798-P: Semaglutide Effect during Mixed-Meal Tolerance Test (MMTT) with Fully Closed-Loop Therapy in Type 1 Diabetes (T1D)
Bibliographic record
Abstract
Introduction and Objective: We assessed glycemia, insulin needs, and C-peptide levels with semaglutide after MMTT in type 1 diabetes. Methods: This is a sub-analysis of a randomized crossover trial assessing semaglutide vs. placebo with automated insulin delivery (AID) in adults with T1D (NCT05205928). Participants performed a MMTT with 6 mL/kg of Boost, while using fully-closed-loop AID, after 12 weeks of semaglutide and placebo, in random order. Plasma glucose and C-peptide levels were measured over 120 minutes. C-peptide levels <0.003 nmol/L assumed to be 0 nmol/L. Paired t-test was performed for parametric comparisons, with Wilcoxin signed-rank test for non-parametric comparisons. Results: Ten participants completed the MMTT, with 8 having C-peptide levels and 7 having pump data; 40% were female, with age 47 (SD 14) years and T1D duration 29 (11) years. All but one had baseline C-peptide of < 0.003 pmol/L. Semaglutide reduced glucose AUC compared to placebo (p=0.006), but C-peptide AUC was not different between arms (p=0.35). Despite having lower glucose AUC, the insulin delivery by the AID was lower for semaglutide than placebo (p = 0.024). Conclusion: Semaglutide reduced glucose AUC during fully closed-loop therapy after weight-adjusted meal replacement, with less insulin output required from the AID. Further studies are needed to understand mechanistic of effects. Disclosure M. Pasqua: Speaker's Bureau; Abbott, Sanofi, Medtronic. J. Doumat: None. A. Jafar: None. M. Tsoukas: Speaker's Bureau; Novo Nordisk, Eli Lilly and Company, Boehringer-Ingelheim, Janssen Pharmaceuticals, Inc, Sanofi. A. Haidar: Research Support; Tandem Diabetes Care, Inc. Consultant; Eli Lilly and Company, Abbott. Research Support; ADOCIA, Dexcom, Inc., Ypsomed AG, Bigfoot Biomedical, Inc. Funding Canada Research Chair in Artificial Pancreas Systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".