免疫细胞介导的关键基因与代谢功能障碍相关脂肪肝风险之间的因果关系:孟德尔随机化和中介分析

IF 5.4 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Gong Feng, Na He, Jing Gao, Xiao-Cheng Li, Fen-Na Zhang, Cheng-Cheng Liu, Giovanni Targher, Christopher D Byrne, Man Mi, Ming-Hua Zheng, Feng Ye
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引用次数: 0

摘要

目的:代谢功能障碍相关性脂肪肝(MAFLD)的无创诊断仍面临挑战。我们旨在确定新的关键基因作为 MAFLD 的非侵入性生物标志物,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:利用已发表的活检证实的MAFLD患者的转录组数据,我们应用微阵列数据线性模型、最小绝对收缩和选择操作(LASSO)回归和接收者操作特征曲线(ROC)分析来识别和验证MAFLD的生物标志物。我们利用表达定量性状位点数据库和由 778 614 名欧洲人组成的队列,采用孟德尔随机法分析了关键生物标志物与 MAFLD 之间的因果关系。此外,我们还进行了中介分析,以研究 731 种免疫表型在这些关系中的参与情况:我们确定了 31 个差异表达基因,LASSO 回归显示,IGFBP2、PEG10 和 P4HA1 这三个枢纽基因在识别 MAFLD 方面的接收者操作特征曲线下面积(AUROC)分别为 0.807、0.772 和 0.791。在开发数据集和验证数据集中,这三个基因的模型的接受者操作特征曲线下面积分别为 0.959 和 0.800。该模型还通过 MAFLD 患者和对照组的血清酶联免疫吸附测定数据进行了验证(AUROC:0.819,95% 置信区间:0.736-0.902)。通过逆方差加权分析,PEG10 与 MAFLD 风险增加有关(几率比 = 1.106,p = 0.032),其中约 30% 的风险由 CD11c + CD62L- 单核细胞的百分比介导: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-单核细胞百分比介导的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causal relationship between key genes and metabolic dysfunction-associated fatty liver disease risk mediated by immune cells: A Mendelian randomization and mediation analysis.

Aim: 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.

Materials and methods: 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.

Results: 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.

Conclusions: The MAFLD panels have good diagnostic accuracy, and the causal link between PEG10 and MAFLD was mediated by the percentage of CD11c + CD62L- monocytes.

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来源期刊
Diabetes, Obesity & Metabolism
Diabetes, Obesity & Metabolism 医学-内分泌学与代谢
CiteScore
10.90
自引率
6.90%
发文量
319
审稿时长
3-8 weeks
期刊介绍: Diabetes, Obesity and Metabolism is primarily a journal of clinical and experimental pharmacology and therapeutics covering the interrelated areas of diabetes, obesity and metabolism. The journal prioritises high-quality original research that reports on the effects of new or existing therapies, including dietary, exercise and lifestyle (non-pharmacological) interventions, in any aspect of metabolic and endocrine disease, either in humans or animal and cellular systems. ‘Metabolism’ may relate to lipids, bone and drug metabolism, or broader aspects of endocrine dysfunction. Preclinical pharmacology, pharmacokinetic studies, meta-analyses and those addressing drug safety and tolerability are also highly suitable for publication in this journal. Original research may be published as a main paper or as a research letter.
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