A high protein diet at the upper end of the Acceptable Macronutrient Distribution Range (AMDR) leads to kidney glomerular damage in normal female Sprague‐Dawley rats
Bibliographic record
Abstract
In setting the AMDR for protein at 10–35% of daily energy, the Institute of Medicine acknowledged a lack of data regarding the safety of long‐term intakes. The current study assessed the impact of chronic (17 months) protein consumption at the upper end of the AMDR on renal function and histology. Using plant and animal whole protein sources, female Sprague‐Dawley rats (70 days old; n=8–11 at 4, 8, 12, or 17 mo.) were randomized to either a normal (NP; 15% of energy) or high protein (HP; 35% of energy) diet. Egg albumin and skim milk replaced carbohydrates in the HP diet. Diets were balanced for energy, fat, vitamins and minerals, and offered ad libitum . Renal function was analyzed by urinary protein levels and serum creatinine. Glomerular hypertrophy, glomerulosclerosis and tubulointerstitial fibrosis were assessed on formalin fixed kidneys sections. Rats consuming the HP compared to NP diet had ~15% higher kidney weights (P = <0.0001) and 2–8 times higher proteinuria (P = <0.0001). Consistent with this, HP compared to NP rats had ~23% larger glomeruli (P = 0.0017) and ~29% more glomerulosclerosis (P = 0.0003). Serum creatinine and tubulointerstitial fibrosis levels were not different between groups. These data in normal female rats suggest that protein intakes at the upper end of the AMDR are detrimental to kidney health in the long term. Further studies in other animal models and in humans are warranted. Funded 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".