Fixed-Ratio Combination of Insulin and GLP-1 RA in Patients with Longstanding Type 2 Diabetes: A Subanalysis of LixiLan-L
Bibliographic record
Abstract
INTRODUCTION: With longer duration and progression of type 2 diabetes (T2D), β-cell function deteriorates and insulin therapy often becomes necessary. Glucagon-like peptide-1 receptor agonists such as lixisenatide that do not rely only on β-cell function and glucagon suppression primarily, but also lower glucose by other (insulin-independent) mechanisms such as delayed gastric emptying, may be appropriate adjuvant therapy to basal insulin in patients with longstanding T2D. METHODS: We assessed the efficacy and safety of insulin glargine (iGlar) versus iGlarLixi, a fixed-ratio combination of iGlar and lixisenatide, stratified by quartiles (Q) of T2D duration (≤ 7.305 [Q1], > 7.305 to ≤ 10.75 [Q2], > 10.75 to ≤ 15.67 [Q3], and > 15.67 years [Q4]) in the LixiLan-L trial (N = 736). RESULTS: Across all quartiles, the reduction in glycated haemoglobin was greater with iGlarLixi versus iGlar, and the difference was most pronounced in patients with the longest duration (Q4; least squares mean difference [standard error] - 0.62 [0.13], P < 0.0001). Additionally, hypoglycaemia rates were significantly lower with iGlarLixi versus iGlar in patients in Q4 (3.3 vs. 6.9 events/patient-year, P < 0.0001). CONCLUSION: iGlarLixi lowered glycated haemoglobin more versus iGlar regardless of T2D duration, with benefit retained even among patients with the longest T2D duration.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".