Intake of Added Sugar and Sugar-Sweetened Drink and Serum Uric Acid Concentration in US Men and Women
Bibliographic record
Abstract
Fructose-induced hyperuricemia might have a causal role in metabolic syndrome, hypertension, and other chronic disease. However, no study has investigated whether sugar added to foods or sugar-sweetened beverages, which are major sources of fructose, are associated with serum uric acid concentration in free-living populations. We examined the relationship between the intakes of added sugars and sugar-sweetened beverages and serum uric acid concentrations in the National Health and Nutrition Examination Survey 2001-2002, a nationally representative sample of men and women. We included 4073 subjects (1988 men and 2085 women) >18 years of age in the current study. Dietary intake was assessed by a single 24-hour recall. We used multivariate linear regression to adjust for age, gender, intake of energy and alcohol, body mass index, use of diuretics, beta-blockers, and other covariates. Male subjects in the highest intake quartile of estimated intake of added sugars or sugar-sweetened drinks had higher plasma uric acid concentrations than those in the lowest intake quartiles (P<0.001 for both) after adjusting for potential confounders, whereas we did not observe significant associations for females (P for trend>0.2; P for interaction <0.01). Further research is needed to confirm causality of these associations and the observed difference by gender.
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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 | 0.000 | 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".