A compositional analysis study of body composition and cardiometabolic risk factors
Bibliographic record
Abstract
OBJECTIVE: This cross-sectional study used compositional data analysis (CoDA) to do the following: 1) analyze the relative associations between fat and lean tissues with cardiometabolic risk factors; and 2) estimate how these risk factors would change if equivalent mass was displaced from one tissue to another. Differences between CoDA and traditional regression were explored. METHODS: A total of 397 adults with overweight or obesity were studied. Body composition consisted of visceral fat, abdominal subcutaneous fat, peripheral subcutaneous fat, other fat depots, skeletal muscle, and other lean tissues. The outcomes were a continuous metabolic syndrome score (primary outcome) and eight other cardiometabolic risk factors (secondary outcomes). Associations were examined using CoDA and traditional linear regression. RESULTS: Visceral fat mass, relative to the mass of the remaining tissues, was significantly associated with the metabolic syndrome score and five of eight remaining risk factors (p < 0.05). The relative contribution of the remaining tissues was not consistently associated with the study outcomes. Displacing equivalent mass from visceral fat into the remaining tissues was associated with meaningful decreases in the metabolic syndrome score. Regression estimates for CoDA and traditional regression differed in size and statistical significance. CONCLUSIONS: These CoDA findings reinforce that excess visceral fat contributes to less-favorable cardiometabolic risk factors.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".