Consumption of higher dairy and dietary protein during diet‐ and exercise‐induced weight loss promotes a metabolically favourable body composition change in overweight and obese young women
Bibliographic record
Abstract
While diet and exercise induced body weight loss has significant health benefits, the ratio of fat:lean tissue loss may be more important. We aimed to determine how daily exercise and different hypoenergetic diets, varying in protein and calcium, affected changes in the tissue composition of weight lost in otherwise healthy young, overweight and obese women over 16wk. Ninety subjects were randomized to 3 groups (n=30 ea): HiDairyPro (HDP), DairyPro (DP) and Control (CON). They differed in the type of protein consumed (high, moderate or low dairy, respectively) and amount (30%, 15% and 15% of energy, respectively). Body composition (DXA) was measured at 0, 8 and 16wk, and a subset of subjects (n=39) underwent MRI to assess visceral adipose tissue (VAT) volume at 0 and 16wk. All groups lost body weight and fat (P<0.05, for all), however, the net loss of fat during 8–16wk was greater in HDP versus DP and CON. HDP gained muscle with a greater increase during 8–16wk than the other groups while DP maintained muscle and CON lost muscle. HDP also lost more VAT versus CON. We conclude that diet and exercise induced weight loss regimens with higher dairy, calcium and protein versus those without promoted weight loss in women with a more metabolically favourable body composition change characterised by greater total and visceral fat loss and muscle mass gain. Grant Funding Source : The Dairy Farmers of Canada, CIHR, and The US Dairy Research Institute
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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 | 0.000 | 0.000 |
| 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".