Ethnic differences in fat and muscle mass and their implication for interpretation of bioelectrical impedance vector analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
According to the World Health Organization Expert Consultation, current body mass index (BMI) cut-offs should be retained as an international classification. However, there are ethnic differences in BMI-associated health risks that may be caused by differences in body fat or skeletal muscle mass and these may affect the interpretation of phase angle and bioelectrical impedance vector analysis (BIVA). Therefore, the aim of this study was to compare body composition measured by bioelectrical impedance analysis among 1048 German, 1026 Mexican, and 995 Japanese adults encompassing a wide range of ages and BMIs (18–78 years; BMI, 13.9–44.3 kg/m 2 ). Regression analyses between body composition parameters and BMI were used to predict ethnic-specific reference values at the standard BMI cut-offs of 18.5, 25, and 30 kg/m 2 . German men and women had a higher fat-free mass per fat mass compared with Mexicans. Normal-weight Japanese were similar to Mexicans but approached the German phenotype with increasing BMI. The skeletal muscle index (SMI, kg/m 2 ) was highest in Germans, whereas in BIVA, the Mexican group had the longest vector, and the Japanese group had the lowest phase angle and the highest extracellular/total body water ratio. Ethnic differences in regional partitioning of fat and muscle mass at the trunk and the extremities contribute to differences in BIVA and phase angle. In conclusion, not only the relationship between BMI and adiposity is ethnic specific; in addition, fat distribution, SMI, and muscle mass distribution vary at the same BMI. These results emphasize the need for ethnic-specific normal values in the diagnosis of obesity and sarcopenia.
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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 it