PhD (2017): Molecular Epidemiology, Department of Medical Sciences, Uppsala University
MD (2013): BM BCh, University of Oxford, UK
BSc-MSc in Psychology (2008): Dipl-Psych, University of Braunschweig, Germany
We use large longitudinal population cohorts and state-of-the-art biochemical platforms that measure large numbers of proteins (proteomics) or metabolites (metabolomics) in order to discover biomarkers for chronic non-communicable diseases, such as type 2 diabetes, heart attack and kidney disease. In combination with genetic data, we apply traditional epidemiologic as well as recently developed methods, including Mendelian randomization analysis and machine learning.
We aim to:
(i) improve our understanding of the (patho)physiologic mechanisms that lead to common cardiometabolic diseases;
(ii) discover new potential treatment targets for prevention and treatment;
(iii) develop better risk prediction models to identify persons most likely to benefit from enhanced screening, targeted prevention and specific treatments;
(iv) characterise different molecular subtypes of common diseases, such as chronic kidney disease, in order to enable tailored person-centered treatment.
key words: molecular epidemiology, proteomics, metabolomics, genomics, complex traits and diseases, Mendelian Randomization, risk prediction, machine learning, type 2 diabetes, cardiovascular disease, chronic kidney disease ...