Protein Source Determines the Effectiveness of High-Protein Diets in Improving Adipose Tissue Function and Insulin Resistance in fa/fa Zucker Rats
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Obesity and insulin resistance are increasing globally. Emerging evidence suggests that not only the quantity but also the source of dietary protein may improve metabolic health outcomes. This study evaluated the effects of high-protein diets (HPDs) containing animal or plant protein sources on obesity and obesity-related metabolic markers in a rodent model of genetic obesity. METHODS: Zucker rats were fed HPDs (35% of energy) containing protein from different sources (casein, egg white protein, soy + pea protein, mixture of egg white + soy + pea proteins) or a normal protein diet (15% of energy) containing casein over 8 weeks. Oral glucose tolerance, weight gain, fat depots, serum biochemistry, adipocyte and pancreatic islet size, and markers of adipose tissue lipolysis, insulin signaling, and immune cells were assessed. RESULTS: Consumption of HPDs containing egg white protein, soy + pea, or their mixture resulted in smaller adipocytes compared to the casein diets, despite greater weight gain, elevated serum NEFA, and more total visceral fat in the HPD plant group. These HPD groups had reduced fasting insulin and no compensatory pancreatic islet enlargement. CD3 levels were elevated in adipose tissue without changes in F4/80, and no differences were observed in ATGL, HSL, Akt or AS160. CONCLUSIONS: The source of dietary protein in HPDs significantly influences metabolic outcomes in obese rats, impacting adipocyte and pancreatic islet size, insulinemia, and immune cell markers in adipose tissue. These findings support the potential of employing targeted dietary protein interventions for managing obesity-related metabolic disorders.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".