Aalborg University is a Danish university located in Aalborg, Esbjerg and Copenhagen. With approximately 20,000 students, Aalborg University is Denmark’s fifth largest university and the leading engineering school in Europe.
PREDICT is a National Research Center of Excellence located at the Department of Clinical Medicine at Aalborg University in Copenhagen. The centre is funded by the Danish National Research Foundation. With the aim of unravelling the cause and prognosis of inflammatory bowel diseases (IBD), the PREDICT center employes researchers, including clinicians, epidemiologists, immunologists, geneticists, statisticians and data scientists working on unique patient samples from the Danish National Biobank combined with longitudinal nationwide registry data.
PREDICT examines genetics, epigenetics, antibodies, inflammatory markers, metabolomics and microbiomes on thousands of patients with IBD and has built a unique infrastructure at the Danish National Genome Center to combine molecular data with long-term clinical data from cohorts and registries. The ambition is to obtain exclusive novel basic science information on the biological mechanisms underlying the development of IBD and the heterogeneous course of IBD, including the differential need for and response to treatment.
Role within INTERCEPT
The role of PREDICT, Aalborg University within the INTERCEPT project is to contribute to a cross-European cohort with pre-diagnostic serum samples for analyses of biomarkers and later linkage to meta-data on clinical features. This will be aligned and harmonized as closely as possible to the PROMISE data structure and sample metadata. Individuals, with at least one serum sample obtained before the diagnosis of Crohn’s disease, will be matched to controls, free from IBD during follow-up and matched by age, sex and calendar period of sample collection.
We will support characterising time-varying trajectories of individual biomarkers and examine their relationship with the risk of developing CD in the cross-European cohort. Using feature selection methods, biomarkers strongly associated with development of CD will be prioritised and evaluated for their predictive ability, individually and in combination, and across different timepoints. This data will be incorporated into the predictive modeling in an iterative process.
Main contacts
Prof. Tine Jess
Center Director, PI
Associate Prof. Aleksejs Sazonovs
Group Leader, Deputy PI
Caesar Szwebs
Project officer