Herring Milt Protein Hydrolysate Improves Insulin Resistance in High-Fat-Diet-Induced Obese Male C57BL/6J Mice
Bibliographic record
Abstract
Protein consumption influences glucose homeostasis, but the effect depends on the type and origin of proteins ingested. The present study was designed to determine the effect of herring milt protein hydrolysate (HPH) on insulin function and glucose metabolism in a mouse model of diet-induced obesity. Male C57BL/6J mice were pretreated with a low-fat diet or a high-fat diet for 6 weeks. Mice on the high-fat diet were divided into four groups where one group continued on the high-fat diet and the other three groups were fed a modified high-fat diet where 15%, 35%, and 70%, respectively, of casein was replaced with an equal percentage of protein derived from HPH. After 10 weeks, mice that continued on the high-fat diet showed significant increases in body weight, blood glucose, insulin, and leptin levels and exhibited impaired oral glucose tolerance, insulin resistance, and pancreatic β-cell dysfunction. Compared to mice fed the high-fat diet, the 70% replacement of dietary casein with HPH protein reduced body weight, semi-fasting blood glucose, fasting blood glucose, insulin, leptin, and cholesterol levels and improved glucose tolerance, homeostasis model assessment of insulin resistance (HOMA-IR), and homeostasis model assessment of β-cell function (HOMA-β) indices. The 35% replacement of dietary casein with HPH protein showed moderate effects, while the 15% replacement of dietary casein with HPH protein had no effects. This is the first study demonstrating that replacing dietary casein with the same amount of protein derived from HPH can prevent high-fat-diet-induced obesity and insulin resistance.
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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 | 0.001 |
| 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 | 0.001 | 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".