Efficacy of glucagon‐like peptide‐1 receptor agonists compared to dipeptidyl peptidase‐4 inhibitors for the management of type 2 diabetes: A meta‐analysis of randomized clinical trials
Bibliographic record
Abstract
AIMS: Glucagon-like peptide-1 (GLP-1) agonists and dipeptidyl peptidase-4 (DPP-4) inhibitors are both incretin-based therapies for type 2 diabetes (T2DM) but have distinct efficacy and side effect profiles. We thus performed a systematic review and meta-analysis to compare the effects of GLP-1 agonists to DPP-4 inhibitors on glycaemic control, weight and incidence of adverse events in adults with T2DM. We also sought to determine whether there was any additional effect in switching from DPP-4 inhibitor to GLP-1 agonist. MATERIALS AND METHODS: We systematically searched PubMed, Embase and ClinicalTrials.gov for (1) randomized controlled trials (RCTs) comparing any GLP-1 agonist to any DPP-4 inhibitor and (2) interventional studies where a DPP-4 inhibitor was switched to a GLP-1 agonist. We assessed pooled data using random-effects model (CRD42017057115). RESULTS: The pooled analysis of 13 RCTs (n = 4330) showed that, compared to DPP-4 inhibitors, GLP-1 agonists yielded a greater mean reduction in glycated haemoglobin (HbA1c) of -0.41% (95% CI -0.53 to -0.30) and in weight of -2.15 kg (-3.04 to -1.27). GLP-1 agonists were associated with greater likelihood of gastrointestinal side effects with no increased risk of hypoglycaemia. In 5 interventional studies (n = 433), switching from DPP-4 inhibitor to GLP-1 agonist yielded further mean reduction in HbA1c of -0.69% (-1.03 to -0.35) and in weight of -2.25 kg (-3.12 to -1.38). CONCLUSIONS: GLP-1 agonists yield greater reduction in HbA1c and weight as compared to DPP-4 inhibitors, with increased incidence of gastrointestinal symptoms but not hypoglycaemia. Replacing a DPP-4 inhibitor with GLP-1 agonist provides additional benefits in glycaemic control and weight loss.
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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 | 0.028 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.069 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".