Metabolic associated fatty liver disease (MAFLD) is nowadays considered the most common cause of chronic liver disease in Western countries, with an estimated prevalence of 30% in the general population. It is also one of the main causes of liver cancer, liver failure and liver transplantation, resulting in an important disease burden for patients and their families and economic cost for the society. The hallmark of MAFLD is lipid accumulation in the liver that results from deregulated lipid metabolism, however, this disease is a complex, heterogeneous and multisystemic disorder, with a natural course that may include cardiovascular, metabolic, neoplastic or liver-related complications. Many factors influence MAFLD development and progression, such as environmental exposure, lifestyle, genetic susceptibility, metabolic status and the gut microbiome. This complexity contributes to the fact that currently there is a lack of approved drug treatments and of tools for non-invasive accurate diagnosis to stage this disease and to establish the risk of complications. Therefore, a multi-omics data integration approach of MAFLD patients could help us to properly sub-phenotype and stratify patients paving the way for precision medicine in MAFLD.
In this project, in coordination with the genomic IMPaCT platform, blood and stool samples, clinical data, and liver and fat biopsy results from our multicentric cohorts will be used to develop and validate a non-invasive metabolites-based signature. Besides, we will evaluate the biological effects of these novel identified metabokines promoting metabolic dysregulation as they might be novel and promising therapeutic targets for MAFLD. Finally, from these results we will develop an integrative analysis through artificial intelligence techniques (available within IMPaCT infrastructure) combining our clinical and other non-invasive data obtained to refine clinical scoring systems in MAFLD.
