Serum uric acid is associated with metabolic risk factors for cardiovascular disease in the Uygur population
Bibliographic record
Abstract
The prevalence of hyperuricemia is low in Uygurs, who have a high prevalence of cardiovascular risk factors such as hypertension, overweight-obesity, dyslipidemia, hyperglycemia, and insulin resistance (IR). This study sought to investigate the relationships between serum uric acid (UA) and these risk factors in this population. A cross-sectional study was conducted in Uygurs (859 males, 1268 females) aged 20 to 70 years. Demographic data, systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), and fasting and postprandial blood were obtained, and biological measurements were determined. The mean of BMI, SBP, DBP, total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides, fasting blood glucose, fasting insulin, and homeostasis model assessment insulin resistance index (HOMA-IR), and the prevalence of hypertension, IR, hyperglycemia, overweight-obesity, hypercholesteremia, hyper-LDL-c, and hypertriglyceridemia increased with UA but the prevalence of hypo-HDL-c decreased (p < 0.05). Logistic regression analysis showed that the odds ratios for IR, overweight-obesity, hypercholesteremia, hyper-LDL-c, and hypertriglyceridemia against the lowest UA group increased but decreased for hypo-HDL-c (p < 0.05). The UA in the hypo-HDL-c group was lower than that of the controls; the prevalence of hypo-HDL-c in hyperuricemia subjects was lower than in those with normal UA (p < 0.05). But the opposite results were observed between overweight-obesity, hyperglycemia, IR, hypercholesteremia, hypertriglyceridemia, and hyper-LDL-c and correspondence controls, respectively (p < 0.05). In Uygur, elevated UA is associated with overweight-obesity, hypercholesteremia, hyper-LDL-c, hypertriglyceridemia, hyperglycemia, and IR. The HDL-c level significantly increases with UA, whereas the prevalence of hypo-HDL-c decreases. Further studies are needed to clarify why UA is positively correlated to HDL-c.
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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 | 0.000 | 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".