Dietary restriction in moderately obese rats improves body size and glucose handling without the renal and hepatic alterations observed with a high-protein diet
Bibliographic record
Abstract
Obesity is increasing worldwide, and high-protein (HP) diets are widely used for weight loss. However, the overall safety of HP diets is not well established in obese individuals, who make up a significant proportion of the population. To evaluate the health effects of an HP diet in obesity, obesity-prone (OP) Sprague-Dawley rats were given high-fat diets for 12 weeks to induce obesity. Following this, for 8 more weeks, these rats were given either a normal-protein (NP) (15% of energy) or an HP (35% of energy) diet ad libitum, or the NP diet at a restricted level to achieve body weights similar to those of the HP group (pair-weighted (PW) group). Obesity-resistant (OR) control rats were also given the NP diet throughout the feeding period. The HP-OP group had higher food intake but lower body weight, improved glucose handling, and lowered serum haptoglobin compared with the NP-OP group. These benefits were also observed in PW-OP rats. In addition, PW-OP rats had less fat accumulation when compared with NP-OP rats, and an improved Lee index, lower liver size, and lower serum alanine aminotransferase when compared with HP-OP rats. On the other hand, kidney size, proteinuria, and serum homocysteine were increased in HP-OP rats compared with NP-OP rats, whereas PW-OP rats did not experience these effects. These results indicate that in obese rats, more benefits are obtained via dietary restriction with an NP diet and without some of the potentially detrimental effects of an HP diet.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".