Lixisenatide reduces glycaemic variability in insulin‐treated patients with type 2 diabetes
Bibliographic record
Abstract
Chronic hyperglycaemia and glucose variability are associated with the development of chronic diabetes-related complications. We conducted a pooled analysis of patient-level data from three 24-week, randomized, phase III clinical trials to evaluate the impact of lixisenatide (LIXI) on glycaemic variability (GV) vs placebo as add-on to basal insulin. The main outcome GV measures were standard deviation (s.d.), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG) level, area under the curve for fasting glucose (AUC-F), and high (HBGI) and low blood glucose index (LBGI). The change in GV metrics over 24 weeks and relationships among baseline GV, patient characteristics and outcomes were assessed. Data were pooled from 1198 patients (665 LIXI, 533 placebo). Values for s.d., MAG level, MAGE, HBGI, and AUC-F significantly decreased with LIXI vs placebo, while LBGI values were unchanged. Higher baseline GV measures correlated with older age, longer type 2 diabetes duration, lower body mass index, higher baseline glycated/haemogobin, greater reduction in postprandial glucose (PPG) level, and higher rates of symptomatic hypoglycaemia. These data show that LIXI added to basal insulin significantly reduced GV and PPG excursions vs placebo, without increasing the risk of hypoglycaemia (LBGI).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".