Effect of glucagon-like peptide-1 receptor agonists on vascular risk factors among adults with type 2 diabetes and established atherosclerotic cardiovascular disease
Bibliographic record
Abstract
Limited data exist on the cardiovascular effectiveness of once-weekly (OW) glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in real-world practice. We assessed the OW GLP-1 RA effects on vascular risk factors in adults with type 2 diabetes and atherosclerotic cardiovascular disease using data from a large-scale US electronic health record database (index date = first prescription of OW GLP-1 RA). Exploratory analyses were performed on patients newly initiating OW GLP-1 RAs (including and excluding semaglutide), and semaglutide. Changes in vascular risk factors were evaluated by comparing mean measures between the 12-month pre- and post-index periods. Analyses were conducted for all three cohorts and subpopulations including stratified by tercile of baseline vascular risk factor value. In the final cohorts (1. OW GLP-1 RA including semaglutide: n = 20,084; 2. OW GLP-1 RA excluding semaglutide: n = 16,894; 3. semaglutide: n = 3,435), significant mean reductions ( P < 0.001) were observed from baseline to post-index in hemoglobin A1c (%, 1] -1.1; 2] -1.1; 3] -1.2), low-density lipoprotein cholesterol (mg/dL, 1] -6.4; 2] -6.4; 3] -6.9), total cholesterol (mg/dL, 1] -11.0; 2] -11.1; 3] -10.7), triglycerides (mg/dL, 1] -31.8; 2] -31.4; 3] -33.1), systolic blood pressure (mmHg, 1] -1.5; 2] -1.2; 3] -3.1), body weight (kg, 1] -2.7; 2] -2.4; 3] -4.3) and body mass index (kg/m 2 ; 1] -0.9; 2] -0.8; 3] -1.4). Largest reductions were observed in the top tercile. Our data suggest GLP-1 RAs are associated with significant reductions in key vascular risk factors in real-world practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".