249, 250-499, 500-999, or > or = 1000 mg/day) were 6.4, 6.1, 6.0, 5.7, and 5.7 mg/dl, respectively (p for trend < 0.001). Greater vitamin C intake was associated with lower prevalence of hyperuricemia (serum uric acid > 6 mg/dl). Multivariate odds ratios for hyperuricemia across total vitamin C intake categories were 1 (reference), 0.58, 0.57, 0.38, and 0.34 (95% CI 0.20-0.58; P for trend < 0.001). When we used dietary data, which were assessed 4-8 years before blood collection, as predictors, we observed similar inverse associations between vitamin C intake and uric acid. CONCLUSION: These population-based data indicate that vitamin C intake in men is inversely associated with serum uric acid concentrations. These findings support a potential role of vitamin C in the prevention of hyperuricemia and 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 | 0.000 | 0.000 |
| 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".