The effect of body fat distribution on ethnic differences in cardiometabolic risk factors of Chinese and Europeans
Bibliographic record
Abstract
This study aimed to examine the differences in body fat distribution and cardiometabolic risk between individuals of Chinese and European origin and the role of body fat distribution on ethnic differences in cardiometabolic risk. A total of 418 participants from the Multicultural Community Health Assessment Trial were assessed for visceral adipose tissue (VAT), subcutaneous abdominal adipose tissue (SAT), anthropometric variables, blood pressure, and lipid, insulin, and glucose levels. Multiple regression analyses were split by sex and adjusted for appropriate covariates in model 1a and further adjusted for VAT in model 1b or SAT in model 1c. A secondary model replaced body mass index (BMI) with waist circumference (WC). Chinese males had higher levels of triglycerides, insulin, homeostasis model assessment, and SAT than European males, as well as higher total cholesterol (TC), glucose, and VAT in the model adjusted for WC. Chinese females had higher glucose levels than European females after adjustment for either BMI or WC. When VAT was added to the models, differences in cardiometabolic risk factors remained significant but were attenuated between Chinese and European males and females; SAT did not attenuate the ethnic difference in cardiometabolic risk. These findings suggest that the higher VAT levels seen in the Chinese population do not fully account for the ethnic disparities in these risk factors. Given the observed interethnic difference in body composition, current BMI and WC cutoffs might be misleading when it comes to identifying Chinese individuals at risk for type 2 diabetes or cardiovascular disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".