Changes in Body Composition in Relation to Metabolic Syndrome: A Compositional Analysis in Adults with Overweight and Obesity
Bibliographic record
Abstract
BACKGROUND/PURPOSE: 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 noncompositional independent variables. Mounting evidence suggests that body tissues are biologically co-dependent and therefore, require a statistical technique that considers this. This study used a compositional data analysis framework to explore the longitudinal association between body composition and a continuous metabolic syndrome score. METHODS: Participants included 288 physically inactive adults (age: 56 ± 12 yr [mean ± SD]; 56% female) with overweight or obesity (body mass index: 31.3 ± 3.5 kg·m 2 ) who participated in randomized controlled trials that determined the effects of exercise on adipose tissue (visceral, abdominal subcutaneous, peripheral subcutaneous, other adipose tissues) and lean tissues (skeletal muscle, other lean tissues) assessed by whole-body magnetic resonance imaging. RESULTS: Visceral adipose tissue, relative to the mass of the remaining tissues, was significantly associated with the metabolic syndrome score preintervention and postintervention ( P < 0.05). The slopes and intercepts of the preintervention and postintervention regression lines between relative visceral adipose tissue mass and metabolic syndrome did not differ ( P > 0.2). For a given weight loss, the greater the relative reduction in visceral adipose tissue, the larger the decrease in the predicted metabolic syndrome score. CONCLUSIONS: This novel compositional data analysis reinforces that visceral adipose tissue is an important marker of cardiometabolic risk and should be a primary target for therapeutic strategies in individuals with overweight or obesity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".