Visceral adipose tissue accumulation differs according to ethnic background: results of the Multicultural Community Health Assessment Trial (MCHAT),”TheAmerican
Bibliographic record
Abstract
Background: It was suggested that body fat distribution differs across ethnic groups, and this may be important when considering risk of disease. Previous studies have not adequately investigated differences in discrete regions of abdominal adiposity across ethnic groups. Objective: We compared the relation between abdominal adipose tissue and total body fat between persons living in Canada of Ab-original, Chinese, and South Asian origin with persons of European origin. Design: Healthy Aboriginal, Chinese, European, and South Asian participants (n 822) aged between 30 and 65 y were matched by sex, ethnicity, and body mass index (BMI; in kg/m2) range. Total abdominal adipose tissue (TAT), subcutaneous abdominal adipose tissue (SAT), visceral adipose tissue (VAT), total body fat mass, lifestyle, and demographics were assessed. Relations between BMI and total body fat, TAT, SAT, and VAT and between total body fat and TAT, SAT, and VAT were investigated. Results: BMI significantly underestimated VAT in all non-European groups. Throughout a range of total body fat mass, VAT was not significantly different between the Aboriginals and the Eu-ropeans. With total body fat 9.1 kg, Chinese participants had increasingly greater amounts of VAT than did the Europeans (P for interaction 0.008). South Asians had less VAT with total body fat 37.4 kg but more VAT below that amount than did Europeans (P for interaction 0.001). Conclusion: Compared with Europeans, the Chinese and South Asian cohorts had a relatively greater amount of abdominal adipose tissue, and this difference was more pronounced with VAT. No significant differences were observed between the Aboriginals and
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.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".