Achieving a clinically relevant composite outcome of an HbA1c of <7% without weight gain or hypoglycaemia in type 2 diabetes: a meta‐analysis of the liraglutide clinical trial programme
Bibliographic record
Abstract
AIM: Effective type 2 diabetes management requires a multifactorial approach extending beyond glycaemic control. Clinical practice guidelines suggest targets for HbA1c, blood pressure and lipids, and emphasize weight reduction and avoiding hypoglycaemia. The phase 3 clinical trial programme for liraglutide, a human glucagon-like peptide 1 analogue, showed significant improvements in HbA1c and weight with a low risk of hypoglycaemia compared to other diabetes therapies. In this context, we performed a meta-analysis of data from these trials evaluating the proportion of patients achieving a clinically relevant composite measure of diabetes control consisting of an HbA1c <7% without weight gain or hypoglycaemia. METHODS: A prespecified meta-analysis was performed on 26-week patient-level data from seven trials (N = 4625) evaluating liraglutide with commonly used therapies for type 2 diabetes: glimepiride, rosiglitazone, glargine, exenatide, sitagliptin or placebo, adjusting for baseline HbA1c and weight, for a composite outcome of HbA1c <7.0%, no weight gain and no hypoglycaemic events. RESULTS: At 26 weeks, 40% of the liraglutide 1.8 mg group, 32% of the liraglutide 1.2 mg group and 6-25% of comparators (6% rosiglitazone, 8% glimepiride, 15% glargine, 25% exenatide, 11% sitagliptin, 8% placebo) achieved this composite outcome. Odds ratios favoured liraglutide 1.8 mg by 2.0- to 10.5-fold over comparators. CONCLUSIONS: As assessed by the composite outcome of HbA1c <7%, no hypoglycaemia and no weight gain, liraglutide was clearly superior to the other commonly used therapies. However, the long-term clinical impact of this observation remains to be shown.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.057 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".