Personalized disease prediction and prevention in chronic inflammatory disorders

Background: Chronic inflammatory diseases (CID) are a group of non-communicable disorders of the immune system with a lifetime prevalence of over 10% in the EU. Rheumatoid arthritis (RA) and inflammatory bowel diseases (IBD) are two archetypal CIDs with a particularly high unmet medical need and impact on European health care systems. Objectives: The PerPrev-CID consortium aims to set up new standards for decision support tool development for preventive and early therapeutic interventions in RA and IBD. Selected main aims are: (i) To define actionable predictors from longitudinal multi-level Omics and clinical data integration In RA and IBD. We will identify markers indicating progression from pre-symptomatic to manifest disease and signatures predicting early disease relapse (ii) To develop new tools for home-based and continuous assessment of patient-related data dimensions using digital health apps/wearables, including more innovative, objective parameters, such as movement patterns, physical activity, and sleep. (iii) To assess the healthpromoting effects of interfering with the tryptophan pathway in a proof-of-concept nutritional study in early RA and IBD. (iv) To study ethical, legal and economic consequences of risk assessment and early low-threshold preventive interventions. Important aspects are the communication of the risk concept, economic implications of broad testing and interventions as well as patients´ perception of the concept of risk prediction. Expected impact: We will develop prototype solutions for privacy-preserving AI-based data analysis for research and clinical application and will test a first proof-of-concept low threshold intervention in RA and IBD. Our approaches will provide a clear health benefit to the citizens of the EU by improving health outcomes, empowering joint decision making and contributing to appropriate action plans to reduce the avoidable burden of the diseases.

The expected outcome of the project:

• Implementation of an international systems medicine framework leveraging harmonized data management and explainable AI (XAI) alongside swarm-learning techniques, providing the optimal environment for biomarker discovery and validation
• Better understanding of molecular factors driving individual variation, resilience and disease risk
• Biomarkers and decision support tools that accurately estimate risks for disease onset
• Novel home-based tools and biomarkers that predict onset of disease flares
• Efficacy of nutritional interventions (FSMP) in preventing disease flares/progression
• Personalized health engagement information package• Informed policy recommendations