Understanding the development of eating disorders by using longitudinal data and machine learning methods

Summary

​​Eating disorders (EDs) are severe mental illnesses affecting up to 15% of young women and 3% of young men in high income countries. The causes of EDs are complex and involve many biological, psychological, and social factors. We are interested in understanding how various factors are associated with both current EDs and future disease risk.

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​We will investigate causes and mechanisms of eating disorders. This will help us understand the basis of diagnostic classifications, which will promote early intervention and the identification of new areas to target in treatments.

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​We will analyse the data already collected in the STRATIFY study (https://stratify-project.org/), including patients with Anorexia Nervosa (N=60), Bulimia Nervosa (N=49), Binge eating disorder (N=27) and healthy controls (N=69). We will also recruit 30 new participants with binge eating disorder using the original STRATIFY protocol to enlarge the binge eating disorder group, so that its sample size is comparable to the other groups.

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​Participants will fill online questionnaires, take an online clinical interview, and undergo a research visit, including one brain scan, blood and urine samples, and a range of social and behavioural measures.

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​We will use neuroimaging, cognitive, psychological and environmental data to uncover similarities as well differences across ED diagnoses. We will use advanced statistical methods such as machine learning based models. We aim to find the behavioural and neural processes that can distinguish people with current EDs from healthy individuals. Furthermore, we will use data from the IMAGEN study (https://imagen-project.org/) - a longitudinal population-based genetic and neuroimaging study - to test if the identified markers can predict future disease risk.​