Does GLP-1 suppress its own basal secretion?
Bibliographic record
Abstract
PURPOSE/AIM: Negative feedback controls in endocrine regulatory systems are well recognized. The incretins and their role in glucose regulation have been of major interest recently. Whether the same negative control system applies to the regulation of incretin secretion is not clear. We sought to examine the hypothesis that exogenous administration of glucagon like peptide-1, GLP-1(7-36) amide or its metabolite GLP-1(9-36) amide, reduces the endogenous basal release of this incretin. MATERIALS AND METHODS: We evaluated the endogenous basal release of GLP-1 using two separate study designs. In protocol A we examined the GLP-1(7-36) amide levels during the infusion of GLP-1(9-36) amide. In protocol B, we used PYY and GLP-2 as biomarkers for the endogenous basal release of GLP-1(7-36) amide and assessed the endogenous basal release of these two hormones during the GLP-1(7-36) infusion. Twelve lean and 12 obese subjects were enrolled in protocol A and 10 obese volunteers in protocol B. RESULTS: The plasma levels of GLP-1(7-36) amide in protocol A and PYY and GLP-2 in protocol B remained unchanged during the exogenous infusion of GLP-1(9-36) and GLP-1(7-36) amide, respectively. CONCLUSIONS: The negative feedback control system as described by inhibition of the release of endogenous hormone while infusing it exogenously was not observed for the basal secretion of GLP-1(7-36) amide.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".