37-LB: GLP-2 Regulation of Dietary Fat Absorption and Intestinal Lipoprotein Production: The Role of the Enteric Nervous System via nNOS Signaling
Bibliographic record
Abstract
Objective: Postprandial dyslipidemia, a condition commonly presented in individuals with type 2 diabetes (T2D), is characterized by the overproduction of triglyceride (TG)-rich lipoproteins (TRL). This results in excess atherogenic chylomicron (CM) remnants in circulation, significantly increasing the risk of cardiovascular disease (CVD). Using a Syrian golden hamster model, our laboratory has revealed that intestinal hormone glucagon-like peptide-2 (GLP-2) regulates dietary fat uptake and increases CM production in insulin-resistant states. However, the mechanisms by which GLP-2 enhances CM production remain unclear. Previous studies have implicated nitric oxide (NO) in the actions of GLP-2, demonstrating the requirement of NO in CM secretion. Therefore, this study investigated the role of neuronal NO synthase (nNOS)-mediated NO generation in lipid uptake and CM formation. Methods: nNOS-/- and C57BL/6 (WT) mice received an intraduodenal injection of olive oil, followed by an acute intraperitoneal (i.p.) injection of GLP-2 (0.25mg/kg; n=7) or vehicle (PBS; n=8). Blood samples were collected over a 2-hour period to assess postprandial lipid accumulation, and TRL fractions were isolated via ultracentrifugation. Immunoblotting was used to measure plasma apolipoprotein B48 (apoB48) protein expression. Results: While GLP-2-treated WT mice showed characteristic rises in postprandial TG and cholesterol in whole plasma and TRL fractions, the effect of GLP-2 in nNOS-/- mice was abolished. The effect of GLP-2 on postprandial plasma apoB48 protein expression was also abolished in nNOS-/- mice relative to GLP-2-treated WT mice. Conclusions: These findings demonstrate a novel mechanism in which nNOS-generated NO is crucial for GLP-2-mediated lipid absorption and CM production in the postprandial state. Understanding this pathway will provide insight for future therapeutic interventions to treat lipid disorders in T2D. Disclosure E.M. Grande: None. K. Adeli: None. Funding Canadian Institutes of Health Research
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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) | 0.000 | 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) | 0.003 | 0.001 |
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".