Patterns of Autonomic Nervous System Biomarkers in Depression (DEANS)

Depression affects over 7% of adult population in Europe and is associated with cardiovascular morbidity and mortality. However, main diagnostic methods are based on subjective measures, include a wide range of profiles and are time consuming. There is a lack of an objective measurement which can be used to monitor patients at risk for depression, allowing the early diagnosis and follow-up of the disease and the evaluation of treatment efficacy. Different approaches have been investigated to improve diagnosis accuracy and treatment efficacy, but no one has been conclusive.

In this Project we will address the early diagnosis and monitoring of depressed patients using a multimodal approach which takes into account different parameters reported to be altered in depression. These parameters include: i) features derived from the processing of physiological signals, such as those related to autonomic nervous system and voice; ii) biochemical and metabolomic markers derived from blood, hair and saliva; iii) stress level derived from psychometric tests. Correlation analysis between the former parameters and depression severity will be done and the redundancy or complementarity between the different subsets will be investigated. Prediction models for depression severity assessment based on the former features will be designed and validated within the Project.

The Project will address the following questions: i) can features derived from physiological signals discriminate depressed patients? ii) can metabolomics analysis aid in assessing depression severity? ii) are current and/or maintained levels of stress associated with more severe depression? iii) are noninvasive measures of autonomic nervous response to a cognitive stress useful to assess depression severity?

To achieve the Project’s objective a multidisciplinary research team is essential. Our research team includes: three clinical groups (ZARADEMP, CIBERSAM-11, CIBERSAM-25), responsible for the recruitment of the database and depression diagnosis, which support the clinical interest of the Project; two external groups (MCL-CIB, FMUZ), responsible for biochemical and metabolomics analysis; one external group (VIVoLAB), responsible for speech analysis; two CIBERBBN groups (GAB, BSICoS), responsible for the analysis of physiological signals related to autonomic nervous system, correlation analysis and prediction models.

The Project will contribute to the research and development of technology for risk stratification, personalized treatment and therapy effectiveness evaluation in the field of depression.