van Houtum LAEM, Baaré WFC, Beckmann CF, Castro-Fornieles J, Cecil CAM, Dittrich J, Ebdrup BH, Fegert JM, Havdahl A, Hillegers MHJ, Kalisch R, Kushner SA, Mansuy IM, Mežinska S, Moreno C, Muetzel RL, Neumann A, Nordentoft M, Pingault JB, Preisig M, Raballo A, Saunders J, Sprooten E, Sugranyes G, Tiemeier H, van Woerden GM, Vandeleur CL, van Haren NEM
Children of parents with mental illness are more likely to develop a mental illness themselves. This so-called intergenerational transmission of mental illness is not given adequate attention in clinical settings, diagnostics, or childcare. This results in delays in identifying mental health issues in young children, missing opportunities for prevention through protective measures and resilience building. This is where the FAMILY project steps in. The EU-funded FAMILY project is a collaboration between researchers from Europe and the US with the goal of understanding why, how, and when mental illnesses are passed from parents to children. The project focuses on changes in the brain, the epigenome, and genetic and environmental risks, comparing children of parents with and without mental illness, and using relevant animal models for research. The project also uses modern technologies like artificial intelligence and machine learning to build a prediction model to help understanding risk for and resilience against mental illness. This model is supposed to estimate the likelihood of a child developing a mental illness if their parents are affected. In addition, the FAMILY project looks at the social and ethical issues related to predicting risks. Overall, this work aims to prepare clinics and hospitals for the potential future use of predictive tools.
This consortium paper summarises the FAMILY project aims to achieve three main objectives:
- advance our understanding of why, how, and when severe mental illnesses are passed down in families and identify the best timing for preventive and intervention measures,
- create statistical models that help predict which children are more likely to develop mental illnesses at specific times given certain risk and resilience factors, and
- provide insights into the social and ethical implications of predicting mental health risks.