Haemoglobin A1c, fasting glucose, serum C-peptide and insulin resistance in relation to serum uric acid levels--the Third National Health and Nutrition Examination Survey
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To evaluate haemoglobin A1c (HbA1c), fasting glucose, serum C-peptide and insulin resistance in relation to serum uric acid levels in a nationally representative sample of men and women. METHODS: Using data from 14,664 participants aged 20 yrs and older in The US Third National Health and Nutrition Examination Survey (1988-1994), we examined the relation between the levels of HbA1c, other biomarkers and serum uric acid levels using multivariate linear regressions stratified by gender. RESULTS: The serum uric acid levels increased with increasing serum HbA1c levels up to the category of 6-6.9%, and thereafter decreased with further increasing HbA1c levels (a bell-shaped relation). Compared with a HbA1c level of < 5%, the multivariate differences among women were 26.8 micromol/l for HbA1c of 6-6.9% and -25.6 micromol/l (95% CI -42.8, -8.3) for HbA1c > or = 9%. The corresponding multivariate differences among men were 8.3 micromol/l (95% CI -3.0, 19.6) and -64.8 micromol/l (95% CI -46.0, -84.5), which were both significantly different from those among women (P-values for interaction by sex <0.001). Fasting glucose levels also showed a bell-shaped relation with serum uric acid levels. Individuals with diabetes showed lower serum uric acid levels and the association was larger among men (P-value for interaction, 0.007). Serum uric acid levels increased linearly with increasing fasting serum C-peptide levels, serum insulin levels or insulin resistance (multivariate P-values for trend, <0.001). CONCLUSIONS: Individuals with moderately elevated HbA1c levels (i.e. pre-diabetes) may be at a higher risk of hyperuricaemia and gout, particularly in women, whereas individuals with diabetes or highly elevated HbA1c levels may be at a lower risk of these conditions, particularly in men.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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