A high protein (HP) diet results in moderate renal and hepatic damage but improves body size, glucose handling and haptoglobin levels in diet‐induced obese rats
Bibliographic record
Abstract
HP diets may aid in weight control and glucose handling, but also can cause minor damage to healthy kidneys. Since obesity itself increases renal damage, the additive effects of obesity and an HP diet were investigated in diet‐induced obese rats. Obesity‐prone and ‐resistant rats were given a high fat diet for 12 wk to induce obesity, followed by either HP [35 en%, ad libitum (AL)] or normal protein [35 en%, NP, either AL or pair‐weighed (PW)] for 8 wk. Obese rats given HP compared to NP diets AL consumed more feed but gained less weight. Renal enlargement in the HP compared to NP AL rats accompanied by higher proteinuria compared to NP AL and PW rats, and hepatic enlargement and elevated serum alanine aminotransferase in HP compared to PW NP rats indicated potential minor renal and hepatic damage. However, in addition to less weight gain, HP compared to NP AL rats also had lower blood glucose levels, homeostatic model assessment2 (HOMA2) scores and lower haptoglobin levels. Thus, the potential risks of HP feeding on minor renal and hepatic damage in obesity should be evaluated against the potential benefits on weight loss, glucose handling and inflammation. Grant Funding Source : CIHR 196330
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".