Evaluation of the specific effects of intranasal glucagon on glucose production and lipid concentration in healthy men during a pancreatic clamp
Bibliographic record
Abstract
AIM: To investigate the specific effects of intranasal glucagon (ING) on plasma glucose, endogenous glucose production (EGP) and lipid concentration. METHODS: We conducted a single-blind, randomized, crossover study at our academic investigation unit. Under pancreatic clamp conditions with tracer infusion, 1 mg ING or intranasal placebo (INP) was administered to 10 healthy men. As pilot studies showed that ING transiently increased plasma glucagon, we infused intravenous glucagon for 30 minutes along with INP to ensure similar plasma glucagon concentrations between interventions. The main outcome measures were plasma glucose, EGP, free fatty acid (FFA) and triglyceride (TG) concentrations. RESULTS: In the presence of similar plasma glucagon concentrations, the increase in plasma glucose under these experimental conditions was attenuated with ING (mean plasma glucose analysis of variance P < .001) with reduction in EGP (P = .027). No significant differences were seen in plasma FFA and TG concentrations. CONCLUSION: ING raises plasma glucose but this route of administration attenuates the gluco-stimulatory effect of glucagon by reducing EGP. This observation invites speculation about a potential central nervous system effect of glucagon, which requires further investigation. If ING is developed as a treatment for hypoglycaemia, this attenuated effect on plasma glucose should be taken into account.
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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 | 0.000 | 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".