Glucagon-like peptide (GLP)-1 regulation of lipid and lipoprotein metabolism
Bibliographic record
Abstract
Metabolic health is highly dependent on intestinal and hepatic handling of dietary and endogenous lipids and lipoproteins. Disorders of lipid and lipoprotein metabolism are commonly observed in patients with insulin resistant states such as obesity, metabolic syndrome, and type 2 diabetes. Evidence from both animal models and human studies indicates that a major underlying factor in metabolic or diabetic dyslipidemia is the overproduction of hepatic and intestinal apolipoprotein (apo)B-containing lipoprotein particles. These particles are catabolized down into highly proatherogenic remnants, which can be taken up into the arterial intima and promote plaque development. Several gut-derived peptides have been identified as key regulators of energy metabolism; one such peptide is the incretin hormone glucagon-like peptide (GLP)-1. Our laboratory has previously demonstrated that GLP-1 can signal both centrally and peripherally to reduce postprandial and fasting lipoprotein secretion. Moreover, we have demonstrated that GLP-1 receptor (GLP-1R) agonists can ameliorate diet-induced dyslipidemia. Recently, we published evidence for a novel vagal neuroendocrine signalling pathway by which native GLP-1 may exert its anti-lipemic effects. Furthermore, we demonstrated a novel role for other gut-derived peptides in regulating intestinal lipoprotein production. Overall, ample evidence supports a key role for GLP-1R on the portal vein afferent neurons and nodose ganglion in modulating intestinal fat absorption and lipoprotein production and identifies other gut-derived peptides as novel regulators of postprandial lipemia. Insights from these data may support identification of potential drug targets and the development of new therapeutics targeting treatment of diabetic dyslipidemia.
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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) | 0.000 | 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) | 0.001 | 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".