Comparative efficacy of <scp>sodium‐glucose</scp> co‐transporter‐2 inhibitors, <scp>glucagon‐like</scp> peptide‐1 receptor agonists and non‐steroidal mineralocorticoid receptor antagonists in chronic kidney disease and type 2 diabetes: A systematic review and network <scp>meta‐analysis</scp>
Bibliographic record
Abstract
AIM: To compare the relative efficacy of sodium-glucose co-transporter 2 inhibitors (SGLT-2is), glucagon-like peptide-1 receptor agonists (GLP-1RAs) and non-steroidal mineralocorticoid receptor antagonists (nsMRAs) in improving the cardiovascular and renal outcomes in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD). MATERIALS AND METHODS: We searched PubMed, Embase and Cochrane Library from inception through 25 November 2022. We selected randomized controlled trials that studied patients with CKD and T2D with a follow-up of at least 24 weeks and compared SGLT-2is, GLP-1RAs and nsMRAs with each other and with placebo. Primary outcomes were major adverse cardiovascular events (MACE) and composite renal outcomes (CRO). Secondary outcomes were cardiovascular death, all-cause death, stroke, myocardial infarction and heart failure hospitalization (HFH). A frequentist approach was used to pool risk ratios (RRs) with 95% confidence intervals (CIs). RESULTS: Twenty-nine studies with 50 938 participants for MACE and 49 965 participants for CRO were included. SGLT-2is did not significantly reduce MACE but were associated with significantly lower risks of CRO compared with GLP-1RAs (RR, 0.77; 95% CI, 0.64-0.91; P = .003) and nsMRAs (RR, 0.78; 95% CI, 0.68-0.90; P = .001). Compared with GLP-1RAs and nsMRAs, SGLT-2is significantly reduced risks of HFH by 31% (RR, 0.69; 95% CI, 0.55-0.88; P = .002) and 22% (RR, 0.78; 95% CI, 0.63-0.95; P = .016), respectively, but did not significantly reduce other secondary outcomes. There were no significant differences between GLP-1RAs and nsMRAs in lowering all outcomes. CONCLUSIONS: SGLT-2is were associated with better cardiorenal protection than GLP-1RAs and nsMRAs in patients with CKD and T2D.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.020 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".