Systematic review and meta-analysis: SGLT2 inhibitors, blood pressure and cardiovascular outcomes
Bibliographic record
Abstract
OBJECTIVE: Clinical trials suggest that SGLT2 inhibitors reduce the risk of cardiovascular mortality in patients with type 2 diabetes, however the mechanism is unclear. Our objective was to test the hypothesis that blood pressure reduction is one potential mechanism underlying the observed improvements in cardiovascular outcomes with SGLT2 inhibitors. METHODS: We searched MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials (inception-June 2019) for randomized controlled trials that reported the effect of SGLT2 inhibitors compared with placebo on cardiovascular outcomes in adults with type 2 diabetes. Two reviewers independently extracted data and assessed study quality. Random effects meta-analyses, stratified meta-analyses and meta-regressions were conducted to evaluate the association between blood pressure reduction in SGLT2 inhibitor treated patients and cardiovascular outcomes. RESULTS: = 0.0%). Meta-regression analysis revealed no detectable difference in cardiovascular mortality (RR 0.93; 95%CI 0.88, 1.13, p = 0.483), 3-point major adverse cardiovascular events (p = 0.839) or congestive heart failure hospitalizations (p = 0.844) with change in mean systolic blood pressure. CONCLUSIONS: Cardiovascular events are reduced in participants with type 2 diabetes treated with SGLT2 inhibitors compared with placebo. There was no significant relationship between the risk of developing adverse cardiovascular events and blood pressure reduction with SGLT2 inhibitors. There is insufficient evidence to suggest that blood pressure reduction is a significant contributor to the cardiovascular benefits observed.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".