Mixed compared with single-source proteins in high-protein diets affect kidney structure and function differentially in obese <i>fa/fa</i> Zucker rats
Bibliographic record
Abstract
Questions remain regarding the potential negative effects of dietary high protein (HP) on kidney health, particularly in the context of obesity in which the risk for renal disease is already increased. To examine whether some of the variability in HP effects on kidney health may be due to source of protein, obese fa/fa Zucker rats were given HP (35% of energy from protein) diets containing either casein, soy protein, or a mixed source of animal and plant proteins for 12 weeks. Control lean and obese rats were given diets containing casein at normal protein (15% of energy from protein) levels. Body weight and blood pressure were measured, and markers of renal structural changes, damage, and function were assessed. Obesity alone resulted in mild renal changes, as evidenced by higher kidney weights, proteinuria, and glomerular volumes. In obese rats, increasing the protein level using the single, but not mixed, protein sources resulted in higher renal fibrosis compared with the lean rats. The mixed-protein HP group also had lower levels of serum monocyte chemoattractant protein-1, even though this diet further increased kidney and glomerular size. Soy and mixed-protein HP diets also resulted in a small number of damaged glomeruli, while soy compared with mixed-protein HP diet delayed the increase in blood pressure over time. Since obesity itself confers added risk of renal disease, an HP diet from mixed-protein sources that enables weight loss but has fewer risks to renal health may be advantageous.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".