The effect of glucagon‐like peptide‐1 receptor agonists liraglutide and semaglutide on cardiovascular and renal outcomes across baseline blood pressure categories: Analysis of the <scp>LEADER</scp> and <scp>SUSTAIN</scp> 6 trials
Bibliographic record
Abstract
It is unknown if the cardioprotective and renal effects of glucagon-like peptide-1 receptor agonists are consistent across blood pressure (BP) categories in patients with type 2 diabetes and at high risk of cardiovascular events. Using data from the LEADER (9340 patients) and SUSTAIN 6 (3297 patients) trials, we evaluated post hoc the cardiorenal effect of liraglutide and semaglutide on major adverse cardiovascular events (MACE) and nephropathy by baseline BP categories using a Cox proportional hazards model (treatment and subgroup as factors; adjusted for cardiorenal risk factors). Data from the two trials were analysed separately. In the LEADER and SUSTAIN 6 trials, the prevalence of stage 1 hypertension was 30% and 31%, respectively, and of stage 2 hypertension 41% and 43%, respectively. There was no statistical heterogeneity across the BP categories for the effects of liraglutide (P = .06 for MACE; P = .14 for nephropathy) or semaglutide (P = .40 for MACE; P = .27 for nephropathy) versus placebo. This implies that liraglutide and semaglutide may be beneficial for patients with type 2 diabetes, irrespective of their baseline BP.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".