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It’s all happening 16–17 September at Digital NAFLD Summit 2021 and here are our top 3 reasons to attend:

  1. Gain deeper insights into the latest thinking in NAFLD/NASH research from the leading minds in the field.
  2. Learn from top clinical and basic researchers from several fields about the cutting-edge developments and future of the NAFLD area.
  3. Discuss measures to overcome the growing health burden that NAFLD is quickly becoming and that many countries are unprepared to deal with.

Learning objectives

At Digital NAFLD Summit 2021, we aim to present you with an update on the epidemiology and public health relevance of this condition, and on the debate on the most appropriate definition to identify individuals at higher risk of liver complications. Starting from the genetic, epigenetic, and metabolic basis of the disease, we will review the most recently discovered mechanisms underlying the pathogenesis of liver damage and the clinical heterogeneity of the disease 

Together at this summit, we will address key challenges in clinical practice and drug developmentThese challenges include:

  • identifying biomarkers, imaging studies, and artificial intelligence-based approaches for patient risk stratification;
  • identifying disease subsets with distinct features;
  • developing therapeutic targets to be tested in early and late phase clinical trials. 


  • Public health relevance of NAFLD 
  • Defining fatty liver disease, and the respective advantages of NAFLD vs MAFLD 
  • Genetic basis of NAFLD 
  • Clinical heterogeneity of NAFLD 
  • Pathogenesis of liver damage in NAFLD: from human genome and microbiome alterations to altered metabolism, intestinal permeability, immune system regulation, and liver fibrogenesis 
  • Current clinical management of NAFLD: challenges in driving lifestyle change and endoscopic/surgical therapy 
  • Progress in the non-invasive assessment of NAFLD: novel biomarkers, including genetic variants and lipid species, lastgeneration imaging approaches, and the implementation of machinelearning algorithms 
  • Identification of new therapeutic targets in preclinical models, including mitochondrial metabolism, state of the art in clinical trials in the field, and update on the design of therapeutic studies 
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