Prevention of skeletal muscle insulin resistance by dietary cod protein in high fat-fed rats
Bibliographic record
Abstract
In the present study, we tested the hypothesis that fish protein may represent a key constituent of fish with glucoregulatory activity. Three groups of rats were fed a high-fat diet in which the protein source was casein, fish (cod) protein, or soy protein; these groups were compared with a group of chow-fed controls. High-fat feeding led to severe whole body and skeletal muscle insulin resistance in casein- or soy protein-fed rats, as assessed by the euglycemic clamp technique coupled with measurements of 2-deoxy-D-[(3)H]glucose uptake rates by individual tissues. However, feeding cod protein fully prevented the development of insulin resistance in high fat-fed rats. These animals exhibited higher rates of insulin-mediated muscle glucose disposal that were comparable to those of chow-fed rats. The beneficial effects of cod protein occurred without any reductions in body weight gain, adipose tissue accretion, or expression of tumor necrosis factor-alpha in fat and muscle. Moreover, L6 myocytes exposed to cod protein-derived amino acids showed greater rates of insulin-stimulated glucose uptake compared with cells incubated with casein- or soy protein-derived amino acids. These data demonstrate that feeding cod protein prevents obesity-induced muscle insulin resistance in high fat-fed obese rats at least in part through a direct action of amino acids on insulin-stimulated glucose uptake in skeletal muscle cells.
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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 | 0.001 | 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".