Associations Between Obstructive Sleep Apnea and Metabolic Dysfunction-Associated Fatty Liver Disease: Insights from Comprehensive Mendelian Randomization and Gene Expression Analysis - Ma T; Liao C; Chen W; Feng J (Nature and Science of Sleep, 2025)
Tianyu Ma,1 Chunyan Liao,2 Wenhui Chen,3 Jia Feng4,5 1School of Medicine, Jinan University, Guangzhou, Guangdong, 510630, People’s Republic of China; 2Department of Anesthesia and Surgery Center, The First Affiliated Hospital of Jinan University, Guangzhou, People’s Republic of China; 3Department of Metabolic and Bariatric Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, People’s Republic of China; 4Department of Cellular Biology, Institute of Biomedicine, Jinan University, Guangzhou, Guangdong, 510632, People’s Republic of China; 5The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, 510630, People’s Republic of ChinaCorrespondence: Jia Feng, Department of Cellular Biology, Institute of Biomedicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou, Guangdong, 510632, People’s Republic of China, Tel/Fax +86 13535224413, Email fengjia412826@163.com Chunyan Liao, Department of Anesthesia and Surgery Center, the First Affiliated Hospital of Jinan University, Guangzhou, People’s Republic of China, Tel +86 13725251007, Fax +86 020 38688580, Email 510039181@qq.comBackground: Obstructive sleep apnea (OSA) is linked to metabolic dysfunction-associated fatty liver disease (MAFLD), yet their exact causality and underlying mechanisms remain inconclusive. We aimed to investigate their causal associations and shared biomarkers using Mendelian randomization (MR) and bioinformatics approaches.Methods: We used OSA-related and MAFLD-related GWAS data to explore their causal relationship and the role of body mass index (BMI) through two-sample and network MR analysis. Gene expression profiles were analyzed to identify intersection genes through differential expression analysis and weighted gene co-expression network analysis (WGCNA). Functional enrichment (GO and KEGG), protein–protein interaction (PPI) networks, and immune cell infiltration analyses (ssGSEA) were performed on the intersecting genes. We then conducted MR analysis to assess the relationship between immune cells and both diseases. Inverse variance weighting (IVW) served as the primary MR method, supplemented by MR-Egger regression, weighted median, and weighted mode.Results: MR analysis revealed that OSA increased the risk of MAFLD [odds ratio (OR)=1.40, 95% CI 1.14– 1.73, p= 0.002], with OSA potentially mediating the effect of BMI on MAFLD, accounting for 62.3% of the mediation. Bioinformatics identified 42 intersection genes. Four hub genes (FOS, EGR1, NR4A1, JUN) were ultimately obtained by PPI network, which were strongly linked to immune cell infiltration. Additionally, three immune cell phenotypes (CD4RA on TD CD4+, HLA DR on CD14+ CD16-monocytes, and HLA DR on CD14+ monocytes) were found to be associated with both OSA and MAFLD.Conclusion: OSA may causally influence MAFLD and mediate the effect of BMI on MAFLD. Four key genes and three immune cell phenotypes play crucial roles in the shared pathogenesis of both diseases.Keywords: obstructive sleep apnea, metabolic dysfunction-associated fatty liver disease, OSA, Mendelian randomization, bioinformatics