Correlation between the rate of weight loss and changes in body composition in obese postmenopausal women after 5 weeks: a pilot study
Bibliographic record
Abstract
Approximately 25% of weight lost during restrictive diets (without exercise) is lean body mass (LBM). No study has yet investigated the impact of the rate of weight loss (RWL) on LBM and fat mass (FM). The purpose of this study was to investigate the relationships between the RWL and body composition in older obese women. Twenty obese postmenopausal women aged between 51 and 74 years enrolled in a 5 week dietary weight loss intervention. Subjects were characterized according to their RWL (low RWL < 0.74 kg.week(-1) (n = 9) vs. high RWL > or = 0.74 kg.week(-1) (n = 11)). Total and trunk FM and LBM (by dual-energy X-ray absorptiometry) were measured before and after weight loss. A significant correlation was observed between the RWL (kg.week(-1)) and changes in LBM (kg.week(-1)) (r = 0.75; p = 0.0002). However, no association was observed with changes in FM (kg.week(-1)) (r = 0.40; p = 0.08). Both groups showed a similar decrease in FM (low RWL, -2.7 +/- 0.9 kg,; high RWL, -3.2 +/- 0.8 kg; p = 0.38), whereas losses in LBM were significantly higher in the high RWL than in the low RWL group (-1.6 +/- 1.2 kg vs. -0.4 +/- 1.1 kg; p = 0.05). An RWL > 0.74 kg.week(-1) was associated with a greater loss of LBM, but had no extra benefits on FM after a 5 week weight loss program. Current guidelines, which recommend RWL up to 0.91 kg.week(-1), might not be optimal to prevent decreases in LBM in postmenopausal women when no exercise is added.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".