A new anthropometric index to predict percent body fat in young adults
Bibliographic record
Abstract
OBJECTIVE: To propose a new anthropometric index that can be employed to better predict percent body fat (PBF) among young adults and to compare with current anthropometric indices. DESIGN: Cross-sectional. SETTING: All measurements were taken in a controlled laboratory setting in Seoul (South Korea), between 1 December 2015 and 30 June 2016. PARTICIPANTS: Eighty-seven young adults (18-35 years) who underwent dual-energy x-ray absorptiometry (DXA) were used for analysis. Multiple regression analyses were conducted to develop a body fat index (BFI) using simple demographic and anthropometric information. Correlations of DXA measured PBF (DXA_PBF) with previously developed anthropometric indices and the BFI were analysed. Receiver operating characteristic curve analyses were conducted to compare the ability of anthropometric indices to identify obese individuals. RESULTS: BFI showed a strong correlation with DXA_PBF (r = 0·84), which was higher than the correlations of DXA_PBF with the traditional (waist circumference, r = 0·49; waist to height ratio, r = 0·68; BMI, r = 0·36) and alternate anthropometric indices (a body shape index, r = 0·47; body roundness index, r = 0·68; body adiposity index, r = 0·70). Moreover, the BFI showed higher accuracy at identifying obese individuals (area under the curve (AUC) = 0·91), compared with the other anthropometric indices (AUC = 0·71-0·86). CONCLUSIONS: The BFI can accurately predict DXA_PBF in young adults, using simple demographic and anthropometric information that are commonly available in research and clinical settings. However, larger representative studies are required to build on our findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".