Abstract 247: Dipeptidyl Peptidase-4 Inhibition With Sitagliptin Acutely Inhibits Intestinal Lipoprotein Particle Secretion in Healthy Humans
Bibliographic record
Abstract
Postprandial dyslipidemia, in part due to overproduction of triglyceride-rich lipoprotein (TRL) particles from the liver and the intestine, contributes to increased CVD risk. The DPP-4 inhibitor sitagliptin, an anti-diabetic agent, has been shown to reduce postprandial lipid excursion following a mixed meal. The underlying mechanism of this effect, however, has not been examined in humans. This study was designed to examine intestinal and hepatic TRL particle production and clearance in response to a single oral dose of sitagliptin. 15 lean, healthy male volunteers were studied in two occasions, 4-6 weeks apart, receiving sitagliptin (100 mg) or placebo in random order. Kinetics of TRL particles of intestinal and hepatic origin were measured using stable isotope tracer infusion techniques and with control of pancreatic hormone levels. Sitagliptin decreased TRL apoB-48 concentration (-32%, P<0.05) by reducing production rate (sitagliptin 47.2+/- 11.0 vs placebo 93.9 +/- 25.2 ug/kg/d, P<0.05). Fractional catabolic rate was not significantly affected. TRL apoB-100 concentration, production rate and fractional catabolic rate were not significantly different between sitagliptin and placebo. Plasma TG, free fatty acids, glucose, pancreatic hormones, and TRL TG were similar between treatments. In conclusion, sitagliptin acutely inhibits intestinal, but not hepatic, lipoprotein particle production, independent of changes in pancreatic hormones and circulating glucose and free fatty acids. This pleotropic effect of sitagliptin explains in part the reduction in postprandial lipemia seen in clinical trials and may provide metabolic benefits beyond glucose lowering.
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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) | 0.004 | 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".