Glucagon‐Like Peptide‐2 Alters Bile Acid Metabolism in Parenteral Nutrition–Associated Liver Disease
Bibliographic record
Abstract
BACKGROUND: We aim to study the mechanisms underlying our previous finding that exogenous glucagon-like peptide-2 (GLP-2) treatment in a preclinical model of neonatal parenteral nutrition-associated liver disease (PNALD) improves cholestasis. METHODS: Neonatal piglets received 17 days of parenteral nutrition (PN) therapy and either saline control (PN/Saline n = 8) or GLP-2 treatment at 11 nmol/kg/d (PN/GLP-2, n = 7). At terminal laparotomy, bile and liver samples were collected. The relative gene expression of enzymes involved in bile acid synthesis, regulation, and transport was measured in liver by reverse-transcriptase quantitative polymerase chain reaction. Bile acid composition in bile was determined using tandem mass spectrometry. Data were analyzed using 1-way analysis of variance (ANOVA) or Kruskal-Wallis ANOVA. RESULTS: GLP-2 increased the expression of bile acid export genes: multidrug resistance-associated proteins 2 (MRP2) (P = .002) and 3 (MRP3) (P = .037) over saline control. GLP-2 increased expression of Farnesoid X receptor (FXR) (P < .001) and CYP7A1 (cytochrome P450, family 7, subfamily A, polypeptide 1) (P = .03). GLP-2 treatment was associated with decreased concentrations of taurohyocholic acid and conjugates of toxic lithocholic acid (P < .01). GLP-2 treatment increased the liver bile acid content. CONCLUSIONS: GLP-2 treatment was associated with alterations in the hepatic expression of genes involved in bile acid metabolism. The transcriptomic results indicate the mechanisms at the transcriptional level acting to regulate bile acid synthesis and increase bile acid export. Differences in bile acid profiles further support a beneficial role for GLP-2 therapy in PNALD.
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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 | 0.000 | 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".