Abstract
Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.
Highlights
Continuous accumulation of fat in hepatocytes leads to Non-Alcoholic Fatty Liver Disease (NAFLD)[1]
HCs across these datasets represented real-world heterogeneity as they included those with normal weight, healthy obese, and those suspected of NAFLD
Multiple tests and scoring systems have been developed for non-invasive diagnostics for NAFLD and Non-Alcoholic Steatohepatitis (NASH)
Summary
Continuous accumulation of fat in hepatocytes leads to Non-Alcoholic Fatty Liver Disease (NAFLD)[1]. Initial screening is based on blood biomarkers such as liver enzymes or insulin resistance, while more complex models incorporate general risk factors such as age, sex, and BMI; these data are used to calculate risk scores which have reasonable performance at detecting NAFLD but have lower discriminatory power to distinguish N ASH12,14–17 Based on these scores, a patient is referred to a hepatologist for further tests involving imaging and/or liver biopsy—an uncomfortable, invasive, expensive, and low throughput procedure which is the gold standard of NASH diagnosis. We hypothesized that a multi-cohort analysis of transcriptome profiles of liver biopsies from patients with NAFLD or NASH would identify a robust signature for NASH that generalizes across the biological, clinical, and technical heterogeneity of the real-world patient population and will be suitable for clinical development
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
