Effects of dietary high protein on renal health in the pig model
Bibliographic record
Abstract
The long‐term impact of a high protein (HP) diet at the upper limit of the acceptable macronutrient distribution range (AMDR) on kidney health is unknown. Therefore, 16 adult female Cotswold pigs were randomized to receive either normal (NP, 15% energy from protein) or HP (35% energy from protein) diets for 16 weeks. Each diet contained whole proteins from animal and plant sources and were balanced for energy, fat, vitamins and minerals. Body composition was measured by dual‐energy X‐ray absorptiometry, renal hypertrophy was assessed by kidney volume and glomerular hypertrophy was analyzed by measuring glomeruli on fixed sections. The HP diet increased kidney volume (p=0.003) and weight (p= 0.0276). Consistant with this, HP compared to NP pigs had larger glomeruli (p=0.078). However, diet had no significant effect on renal function as measured by glomerular filtration rate and proteinuria. HP consumption decreased body weight (p=0.0232) and body fat percentage (p<0.05) and increased percent bone mineral content (p=0.036) and lean mass (p<0.05). These findings suggest that despite the potential benefit of the HP diet on body composition, protein intakes at the upper limit of the AMDR may alter renal hemodynamics by increasing kidney and glomerular volume. The long term effects on glomerular injury remain to be elucidated (Supported by Canadian Institutes of Health Research).
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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 | 0.001 | 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) | 0.002 | 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".