Regulation of intestinal lipid and lipoprotein metabolism by the proglucagon-derived peptides glucagon like peptide 1 and glucagon like peptide 2
Bibliographic record
Abstract
PURPOSE OF REVIEW: The intestine is highly efficient at absorbing and packaging dietary lipids onto the structural protein apoB48 for distribution throughout the body. Here, we summarize recent advances into understanding the physiological and pharmacological actions of the proglucagon-derived peptides: glucagon like peptide 1 (GLP-1) and glucagon like peptide 2 (GLP-2) on intestinal lipoprotein secretion. RECENT FINDINGS: Several recent studies have elucidated mechanisms underlying the paradoxical effects of GLP-1 and GLP-2 on intestinal production of triglyceride-rich lipoproteins (TRLs). Both gut-derived peptides are secreted on an equimolar basis in response to the same nutrient stimulus. Despite neither receptor demonstrating clear localization to enterocytes, a single injection of a GLP-1R agonist rapidly decreases delivery of intestinally packaged fatty acids into the plasma, while conversely GLP-2 receptor (GLP-2R) activation acutely increases TRL concentrations in plasma. SUMMARY: The regulation of TRL secretion is dependent on the coordination of many processes: fatty acid availability uptake, assembly onto the apoB48 polypeptide backbone, secretion and reuptake, which the hormonal, neural, inflammatory and metabolic milieu can all strongly influence. Understanding of how GLP-1 and GLP-2 receptor agonists control TRL production has clinical importance given that GLP1R agonists were recently demonstrated not only to provide glycemic control but also to prevent major adverse cardiovascular events in patients with T2DM and the success of GLP-2R agonists in treating short bowel disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".