Population Impact Attributable to Modifiable Risk Factors for Hyperuricemia
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: ), 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 PARs 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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".