Efficacy of lixisenatide in patients with type 2 diabetes: A post hoc analysis of patients with diverse β-cell function in the GetGoal-M and GetGoal-S trials
Bibliographic record
Abstract
AIMS: To evaluate the impact of β-cell function on the efficacy of lixisenatide, a once-daily prandial glucagon-like peptide-1 receptor agonist, in patients with type 2 diabetes (T2D). MATERIALS AND METHODS: In this post hoc analysis, patients from the Phase 3 GetGoal-M and GetGoal-S clinical trials randomized to lixisenatide 20μg once daily were stratified into quartiles by baseline β-cell function, as measured by the secretory units of islet in transplantation (SUIT) index. RESULTS: Patients (N=437) were distributed evenly among SUIT index quartiles 1 to 4 (lowest to highest β-cell function). Clinical outcomes improved from baseline across all SUIT quartiles; mean changes at week 24 were: glycated hemoglobin (HbA1c; % [mmol/mol]), -0.99 (-10.8), -0.87 (-9.5), -0.86 (-9.4), -0.83 (-9.1); and postprandial plasma glucose (PPG; mmol/L), -7.9, -5.6, -5.5, -4.3 (overall effect P<0.0001). Furthermore, postprandial glucagon was reduced in all SUIT quartiles, while insulinogenic index improved only in patients with higher baseline SUIT (overall effect P=0.0286). No severe symptomatic hypoglycemic events were reported. CONCLUSIONS: Lixisenatide treatment resulted in reductions in HbA1c and PPG levels across all SUIT quartiles. This suggests that non-insulin-related actions of lixisenatide contribute to improved glycemic control in T2D.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| 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 | 0.001 |
| 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".