Human Translational Genetics – Stefano Romeo Group

At the intersection of genetics, metabolism, and clinical medicine, the Human Translational Genetics Group, led by Professor Stefano Romeo, aims to uncover genetic and molecular mechanisms underlying metabolic diseases. Through innovative translational research, this team bridges fundamental discoveries and clinical applications, striving to improve patient outcomes by addressing liver disease, diabetes, and cardiovascular health.

Our research

Professor Stefano Romeo's research centers on uncovering genetic variants and molecular pathways that govern human metabolic processes, focusing primarily on liver disease—especially metabolic dysfunction-associated steatotic liver disease (MASLD)—diabetes, and cardiovascular diseases. Employing an integrative approach that combines genomics, bioinformatics, molecular biology techniques, and clinical investigation, his research has uncovered crucial genetic factors contributing to MASLD, such as variants in the PNPLA3 and MBOAT7 genes.

Professor Romeo has also pioneered the development of an innovative multilineage 3D in vitro model of fatty liver disease, enabling the exploration and testing of novel therapeutic approaches. Additionally, his team has identified a protective genetic variant within the PSD3 gene, demonstrating its beneficial role in fatty liver disease through studies utilizing human-derived liver organoids and animal models.

His most significant achievement is the recent identification of two distinct MASLD types with different cardiometabolic risk profiles. This research utilized partitioned polygenic risk scores to distinguish between a liver-specific MASLD, associated with rapid progression of liver disease but limited cardiovascular risk, and a systemic MASLD, linked to higher risks of cardiovascular disease and type 2 diabetes. These findings emphasize the disease's complexity and the necessity for targeted treatment approaches.

In cardiovascular research, Professor Romeo has contributed significantly by creating a machine learning-based algorithm for diagnosing familial hypercholesterolemia (FH), highlighting the role of lipoprotein(a) as an independent cardiovascular risk factor in FH, and identifying new mutations in the LPL gene associated with severe hypertriglyceridemia. His translational efforts aim to leverage genetic insights into personalized medical strategies, enhancing disease prediction, prevention, and therapeutic outcomes.