Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Nov 27, 2021; 13(11): 1584-1610
Published online Nov 27, 2021. doi: 10.4254/wjh.v13.i11.1584
Heterogeneity of non-alcoholic fatty liver disease: Implications for clinical practice and research activity
Partha Pal, Rajan Palui, Sayantan Ray
Partha Pal, Department of Medical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad 500082, India
Rajan Palui, Department of Endocrinology, The Mission Hospital, Durgapur 713212, West Bengal, India
Sayantan Ray, Department of Endocrinology, Jagannath Gupta Institute of Medical Sciences and Hospital, Kolkata 700137, West Bengal, India
Sayantan Ray, Diabetes and Endocrinology, Apollo Clinic, Ballygunge, Kolkata 700019, West Bengal, India
Author contributions: Pal P performed the literature search, wrote the first draft and provided intellectual input; Palui R conceptualized the work, performed a literature search, supervised the writing; Ray S supervised the literature search, the writing, provided intellectual input and critically revised the manuscript.
Conflict-of-interest statement: None to declare.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Sayantan Ray, MBBS, MD, Department of Endocrinology, Jagannath Gupta Institute of Medical Sciences and Hospital, KP Mondal Road, Budge Budge, Kolkata 700137, West Bengal, India.
Received: March 14, 2021
Peer-review started: March 14, 2021
First decision: July 18, 2021
Revised: July 29, 2021
Accepted: October 14, 2021
Article in press: October 14, 2021
Published online: November 27, 2021

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.