Stabilization of kidney function and reduction in heart failure events with sodium‐glucose co‐transporter 2 inhibitors: A meta‐analysis and meta‐regression analysis
Bibliographic record
Abstract
Abstract Aims Sodium‐glucose co‐transporter 2 (SGLT2) inhibitors reduce the risk of heart failure (HF) events regardless of diabetes status. However, factors associated with their efficacy in HF reduction remain unknown. This study aims to identify clinically relevant markers for the efficacy of SGLT2 inhibitors in HF risk reduction. Materials and methods We searched PubMed/MEDLINE and EMBASE for randomized placebo‐controlled trials of SGLT2 inhibitors reporting a composite of HF hospitalization or cardiovascular death in participants with or without type 2 diabetes published until 28 February 2023. Random‐effects meta‐analysis and mixed‐effects meta‐regression were conducted to evaluate the association between the outcomes and clinical variables, including changes in glycated haemoglobin, body weight, systolic blood pressure, haematocrit and overall/chronic estimated glomerular filtration rate (eGFR) slope. Results Thirteen trials with 90 413 participants were included. SGLT2 inhibitors reduced the hazard ratio of the composite of HF hospitalization or cardiovascular death (hazard ratio 0.77; 95% confidence interval, 0.74‐0.81; p < .0001). In meta‐regression analysis, chronic eGFR slope (eGFR change after the initial dip) was significantly associated with the composite outcome ( p = .017), and each 1 ml/min/1.73 m 2 /year improvement in chronic eGFR slope led to a 14% reduction in the composite outcome. By contrast, changes in the other parameters showed no significant associations. Conclusions Improvement in chronic eGFR slope, which reflects the stabilization of kidney function, is significantly associated with the efficacy of the SGLT2 inhibitor in HF, highlighting the cardiorenal axis role in the beneficial effects on HF. The chronic eGFR slope can be a surrogate marker of the effects of SGLT2 inhibitors on HF reduction.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.068 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".