Population Impact Attributable to Modifiable Risk Factors for Hyperuricemia
Why this work is in the frame
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Bibliographic record
Abstract
Objective To examine modifiable risk factors in relation to the presence of hyperuricemia and to estimate the proportion of hyperuricemia cases in the general population that could be prevented by risk factor modification, along with estimates of the variance explained. Methods Using data obtained from 14,624 adults representative of the US civilian noninstitutionalized population, we calculated adjusted prevalence ratios for hyperuricemia, population attributable risks ( PAR s), and the variance explained according to the following 4 factors: body mass index ( BMI ; ≥25 kg/m 2 ), alcohol intake, nonadherence to a Dietary Approaches to Stop Hypertension ( DASH ) diet, and diuretic use. Results BMI , alcohol intake, adherence to a DASH ‐style diet, and diuretic use were all associated with serum urate levels and the presence of hyperuricemia in a dose‐dependent manner. The corresponding PAR s of hyperuricemia cases for overweight/obesity (prevalence 60%), nonadherence to a DASH ‐style diet (prevalence 82%), alcohol use (prevalence 48%), and diuretic use (prevalence 8%) were 44% (95% confidence interval [95% CI ] 41%, 48%), 9% (95% CI 3%, 16%), 8% (95% CI 5%, 11%), and 12% (95% CI 11%, 14%), respectively, whereas the corresponding variances explained were 8.9%, 0.1%, 0.5%, and 5.0%. Our simulation study showed the variance nearing 0% as exposure prevalence neared 100%. Conclusion In this nationally representative study, 4 modifiable risk factors ( BMI , the DASH diet, alcohol use, and diuretic use) could be used to individually account for a notable proportion of hyperuricemia cases. However, the corresponding serum urate variance explained by these risk factors was very small and paradoxically masked their high prevalences, providing real‐life empirical evidence for its limitations in assessing common risk factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 0.001 |
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