Comparing body composition techniques against an adapted multicompartment model in individuals with excess body weight
Bibliographic record
Abstract
• Body composition methods were compared against a 4C model in adults with excess body weight. • ADP showed the best agreement with 4C, but its accuracy was lower in individuals with obesity. • DXA and BIA consistently overestimated FM, and underestimated FFM. • DXA accuracy was better in individuals with obesity than in those with overweight. • BIA-derived TBW could be overestimated, which would inflate 4C-derived FFM estimates. Accurate body composition assessment is critical for detecting individuals at increased health risk from excess adiposity; however, many measurement techniques lose accuracy in those with a higher body mass index (BMI). This study evaluated the accuracy of body composition techniques against a 4-compartment (4C) model in individuals with overweight or obesity. N=75 participants were categorized as having overweight (n=56, BMI 25–29.9 kg/m 2 ) or obesity (n=19, BMI ≥30 kg/m 2 ). Body composition was assessed by bioelectrical impedance analysis (BIA), air-displacement plethysmography (ADP), and dual-energy X-ray absorptiometry (DXA). An adapted 4C model used body mass, body volume (via ADP), bone mineral content (via DXA), and total body water (TBW, via BIA). Accuracy was assessed as mean differences (MD) ± standard deviation (comparator - 4C) and 95% limits of agreement (LoA). ADP demonstrated the smallest overall difference in body fat percentage (BF%; MD= 0.10±1.70%, LoA [-3.23, 3.43], p=0.620), but its accuracy reduced in individuals with obesity. Both BIA (MD= 1.73±1.72%, LoA [-1.52, 4.98]) and DXA (MD= 1.86±1.79%, LoA [-1.65, 5.37]) overestimated BF% (both p<0.001). Overall, ADP demonstrated the best accuracy, while DXA had the greatest differences compared to the 4C model. This may have resulted from TBW being overestimated by BIA, which would inflate 4C-derived fat-free mass. Although all methods demonstrated strong agreement for group-level BF%, a substantial individual-level variability (up to 5% error) highlights the need for caution when interpreting results in clinical or personalized assessment contexts in individuals with excess body weight.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".