Exercise and Dairy Protein have Distinct Effects on Indices of Liver and Systemic Lipid Metabolism
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore the individual and combined effects of skim milk powder (SMP) and exercise on indices of systemic and liver lipid metabolism in male obese rats. METHODS: Rats were fed a high-fat (~ 40% kcal from fat), high-sugar diet for 8 weeks. At 12 weeks of age, rats were assigned to one of four weight-matched, isocaloric, high-fat, high-sugar groups for 6 weeks: (1) casein-sedentary, (2) casein-exercise, (3) SMP-sedentary, and (4) SMP-exercise. Nonfat SMP or casein was the sole protein source in the dairy and control casein diets, respectively. Exercise training occurred 5 d/wk for 60 minutes on a motorized treadmill. Whole-body metabolism was assessed by a Comprehensive Lab Animal Monitoring System. Lipidomics, Western blot, and polymerase chain reaction were used to assess markers of hepatic lipid metabolism. RESULTS: Exercise, but not SMP, altered the fatty acid composition of liver triglycerides, reduced indices of lipogenesis, and increased expression of genes linked to oxidative metabolism, in conjunction with increases in whole-body fat oxidation. SMP and exercise reduced plasma triglycerides in an additive manner. CONCLUSIONS: These findings provide evidence that SMP and exercise exert distinct effects on whole-body and hepatic carbohydrate and lipid metabolism and that they could work in a synergistic manner to reduce serum triglyceride concentrations.
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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) | 0.001 | 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".