Effectiveness of SGLT2 inhibitors, incretin-based therapies, and finerenone on cardiorenal outcomes: a meta-analysis and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Several societies recommend sodium-glucose co-transporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1RA), and finerenone for cardiorenal risk reduction in select populations. This updated meta-analysis assessed the impact of SGLT2i, incretin-based therapies (i.e. GLP-1RAs and tirzepatide), and finerenone on cardiorenal outcomes in established and emerging populations. Additionally, a network meta-analysis (NMA) compared the relative efficacy between treatment classes. METHODS: A systematic search of MEDLINE and CENTRAL from January 2023 to April 2025, supplemented by studies evaluated in our prior meta-analyses, identified 33 randomized controlled trials. Random-effects models were used to generate hazard ratios for outcomes including cardiovascular (CV) mortality, all-cause mortality, heart failure (HF) hospitalization/event, non-fatal myocardial infarction (MI), non-fatal stroke, and kidney composite outcomes in various subpopulations including patients with type 2 diabetes (T2D) with atherosclerotic cardiovascular disease (ASCVD) or high CV risk, chronic kidney disease (CKD), HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), HFpEF with obesity, post-MI, acute HF, and ASCVD with overweight/obesity without T2D. NMA was performed when two or more treatment classes were reported for a given outcome within a subpopulation. RESULTS: Incretin-based therapies decreased CV mortality, all-cause mortality, non-fatal MI, and kidney composite outcomes in CKD, and reduced these outcomes as well as HF hospitalization/events and non-fatal stroke in T2D with ASCVD/high CV risk. In HFpEF with obesity, incretin-based therapies reduced HF hospitalization/events. SGLT2i reduced CV mortality, all-cause mortality, HF hospitalization/events, and kidney composite outcomes in HFrEF, and reduced these outcomes as well as non-fatal MI in CKD and T2D with ASCVD/high CV risk. SGLT2i decreased HF hospitalization/events in HFpEF, and lowered HF hospitalizations in post-MI and acute HF populations. Finerenone reduced HF hospitalizations and kidney composite outcomes in diabetic CKD and reduced HF hospitalizations in HFpEF. Using placebo as the common comparator in the NMA, SGLT2i conferred significantly greater reductions in HF hospitalization/events and kidney composite outcomes compared to incretin-based therapies in T2D with ASCVD/high CV risk and CKD. CONCLUSIONS: These findings confirm the role of SGLT2i, incretin-based therapies, and finerenone in cardiorenal risk reduction across established high-risk groups, including T2D and CKD, and extend benefits to newer populations, including acute HF and post-MI for SGLT2i, and HFpEF with obesity for incretin-based therapies. Indirect NMA evidence further suggests SGLT2i may reduce HF hospitalization/events and kidney composite outcomes more than incretin-based therapies in T2D with ASCVD/high CV risk and CKD populations.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.067 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".