Changes In Body Composition In Relation To The Metabolic Syndrome: A Compositional Data Analysis
Bibliographic record
Abstract
Current knowledge of the association between body composition and health outcomes is based on traditional regression techniques, where the components of body composition are treated as non-compositional independent variables. Mounting evidence suggests that body tissues are biologically co-dependent and therefore, require a statistical technique that considers this. Compositional data analysis (CoDA) allows for the study of compositional co-dependent variables and is the optimal statistical technique for body composition research. PURPOSE: To use a CoDA framework to: (i) describe changes in body composition in response to exercise and/or caloric restriction; and (ii) explore associations between change in body composition and change in the metabolic syndrome (MetS). METHODS: Participants included 288 physically inactive adults (Age: 55.7 ± 11.9 years [mean ± SD]; 56.3% female) with overweight or obesity (BMI: 31.3 ± 3.5 kg/m2; waist circumference: 105 ± 11 cm) who participated in randomized controlled trials to determine the effects of exercise and/or caloric restriction on abdominal and peripheral subcutaneous fat, visceral fat, other fat (e.g., intramuscular fat, pericardial fat), skeletal muscle and other lean tissues (e.g., organs, bone), assessed by whole-body magnetic resonance imaging. The primary outcome was a continuous MetS score. Associations were examined using CoDA. RESULTS: There were significant differences in body composition in response to treatment (P < .001), with visceral fat’s relative contribution to body composition changing the most (-18.3%). Visceral fat mass, relative to the mass of the remaining tissues, was significantly associated with the MetS score pre- and post-intervention (P < .05). These associations were not consistently observed for the other tissues. Greater changes in the MetS occurred when visceral fat’s relative contribution to the change in body composition was greater. CONCLUSIONS: This novel analysis reinforces the singular importance of visceral fat as a marker of cardiometabolic risk and consequently, that it be a primary target for therapeutic strategies. Funding: Canadian Institutes of Health Research; Medical Research Council Canada
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".