Effects of glucagon‐like peptide‐1 receptor agonists liraglutide and semaglutide on cardiovascular and renal outcomes across body mass index categories in type 2 diabetes: Results of the <scp>LEADER</scp> and <scp>SUSTAIN</scp> 6 trials
Bibliographic record
Abstract
Abstract Associations between body mass index (BMI) and the cardiovascular (CV) and kidney efficacy of glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) in patients with type 2 diabetes (T2D) are uncertain; therefore, data analysed separately from the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial and the Trial to Evaluate Cardiovascular and Other Long‐term Outcomes with Semaglutide in Subjects with Type 2 Diabetes (SUSTAIN 6) were examined. These international, randomized, placebo‐controlled trials investigated liraglutide and semaglutide (both subcutaneous) in patients with T2D and at high risk of CV events. In post hoc analyses, patients were categorized by baseline BMI (<25, ≥25‐<30, ≥30‐<35 and ≥35 kg/m 2 ), and CV and kidney outcomes with GLP‐1 RA versus placebo were analysed. All baseline BMI data from LEADER (n = 9331) and SUSTAIN 6 (n = 3290) were included (91% and 92% of patients with overweight or obesity, respectively). In SUSTAIN 6, nominally significant heterogeneity of semaglutide efficacy by baseline BMI was observed for CV death/myocardial infarction/stroke (major adverse CV events, primary outcome of both; P interaction = .02); otherwise, there was no statistical heterogeneity for either GLP‐1 RA versus placebo across BMI categories for key CV and kidney outcomes. The lack of statistical heterogeneity from these cardiorenal outcomes implies that liraglutide and semaglutide may be beneficial for many patients and is probable not to depend on their baseline BMI, but further study is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".