Heterogeneity of non-alcoholic fatty liver disease: Implications for clinical practice and research activity

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a heterogeneous condition with a wide spectrum of clinical presentations and natural history and disease severity. There is also substantial inter-individual variation and variable response to a different therapy. This heterogeneity of NAFLD is in turn influenced by various factors primarily demographic/dietary factors, metabolic status, gut microbiome, genetic predisposition together with epigenetic factors. The differential impact of these factors over a variable period of time influences the clinical phenotype and natural history. Failure to address heterogeneity partly explains the sub-optimal response to current and emerging therapies for fatty liver disease. Consequently, leading experts across the globe have recently suggested a change in nomenclature of NAFLD to metabolic-associated fatty liver disease (MAFLD) which can better reflect current knowledge of heterogeneity and does not exclude concomitant factors for fatty liver disease (e.g. alcohol, viral hepatitis, etc.). Precise identification of disease phenotypes is likely to facilitate clinical trial recruitment and expedite translational research for the development of novel and effective therapies for NAFLD/MAFLD.

Keywords: Non-alcoholic fatty liver disease; Metabolic-associated fatty liver disease; Heterogeneity; Phenotypes; nomenclature; Clinical trial; Effective therapies

Core Tip: It is being increasingly recognized that non-alcoholic fatty liver disease (NAFLD) is a heterogenous condition with wide variability in clinical presentation and natural history. This heterogeneity is driven by genetic predisposition, metabolic factors, gut microbiota, diet and demographic factors. The suboptimal response to current pharmacotherapy in NAFLD highlights the failure to recognize this heterogeneity. Experts believe that updating NAFLD nomenclature is the first step towards this. Identification of disease subtypes can help development of preclinical model evaluating novel targets. This would in turn help clinical trial design by comparing and pooling results and thus improve disease outcomes.