Peanut protein reduces body protein mass and alters skeletal muscle contractile properties and lipid metabolism in rats
Bibliographic record
Abstract
It is well known that diets high in nuts or peanuts favourably affect plasma lipid concentrations. However, few studies have examined the effects of nut and peanut protein (PP) on body composition and skeletal muscle properties. The present study was aimed at evaluating the effect of dietary PP compared with two animal proteins, casein (C) and cod protein (CP) on body composition, skeletal muscle contractile properties and lipid metabolism in rats. Thirty-two male rats were assigned to one of the following four diets containing either C, CP, PP or C+peanut protein (CPP, 50:50) mixture. After 28 d of ad libitum feeding and after 12-h fast, blood, liver and muscle were collected for measurements of plasma and hepatic cholesterol and TAG, plasma glucose and insulin and contractile properties. Rats fed with the low-quality protein, PP, had lower body weight gain, body protein mass, soleus mass and liver weight than those fed with the high-quality dietary proteins, C and CP. PP also caused a deficit in contractile properties in soleus. Likewise, PP increased plasma cholesterol and body fat mass compared with CP. However, these elevations were accompanied with increased hepatic TAG concentrations and lowered intestinal fat excretion. These results show that PP intake alters body composition by reducing skeletal muscle mass and liver weight as well as muscle contractility and lipid metabolism. Adding a complete protein such as C might partially counteract these adverse effects.
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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 | 0.001 |
| 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".