Impact of the Glucagon Assay When Assessing the Effect of Chronic Liraglutide Therapy on Glucagon Secretion
Bibliographic record
Abstract
Context: Glucagon-like peptide-1 agonists acutely lower serum glucagon. However, in the Liraglutide and β-Cell Repair (LIBRA) Trial, 48-week treatment with liraglutide yielded lower/unchanged fasting glucagon but, surprisingly, enhanced postchallenge glucagonemia [measured by R&D Systems (Minneapolis, MN) assay]. Objective: Because differences between glucagon assays potentially could explain these unexpected findings, we have remeasured glucagon in all 1222 samples from this trial using the highly-sensitive/specific Mercodia assay to compare the findings between assays. Design/Setting/Participants/Intervention: In LIBRA, 51 patients with type 2 diabetes of 2.6 ± 1.9 years duration were randomized to daily subcutaneous liraglutide or placebo injection and followed for 48 weeks, with serial oral glucose tolerance test (OGTT) every 12 weeks (with liraglutide/placebo last administered ∼24 hours earlier). Outcome: Serum glucagon was measured every 30 minutes on each OGTT, enabling determination of the area under the glucagon curve (AUCglucagon). Results: With the Mercodia assay, fasting glucagon was higher in the liraglutide arm than placebo at 12 weeks (P = 0.01), with no between-group differences at 24, 36, and 48 weeks. There was no difference in AUCglucagon between the groups at any visit. Mercodia and R&D Systems glucagon measurements correlated at postchallenge time points but not at fasting. Conclusion: The Mercodia assay did not replicate the R&D Systems glucagon findings. Although neither assay demonstrated lower postchallenge glucagonemia with chronic liraglutide last administered ∼24 hours earlier, the differential response reported by these assays precludes definitive conclusion and highlights the critical role of assay selection when measuring glucagon in clinical studies.
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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 | 0.006 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".