摘要
既往研究已发现免疫球蛋白G(immunoglobulin G,IgG)N-糖基化与代谢特征之间存在关联,但它们之间是否存在因果关联尚有待研究。本研究使用孟德尔随机化(Mendelian randomization,MR)研究方法整合全基因组关联研究(genome-wide association studies,GWAS)和数量性状基因座(quantitative trait loci,QTL)数据探究IgG N-糖基化与代谢特征之间的双向因果关联。在正向MR分析中,通过整合IgG N-糖基-QTL遗传变异与GWAS数据和代谢特征进行分析,分别发现59个包括影响体质指数(body mass index,BMI)的9个IgG N-糖基(glycan peaks,GP)(GP1和GP6等)和影响空腹血糖(fasting plasma glucose,FPG)的7个IgG N-糖基(GP1和GP5等)以及15个[包括影响BMI的5个IgG N-糖基(GP2和GP11等)和影响FPG的4个IgG N-糖基(GP1和GP10等)]由遗传决定的IgG N-糖基在单样本和两样本MR研究中与代谢特征存在因果关联(全部P<0.05)。相应地,对整合代谢特征-QTL-遗传变异与GWAS结果和IgG N-糖基进行MR分析的结果显示,在单样本和两样本MR研究中,分别发现72个包括影响GP1的1个因果代谢特征[高密度脂蛋白胆固醇(high-density lipoprotein cholesterol,HDL-C)]和影响GP2的5个因果代谢特征[FPG、收缩压(systolic blood pressure,SBP)等]和4个[包括影响GP3的1个因果代谢特征(HDL-C)和影响GP9的1个代谢特征(HDL-C)]由遗传决定的代谢特征与IgG N-糖基之间存在因果关联(全部P<0.05)。值得注意的是,在单样本和两样本的MR分析中均发现了遗传决定的高水平的GP11与BMI水平增高存在因果关联[固定效应模型-Beta(SE):0.106(0.034)和0.010(0.005)]和高水平的HDL-C与GP9水平降低存在因果关联[-0.071(0.022)和-0.306(0.151)],且这一结果在单样本和两样本的meta汇总分析中得到了进一步验证[固定效应模型-Beta(95%置信区间)分别为:0.0109(0.0012,0.0207)和-0.0759(-0.1186,-0.0332)]。综上所述,本研究全面的双向MR分析提供了IgG N-糖基化与代谢特征之间双向因果关联的证据,在一定程度上揭示了IgG N-糖基化与代谢特征之间的生物学机制。
Although the association between immunoglobulin G(IgG)N-glycosylation and metabolic traits has been previously identified,the causal association between them remains unclear.In this work,we used Mendelian randomization(MR)analysis to integrate genome-wide association studies(GWASs)and quantitative trait loci(QTLs)data in order to investigate the bidirectional causal association of IgG Nglycosylation with metabolic traits.In the forward MR analysis,59(including nine putatively causal glycan peaks(GPs)for body mass index(BMI)(GP1,GP6,etc.)and seven for fasting plasma glucose(FPG)(GP1,GP5,etc.))and 15(including five putatively causal GPs for BMI(GP2,GP11,etc.)and four for FPG(GP1,GP10,etc.))genetically determined IgG N-glycans were identified as being associated with metabolic traits in one-and two-sample MR studies,respectively,by integrating IgG N-glycan-QTL variants with GWAS results for metabolic traits(all P<0.05).Accordingly,in the reverse MR analysis of the integrated metabolic-QTL variants with the GWAS results for IgG N-glycosylation traits,72(including one putatively causal metabolic trait for GP1(high-density lipoprotein cholesterol(HDL-C))and five for GP2(FPG,systolic blood pressure(SBP),etc.))and four(including one putatively causal metabolic trait for GP3(HDL-C)and one for GP9(HDL-C))genetically determined metabolic traits were found to be related to the risk of IgG N-glycosylation in one-and two-sample MR studies,respectively(all P<0.05).Notably,genetically determined associations of GP11?BMI(fixed-effects model-Beta with standard error(SE):0.106(0.034)and 0.010(0.005))and HDL-C?GP9(fixed-effects model-Beta with SE:-0.071(0.022)and-0.306(0.151))were identified in both the one-and two-sample MR settings,which were further confirmed by a meta-analysis combining the one-and two-sample MR results(fixed-effects modelBeta with 95%confidence interval(95%CI):0.0109(0.0012,0.0207)and-0.0759(-0.1186,-0.0332),respectively).In conclusion,the comprehensively bidirectional MR analyses provide suggestive evidence of bidirectional causality between IgG N-glycosylation and metabolic traits,possibly revealing a new richness in the biological mechanism between IgG N-glycosylation and metabolic traits.
作者
孟晓妮
曹维杰
刘迪
Isinta Maranga Elijah
邢薇佳
侯海峰
徐希柱
宋曼殳
王友信
Xiaoni Meng;Weijie Cao;Di Liu;Isinta Maranga Elijah;Weijia Xing;Haifeng Hou;Xizhu Xu;Manshu Song;Youxin Wang(Beijing Key Laboratory of Clinical Epidemiology,School of Public Health,Capital Medical University,Beijing 100069,China;Centre for Precision Health,Edith Cowan University,Perth,WA 6027,Australia;Centre for Biomedical Information Technology,Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;School of Public Health,Shandong First Medical University&Shandong Academy of Medical Sciences,Jinan 250117,China;School of Medical and Health Sciences,Edith Cowan University,Perth,WA 6027,Australia)
出处
《Engineering》
SCIE
EI
CAS
CSCD
2023年第7期74-88,I0004,共16页
工程（英文）
基金
supported by grants from the National Natural Science Foundation of China(81872682)
the Young Taishan Scholars Program of Shandong Province of China(tsqn20161046)
the Academic Promotion Programme of Shandong First Medical University(2019RC010)
the Shandong Province Higher Educational Young and Innovation Technology Supporting Program(2019KJL004)
the Doctoral Scientific Research Foundation of Shandong First Medical University.
