Milk supplementation facilitates appetite control in obese women during weight loss: a randomised, single-blind, placebo-controlled trial
Bibliographic record
Abstract
Dairy products provide Ca and protein which may facilitate appetite control. Conversely, weight loss is known to increase the motivation to eat. This randomised controlled trial verified the influence of milk supplementation on appetite markers during weight loss. Low Ca consumer women participated in a 6-month energy-restricted programme (-2508 kJ/d or -600 kcal/d) and received either a milk supplementation (1000 mg Ca/d) or an isoenergetic placebo (n 13 and 12, respectively). Fasting appetite sensations were assessed by visual analogue scales. Anthropometric parameters and fasting plasma concentrations of glucose, insulin, leptin, ghrelin and cortisol were measured as well. Both groups showed a significant weight loss (P < 0·0001). In the milk-supplemented group, a time x treatment interaction effect showed that weight loss with milk supplementation induced a smaller increase in desire to eat and hunger (P < 0·05). Unlike the placebo group, the milk-supplemented group showed a lower than predicted decrease in fullness (-17·1 v. -8·8; -2·7 v. 3·3 mm, P < 0·05, measured v. predicted values, respectively). Even after adjustment for fat mass loss, changes in ghrelin concentration predicted those in desire to eat (r 0·56, P < 0·01), hunger (r 0·45, P < 0·05) and fullness (r -0·40, P < 0·05). However, the study did not show a between-group difference in the change in ghrelin concentration in response to the intervention. These results show that milk supplementation attenuates the orexigenic effect of body weight loss.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".