An open and sustainable EU Child
Cohort Network

We will set up a European pregnancy and childhood cohort network, the EU CHILD Cohort Network, which brings together extensive existing data from more than 250,000 European children and their parents. These cohorts enable studies across multiple generations, because of their detailed phenotyping of both parents and children. This EU CHILD Cohort Network enables optimal exploitation of available research and biobank data in research on health across the lifecycle in different European subpopulations. We will develop a governance structure, safe data sharing platform and data harmonization strategies. Collaboration between European pregnancy and child cohorts will be stimulated by meetings open to cohort researchers in and outside the LifeCycle Project, a Fellowship Programme for exchange of junior researchers between cohorts, and e-learning modules for life course health studies. Strategies shall be developed to ensure the sustainability of the EU Child Cohort Network after LifeCycle. For detailed information see The EU CHILD Cohort Network

We will enrich European pregnancy and child cohort studies participating in the EU Child Cohort Network with novel integrated data on early-life stressors related to socioeconomic, migration, urban environment and lifestyle determinants, based on data available within the cohorts and new external data from registries. Integrated data will also be used to construct a novel holistic ‘dynamic early-life exposome’ model, which will encompass many human environmental exposures during various stages of early life, complementing the genome.

We will use the integrated and harmonized data from the EU Child Cohort Network for identification of early-life stressors influencing cardio-metabolic, respiratory and mental developmental adaptations and health trajectories during the full LifeCycle. Developmental adaptations and NCD risk factors have been assessed by advanced clinical and imaging techniques. We will use repeatedly measured data obtained at many time points to compare different life course models including those assuming specific critical periods and those assuming interactive and cumulative effects.

We will evaluate analytical approaches and where needed develop new approaches for causal inference and longitudinal trajectories modelling in observational studies. These two topics are among the most important challenges for observational life course trajectory studies. The large numbers of participants in the EU Child Cohort Network allow comparison and integration of different casual inference methods and implementation of methods for longitudinal modelling to examine the relationships between changes in stressors, mediators and outcomes. We will develop tutorials, organize methodological workshops and develop e-learning modules that will be widely disseminated to the research community.

We will identify DNA methylation loci that mediate the relations of early-life stressors with health trajectories during the full life cycle. We will use available epigenome-wide data on DNA methylation in different age windows for state-of-the-art epigenome-wide association studies (EWAS) meta-analyses to identify epigenetic markers, and subsequently assess their persistency and change throughout the life course and their functionality in relation to RNA expression.

We will review findings from research and develop recommendations for public health strategies on individual and population level on modification of early-life stressors (socioeconomic, migration, urban environment and lifestyle determinants), related to life cycle health trajectories. Since experimental studies on these exposures are difficult to perform, we will use evidence from observational studies on causal inference, population-based attributable risks and populations at risk. We will develop recommendations for multiple target groups.

We will use pregnancy and childhood data from the EU CHILD Cohort Network to develop models to predict the onset and evolution of risk factors for cardio-metabolic, respiratory and mental outcomes throughout the life course. As compared to global prediction models, we will take account of baseline risk estimated on early-life stressors. These models will be translated into mobile eHealth applications to provide customized advice for pregnant women and young children.