Creatine supplementation to total parenteral nutrition increases muscle and organ creatine and lowers liver cholesterol in Yucatan miniature piglets (820.17)
Bibliographic record
Abstract
In TPN‐fed pigs, we previously demonstrated that tissue creatine concentrations were proportionate to the IV arginine intake, suggesting that arginine availability limited creatine synthesis. TPN is devoid of creatine, so the requirement must be met entirely by de novo synthesis requiring arginine, glycine and methionine as a methyl donor. L‐arginine:glycine amidinotransferase (AGAT) converts arginine and glycine into guanidinoacetic acid (GAA); GAA is methylated via Guanidinoacetate methyltransferase (GAMT) to form creatine. The addition of creatine to TPN may spare arginine and methionine for protein synthesis and other metabolic processes. Piglets (3‐5 d old, N = 14) were fed complete TPN with (CRE, 0.1 g/kg/d) or without creatine (CON) for 14 d. The CRE group had lower kidney and pancreas AGAT activity and lower plasma GAA concentration (P < 0.01) demonstrating a down‐regulation of creatine biosynthesis. CRE pigs also had greater creatine in plasma, liver, kidney and pancreas (P < 0.01). Total creatine in muscle was also higher in CRE (P = 0.05); however, brain creatine was not affected by treatment. There was no difference in liver weight or triglyceride concentration, but total liver cholesterol was ~50% lower in the CRE piglets compared to CON (P < 0.01). Altered lipid metabolism suggests that creatine may have a role in ameliorating TPN‐induced liver damage. The neonatal brain appears to have priority for creatine when supply is limited, but the addition of creatine to TPN may be necessary to support total body creatine accretion in rapidly growing neonates. Grant Funding Source : Supported by Janeway
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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 | 0.001 | 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".