Cod and soy proteins compared with casein improve glucose tolerance and insulin sensitivity in rats
Bibliographic record
Abstract
The aim of the present study was to determine the effects of feeding various dietary proteins on insulin sensitivity and glucose tolerance in rats. Male Wistar rats were fed for 28 days with isoenergetic diets containing either casein, soy protein, or cod protein. Cod protein-fed and soy protein-fed rats had lower fasting plasma glucose and insulin concentrations compared with casein-fed animals. After intravenous glucose bolus, cod protein- and soy protein-fed rats induced lower incremental areas under glucose curves compared with casein-fed animals. Improved peripheral insulin sensitivity was confirmed by higher glucose disposal rates in cod protein- and soy protein-fed rats (15.2 +/- 0.3 and 13.9 +/- 0.6 mg. kg(-1). min(-1), respectively) compared with casein-fed animals (6.5 +/- 0.7 mg. kg(-1). min(-1), P < 0.05). Moreover, test meal experiments revealed that, in the postprandial state, the lower plasma insulin concentrations in cod protein- and soy protein-fed animals could be also due to decreased pancreatic insulin release and increased hepatic insulin removal. In conclusion, the metabolic responses to three common dietary proteins indicate that cod and soy proteins, when compared with casein, improve fasting glucose tolerance and peripheral insulin sensitivity in rats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".