Generalized Equations for Estimating DXA Percent Fat of Diverse Young Women and Men
Bibliographic record
Abstract
PURPOSE: Popular generalized equations for estimating percent body fat (BF%) developed with cross-sectional data are biased when applied to racially/ethnically diverse populations. We developed accurate anthropometric models to estimate dual-energy x-ray absorptiometry BF% (DXA-BF%) that can be generalized to ethnically diverse young adults in both cross-sectional and longitudinal field settings. METHODS: This longitudinal study enrolled 705 women and 428 men (aged 17-35 yr) for 30 wk of exercise training (3 d·wk(-1) for 30 min·d(-1) of 65%-85% predicted V˙O2max). The distribution of ethnicity was as follows: 37% non-Hispanic white, 29% Hispanic, and 34% African-American. DXA-BF%, skinfold thicknesses, and body mass index (BMI) were collected at baseline and after 15 and 30 wk. RESULTS: Skinfolds, BMI, and race/ethnicity were significant predictors of DXA-BF% in linear mixed model regression analysis. For comparable anthropometric measures (e.g., BMI), DXA-BF% was lower in African-American women and men but higher in Hispanic women compared with non-Hispanic white. Addition of BMI to the skinfold model improved the SEE for women (3.6% vs 4.0%), whereas BMI did not improve prediction accuracy of men (SEE = 3.1%). CONCLUSIONS: These equations provide accurate predictions of DXA-BF% for diverse young women and men in both cross-sectional and longitudinal settings. To our knowledge, these are the first published body composition equations with generalizability to multiple time points, and the SEE estimates are among the lowest published in the literature.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".