The ARTEMIs project is motivated by the urgent need to address the global epidemic of Metabolic dysfunction-associated steatotic liver disease (MASLD). This condition, marked by fat accumulation in the liver, is the leading cause of chronic liver disease in Europe, with a prevalence of over 25%. Its prevalence is rising due to increased rates of associated metabolic disorders like obesity, type 2 diabetes, and high blood pressure, which are caused by unhealthy lifestyles. While disease progression is slow in most patients, an estimated 10% develop advanced fibrosis, risking complications like cirrhosis. Cirrhosis is a non-curable disease that significantly contributes to morbidity and mortality in Europe and is a major risk factor for hepatocellular carcinoma (HCC). MASLD is also linked to an increased risk of cardiovascular diseases, which are the primary cause of death in MASLD patients. Effective management is currently hindered by a limited understanding of MAsLD progression and the complex interactions of the "liver-heart axis".
The project's main aim is to tackle these challenges by proposing a new method to control the MAsLD epidemic using advanced "virtual twin" liver-heart models. Building on previous successful work with liver computational models, the primary goal is to integrate existing multilevel computational models and expand them into a multi-organ approach that includes the heart and circulatory systems. This will be achieved using both mechanistic and machine-learning techniques. The models will be validated in clinical practice through Proof of Concept demonstrators to provide meaningful clinical insights to healthcare professionals.
To achieve this, ARTEMIs must address several key challenges. The models must be clinically relevant and integrated into understandable decision support tools to deliver actionable, personalized insights to clinicians. This requires virtual twins capable of integrating models across multiple scales (molecular, cellular, tissue, organ) and systems to simulate disease progression and predict comorbidities dynamically. Access to a large, high-quality set of multimodal data from diverse patient populations is essential, while adhering to data protection and sovereignty regulations. It is also critical to maintain clinicians' trust in the models' accuracy. Finally, to ensure acceptance and market readiness, the project involves clinical Key Opinion Leaders (KOLs), patients, and industrialists to address clinical relevance, regulatory aspects, and interoperability.
