Effects of a weight loss program on body composition and the metabolic profile in obese postmenopausal women displaying various obesity phenotypes: a MONET group study
Bibliographic record
Abstract
Obesity is a heterogeneous condition, since the metabolic profile may differ greatly from one individual to another. The objective of this study was to compare the effect of a 6-month diet-induced weight loss program on body composition and the metabolic profile in obese individuals displaying different obesity phenotypes. Secondary analyses were done on 129 obese (% body fat: 46% ± 4%) postmenopausal women (age: 57 ± 4 years). Outcome measures included body composition, body fat distribution, glucose homeostasis, fasting lipids, and blood pressure. Obesity phenotypes were determined based on lean body mass (LBM) index (LBMI = LBM/height(2)) and visceral fat (VF) accumulation, as follows: 1, lower VF and lower LBMI (n = 35); 2, lower VF and higher LBMI (n = 19); 3, higher VF and lower LBMI (n = 14); and 4, higher VF and higher LBMI (n = 61). All groups had significantly improved measures of body composition after the intervention (P < 0.0001). Greater decreases in LBM and LBMI were observed in the higher LBMI groups than in the lower LBMI groups (P < 0.0001). Similarly, decreases in VF were greater in the higher VF groups than in the lower VF groups (P < 0.05). Overall, fasting insulin levels and glucose disposal improved following the intervention, with higher LBMI groups showing a trend for greater improvements (P = 0.06 and 0.07, respectively). Overall, no difference was observed among the different obesity phenotypes regarding improvements in the metabolic profile in response to weight loss. Individuals displaying higher VF or higher LBMI at baseline experienced significantly greater decreases for these variables after the intervention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".