Optimising cohort data in Europe

1. Background SYNCHROS (SYNergies for Cohorts in Health: integrating the Role of all Stakeholders) is an EU-funded Coordination and Support Action (H2020, ref. no. 825884; period January 2019- June 2022) aimed at developing a sustainable strategy for the integration and harmonisation of health cohort initiatives and networks across Europe and internationally. Through intensive collaboration with stakeholders (e.g. researchers, funding bodies, coordinators of cohort harmonisation and integration initiatives, policy-makers), SYNCHROS created a strategic agenda for an enhanced international coordination and sustainable recommendations for better collaboration of cohorts globally, towards the development of stratified or personalised medicine. An EU priority is the optimisation of health-related data to support personalised medicine (1) and efficient knowledge transfer from research to clinical practice. Precision or personalised medicine demands that, ideally, data on a multitude of individual behavioural and lifestyle determinants are combined with genetic and environmental information in order to refine predictions and tailor interventions to individual patients. This requires large amounts of evidentiary resources and sophisticated analytical methods, some of which have only recently become available. Population and patient cohort studies, including those from clinical trials, are particularly valuable sources of data because they can detect and quantify changes in health-related parameters. Identifying patterns in the specific disease outcomes and their determinants across these diverse population groups offers a robust evidence-based background for improving medical science and health care practices. Data sharing and optimisation initiatives have taken place at various levels in Europe. Several patient and population consortiums have joined their efforts to create an unparalleled information resource, which can reveal insights into disease processes and provide pointers for interventions (e.g. LifeCycle, CLOSER, ATHLOS, NETCAL and WW Finger). This integration of data from studies involving similar cohort populations is valuable as it increases the longitudinal power and range of health-related parameters from heterogeneous data sources. More complex is integrating data “horizontally“ across diverse cohort studies, either across different population cohorts, across patient cohorts for different diseases or across general population and patient cohorts. However, exploring commonalities across what are seemingly very different populations would offer a unique opportunity to fully exploit the information contained in these large datasets. In the case of combining data from relatively homogenous patient populations (e.g. participants in clinical trial data) or from heterogeneous populations (e.g. population and patient cohorts), in both cases, there are many obstacles related to the integration

(1) http://data.consilium.europa.eu/doc/document/ST-15054-2015-INIT/en/pdf