Abstract 9654: Dipeptidyl Peptidase-4 Inhibitors May Reduce the Risk of Major Adverse Cardiovascular Events and All-cause Mortality In Patients With Type 2 Diabetes: Meta-analyses Oof Randomized Controlled Trials
Bibliographic record
Abstract
Background: Dipeptidyl peptidase-4 (DPP-4) inhibitors and glucagon-like peptide-1 (GLP-1) agonists, both of which have an incretin effect, represent novel, effective therapeutic approaches to treating hyperglycemia in type 2 diabetes mellitus (T2DM). Their impact on cardiovascular (CV) outcomes, however, is still under investigation. Hypotheses: We assessed the impact of DPP-4 inhibitors and GLP-1 agonists on the risk of a composite of cardiovascular death, myocardial infarction, and stroke (MACE), and on all-cause mortality (ACM) in T2DM. Methods: We searched Medline, EMBASE, and Cochrane Library for randomized controlled trials (RCTs) published up to February 2010 that compared an incretin agent versus usual care, placebo, or other active agents for patients with T2DM, and reported a pre-specified CV outcome. We also searched a major trial registry, FDA and EMEA databases, and contacted authors about unclear CV outcome information. We undertook Der-Simonian and Laird random effect meta-analyses of MACE and all-cause mortality, using inverse-variance weights and continuity correction of 0.005. We excluded total-zero-event trials. We conducted subgroup analyses using a small number of pre-specified hypotheses, including study duration, type of control, treatment mode, and risk of bias. Results: Of 61 eligible trials, data on MACE was available in 33 trials of DPP-4 inhibitors (107 events /24341 patients), and 9 of GLP-1 agonists (12/3745), and ACM in 20 trials of DPP-4 inhibitors (45/17317), and 3 of GLP-1 (3/1404). There was a significant reduction in MACE (RR 0.53, 95% CI 0.33 to 0.85) between DPP-4 inhibitors and control, and a non-significant reduction in ACM (RR 0.46, 95% CI 0.19 to 1.07). No significant differences were found between GLP-1 agonists and control in MACE (RR 0.77, 95% CI 0.13 to 4.61) or ACM (RR 3.53,0 to >999). There was no significant heterogeneity detected in all analyses (p>0.1). Subgroup analyses did not show any significant differences in treatment effect, except that the effect on all-cause mortality differs by treatment mode (monotherapy vs. combination/add-on therapy, interaction p=0.04). Conclusion: DPP-4 inhibitors may reduce the risk of MACE and ACM. The effect of GLP-1 agonists on CV outcomes remains uncertain.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.060 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".