Identification of individuals with non-alcoholic steatohepatitis (NASH) among obese patients is crucial, as they face a high risk of developing severe liver conditions, such as cirrhosis. The standard practice for assessing obesity-related fatty liver disease involves a resource-intensive and invasive liver biopsy.
In WP4, we aim to identify non-invasive diagnostic and prognostic biomarkers that can proactively detect at-risk patients, allowing for early intervention and improved long-term outcomes. To that end, we plan to map molecular disease trajectories by analyzing detailed molecular compositions of tissues, combining data from ATLAS 1.0 and new data generated in ATLAS 2.0.
This approach will reveal molecular disease stages of MAFLD and enable the identification of novel molecular markers associated with distinct disease stages. We plan to use contemporary machine learning approaches to optimize diagnostic and prognostic algorithms based on these novel markers, as well as shotgun proteomics and clinical information to achieve higher accuracy compared to traditional single or composite biomarker approaches.
Thus, the expected outcomes of WP4 include the identification of molecular states and drivers of transitions in MAFLD, as well as validated diagnostic and prognostic algorithms predicting current and future liver health status.
Work Package Leaders: Jesper G. S. Madsen and Mette M. Lauridsen
Key publications
- JOINTLY: interpretable joint clustering of single-cell transcriptomes
A.F. Møller, J.G.S. Madsen
Nature Communications (2023)