Body composition indices of a load–capacity model: gender- and BMI-specific reference curves
Bibliographic record
Abstract
OBJECTIVE: Fat mass (FM) and fat-free mass (FFM) are frequently measured to define body composition phenotypes. The load-capacity model integrates the effects of both FM and FFM to improve disease-risk prediction. We aimed to derive age-, gender- and BMI-specific reference curves of load-capacity model indices in an adult population (≥18 years). DESIGN: Cross-sectional study. Dual-energy X-ray absorptiometry was used to measure FM, FFM, appendicular skeletal muscle mass (ASM) and truncal fat mass (TrFM). Two metabolic load-capacity indices were calculated: ratio of FM (kg) to FFM (kg) and ratio of TrFM (kg) to ASM (kg). Age-standardised reference curves, stratified by gender and BMI (<25.0 kg/m2, 25.0-29.9 kg/m2, ≥30.0 kg/m2), were constructed using an LMS approach. Percentiles of the reference curves were 5th, 15th, 25th, 50th, 75th, 85th and 95th. SETTING: Secondary analysis of data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES). SUBJECTS: The population included 6580 females and 6656 males. RESULTS: The unweighted proportions of obesity in males and females were 25.5 % and 34.7 %, respectively. The average values of both FM:FFM and TrFM:ASM were greater in female and obese subjects. Gender and BMI influenced the shape of the association of age with FM:FFM and TrFM:ASM, as a curvilinear relationship was observed in female and obese subjects. Menopause appeared to modify the steepness of the reference curves of both indices. CONCLUSIONS: This is a novel risk-stratification approach integrating the effects of high adiposity and low muscle mass which may be particularly useful to identify cases of sarcopenic obesity and improve disease-risk prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".