Incretin based treatments and mortality in patients with type 2 diabetes: systematic review and meta-analysis
Bibliographic record
Abstract
Objective To assess the impact of incretin based treatment on all cause mortality in patients with type 2 diabetes. Design Systematic review and meta-analysis of randomised trials. Data sources Medline, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov. Eligibility criteria Randomised controlled trials that compared glucagon-like peptide-1 (GLP-1) receptor agonists or dipeptidyl peptidase-4 (DPP-4) inhibitors with placebo or active anti-diabetic drugs in patients with type 2 diabetes. Data collection and analysis Paired reviewers independently screened citations, assessed risk of bias of included studies, and extracted data. Peto’s method was used as the primary approach to pool effect estimates from trials, sensitivity analyses were carried out with other statistical approaches, and meta-regression was applied for six prespecified hypotheses to explore heterogeneity. The GRADE approach was used to rate the quality of evidence. Results 189 randomised controlled trials (n=155 145) were included, all of which were at low to moderate risk of bias; 77 reported no events of death and 112 reported 3888 deaths among 151 614 patients. Meta-analysis of 189 trials showed no difference in all cause mortality between incretin drugs versus control (1925/84 136 v 1963/67 478; odds ratio 0.96, 95% confidence interval 0.90 to 1.02, I2=0%; risk difference 3 fewer events (95% confidence interval 7 fewer to 1 more) per 1000 patients over five years; moderate quality evidence). Results suggested the possibility of a mortality benefit with GLP-1 agonists but not DPP-4 inhibitors, but the subgroup hypothesis had low credibility. Sensitivity analyses showed no important differences in the estimates of effects. Conclusions Current evidence does not support the suggestion that incretin based treatment increases all cause mortality in patients with type 2 diabetes. Further studies are warranted to examine if the effect differs between GLP-1 agonists versus DPP-4 inhibitors.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".