Department of Infectious Disease, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Institute of General Practice, Xi'an Medical University, Xi'an, China.
Diabetes Obes Metab. 2024 Dec;26(12):5590-5599. doi: 10.1111/dom.15925. Epub 2024 Sep 4.
Non-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.
Utilizing 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.
We 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.
The MAFLD panels have good diagnostic accuracy, and the causal link between PEG10 and MAFLD was mediated by the percentage of CD11c + CD62L- monocytes.
代谢相关脂肪性肝病（MAFLD）的无创诊断仍然具有挑战性。本研究旨在寻找新的关键基因作为 MAFLD 的无创生物标志物，阐明生物标志物与 MAFLD 之间的因果关系，并确定免疫细胞作为潜在介质的作用。
利用已发表的 MAFLD 患者的转录组数据，我们应用线性模型进行微阵列数据分析、最小绝对值收缩和选择操作（LASSO）回归以及接受者操作特征（ROC）曲线分析，以识别和验证 MAFLD 的生物标志物。使用表达数量性状基因座数据库和一个包含 778614 名欧洲人的队列，我们使用孟德尔随机化分析关键生物标志物与 MAFLD 之间的因果关系。此外，还进行了中介分析，以研究 731 种免疫表型在这些关系中的作用。
我们鉴定出 31 个差异表达基因，LASSO 回归显示三个核心基因 IGFBP2、PEG10 和 P4HA1 用于识别 MAFLD 的接收者操作特征曲线下面积分别为 0.807、0.772 和 0.791。这三个基因的模型在开发和验证数据集的接收者操作特征曲线下面积分别为 0.959 和 0.800。该模型还使用 MAFLD 患者和对照者的基于血清的酶联免疫吸附试验数据进行了验证（AUROC：0.819，95%置信区间：0.736-0.902）。PEG10 通过逆方差加权分析与 MAFLD 风险增加相关（优势比=1.106，p=0.032），大约 30%的风险是由 CD11c+CD62L-单核细胞的百分比介导的。
MAFLD 面板具有良好的诊断准确性，PEG10 与 MAFLD 之间的因果关系是由 CD11c+CD62L-单核细胞的百分比介导的。
