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1. Simulation of Glucagon-like peptide-1 receptor agonists (GLP1-RA) effects in the primary prevention setting: GLP-1RA have demonstrated significant cardiovascular benefits in individuals with established cardiovascular disease (CVD). However, their potential in the primary prevention of CVD remains largely unexplored. This study aimes to model the potential effect of GLP-1RA therapy on incident CVD events and mortality in a population without pre-existing CVD, using effect estimates derived from the SELECT trial.
2. Itemizing residual CVD risk by integrated analysis of exposomal, genetic and classical cardiovascular risk factors for CVD and mortality. Classical risk factors explain about half of the global 10-year CVD risk. However, substantial residual risk remains. Using the Global Cardiovascular Risk Consortium (GCVRC) dataset, this project aims to assess the relative contributions of exposomal, genetic, and classical risk factors to CVD and all-cause mortality.
3. Sex-specific cardiovascular risk prediction utilizing machine learning to identify exposomal, blood-based, and imaging biomarkers: Women and men differ in incidence, symptoms and prognosis of different CVD subtypes. This project aims to improve early, personalized CVD prediction by developing multimodal, data-driven models that incorporate emerging risk factors and account for sex-specific differences. Key goals include identifying novel exposomal, blood-based, and imaging predictors, quantifying their impact, and translating findings into pragmatic clinical risk prediction tools.
Elisa Großmann,
PhD student;
Aisouda Hoshiyar,
statistician;
Julia Munzinger,
epidemiologist, PhD student;
Thiess Lorenz,
head of data centre, epidemiologist, PhD student;