<scp>iGlarLixi</scp> effectively reduces residual hyperglycaemia in patients with type 2 diabetes on basal insulin: A post hoc analysis from the <scp>LixiLan‐L</scp> study
Bibliographic record
Abstract
Globally, nearly half of patients with type 2 diabetes (T2D) do not successfully achieve target HbA1c with basal insulin, despite meeting fasting plasma glucose (FPG) targets. In this post hoc analysis of the LixiLan-L study, we determined whether iGlarLixi, a fixed-ratio combination of insulin glargine Gla-100 (iGlar) and the glucagon-like peptide-1 receptor agonist lixisenatide (Lixi), addresses the challenge of reducing residual hyperglycaemia in patients with T2D. In LixiLan-L, a randomized, open-label study, 1018 patients with T2D on basal insulin for ≥6 months ± oral antidiabetes drugs entered a 6-week run-in period, during which they were switched to and/or optimized for a daily dose of iGlar while continuing only metformin. Following the run-in period, 736 patients were then randomized to receive iGlarLixi or were continued on iGlar for 30 weeks ± metformin. Residual hyperglycaemia was defined as HbA1c ≥ 7.0% despite FPG of <140 mg/dL. The proportion of patients with residual hyperglycaemia was similar in both treatment arms at screening (~~42%), and increased after the run-in period (~~62%). After 30 weeks, the proportion of patients with residual hyperglycaemia declined to 23.8% in the iGlarLixi versus 47.1% in the iGlar arm (P < .0001). The proportion of patients achieving both HbA1c (<7.0%) and FPG (<140 mg/dL) targets was higher in the iGlarLixi compared with the iGlar arm (50.3% vs. 27.4%, respectively; P < .0001). iGlarLixi effectively reduces residual hyperglycaemia in patients with T2D on basal insulin therapy.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".