Improving Health Care in Major Depressive Disorder

This project aims to improve the health care provided to people with major depressive disorder (MDD), a disease which is a top cause of disability worldwide. One of the main obstacles to a more effective health care in these patients is represented by clinical heterogeneity, which has not completely elucidated biological correlates. Using a large sample of people with MDD already recruited (n=29,400), the investigators develop a clustering algorithm based on genetic-environmental and brain imaging predictors aimed at identifying homogeneous MDD subgroups. The researchers will then link these subgroups with relevant health outcomes, such as disease recurrency and severity, well-being and functioning, risk of psychiatric and medical comorbidities (e.g. cardiovascular disorders). Replication in independent samples already recruited(n=1380) will prove the validity of the subgroups and expand their clinical characterization. The investigators will develop a classification tool to link the individual's characteristics to the relevant health outcomes and provide corresponding clinical recommendations. The prognostic support tool will be applied to newly recruited samples, feasibility and usefulness according to clinicians's opinion will be assessed (n=120, ongoing recruitment).