Introduction: Several reports highlight bariatric surgery as an efficient and long-lasting strategy for weight loss. Herein, we aimed to evaluate the impact of bariatric surgery on 10-year cardiovascular disease (CVD) risk and to compare the effectiveness of different surgical procedures, employing the Framingham Risk Score (FRS).
Methods: Retrospective longitudinal observational study of patients undergoing bariatric surgery. Data was assessed preoperatively and during a 4-year follow-up period.
Results: We evaluated 1449 individuals, 85.2% female, age of 42.4 ± 10.6 years, and preoperative BMI of 44.3 ± 5.8 kg/m; 58.0% underwent Roux-en-Y gastric bypass (RYGB), 23.4% sleeve gastrectomy (SG), and 18.6% adjustable gastric band (AGB). The 10-year CVD risk decreased 43.6% in the first postoperative year. The decrease in FRS was more pronounced in the RYGB group (50.5% in the first postoperative year) (p < 0.001). Although there was a subsequent slight increase in FRS during the follow-up period, the cardiovascular benefits were maintained when compared with baseline. For all surgical procedures, CVD risk showed a quadratic trend with a J-shaped curve. A negative interaction between the RYGB group CVD risk and time was observed (β = - 0.072 (95% CI, - 0.109; - 0.035)). In the RYGB group, FRS decreased more when compared with the SG and AGB groups and, from the second postoperative year onwards, increased more slowly, regardless of gender. The SG group showed similar trend as that of the AGB (β = - 0.002 (95% CI, - 0.049; 0.053)).
Conclusion: Our study showed a significant reduction of 10-year CVD risk after bariatric surgery. This decrease was more pronounced in the first postoperative year, and RYGB was the procedure with the greatest decrease of the 10-year CVD risk.
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http://dx.doi.org/10.1007/s11695-019-04237-0 | DOI Listing |
BMC Res Notes
January 2025
UQ Centre for Clinical Research, Faculty of Health Medicine and Behavioural Sciences, The University of Queensland, Brisbane, Australia.
Objectives: This data note presents a comprehensive geodatabase of cardiovascular disease (CVD) hospitalizations in Mashhad, Iran, alongside key environmental factors such as air pollutants, built environment indicators, green spaces, and urban density. Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions.
Data Description: This dataset includes detailed epidemiologic and geospatial information on CVD hospitalizations in Mashhad, Iran, from January 1, 2016, to December 31, 2020.
Cardiovasc Diabetol
January 2025
Medical Big Data Center, Department of General Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, Suzhou, 215001, Jiangsu, China.
Background: Triglyceride-glucose (TyG) related indices, which serve as simple markers for insulin resistance, have been closely linked to metabolic dysfunction-associated steatotic liver disease (MASLD), cardiovascular disease (CVD), and mortality. However, the prognostic utility of TyG-related indices in predicting the risk of CVD and mortality among patients with MASLD remains unclear.
Methods: Data of 97,331 MASLD patients, with a median age of 58.
NPJ Digit Med
January 2025
Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany.
Aging affects the 12-lead electrocardiogram (ECG) and correlates with cardiovascular disease (CVD). AI-ECG models estimate aging effects as a novel biomarker but have only been evaluated on single ECGs-without utilizing longitudinal data. We validated an AI-ECG model, originally trained on Brazilian data, using a German cohort with over 20 years of follow-up, demonstrating similar performance (r = 0.
View Article and Find Full Text PDFCMAJ
January 2025
Schools of Health and Wellbeing (Nakada, Pell, Ho), and Cardiovascular and Metabolic Health (Welsh, Celis-Morales), University of Glasgow, Glasgow, UK; Human Performance Laboratory, Education, Physical Activity and Health Research Unit (Celis-Morales), Universidad Católica del Maule, Talca, Chile; Centro de Investigación en Medicina de Altura (CEIMA) (Celis-Morales), Universidad Arturo Prat, Iquique, Chile.
Background: Anxiety and depression are associated with cardiovascular disease (CVD). We aimed to investigate whether adding measures of anxiety and depression to the American Heart Association Predicting Risk of Cardiovascular Disease Events (PREVENT) predictors improves the prediction of CVD risk.
Methods: We developed and internally validated risk prediction models using 60% and 40% of the cohort data from the UK Biobank, respectively.
Prev Med
January 2025
Department of Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA; Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, USA; School of Nursing, University of California Los Angeles, Los Angeles, CA, USA. Electronic address:
Aims: Cardiovascular disease (CVD) is the leading cause of death in the United States (U.S.).
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