Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. The aim of this project is the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD.

In this phase of the project a naturalistic cohort of 300 MDD patients will be recruited. The data collected will be used to assess, in real-world conditions, the capability of an innovative algorithm (integrating clinical, omics and gender data of other 300 patients con MDD) to predict the treatment outcomes. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

Inclusion Criteria:

• A current diagnosis of moderate to severe MDD according to the DSM-IV was confirmed using the SCID-I diagnostic scale

Exclusion Criteria:

• Mental retardation or cognitive disorder
• A lifetime history of schizophrenic, schizoaffective, or bipolar disorder Substance abuse in the last 3 months
• Personality disorders, substance abuse, alcohol abuse, obsessive compulsive disorder, post-traumatic stress disorder, as primary diagnosis
• Comorbidity with eating disorders
• Substance or alcohol dependence

MDD in-patients or out-patients referred to psychiatric services are voluntarily enrolling in the study