Ethnic‐Specific Differences in Abdominal Subcutaneous Adipose Tissue Compartments
Bibliographic record
Abstract
South Asians have a higher prevalence of cardiovascular disease (CVD) than Europeans. Studies have identified distinct subcompartments of subcutaneous adipose tissue (SAT) that provide insight into the relationship between abdominal obesity and metabolic risk factors in different ethnic groups. Our objective was to determine the relationship between SAT compartments and fat-free mass (FFM) between South Asian and European cohorts, and between men and women. Healthy Europeans and South Asians (n = 408) were assessed for FFM via dual energy X-ray absorptiometry, and SAT areas by computed tomography (CT). SAT was subdivided into superficial subcutaneous abdominal adipose tissue (SSAT) and deep subcutaneous abdominal adipose tissue (DSAT). Linear regression analyses were performed using DSAT and SSAT as separate dependent variables and FFM and ethnicity as primary independent variables adjusting for age, gender, income, education, and smoking status. Results showed that South Asian men had significantly higher amounts of DSAT (median 187.65 cm(2) vs. 145.15 cm(2), P < 0.001), SSAT (median 92.0 cm(2) vs. 76.1 cm(2), P = 0.046), and body fat mass (BFM) (25.1 kg vs. 22.6 kg, P = 0.049) than European men. In a fully adjusted model, South Asians showed significantly greater DSAT at any FFM than Europeans. Women had more SSAT at any given FFM than men and less DSAT at any given FFM than men, irrespective of ethnic background. In conclusion, South Asians had more DSAT than Europeans and men had relatively more DSAT than women. These data suggest that specific fat depots are influenced by ethnicity and gender; therefore, could provide insight into the relationship between ethnicity, gender and subsequent risk for CVD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".