Serum Concentrations of Uric Acid and the Metabolic Syndrome Among US Children and Adolescents
Bibliographic record
Abstract
BACKGROUND: The association between concentrations of uric acid and the metabolic syndrome in children and adolescents remains incompletely understood. The objective of this study was to examine how these 2 were associated in a nationally representative sample of US children and adolescents. METHODS AND RESULTS: We performed a cross-sectional analysis of 1370 males and females aged 12 to 17 years using data from the National Health and Nutrition Examination Survey 1999-2002. The prevalence of the metabolic syndrome was < 1% among participants in the lowest quartile of serum concentration of uric acid, 3.7% in the second quartile, 10.3% in the third quartile, and 21.1% in the highest quartile. Compared with the lowest 2 quartiles of uric acid together (< or = 291.5 micromol/L), the odds ratios were 5.80 (95% confidence interval, 3.22 to 10.46) for those in the third quartile (> 291.5 to < or = 339 micromol/L or > 4.9 to < or = 5.7 mg/dL) and 14.79 (95% confidence interval, 7.78 to 28.11) for those in the top quartile (> 339 micromol/L) after adjustment for age, sex, race or ethnicity, and concentrations of C-reactive protein. Starting with the lowest quartile of concentration of uric acid, mean concentrations of serum insulin were 66.2, 66.7, 79.9, and 90.9 pmol/L for ascending quartiles, respectively (P for trend <0.001). CONCLUSIONS: Among US children and adolescents, serum concentrations of uric acid are strongly associated with the prevalence of the metabolic syndrome and several of its components.
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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 | 0.001 | 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".