Benefits of combination of insulin degludec and liraglutide are independent of baseline glycated haemoglobin level and duration of type 2 diabetes
Bibliographic record
Abstract
AIM: To evaluate, using post hoc analyses, whether the novel combination of a basal insulin, insulin degludec, and a glucagon-like peptide-1 receptor agonist, liraglutide (IDegLira), was consistently effective in patients with type 2 diabetes (T2D), regardless of the stage of T2D progression. METHODS: Using data from the DUAL I extension [insulin-naïve patients uncontrolled on oral antidiabetic drugs (OADs), n = 1660, 52 weeks] and DUAL II (patients uncontrolled on basal insulin plus OADs, n = 398, 26 weeks) randomized trials, the efficacy of IDegLira was investigated with regard to measures of disease progression stage including baseline glycated haemoglobin (HbA1c), disease duration and previous insulin dose. RESULTS: Across four categories of baseline HbA1c (≤7.5-9.0%), HbA1c reductions were significantly greater with IDegLira (1.1-2.5%) compared with IDeg or liraglutide alone in DUAL I. In DUAL II, HbA1c reductions were significantly greater with IDegLira (0.9-2.5%) than with IDeg in all but the lowest HbA1c category. In DUAL I, insulin dose and hypoglycaemia rate were lower across all baseline HbA1c categories for IDegLira versus IDeg, while hypoglycaemia was higher with IDegLira than liraglutide, irrespective of baseline HbA1c. In DUAL II, insulin dose and hypoglycaemia rate were similar with IDegLira and IDeg (maximum dose limited to 50 U) independent of baseline HbA1c. The reduction in HbA1c with IDegLira was independent of disease duration and previous insulin dose but varied depending on pre-trial OAD treatment. CONCLUSIONS: IDegLira effectively lowered HbA1c across a range of measures, implying suitability for patients with either early or advanced T2D.
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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) | 0.002 | 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 | 0.000 | 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".