Sex difference in the association of serum uric acid with metabolic syndrome and its components: a cross-sectional study in a Chinese Yi population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.000 | 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