Bipolar disorder (BD) is a complex and chronic psychiatric disorder for which there is still no biomarker. The diagnosis of the disease is based on clinical interview with no biomolecular support that can be used to increase diagnosis confidence or to guide prognosis. Moreover, polypharmacy is the rule in BD treatment, and patients are subjected to long and unhelpful therapy trials until reaching clinical stabilization, far from a personalized and preventive medicine.
Several potential biomarkers have been proposed in the literature or even commercialized. But in fact, their lack of robustness led to a marked withdrawal or not being clinically used due to poor diagnosis reliability. There is therefore a clear need for a comprehensive study which can combine all these approaches (clinical interview, proteins, metabolites and imaging) into a single predictive model. This is precisely what this research project intends to do, gathering: i) a clinical team with long experience in the diagnosis of thousands of patients in the country's largest hospital, with a track record on international consortiums, and in the development of improved diagnostic procedures; ii) a mass spectrometry team, a reference lab for the world leading MS seller with a track record in proteomics, and several private research contracts on metabolite profiling and quantification; and iii) ICNAS as a leading neuroimaging research unit.
The research project will be based on extensive clinical characterization with state of the art diagnostic procedures with combined information from international referenced guidelines. Each individual sample will be also characterized with routine blood tests and complemented with immunophenotyping in collaboration with the Portuguese Blood Institute. Then, three parallel studies will be performed: i) proteomics, ii) metabolomics, and iii) fMRI. The proteomics screening will analyze not only PBMC's, serum and plasma but also phosphopeptides, cysteine modified proteins and N-terminals resulting from proteolytic activity. The metabolomics screening will use a novel SWATH-MS approach to produce screening information besides quantitative data. Moreover, structural and functional neuroimaging, both in resting state fMRI will complement the biochemical assays. Our rationale is that it will be the combination of all the parameters with different weighting factor that can create the predictive model. These multiple factors will be validated with different populations.
The novelty of the project is not on each individual task, but the depth of the screening performed in each patient, and the overall capacity to integrate the information from four completely independent screens. By delivering a predictive model based on a reduced panel of biomarkers we will provide enough information to be used as diagnosis or prognosis for BD, or to detect early risk biomarkers in patients with unipolar depression, alone or in combination with the clinical interview.