IDegLira Improves Both Fasting and Postprandial Glucose Control as Demonstrated Using Continuous Glucose Monitoring and a Standardized Meal Test
Bibliographic record
Abstract
OBJECTIVE: IDegLira is a novel, fixed-ratio combination of the long-acting basal insulin, insulin degludec, and the long-acting glucagon-like peptide-1 analog liraglutide. We studied the effect of IDegLira versus its components on postprandial glucose (PPG) in type 2 diabetes. METHODS: In this substudy, 260 (15.6%) of the original 1663 patients with inadequate glycemic control participating in a 26-week, open-label trial (DUAL I) were randomized 2:1:1 to once-daily IDegLira, insulin degludec or liraglutide. Continuous glucose monitoring (CGM) for 72 hours and a meal test were performed. RESULTS: At week 26, IDegLira produced a significantly greater decrease from baseline in mean PPG increment (normalized iAUC0-4h) than insulin degludec (estimated treatment difference [ETD] -12.79 mg/dl [95% CI: -21.08; -4.68], P = .0023) and a similar magnitude of decrease as liraglutide (ETD -1.62 mg/dl [95% CI: -10.09; 6.67], P = .70). CGM indicated a greater reduction in change from baseline in PPG increment (iAUC0-4h) for IDegLira versus insulin degludec over all 3 main meals (ETD -6.13 mg/dl [95% CI: -10.27, -1.98], P = .0047) and similar reductions versus liraglutide (ETD -1.80 mg/dl [95% CI: -2.52, 5.95], P = .4122). Insulin secretion ratio and static index were greater for IDegLira versus insulin degludec (P = .048 and P = .006, respectively) and similar to liraglutide (P = .45 and P = .895, respectively). CONCLUSIONS: Once-daily IDegLira provides significantly better PPG control following a mixed meal test than insulin degludec. The improvement is at least partially explained by higher endogenous insulin secretion and improved beta cell function with IDegLira. The benefits of liraglutide on PPG control are maintained across all main meals in the combination.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".