Sex difference in the association of serum uric acid with metabolic syndrome and its components: a cross-sectional study in a Chinese Yi population
Bibliographic record
Abstract
OBJECTIVES: Since the association between serum uric acid (SUA) and metabolic syndrome (MetS) has been reported extensively, it remains unclear whether SUA is associated with MetS and its components in a Chinese Yi population. METHODS: This study recruited 1,903 people (912 men, 991 women) older than 18 years old from the Liangshan region in Sichuan province. Anthropometric measurements and biochemical indexes were measured by a standard protocol. SUA levels were divided into four quartiles by sex. RESULTS: The prevalence of hyperuricemia and MetS is 21.0% and 17.1%, respectively. The levels of SUA were positively correlated with waist circumference, body mass index and triglycerides while negatively correlated with high-density lipoprotein cholesterol in both sexes. Increased SUA levels were accompanied with prevalence of MetS and several components in both sexes (P < 0.05). Men with the highest SUA quartile had an increased risk of MetS [OR (95% CI): 3.101 (1.281-7.504)], and men with higher SUA levels had an increased risk of central obesity, high blood pressure and hypertriglyceridemia compared to the lowest SUA quartile. Women with higher SUA levels had an increased risk of MetS, central obesity, hypertriglyceridemia and a lower risk of high blood pressure compared to the lowest SUA quartile. CONCLUSIONS: SUA levels were closely associated with MetS and several components by sex in Chinese Yi population.
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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 | 0.000 |
| 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".