Glucose and GLP-2 (Glucagon-Like Peptide-2) Mobilize Intestinal Triglyceride by Distinct Mechanisms
Bibliographic record
Abstract
OBJECTIVE: Dietary triglycerides are partially retained in the intestine within intracellular or extracellular compartments, which can be rapidly mobilized in response to several stimuli, including glucose and GLP-2 (glucagon-like peptide-2). To elucidate the mechanism of intestinal lipid mobilization, this study examined the patterns and time course of lymph flow and triglycerides after glucose and GLP-2 treatment in rats. Approach and Results: Lymph flow, triglyceride concentration, and triglyceride output were assessed in mesenteric lymph duct-cannulated rats in response to an intraduodenal (i.d.) lipid bolus followed 5 hours later by either (1) i.d. saline+intraperitoneal (i.p.) saline (placebo), (2) i.d. glucose plus i.p. saline, (3) i.d. saline+i.p. GLP-2, or (4) i.d. glucose+i.p. GLP-2. GLP-2 and glucose administered alone or in combination stimulated total triglyceride output to a similar extent, but the timing and pattern of stimulation differed markedly. Whereas GLP-2 rapidly increased lymph flow with no effect on lymph triglyceride concentration or triglyceride:apoB48 (apolipoprotein B48) ratio (a surrogate marker of chylomicron size) compared with placebo, glucose transiently decreased lymph flow followed by delayed stimulation of lymph flow and increased lymph triglyceride concentration and triglyceride:apoB48 ratio. CONCLUSIONS: Glucose and GLP-2 robustly enhanced intestinal triglyceride output in rats but with different effects on lymph flow, lymph triglyceride concentration, and chylomicron size. GLP-2 stimulated triglyceride output primarily by enhancing lymph flow with no effect on chylomicron size, whereas glucose mobilized intestinal triglycerides, stimulating secretion of larger chylomicrons. This suggests that these 2 stimuli mobilize intestinal lipid by different mechanisms.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".