Long-term effects of dietary high protein on renal health in the pig model
Bibliographic record
Abstract
The impact of habitually consuming a high protein (HP) diet at the upper limit of the acceptable macronutrient distribution range (AMDR) on kidney health is unknown. The current study was designed to test the hypothesis that long-term consumption of a diet providing 35% of energy as protein will have negative consequences on renal health, as assessed in a pig model. Methods: Adult female, non-pregnant, commercial pigs (Genesus) were randomized to receive either NP (15% energy from protein) or HP (35% energy from protein) isocaloric diets for either 4 or 8 months. Diets contained whole protein sources with an animal: plant ratio of 2:1 in the NP diet to mimic the average Canadian diet. The increased protein in the HP diet was achieved by increasing egg and dairy protein sources. Body composition was measured by dual-energy X-ray absorptiometry. Glomerular volume and kidney fibrosis were evaluated on kidney sections by quantitative image analysis. The inflammatory marker monocyte chemoattractant protein-1 (MCP-1) and the growth factor transforming growth factor beta-1(TGFβ1) were assessed in renal tissue using commercial ELISA kits. Results: Pigs given the HP diet had lower body weights and percentage of body fat. Pigs consuming the HP diet had significantly higher glomerular filtration rates (GFR) and larger kidneys. Renal MCP-1 levels and renal fibrosis also were significantly higher in pigs given the HP diet, while proteinuria and renal TGFβ1 expression did not differ. Conclusion: These findings suggest that, despite the potential benefit of the HP diet on body composition, long-term intakes of protein at the upper limit of the AMDR may compromise renal health in healthy female pigs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".