A Mixed Protein Diet at the Upper End of the Acceptable Macronutrient Distribution Range for Protein Increases Renal Fibrosis in the Pig Model
Bibliographic record
Abstract
The acceptable macronutrient distribution range (AMDR) for protein has been set at 10‐35% of energy. The current study assessed the consequences of long‐term consumption of a mixed high protein diet at the upper end of the AMDR on the pig kidney. Methods Using whole protein sources, adult female pigs were given either normal (NP, 15% of energy) or high protein (HP, 35% of energy) isocaloric diets for 4 or 8 months. The protein in the NP diet was derived from animal and plant sources in a 2:1 ratio; the increased protein in the HP diet was achieved by increasing egg and dairy protein. Glomerular volume and renal fibrosis were quantified by image analysis. The renal inflammatory marker monocyte chemoattractant protein‐1 (MCP‐1) and the growth factor transforming growth factor beta‐1 (TGFβ1) were determined by ELISA. Results Pigs given the HP diet at 4, but not 8 months, had lower body weights and body fat, higher renal MCP‐1 levels and a trend towards higher glomerular filtration rates. At 8 months, kidney and glomerular volume, and tubulointerstitial fibrosis were significantly higher in pigs given the HP diet, while proteinuria and renal TGFβ1 expression did not differ. Conclusion Despite the potential benefits on body composition, long‐term intakes of protein at the upper limit of the AMDR may compromise renal health in healthy female pigs. Supported by CIHR.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".