Using simple anthropometric measures to predict body fat in South Asians
Bibliographic record
Abstract
Previously determined predictive equations for body fat mass (BFM) are primarily derived from populations of European origin, which may not be appropriate for all ethnic groups. The objective of this study was to develop an improved predictive equation for BFM specific to South Asians and derived from common anthropometric measurements that include measures of central adiposity. A total of 208 apparently healthy South Asian men and women, aged 30-65 years, were recruited. Anthropometric measurements and BFM by dual energy X-ray absorbitometry (DEXA) were obtained. Sex-specific equations predicting BFM were developed using regression models on a reference subset (68 men, 70 women) and tested on a validation group. New predictive equations (BFMNEW) were tested for agreement with Durin and Wormersley and Siri equations and with the reference method, DEXA. The best predictive sex-specific equation involved a combination of skinfolds, waist circumference, hip circumference, humerus breadth, height, mass, and age. Models significantly correlated with BFM determined by DEXA (r = 0.946 for men; r = 0.974 for women; p < 0.001). The estimates of BFM from reference and validation groups had excellent correlations and displayed excellent agreement to DEXA measures. We demonstrated new predictive equations for BFM that are specific to South Asians and incorporate measures of central adiposity. This may help resolve issues surrounding inaccurate determination of adiposity in South Asians, and consequently provide better estimations of disease risk.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".