Differences in subcutaneous abdominal adiposity regions in four ethnic groups
Bibliographic record
Abstract
OBJECTIVE: Previous studies have identified ethnic specific differences in visceral adipose tissue (VAT), which may account for ethnic differences in cardio-metabolic risk. However, two distinctive sub-compartments of abdominal subcutaneous adipose tissue (SAT) have been recently identified that may also differ among ethnic groups. Therefore, the relationship between SAT compartments and body fat mass (BFM) between Aboriginal, Chinese, and South Asian cohorts compared to Europeans was investigated. DESIGN AND METHODS: Healthy Aboriginal, Chinese, European, and South Asian (n = 822) men and women (30-65 years) were assessed for BFM via dual energy X-ray absorptiometry, and SAT areas using computer tomography. SAT was subdivided into superficial SAT (SSAT) and deep SAT (DSAT) via the fascia-superficialis. Linear regression was performed using DSAT and SSAT as separate dependent variables and BFM and ethnicity as primary independent variables adjusting for confounders. RESULTS: Aboriginal (181.0 cm(2) ; p = 0.045) and South Asians (178.3 cm(2) ; p = 0.013) had significantly higher amounts of DSAT, whereas the Chinese cohort had significantly less when compared with Europeans (114.3 cm(2) ; p = <0.001). The Aboriginal cohort had a significantly higher amount of SSAT than Europeans (123.13 cm(2) vs. 108.7 cm(2) ; p = 0.04). Ethnicity modified the relationship between DSAT and BFM (p < 0.001 for interaction) such that Aboriginals and majority of South Asians had a significantly greater DSAT. CONCLUSION: These data further demonstrate ethnic differences in body fat distribution such that Aboriginals and South Asians have greater amounts of DSAT. This may contribute to the increased cardio-metabolic risk in these 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".