Ethnic differences in anthropometric measures and abdominal fat distribution: a cross-sectional pooled study in Inuit, Africans and Europeans
Bibliographic record
Abstract
BACKGROUND: Ethnic variation in abdominal fat distribution may explain differences in cardiometabolic risk between populations. However, the ability of anthropometric measures to quantify abdominal fat is not clearly understood across ethnic groups. The aim of this study was to investigate the associations between anthropometric measures and visceral (VAT) and subcutaneous abdominal adipose tissue (SAT) in Inuit, Africans and Europeans. METHODS: We combined cross-sectional data from 3 studies conducted in Greenland, Kenya and Denmark using similar methodology. A total of 5275 individuals (3083 Inuit, 1397 Africans and 795 Europeans) aged 17-95 years with measures of anthropometry and ultrasonography of abdominal fat were included in the study. Multiple regression models with fractional polynomials were used to analyse VAT and SAT as functions of body mass index (BMI), waist circumference, waist-to-hip ratio, waist-to-height ratio and body fat percentage. RESULTS: The associations between conventional anthropometric measures and abdominal fat distribution varied by ethnicity in almost all models. Europeans had the highest levels of VAT in adjusted analyses and Africans the lowest with ethnic differences most apparent at higher levels of the anthropometric measures. Similar ethnic differences were seen in the associations with SAT for a given anthropometric measure. CONCLUSIONS: Conventional anthropometric measures like BMI and waist circumference do not reflect the same amount of VAT and SAT across ethnic groups. Thus, the obesity level at which Inuit and Africans are at increased cardiometabolic risk is likely to differ from that of Europeans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".