Causal relationship between key genes and metabolic dysfunction‐associated fatty liver disease risk mediated by immune cells: A Mendelian randomization and mediation analysis

Author:

Feng Gong12ORCID,He Na3,Gao Jing45,Li Xiao‐Cheng2,Zhang Fen‐Na3,Liu Cheng‐Cheng2,Targher Giovanni67ORCID,Byrne Christopher D.8ORCID,Mi Man2,Zheng Ming‐Hua910ORCID,Ye Feng1

Affiliation:

1. Department of Infectious Disease, The First Affiliated Hospital of Xi'an Jiaotong University Xi'an China

2. Institute of General Practice Xi'an Medical University Xi'an China

3. Department of Gastroenterology The First Affiliated Hospital of Xi'an Medical University Xi'an China

4. School of Medicine Xiamen University Xiamen China

5. Department of Emergency Medicine Affiliated Hospital of Xizang Minzu University Xianyang China

6. Department of Medicine University of Verona Verona Italy

7. Metabolic Diseases Research Unit IRCCS Sacro Cuore‐Don Calabria Hospital Negrar di Valpolicella Italy

8. Southampton National Institute for Health and Care Research Biomedical Research Centre University Hospital Southampton and University of Southampton, Southampton General Hospital Southampton UK

9. Department of Hepatology, MAFLD Research Center The First Affiliated Hospital of Wenzhou Medical University Wenzhou China

10. Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province Wenzhou China

Abstract

AbstractAimNon‐invasive diagnostics for metabolic dysfunction–associated fatty liver disease (MAFLD) remain challenging. We aimed to identify novel key genes as non‐invasive biomarkers for MAFLD, elucidate causal relationships between biomarkers and MAFLD and determine the role of immune cells as potential mediators.Materials and MethodsUtilizing published transcriptome data of patients with biopsy‐proven MAFLD, we applied linear models for microarray data, least absolute shrinkage and selector operation (LASSO) regressions and receiver operating characteristic (ROC) curve analyses to identify and validate biomarkers for MAFLD. Using the expression quantitative trait loci database and a cohort of 778 614 Europeans, we used Mendelian randomization to analyse the causal relationships between key biomarkers and MAFLD. Additionally, mediation analysis was performed to examine the involvement of 731 immunophenotypes in these relationships.ResultsWe identified 31 differentially expressed genes, and LASSO regression showed three hub genes, IGFBP2, PEG10, and P4HA1, with area under the receiver operating characteristic (AUROC) curve of 0.807, 0.772 and 0.791, respectively, for identifying MAFLD. The model of these three genes had an AUROC of 0.959 and 0.800 in the development and validation data sets, respectively. This model was also validated using serum‐based enzyme‐linked immunosorbent assay data from MAFLD patients and control subjects (AUROC: 0.819, 95% confidence interval: 0.736–0.902). PEG10 was associated with an increased MAFLD risk (odds ratio = 1.106, p = 0.032) via inverse variance–weighted analysis, and about 30% of this risk was mediated by the percentage of CD11c + CD62L– monocytes.ConclusionsThe MAFLD panels have good diagnostic accuracy, and the causal link between PEG10 and MAFLD was mediated by the percentage of CD11c + CD62L– monocytes.

Publisher

Wiley

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