Adding Once-Daily Lixisenatide for Type 2 Diabetes Inadequately Controlled With Newly Initiated and Continuously Titrated Basal Insulin Glargine
Bibliographic record
Abstract
OBJECTIVE: When oral therapy for type 2 diabetes is ineffective, adding basal insulin improves glycemic control. However, when glycated hemoglobin (HbA1c) remains elevated because of postprandial hyperglycemia, the next therapeutic step is controversial. We examined the efficacy and safety of lixisenatide in patients with HbA1c still elevated after initiation of insulin glargine. RESEARCH DESIGN AND METHODS: This double-blind, parallel-group trial enrolled patients with HbA1c 7-10% despite oral therapy. Insulin glargine was added and systematically titrated during a 12-week run-in, after which candidates with fasting glucose ≤ 7.8 mmol/L and HbA1c 7-9% were randomized to lixisenatide 20 µg or placebo for 24 weeks while insulin titration continued. The primary end point was HbA1c change after randomization. RESULTS: The randomized population (n = 446) had mean diabetes duration of 9.2 years, BMI 31.8 kg/m(2), and daily glargine dosage of 44 units. HbA1c had decreased during run-in from 8.6 to 7.6%; adding lixisenatide further reduced HbA1c by 0.71 vs. 0.40% with placebo (least squares mean difference, -0.32%; 95% CI, -0.46 to -0.17; P < 0.0001). More participants attained HbA1c <7% with lixisenatide (56 vs. 39%; P < 0.0001). Lixisenatide reduced plasma glucose 2 h after a standardized breakfast (difference vs. placebo -3.2 mmol/L; P < 0.0001) and had a favorable effect on body weight (difference vs. placebo -0.89 kg; P = 0.0012). Nausea, vomiting, and symptomatic hypoglycemia <3.3 mmol/L were more common with lixisenatide. CONCLUSIONS: Adding lixisenatide to insulin glargine improved overall and postprandial hyperglycemia and deserves consideration as an alternative to prandial insulin for patients not reaching HbA1c goals with recently initiated basal insulin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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".