The role of omics in the pathophysiology, diagnosis and treatment of non-alcoholic fatty liver disease

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a multifaceted metabolic disorder, whose spectrum covers clinical, histological and pathophysiological developments ranging from simple steatosis to non-alcoholic steatohepatitis (NASH) and liver fibrosis, potentially evolving into cirrhosis, hepatocellular carcinoma and liver failure. Liver biopsy remains the gold standard for diagnosing NAFLD, while there are no specific treatments. An ever-increasing number of high-throughput Omics investigations on the molecular pathobiology of NAFLD at the cellular, tissue and system levels produce comprehensive biochemical patient snapshots. In the clinical setting, these applications are considerably enhancing our efforts towards obtaining a holistic insight on NAFLD pathophysiology. Omics are also generating non-invasive diagnostic modalities for the distinct stages of NAFLD, that remain though to be validated in multiple, large, heterogenous and independent cohorts, both cross-sectionally as well as prospectively. Finally, they aid in developing novel therapies. By tracing the flow of information from genomics to epigenomics, transcriptomics, proteomics, metabolomics, lipidomics and glycomics, the chief contributions of these techniques in understanding, diagnosing and treating NAFLD are summarized herein.

Perspectives on Precision Medicine Approaches to NAFLD Diagnosis and Management

“…Gathering and layering of all pertinent clinical, genetic (i.e., genotyping of major genetic variants or use of PRS), microbiome-related, and cardio-metabolic data should contribute to better defining disease trajectory, risk of progression, and likelihood of response to different therapeutic options in any given patient with NAFLD. Also, in the future, the use of high-throughput omics (i.e., lipidomics proteomics, metabolomics, and glycomics) investigations will help to generate comprehensive biochemical snapshots allowing one to discriminate between patient subgroups [ 185 , 186 ]. Also, artificial intelligence tools to integrate and analyze big data as well as to develop algorithms combining the information through machine learning strategies [ 187 – 189 ] may be of help to better stratifying patients and defining tailored treatment strategies.…”

Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis

“…Other published studies have examined the utility of ML to predict or diagnose various forms of NAFLD. 6 , 16–20 Perakakis et al (2020) have compared a wide variety of models using different types of omics on a variety of NAFLD prediction problems. We compare a few such models and their differences (objectives, target cohort, methods, type of variables used, outcomes and applicability) to our study ( Table 4 ).…”

“…There remains a need to develop a noninvasive, accurate, easy-to-use tool to identify patients with a high probability of NASH. 6 …”

Nutrigenomics and Nutrigenetics in Metabolic- (Dysfunction) Associated Fatty Liver Disease: Novel Insights and Future Perspectives

“…Thanks to an increasing number of high-throughput Omics investigations published in the last few years, the molecular pathobiology of MAFLD, considered as a systemic disorder, has been better clarified. This fact represented an important starting point to properly debate regarding the appropriacy of the holistic view on MAFLD pathophysiology, potentially leading to some important diagnostic and therapeutic advantages as well [ 103 ]. As part of this tangled biologic network, nutrigenomics appears to be crucial in this setting, particularly considering the potential benefit that could derive from the dietary therapeutic application and tailored clinical management.…”