Ethnic differences in acylation stimulating protein (ASP) in Xinjiang Uygur autonomous region, China.
Bibliographic record
Abstract
BACKGROUND: Acylation Stimulating Protein (ASP) stimulates adipocyte triglyceride synthesis and glucose transport. The aim was to examine ethnic difference in ASP and the relation to lipid profile and other parameters among Han, Uygur, and Kazak healthy populations matched for BMI, age and gender distribution. METHODS: 331 healthy persons were recruited in total (age 30-60 yr): 137 Han, 114 Uygur, and 80 Kazak. Anthropometric measurements including height, weight, waist circumference, hip circumference, blood pressure, ankle brachial index (ABI), and pulse wave velocity (PWV) were measured in all participants. Fasting concentrations of fasting glucose, uric acid, and lipids, including triglyceride (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), ASP, complement C3, insulin, non-esterified fatty acid (NEFA) and C-reactive protein (CRP) were measured. RESULTS: ASP in Uygurs was significantly lower than Han subjects (P=0.0003). The Uygurs demonstrated the highest C3 (P<0.001), CRP (P=0.001), and NEFA concentrations (P=0.008), the lowest %ASP/C3 (P<0.001) and TC levels (P=0.0008) vs those in Han and Kazak populations. In the Han group, glucose, the average ABI (an index of peripheral response) and diastolic blood pressure were significantly different from both Uygur and Kazak group (P=0.0007, P=0.0003, P=0.0001) while Kazaks show the lowest waist/hip circumference (WHR) (P=0.0003). CONCLUSION: There are ethnic differences in ASP, C3, CRP and lipid profiles in healthy Han, Uygur, and Kazak populations. Overall, the Uygur populations presents with a disadvantageous metabolic profile as compared to Han and Kazak groups.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".