This article is original by Translational Medicine Network. Please indicate the source when reprinting Author: Lily Introduction: Non-alcoholic fatty liver disease (NAFLD) has not yet been approved for specific drug treatment; therefore, in order to monitor disease progression an

2025/07/0100:53:36 science 1233

This article is original from Translational Medicine Network. Please indicate the source when reprinting

Author: Lily

Introduction : Non-alcoholic fatty liver disease (NAFLD) has not yet been approved for specific drug treatment; therefore, in order to monitor disease progression and treatment response, we urgently need to determine the potential drug targets of NAFLD and the biomarker . Recently, a research team from the Iceland University of conducted a large-scale genome-wide NAFLD association study, and determined sequence variants related to NAFLD—including rare protective loss-of-function variants pointing to potential drug targets; and used proteomic data to construct a model that can distinguish NAFL from cirrhosis , providing a non-invasive tool for the diagnosis and evaluation of NAFLD.

Nonalcoholic fatty liver disease (NAFLD)

Nonalcoholic fatty liver disease (NAFLD) affects about a quarter of the world's population and has become an increasingly serious health problem. Nonalcoholic fatty liver (NAFL) is the first stage of NAFLD—at this time, fat accumulation caused by unknown causes (except excessive drinking or other clear liver damage factors) accounts for 5% or more of the entire liver. NAFL can progress to nonalcoholic steatohepatitis (NASH); more severely, some patients with NASH will further develop cirrhosis and hepatocellular liver cancer (HCC). Obesity, metabolic syndrome, diabetes and hypertension are recognized risk factors for NAFL and NASH; cirrhosis associated with NASH is the second most common indication for liver transplantation.

NAFLD diagnosis and staging are challenging: Although elevated liver enzymes are a common manifestation of NAFLD, liver enzymes are nonspecific and do not well predict NAFLD progression. Although proton density fat fraction (PDFF) derived from magnetic resonance imaging (MRI) can accurately quantify liver fat; liver biopsy is still an indispensable means for the diagnosis and staging of non-alcoholic fatty liver (NASH)—unfortunately, biopsy involves sampling variability issues and can cause complications. At present, NASH has not yet been approved for specific drug treatment; therefore, in order to monitor disease progression and treatment response, we urgently need to identify potential drug targets and biomarkers for NAFLD.

NAFLD Large-scale Genome-wide Association Research

On October 24, a research team from the University of Iceland published a research paper entitled "Multiomics study of nonalcoholic fatty liver disease" in the journal "Nature Genetics). This study conducted a large genome-wide association study on non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular hepatocarcinoma (HCC), and identified sequence variants associated with NAFLD—including rare protective loss-of-function variants pointing to potential drug targets.

In addition, through plasma proteomic analysis, this study revealed the pathogenesis of NAFLD, and then identified potential biomarkers of disease, disease progression, and target ligation; and used proteomics data to construct a model that can distinguish NAFL from cirrhosis, providing a non-invasive tool for the diagnosis and evaluation of NAFLD.

https://www.nature.com/articles/s41588-022-01199-5

GWAS analysis

Regarding NAFL, the researchers used 9,491 clinical cases and proton density fat fractions (PDFF) extracted from 36,116 liver magnetic resonance images. MR images were generated using two acquisition techniques: 8,448 images were generated using gradient multiple echo (GRE) and 27,668 images were generated using the iterative water-lipid separation image (IDEAL) using echo asymmetry and least squares method. 20.0% IDEAL images and 23.6% GRE images were 5%. Of the 270 (IDEAL) and 97 (GRE) individuals diagnosed with NAFL, 179 (66.3%) and 76 (78.4%) had 5.0%, respectively. The PDFF estimates of this study have a good correlation with the 3,869 PDF values calculated by other studies using LiverMultiScan26 (r2=0.96).

Figure 1. Distribution of PDFF Assessment Values:

Research team conducted a genome-wide association study (GWAS) on the PDFF estimates (n=36,116) using the first available MRI measurement value of each individual. In addition, meta analysis was performed on 9,491 cases of clinically diagnosed NAFL —785 were from Iceland (deCODE genetics database), 5,921 were from the UK (Biobank database), and 2,134 were from the US (Intermountain INSPIRE and HerediGene database). The same analysis was also performed in 876,210 control groups.

This study found 18 independent sequence variants at 17 points in GWAS analysis, 4 of which have not been reported in previous GWAS analyses related to NAFL (in or near PNPLA2, TOR1B, APOH and GUSB). The effects of these variants on the two phenotypes are comparable, means that the variant that causes the PDFF value to increase also increases the risk of NAFL and vice versa.

Figure 2. Comparison of the effects of sequence variation on PDFF and its effects on liver disease, liver enzymes and lipids:

study results and significance

study identified 18 sequence variants related to NAFL and 4 sequence variants related to cirrhosis; and rare and predictive loss of protective function were found in MTARC1 and GPAM—emphasizing that they are potential drug targets.

By observing the association with 52 other phenotypes and traits, this study explores the pleiotropic effects of identified variants. BMI is one of the most common risk factors for NAFLD, and longitudinal PDFF measurements show that carriers of p.Ile148Met (a well-known NAFLD risk variant) in PNPLA3 are more susceptible to changes in BMI than non-carriers.

To study whether plasma proteins can effectively distinguish NAFL from cirrhosis, this study used liver enzymes, age, gender, and BMI as baselines and used plasma proteome and genetic risk scores (GRSs) to train a punitive logistic regression model. study showed that in the Icelandic cohort and the UK Biobank cohort, models trained with plasma proteome were better than other models in distinguishing between NAFL and cirrhosis, NAFL and the general population, cirrhosis and the general population.

This study used multiomic data to clarify the genetic basis of NAFLD, and the analysis of predicted LoF (pLOF) variants, blood RNA expression and plasma proteomic data pointed out whether changes in pathogenic genes and their functions contribute to pathogenic mechanisms. At the same time, the different effects of NAFL risk alleles on other diseases and traits, including lipid and proteins, were demonstrated, and it was shown that plasma proteomics has the potential for staged NAFLD. To date, the study is one of the largest studies conducted to elucidate the genetic basis of NAFLD, and its results are expected to help develop diagnostic tools or therapies for NAFLD.

Reference materials:

https://www.nature.com/articles/s41588-022-01199-5

Note: This article aims to introduce the progress of medical research and cannot be used as a reference for treatment plans. If you need health guidance, please go to a regular hospital for treatment.

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