Long‐term efficacy and safety of combined insulin and glucagon‐like peptide‐1 therapy: Evidence from the LEADER trial
Bibliographic record
Abstract
AIM: Glucagon-like peptide-1 receptor agonist (GLP-1RA) and insulin combination therapy is an effective treatment option for type 2 diabetes, but long-term data are lacking. The aim was to assess the long-term efficacy of the GLP-1RA liraglutide in subgroups by insulin use in the LEADER trial. MATERIALS AND METHODS: LEADER assessed cardiovascular (CV) safety and efficacy of liraglutide (1.8 mg) versus placebo (plus standard of care therapy) in 9340 patients with type 2 diabetes and high risk of CV disease, for up to 5 years. We analyzed CV events, metabolic parameters and hypoglycaemia post hoc in three subgroups by baseline insulin use (basal-only insulin, other insulin or no insulin). Insulin was a non-random treatment allocation as part of standard of care therapy. RESULTS: At baseline, 5171 (55%) patients were not receiving insulin, 3159 (34%) were receiving basal-only insulin and 1010 (11%) other insulins. Insulin users had a longer diabetes duration and slightly worse glycaemic control (HbA1c) than the no-insulin subgroup. Liraglutide reduced HbA1c and weight versus placebo in all three subgroups (P < .001), and severe hypoglycaemia rate in the basal-only insulin subgroup. The need for insulin was less with liraglutide. CV risk reduction with liraglutide was similar to the main trial results in the basal-only and no-insulin subgroups. CONCLUSIONS: In patients on insulin, liraglutide improved glycaemic control, weight and need for insulin versus placebo, for at least 36 months with no increased risk of severe hypoglycaemia, while maintaining CV safety/efficacy, supporting the combination of liraglutide and insulin for management of type 2 diabetes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".