The Use of BMI and Waist Circumference as Surrogates of Body Fat Differs by Ethnicity
Bibliographic record
Abstract
OBJECTIVE: To compare the prediction of percentage body fat using BMI and visceral adipose tissue (VAT) using waist circumference (WC) in individuals of Chinese, European, and South Asian origin. RESEARCH METHODS AND PROCEDURES: Healthy men and women of Chinese, European, and South Asian origin (n = 627) between the ages of 30 and 65 years were recruited to ensure equal distribution of gender and representation across BMI ranges (18.5 to 24.9, 25 to 29.9, and >or=30 kg/m(2)). Participants were assessed for demographics, anthropometry, lifestyle, and regional adiposity. Percentage body fat and VAT were measured by DXA and computer tomography scan, respectively. RESULTS: BMI and WC were highly correlated with total and regional measures of adiposity in each ethnic group. At any BMI, the percentage body fat of Chinese participants was similar to that of Europeans, but that of South Asians was greater by 3.9% (p < 0.001). Above a WC of 71.0 cm, the Chinese participants had an increasingly greater amount of VAT than the Europeans (p = 0.017 for interaction). South Asians had significantly more VAT than the Europeans at all but the most extreme WC (above 105 cm) (p < 0.05). DISCUSSION: Compared with Europeans, percentage body fat was higher for a given BMI in South Asians, whereas VAT was higher for a given WC in both Chinese and South Asian men and women. These findings support the use of ethnic-specific anthropometric targets.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".