Body Fat Content Determination in Premenopausal, Overweight, and Obese Young Women Using DXA and FT‐NIR
Bibliographic record
Abstract
Even though BMI is the most commonly used method for assessing and monitoring obesity, it does not take into account the individual's body fat content assuming instead that body mass is closely associated with body fat, which is a tenuous assumption. The aim of this study was to make a direct comparison between measurements of body fat content using a convenient and rapid Fourier transform near-infrared (FT-NIR) spectroscopy and dual-energy X-ray absorptiometry (DXA). We recruited 52, premenopausal women (age range 19-45), all of whom had a BMI that classified them as either overweight or obese (range: 27-40 kg/m(2), mean: 31.1 ± 3.7 kg/m(2)) and indicated a statistically significant linear relationship between the fat content in kilograms measured by FT-NIR and DXA (r = 0.95, P < 0.001). Bland-Altman analysis showed that almost all the differences between two measurements fell within 2 s.d. We report here that the FT-NIR method provided comparable measurements of subcutaneous body fat content similar to those of total fat obtained using DXA. The FT-NIR method is a lower cost, easy to use and transport, and, based on comparison with DXA, an accurate method to measure body fat content. We propose that FT-NIR is an ideal method for safe repeat measurements in large trials or in screening and monitoring individuals during interventions in which changes in body fat will occur.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".