With the obesity epidemic and prevalence of 25%, metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disease in the world. Despite the enormous economic and clinical burden that MASLD poses, there are still no approved therapies. Mechanisms regulating disease progression are largely unknown, but the crosstalk of liver and visceral adipose tissue (VAT) in the context of obesity, appears to be instrumental in driving the onset of steatosis and progression to metabolic dysfunction associated steatohepatitis (MASH) and fibrosis. This is also due to the role that VAT has in mediating immune modulation, inflammatory and metabolic alterations. The hypothesis is that VAT-driven intercellular communication involving hepatocytes and liver non-parenchymal cells (NPCs), has a significant role in MASLD progression. To study the liver-VAT axis in vitro, it is of utmost importance to reproduce the complex and multicellular nature of these tissues. Contrary to previous studies based on simplistic two-dimensional co-cultures of single cell types, XB-LIVAT proposes to use a holistic, interdisciplinary and translational approach to study liver-VAT axis. XB-LIVAT will use a novel co-culture system consisting of an advanced human hetero-cellular liver three-dimensional spheroid model and clinical VAT explants, together with state-of-the-art ‘omics’ technologies to unravel key cell players, early molecular events, and targets in MASLD. In combination with in-depth molecular analysis of MASLD liver biopsies, this advanced in vitro co-culture model with high translational value will shed light on molecular mechanisms in MASLD, with particular focus on the role of NPCs. In addition to contributing to the discovery of novel therapeutic targets and interventional approaches for improving the clinical management of MASLD, XB-LIVAT will shape the researcher into an expert in advanced in vitro disease modeling and translational hepatology.

The incidence of undiagnosed diabetes accounts for 36% European adults, while 541M adults worldwide have Impaired Glucose Tolerance (IGT), an important risk factor for further T2D development. Both IGT and/or Impaired Fasting Glucose (IFG) are intermediate glucose mishandling (i.e. intermediate conditions in the healthy-T2D transition) and are manifestations of the so-called prediabetes condition. Prediabetes itself is not an extensively studied condition compared to the overt T2D, but it is also a condition that can be reversed without the prescription usage to not proceed into T2D. The aim of our project is to develop a prototype tool for the real-time prediction of the prediabetic risk based on a series of patient-specific mathematical models (firstly developed during the FP7 MISSION-T2D project) that simulate metabolism, pancreas hormone production, microbiome metabolites, inflammatory process and immune system response. The prediction algorithm will be based on a “physics-informed machine learning” approach. A rich dataset of real-life data will be combined with a mathematical model to overcome the limits of a “black-box” ML approach, while reducing the computational time for simulating the solutions of a heavy mathematical models and improving its prediction performances.We will collect the necessary training data (e.g., diet questionnaire, physical activity, blood metabolites and microbiome) from already existing clinical studies (used as retrospective trials) which are representative of the real-life scenarios of a prediabetes/diabetes risk insurgence in adulthood (20-80y): family history, Metabolic Syndrome, Liver disease and obesity. A newly dedicated multicentric pilot prospective observational study will be also performed, during which we will also equip the participants with wearable sensors (e.g. glucose monitoring, bioimpedance, heart rate, accelerometer).