Back

Cardiovascular Outcomes in Low-Risk Non-Diabetic Patients Treated with Semaglutide

Komatsoulis, G.; Shvets, A.; Flamholz, Z.; Khera, R.; Pindak, S.; Tann, N.; Wolinsky, J.; Hudzik, A.; Stanford, R.; Patel, M.; Phillips, H.

2025-05-21 cardiovascular medicine
10.1101/2025.05.20.25327768 medRxiv
Show abstract

Semaglutide use has been shown to reduce cardiovascular events in overweight/obese patients with diabetes and preexisting cardiovascular disease. However, data are limited on the benefit of semaglutide in populations without diabetes and with a low level of preexisting cardiovascular disease. Using claims data from Optums de-identified Market Clarity Data, we conducted an observational cohort study to evaluate mortality and cardiovascular outcomes associated with use of semaglutide in overweight/obese patients without diabetes or prior cardiovascular disease (myocardial infarction, stroke, or symptomatic peripheral arterial disease). A total of 38,234 patients who initiated semaglutide between April 2021 and March 2023 were propensity score matched 1:1 to control patients meeting study inclusion and exclusion criteria. Semaglutide patients were treated for a mean (SD) duration of 355 {+/-} 261 days. Semaglutide patients and controls were followed for a mean (SD) duration of 20.2 {+/-} 6.7 and 20.0 {+/-} 6.5 months, respectively. In a time to first event analysis, a composite cardiovascular endpoint (all-cause mortality, nonfatal myocardial infarction or nonfatal ischemic stroke) occurred in 448 (1.2%) semaglutide patients and 645 (1.7%) control patients (HR = 0.73; 95% CI: [0.65, 0.82]; P<0.001). Individual components of the composite endpoint were also significantly lower in the semaglutide group. These findings provide evidence of potential benefit of semaglutide for cardiovascular risk reduction in a population with minimal baseline cardiometabolic complications. Given the population health implications, clinical trials of semaglutide in low risk populations are indicated.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.