Intake of Added Sugar and Sugar-Sweetened Drink and Serum Uric Acid Concentration in US Men and Women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it