Dairy Protein Attenuates Weight Gain in Obese Rats Better Than Whey or Casein Alone
Bibliographic record
Abstract
Evidence suggests that dietary calcium (Ca) and particularly dairy foods may attenuate weight gain and improve symptoms of the metabolic syndrome. The purpose of this study was to determine the effect of different Ca-enriched dairy protein sources on the prevention of weight gain in Sprague-Dawley diet-induced obese (DIO) rats. Twelve week-old DIO rats were assigned to one of eight ad libitum diets that varied in protein source (casein, whey, or complete dairy), Ca content (0.67 or 2.4%) and energy level (high fat/high sucrose (HFHS); or normal calorie density (NC)). Body composition and response to a meal tolerance test (MTT) were measured. Average daily caloric intake did not differ within normal or high energy density groups. At the end of 8 weeks, the dairy/HFHS/0.67% and 2.4% groups had significantly lower body weight than all other HFHS groups. The dairy/HFHS/0.67% and 2.4% groups also had lower body fat and greater lean mass expressed as a percent (P < 0.05). Homeostatic model assessment of insulin resistance (HOMA(IR)) was lowest for dairy/HFHS/0.67% and significantly different from whey/HFHS/0.67% and 2.4%. Independent of protein source, high Ca decreased plasma insulin at 30 min in the MTT more so than low Ca (P < 0.05). Hepatic sterol regulatory element-binding protein (SREBP1c) and peroxisome proliferator-activated receptor-gamma (PPARgamma) mRNA was downregulated by dairy and whey compared to casein in the HFHS/0.67% diets. Overall, these data suggest that complete dairy improves body composition and insulin sensitivity to a greater extent than whey or casein alone.
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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".