The effect of dairy protein and calcium on the prevention of weight gain in Sprague‐Dawley Diet Induced Obese (DIO) rats
Bibliographic record
Abstract
Recent evidence suggests that dietary calcium (Ca 2+ ) may attenuate weight gain and amplify weight loss via regulation of adipocyte intracellular Ca 2+ . Dairy sourced Ca 2+ may specifically augment these responses. The purpose of this study was to determine the role of various dairy proteins as part of a high fat, high sucrose diet enriched with 2.4% Ca 2+ on preventing weight gain in Sprague Dawley diet induced obese (DIO) rats. Forty eight 12 week old DIO rats consumed one of six, ad libitum diets for eight weeks: (1) Low calorie with casein (0.67% Ca 2+ ); (2) Low calorie with casein (2.4% Ca 2+ ); (3) High calorie with casein (2.4% Ca 2+ ); (4) High calorie with dairy (skim milk powder) (0.67% Ca 2+ ); (5) High calorie with dairy (2.4% Ca 2+ ); or (6) High calorie with whey (2.4% Ca 2+ ). Mean weight gain across all diets was 82.2 ± 42.3 g (mean ± SD). Rats consuming the high calorie dairy, 2.4% Ca 2+ diet gained the least amount of weight (24.1 ± 15.8 g) and rats consuming the dairy diet with 0.67% Ca 2+ gained the second lowest amount of weight (52.0 ± 15.3 g). The results from the dairy 2.4% Ca 2+ diet show significantly less weight gain than all other diet groups (p<0.05), including the two low calorie diets. Plasma levels of satiety hormones and expression of genes involved in thermogenesis and appetite regulation are being analyzed as mechanisms of action. In conclusion, it appears dairy protein is able to prevent excessive weight gain associated with a high calorie diet more than casein or whey. The addition of Ca 2+ further attenuated weight gain suggesting that dairy protein supplemented with 2.4% Ca 2+ decreases weight gain in Sprague Dawley DIO rats when provided in an ad libitum high fat, high sucrose diet. Funded by Dairy Farmers of Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".