Sugar‐sweetened soft drinks, diet soft drinks, and serum uric acid level: The third national health and nutrition examination survey
Bibliographic record
Abstract
OBJECTIVE: Sugar-sweetened soft drinks contain large amounts of fructose, which may significantly increase serum uric acid levels and the risk of gout. Our objective was to evaluate the relationship between sugar-sweetened soft drink intake, diet soft drink intake, and serum uric acid levels in a nationally representative sample of men and women. METHODS: Using data from 14,761 participants age>or=20 years from the Third National Health and Nutrition Examination Survey (1988-1994), we examined the relationship between soft drink consumption and serum uric acid levels using linear regression. Additionally, we examined the relationship between soft drink consumption and hyperuricemia (serum uric acid level>7.0 mg/dl for men and >5.7 mg/dl for women) using logistic regression. Intake was assessed by a food-frequency questionnaire. RESULTS: Serum uric acid levels increased with increasing sugar-sweetened soft drink intake. After adjusting for covariates, serum uric acid levels associated with sugar-sweetened soft drink consumption categories (<0.5, 0.5-0.9, 1-3.9, and >or=4 servings/day) were greater than those associated with no intake by 0.08, 0.15, 0.33, and 0.42 mg/dl, respectively (95% confidence interval 0.11, 0.73; P<0.001 for trend). The multivariate odds ratios for hyperuricemia according to the corresponding sweetened soft drink consumption levels were 1.01, 1.34, 1.51, and 1.82, respectively (P=0.003 for trend). Diet soft drink consumption was not associated with serum uric acid levels or hyperuricemia (multivariate P>0.13 for trend). CONCLUSION: These findings from a nationally representative sample of US adults suggest that sugar-sweetened soft drink consumption is associated with serum uric acid levels and frequency of hyperuricemia, but diet soft drink consumption is not.
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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".