Development Of Three Anthropometric Methods To Predict DXA-derived Body Fat Mass In Mexican Athletes
Bibliographic record
Abstract
The measurement of body fat mass (BFM) is a fundamental component of fitness. Traditionally, the skinfold thickness (SKF) technique has been used for this purpose; however, its limitations in terms of precision and reliability are significant, leading to a quest to develop more robust indicators. In this context, using body surface area (BSA), central circumferences, and height might help improve BFM estimation from SKF. PURPOSE: To develop and validate BFM prediction models from anthropometric methods based on BSA, SKF, central circumferences, and height. METHODS: A total of 280 male athletes (weight: 72.09 ± 9.83 kg, height: 1.75 ± 0.08 m, age 23.9 ± 4.4; BFM: 11.9 ± 4.2 kg) underwent a whole-body DXA scan (Hologic QDR-4500) to assess their BFM as the reference. Anthropometric measurements were taken by certified personnel according to the ISAK protocol. The anthropometric variables included four central circumferences (chest, waist, abdomen, hips) and eight skinfolds (triceps, biceps, subscapular, iliac crest, supraspinale, abdominal, thigh, calf). We calculated three anthropometric methods to estimate BFM individually: Body Surface Area method (BSAM), Trunk Adiposity method (TAM), and Central Adiposity method (CAM). BSAM was calculated as = BSA * Average SKF (cm); the BSA was calculated as = (weight * height)/36000.5 TAM was calculated as = Height (m) * Average SKF (cm) * Average of Central circumferences (cm). CAM was calculated as = Height (m) * Average SKF (cm) * Average (Chest and Waist circumferences, cm). We used simple linear regressions to predict BFM from each anthropometric method and Bland-Altman analyses to compare the predicted scores with DXA. RESULTS: The mean BSAM, TAM, and CAM were 1.98 ± 0.78, 157.97 ± 62.39 and 164.09 ± 66.16, respectively. The obtained regression equations were BFM = 4.91 + 2.16 * BSAM (SEE = 1.82, R2 = 0.78), BFM =0.05 + 2.88 * TAM (SEE = 1.27 kg, R2 = 0.87) and BFM = 0.06 + 2.42 * CAM (SEE = 1.80, R2 = 0.79). The mean difference vs DXA (95% limits of agreement) was 0.10 (-3.79 to 3.99) for BSAM, 0.19 (-2.38 to 2.76) for TAM and 0.10 (-3.65 to 3.85) for CAM. CONCLUSIONS: The BSAM, TAM, and CAM are valid predictors for estimating BFM, and TAM seems to be the most accurate of them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| 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".