Medical Comorbidities in Bipolar Disorder

WP1: Project Management (Leader: P1-IT) This WP ensures effective project coordination, quality control, and risk management. It includes: * Organizing the Kick-off Meeting (KoM) * Managing and monitoring project progress based on predefined indicators * Developing a Data Management Plan (DMP) to define data collection, processing, sharing, and storage * Addressing ethical issues to ensure compliance with regulations and obtaining necessary authorizations * Overseeing risk assessment throughout the project * Managing project reporting, including consortium agreements, ethical guidelines, and periodic reports WP2: Training Set - Register Study (Leader: P3-SE) This WP leverages Swedish and Norwegian national health registries to analyze cases of bipolar disorder (BD) and its comorbidities, particularly metabolic syndrome (MetS). * Identification of BD patients from national registries and analysis of co-existing medical conditions * Development of machine learning (ML) models to predict MetS and related outcomes * Application of ML models using Swedish and Norwegian datasets, validated with genetic data * Outputs: Dataset linkage, reports on BD prevalence and comorbidities, risk stratification models, and open-access ML algorithms WP3: Real-World Risk Factors Study (Leader: P2-DE) This WP gathers real-world clinical data from five European centers to analyze BD comorbidities. * Creation of a database using electronic medical records from 1,500 BD patients * Collection of 24 key socio-demographic and clinical variables, including medical history, psychiatric hospitalizations, metabolic indicators, and medication history * Development of a real-world dataset to investigate changes in MetS risk factors in BD patients over time * Outputs: Standardized data collection tools (Medical Record Abstraction Tool - MRAT), anonymized database, and analysis of risk factors WP4: Exploratory Clinical Study (ECS) - Calibration Phase (Leader: P5-FR) This WP aims to validate data from WP2 and WP3 through a prospective clinical study conducted across five European sites. * Preparatory phase: Development of protocols, recruitment procedures, and sample collection guidelines * Patient recruitment and baseline assessments: 400 BD patients stratified by age, sex, and disease severity * Cross-sectional assessment of MetS in BD patients, identifying common diagnostic patterns and risk factors * One-year follow-up to monitor MetS progression and treatment impact * Physical activity monitoring: Using digital tools (ActiGraph GT9X Link) to track sedentary behavior and its association with MetS * Creation of the BIPCOM biobank, collecting biological samples for proteomics, metabolomics, and genetic analyses * Outputs: Clinical dataset, biomarker reports, biobank setup, and final study analyses WP5: Development and Validation of a Precision Medicine (PM) Tool (Leader: P4-NO) This WP focuses on developing predictive models and a Clinical Support Tool (CST) to enhance risk assessment and personalized care in BD patients with metabolic syndrome (MetS). * Prediction Model Development (M13-36) o Creation of Polygenic Hazard Scores (PHS) to improve risk prediction beyond standard Polygenic Risk Scores (PRS). * Integration of genetic, clinical, and lifestyle data to develop machine learning (ML)-based multimodal stratification algorithms. * Model calibration using data from WP2, WP3, and WP4. * Development of a Clinical Support Tool (CST) (M20-36) * A risk calculator for clinicians to assess comorbidity risk in BD. * CST components: 1. Electronic risk assessment questionnaire for patients and clinicians. 2. Best practice recommendations for managing comorbidities. 3. Personalized educational materials outlining individual risk levels. * Refinement of the CST beta version with stakeholder input (WP6). * Clinical Validation in Real-World Settings (M22-34) * Testing the CST in 5-10 patients per site from the WP4 Exploratory Clinical Study (ECS). * Retrospective validation of MetS prediction using ECS baseline data. * Prospective validation with clinicians assessing the tool's usability and impact. WP6: Stakeholder Involvement and Participation (Leader: P1-IT) This WP ensures active involvement of patients, clinicians, and policymakers to optimize the implementation of PM approaches in BD care. * Mobilization of an International Stakeholder Group (M1-36) * Establishment of a Patient and Caregiver Advisory Board (PCAB) to oversee recruitment, retention, and public engagement. * Creation of a Scientific Advisory Board (SAB) with leading BD experts. * Focus on patient empowerment and education for healthcare providers about PM in psychiatry. * Focus Groups (M6-34) o Patient focus groups in five sites to assess perspectives on BD comorbidities, unmet needs, and care improvements. * Clinician focus groups to evaluate PM implementation in BD treatment. * Thematic analysis using nVIVO software, with data stored under GDPR compliance. * CST Fine-Tuning (M32-36) * The CST (developed in WP5) will be refined based on stakeholder feedback. * The final version will include clinical decision-support recommendations to enhance BD comorbidity management. WP7: Dissemination, Public Engagement, and Exploitation (Leader: P6-ES) This WP ensures effective knowledge transfer to the scientific community, policymakers, and the public. * Dissemination and Public Engagement Planning (M1-2) o Strategy development for knowledge transfer across research, healthcare, and policy sectors. * Website Development and Maintenance (M1-36) o Launch of a project website by M3 for real-time updates, publications, and events. * Dissemination Activities (M1-36) * Scientific publications in top-tier and national journals. * Conference participation, including ICPerMed and BD-related congresses. * BIPCOM mid-term and final events in Frankfurt (M19) and Barcelona (M35). * Public Engagement (M1-36) o Promotion via social media, press releases, and media outreach. * Exploitation of Results (M12-M36) o Development of an exploitation plan for sustainable implementation of findings.