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Background: The effects of semaglutide on non-overweight patients with type 2 diabetes (T2D) remain unclear. We retrospectively compared all-cause mortality, cardiovascular outcomes, and adverse events in patients with T2D with a body mass index (BMI) <25 kg/m² who received semaglutide or dipeptidyl peptidase 4 (DPP-4) inhibitors.
Methods: Based on the TriNetX database of electronic medical records between 2018 and 2020, we identified 340,721 patients with T2D with a BMI <25 kg/m². Of the 6,789 patients who received semaglutide, 2,454 who received DPP-4 inhibitors after diagnosis were excluded. Of the 41,141 patients who received DPP-4 inhibitors, 5,252 patients who received GLP-1 receptor agonists after diagnosis were excluded. After propensity score matching, 4,194 patients were included in each group. The primary outcome was the 3-year cumulative incidence of all-cause mortality; the secondary outcomes were acute myocardial infarction (AMI) and stroke. The adverse events included nausea, vomiting, diarrhoea, and hypoglycaemia.
Results: The semaglutide group had a significantly lower risk of all-cause mortality compared to the DPP-4 inhibitor group (6.1% vs. 10.7%, p<0.001; hazard ratio [HR] 0.54, 95% confidence interval [CI] 0.45-0.65). Semaglutide was not associated with the incidence of AMI (6.1% vs. 7.1%, p=0.173; HR 0.87, 95%CI 0.72-1.06) or stroke (8.4% vs. 7.7%, p=0.220; HR 1.11, 95%CI 0.94-1.32). Adverse events, including nausea and vomiting, diarrhoea, and hypoglycaemia, were not significantly different between the groups.
Conclusions: In patients with T2D and BMI <25 kg/m2, semaglutide was associated with a lower 3-year risk of all-cause mortality than DPP-4 inhibitors.
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http://dx.doi.org/10.1093/ehjqcco/qcaf065 | DOI Listing |
Ann Intern Med
September 2025
Department of Medicine, Johns Hopkins University School of Medicine, and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.B.S.).
Electronic health record (EHR) data are increasingly used to develop prediction models that guide clinical decision making at the point of care. These include algorithms that use high-frequency data, like in sepsis prediction, as well as simpler equations, such as the Pooled Cohort Equations for cardiovascular outcome prediction. Although EHR data used in prediction models are often highly granular and more current than other data, there is systematic and nonsystematic missingness in EHR data as there is with most data.
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Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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View Article and Find Full Text PDFAnn Am Thorac Soc
September 2025
University of Gothenburg Sahlgrenska Academy, Department of Internal Medicine and Clinical Nutrition, Gothenburg, Sweden.
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Materials And Methods: We investigated the relationship between COMISA and uncontrolled hypertension in the Swedish CardioPulmonary BioImage Study (SCAPIS). A cross-sectional analysis including participants from the SCAPIS Gothenburg cohort (n=3832, 46% males, age 57.
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View Article and Find Full Text PDFJ Vis Exp
August 2025
Department of Surgery, Division of Cardiothoracic Surgery, Warren Alpert Medical School, Brown University; Cardiovascular Research Center, Rhode Island Hospital.
Reproducibility and research integrity are foundational tenets to scientific discovery, which are produced utilizing well-established, proven principles and protocols. Furthermore, with the ever-increasing prevalence and burden cardiovascular disease (CVD) places on individuals and society at large, it deems essential to cultivate robust and validated model for investigation. Our group utilizes a two-surgery protocol in a swine model that has been progressively refined over the last twenty years, in which we first induce chronic myocardial ischemia by placement of an ameroid constrictor mimicking the pathophysiology of coronary artery disease (CAD) in humans.
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