METABOLIC dysfunction-associated fatty liver disease (MAFLD), previously termed nonalcoholic fatty liver disease (NAFLD), has become a global health challenge, affecting an estimated 25% of adults worldwide. Its prevalence ranges from 13% in Africa to 42% in Southeast Asia, creating significant clinical and socioeconomic burdens. The disease encompasses a continuum of liver pathologies, from simple fat accumulation to metabolic dysfunction-associated steatohepatitis (MASH), which can progress to cirrhosis and hepatocellular carcinoma. Yet, the causal immune mechanisms driving this progression remain poorly understood.
Emerging evidence points to the role of CD4+ T cell subsets in fuelling MAFLD-related inflammation and fibrosis. However, traditional tools such as flow cytometry cannot fully capture transcriptional heterogeneity in immune cells. While single-cell RNA sequencing (scRNA-seq) has revealed disease-specific immune signatures, these associations fall short of establishing causality. To bridge this gap, researchers integrated scRNA-seq with Mendelian randomisation (MR), a genetic epidemiological method designed to infer causal links while reducing confounding bias.
The study identified 212 differentially expressed genes (DEGs) in immune cells from MAFLD patients and narrowed these down to 37 causal candidates using MR. Among them, EVI2B and KLHL24 were found to increase disease risk, while PRF1, CST7, and GNG2 appeared protective. Notably, overexpression of EVI2B in hepatocytes triggered lipid accumulation, confirming its role as a pro-steatotic driver.
To translate these findings into diagnostics, the team developed a machine learning model combining five key genes, which achieved high diagnostic accuracy across validation cohorts. This five-gene signature offers promise as a minimally invasive biomarker for early MAFLD detection.
The work highlights the value of integrating omics approaches with causal inference. Nevertheless, limitations remain, including modest sample sizes, the absence of gene–environment interaction analyses, and the need for functional studies in fibrosis-competent models.
Overall, this research provides new insights into the immune–metabolic axis underlying MAFLD, identifies novel risk and protective genes, and establishes a diagnostic roadmap that could inform future precision medicine strategies in metabolic liver disease.
Reference
Qiao J et al. Immune metabolic changes identify causal candidate genes and enable diagnostic frameworks in MAFLD. Sci Rep. 2025;15(1):31751.
