Glucagon‐Like Peptide 2 Improves Cholestasis in Parenteral Nutrition–Associated Liver Disease
Bibliographic record
Abstract
BACKGROUND: Parenteral nutrition-associated liver disease (PNALD) remains a significant cause of morbidity and mortality in neonates with intestinal failure. Although glucagon-like peptide-2 (GLP-2) is being advanced as therapy, the effect of GLP-2 treatment on PNALD is unknown. We aim to investigate the effect of exogenous GLP-2 administration on hepatic function in a neonatal piglet model of PNALD. METHODS: Neonatal piglets (aged 2-6 days) underwent jugular venous catheterization to receive isonitrogenous, isocaloric parenteral nutrition (PN). Piglets were allocated to 2 groups: group 1 (n = 8) received saline while group 2 (n = 7) received GLP-2 (at 11 nmol/kg/d). After 17 days, piglets underwent terminal laparotomy, and bile flow was measured. Liver specimens were analyzed histologically and with immunoperoxidase staining. Age-matched sow-reared control piglets (group 3, n = 8) were used for comparison. RESULTS: Both groups 1 and 2 receiving PN developed cholestasis relative to sow-reared controls, as evidenced by a decrease in bile flow and increase in serum total bilirubin. However, group 2 had improved bile flow (1.35 vs 0.73 µL/g; P = .02) and diminished bilirubin (38.0 vs 78.5 µmol/L; P = .008) compared with group 1. Group 2 also had lower serum alanine aminotransferase levels, a marker of liver injury. Histologically, the liver specimens in group 1 had marked hepatocyte pigmentation, which was decreased in group 2 specimens. CONCLUSIONS: The exogenous administration of GLP-2 is associated with the improvement of cholestasis and liver injury. This study introduces a novel role for GLP-2 in improving PNALD in the setting of prolonged PN duration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".