Protein source in a high‐protein diet modulates reductions in insulin resistance and hepatic steatosis in <i>fa/fa</i> Zucker rats
Bibliographic record
Abstract
OBJECTIVE: High-protein diets are being promoted to reduce insulin resistance and hepatic steatosis in metabolic syndrome. Therefore, the effect of protein source in high-protein diets on reducing insulin resistance and hepatic steatosis was examined. METHODS: Fa/fa Zucker rats were provided normal-protein (15% of energy) casein, high-protein (35% of energy) casein, high-protein soy, or high-protein mixed diets with animal and plant proteins. RESULTS: The high-protein mixed diet reduced area under the curve for insulin during glucose tolerance testing, fasting serum insulin and free fatty acid concentrations, homeostatic model assessment index, insulin to glucose ratio, and pancreatic islet cell area. The high-protein mixed and the high-protein soy diets reduced hepatic lipid concentrations, liver to body weight ratio, and hepatic steatosis rating. These improvements were observed despite no differences in body weight, feed intake, or adiposity among high-protein diet groups. The high-protein casein diet had minimal benefits. CONCLUSIONS: A high-protein mixed diet was the most effective for modulating reductions in insulin resistance and hepatic steatosis independent of weight loss, indicating that the source of protein within a high-protein diet is critical for the management of these metabolic syndrome parameters.
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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) | 0.002 | 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".