| Title: | Investigation of NAFLD/NASH biomarkers and development of predictive models Using Machine Learning Methods |
| Lead Institution: | Queen Mary University of London |
| Principal investigator: | Professor Panos Deloukas |
Application 53723
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About
Non-Alcoholic Fatty Liver Disease (NAFLD) is the most common form of liver disease in many parts of the world. In Western populations NAFLD affects approximately 50% of diabetics and 76% of obese people. NAFLD covers a spectrum of liver conditions ranging from mild (steatosis) to very severe pathology (cirrhosis). The prevalence of NAFLD (14-30% of the general population) is rapidly increasing, making it a global concern for public health. The timely and valid diagnosis of NAFLD would bring substantial benefits for patient care allowing early treatment to prevent damaging effects. Therefore, it is of upmost importance to identify early on individuals at high-risk of NAFLD to prevent hepatocytes' damage, heart failure etc. Our consortium, MAST4HEALTH, is taking a multidisciplinary approach to assess the use of a non-pharmacological treatment for managing NAFLD of intermediate severity (NASH). We are exploring the effect of Mastiha, a natural product of Greece which was recently shown to possess antioxidant/anti-inflammatory and lipid lowering properties. As part of this project we are investigating genetic and biochemical markers associated with NAFLD. The aim of the proposed study is to use the UK Biobank data set in combination with data from the MAST4HEALTH research program to identify risk factors associated with NAFLD and to develop predictive models and a reliable clinical decision making tool for diagnosis using Machine Learning approaches.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14801 | Development of a Polygenic Risk Score for Metabolic Dysfunction-Associated Steatotic Liver Disease Prediction in UK Biobank | Panagiota Giardoglou (+7) | 2024 | Genes |
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