Dietary betaine modifies hepatic metabolism but not renal injury in rat polycystic kidney disease
Bibliographic record
Abstract
We undertook a morphometric and proton nuclear magnetic resonance ((1)H-NMR) study to test the hypothesis that 1% dietary betaine supplementation would ameliorate renal disease in the heterozygous Han:SPRD-cy rat, a model of polycystic kidney disease (PKD) and progressive chronic renal failure. After 8 wk of pair feeding, betaine had no effect on renal cystic change, renal interstitial fibrosis, serum creatinine, serum cholesterol, or serum triglycerides. (1)H-NMR spectroscopy of renal tissue revealed no change in renal osmolytes, including betaine, or renal content of other organic anions in response to diet. (1)H-NMR spectroscopy of hepatic tissue performed to explore the metabolic fate of ingested betaine revealed that heterozygous animals fed the control diet had elevated hepatic levels of gluconeogenic amino acids, increased beta-hydroxybutyrate, and increased levels of some citric acid cycle metabolites compared with animals without renal disease. Betaine supplementation eliminated these changes. Chronic renal failure in the Han:SPRD-cy rat is associated with disturbances of hepatic metabolism that can be corrected with betaine therapy, suggesting the presence of a reversible methylation defect in this form of chronic renal failure.
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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 | 0.001 |
| 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".