In view of the total global burden of non-communicable diseases (NCDs), there is an unmet need for novel strategies for prevention and treatment. In particular, for many diseases affecting the central nervous system (CNS), treatment and prevention options are limited. Based on our innovative translational methodology, TrEpi aims at discovering significant causal mechanisms.
Brain tumours, neurodegenerative and neurodevelopmental disorders are major NCDs in the central nervous system (CNS). Neurodevelopmental disorders affect individuals from early childhood, and treatment has been unchanged for decades. For many neurodegenerative diseases, no strong preventive measures or disease-modifying treatments are available. Despite standard treatment, median survival of glioblastoma is poor (15 months) because of the rapidly progressive nature of the disease.
The Centre aims at discovering significant causal mechanisms behind NCDs in the CNS from big registry, cohort and biomedical data that lead to new biological insight, and that can be transformed into novel preventive measures both at population and individual level to improve public health (precision public health) and to therapeutic measures, such as drug design and precision medicine.
Results based on observational studies/real-world data (RWD) need to be validated by experiments and, similarly, results from experiments need support by RWD. Also, expanding data sources highlight the need for high-dimensional statistics and machine learning (ML) methods for multi-source data integration.
Our translational framework requires closely linked interdisciplinary teams, and dynamic iterative processes with feedback loops, where the outcomes and lessons learned from one scientific discipline will influence the direction of others. Results from analyses and experiments in individual disciplines may inform further hypotheses and investigations when available to the interdisciplinary working groups. The framework facilitates the use of multiple approaches with different data, designs, methods, and sources of bias. Such triangulation is ideal when assessing causal hypotheses.
Our framework builds on two main translational pipelines that will be developed to verify causal inferences from registry data in experimental model systems, and vice versa.