SGLT2 inhibitors for the prevention of kidney failure in patients with type 2 diabetes: a systematic review and meta-analysis
Bibliographic record
Abstract
Background The effects of sodium-glucose co-transporter-2 (SGLT2) inhibitors on kidney failure, particularly the need for dialysis or transplantation or death due to kidney disease, is uncertain. Additionally, previous studies have been underpowered to robustly assess heterogeneity of effects on kidney outcomes by different levels of estimated glomerular filtration rate (eGFR) and albuminuria. We aimed to do a systematic review and meta-analysis to assess the effects of SGLT2 inhibitors on major kidney outcomes in patients with type 2 diabetes and to determine the consistency of effect size across trials and different levels of eGFR and albuminuria. Methods We did a systematic review and meta-analysis of randomised, controlled, cardiovascular or kidney outcome trials of SGLT2 inhibitors that reported effects on major kidney outcomes in people with type 2 diabetes. We searched MEDLINE and Embase from database inception to June 14, 2019, to identify eligible trials. The primary outcome was a composite of dialysis, transplantation, or death due to kidney disease. We used random-effects models to obtain summary relative risks (RRs) with 95% CIs and random-effects meta-regression to explore effect modification by subgroups of baseline eGFR, albuminuria, and use of renin–angiotensin system (RAS) blockade. This review is registered with PROSPERO (CRD42019131774). Findings From 2085 records identified, four studies met our inclusion criteria, assessing three SGLT2 inhibitors: empagliflozin (EMPA-REG OUTCOME), canagliflozin (CANVAS Program and CREDENCE), and dapagliflozin (DECLARE–TIMI 58). From a total of 38 723 participants, 252 required dialysis or transplantation or died of kidney disease, 335 developed end-stage kidney disease, and 943 had acute kidney injury. SGLT2 inhibitors substantially reduced the risk of dialysis, transplantation, or death due to kidney disease (RR 0·67, 95% CI 0·52–0·86, p=0·0019), an effect consistent across studies ( I 2 =0%, p heterogeneity =0·53). SGLT2 inhibitors also reduced end-stage kidney disease (0·65, 0·53–0·81, p<0·0001), and acute kidney injury (0·75, 0·66–0·85, p<0·0001), with consistent benefits across studies. Although we identified some evidence that the proportional effect of SGLT2 inhibitors might attenuate with declining kidney function (p trend =0·073), there was clear, separate evidence of benefit for all eGFR subgroups, including for participants with a baseline eGFR 30–45 mL/min per 1·73 m 2 (RR 0·70, 95% CI 0·54–0·91, p=0·0080). Renoprotection was also consistent across studies irrespective of baseline albuminuria (p trend =0·66) and use of RAS blockade (p heterogeneity =0·31). Interpretation SGLT2 inhibitors reduced the risk of dialysis, transplantation, or death due to kidney disease in individuals with type 2 diabetes and provided protection against acute kidney injury. These data provide substantive evidence supporting the use of SGLT2 inhibitors to prevent major kidney outcomes in people with type 2 diabetes. Funding None.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".