Association of Sedentary Behavior and Physical Activity With Hyperuricemia and Sex Differences: Results From the China Multi-Ethnic Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To determine the association of physical activity (PA) and sedentary time (ST; leisure and total ST), commuting mode with hyperuricemia in a multiethnic Chinese population, and to analyze the difference between sexes. METHODS: Baseline data were analyzed from 22,094 participants from the China Multi-Ethnic Cohort study in the Yunnan region, China. PA and sedentary behavior were assessed through questionnaires. Hyperuricemia was defined as serum urate > 7.0 mg/dL among men and > 6.0 mg/dL among women. A restricted cubic spline (RCS) was created to model the possible nonlinear relationship of PA and ST with hyperuricemia. Logistic regression was used to estimate the odds ratio (OR) and 95% CI. RESULTS: Hyperuricemia prevalence in the observed population was 15.5% (men 25.5%, women 10.7%). Compared to those with light PA, participants with moderate-to-vigorous PA had lower odds of hyperuricemia (adjusted ORs were 0.85 [95% CI 0.77-0.94] and 0.88 [95% CI 0.79-0.97]). However, RCS showed a U-shaped nonlinear relationship between PA and hyperuricemia, and a linear relationship between hyperuricemia prevalence and increasing ST. Total ST ≥ 4 hours/day increased the risk of hyperuricemia in women but not in men. Mode of transportation revealed that sedentary behavior increased the risk of hyperuricemia, but there were inconsistent results based on sex. CONCLUSION: Moderate PA may be more beneficial in reducing the risk of hyperuricemia. Reducing ST may have a greater effect on preventing hyperuricemia in females than in males.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".