Efficacy of antihyperglycemic therapies on cardiovascular and heart failure outcomes: an updated meta-analysis and meta-regression analysis of 35 randomized cardiovascular outcome trials
Bibliographic record
Abstract
Abstract Background Effects of antihyperglycemic therapies on cardiovascular and heart failure (HF) risks have varied widely across cardiovascular outcome trials (CVOTs), and underlying factors remain incompletely understood. We aimed to determine the relationships of glycated hemoglobin (HbA1c) or bodyweight changes with these outcomes in all CVOTs of antihyperglycemic therapies. Methods We searched PubMed and EMBASE up to 25 January 2023 for all randomized controlled CVOTs of antihyperglycemic therapies reporting both major adverse cardiovascular events (MACE) and HF outcomes in patients with type 2 diabetes or prediabetes. We performed meta-regression analyses following random-effects meta-analyses to evaluate the effects of HbA1c or bodyweight reductions on each outcome. Results Thirty-five trials comprising 256,524 patients were included. Overall, antihyperglycemic therapies reduced MACE by 9% [risk ratio (RR): 0.91; 95% confidence interval (CI) 0.88–0.94; P < 0.001; I 2 = 36.5%]. In meta-regression, every 1% greater reduction in HbA1c was associated with a 14% reduction in the RR of MACE (95% CI 4–24; P = 0.010), whereas bodyweight change was not associated with the RR of MACE. The magnitude of the reduction in MACE risk associated with HbA1c reduction was greater in trials with a higher baseline prevalence of atherosclerotic cardiovascular disease. On the other hand, antihyperglycemic therapies showed no overall significant effect on HF (RR: 0.95; 95% CI 0.87–1.04; P = 0.28; I 2 = 75.9%). In a subgroup analysis based on intervention type, sodium-glucose cotransporter-2 inhibitors (SGLT2i) conferred the greatest HF risk reduction (RR: 0.68; 95% CI 0.62–0.75; P < 0.001; I 2 = 0.0%). In meta-regression, every 1 kg bodyweight reduction, but not HbA1c reduction, was found to reduce the RR of HF by 7% (95% CI 4–10; P < 0.001); however, significant residual heterogeneity ( P < 0.001) was observed, and SGLT2i reduced HF more than could be explained by HbA1c or bodyweight reductions. Conclusions Antihyperglycemic therapies reduce MACE in an HbA1c-dependent manner. These findings indicate that HbA1c can be a useful marker of MACE risk reduction across a wide range of antihyperglycemic therapies, including drugs with pleiotropic effects. In contrast, HF is reduced not in an HbA1c-dependent but in a bodyweight-dependent manner. Notably, SGLT2i have shown class-specific benefits for HF beyond HbA1c or bodyweight reductions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.118 | 0.188 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".