摘要
目的探究血液代谢物和瘢痕疙瘩之间的因果关系。方法该研究为基于两样本双向孟德尔随机化(MR)分析的研究。从全基因组关联研究(GWAS)数据库中获取7 824名成年人志愿者及8 299名受试者的血液代谢物和481 912例瘢痕疙瘩患者相关数据进行研究。通过设置显著阈值P<1.0×10^(-5)、连锁不平衡分析[r^(2)=0.001、千碱基对(kb)=10 000], 以及F统计量(F≥10), 筛选与血液代谢物和瘢痕疙瘩显著相关的单核苷酸多态性(SNPs), 作为工具变量纳入MR分析。采用MR分析的5种方法, 即以逆方差加权法(IVW)为主, MR-Egger回归、加权中位数法、简单模型法及加权模型法作为辅助方法, 分析血液代谢物(暴露因素)和瘢痕疙瘩(结局变量)的因果关系。对符合条件的血液代谢物SNPs进行敏感性分析, 评估研究结果的可靠性和稳定性:通过CochranQ检验和MR-Egger回归检验评估其异质性, MR Egger截距检测排除其水平多效性, 留一法检验评估是否存在单个SNPs对MR分析结果产生显著影响, MR-PRESSO法检验SNPs异常值, 通过错误发现率(FDR)进行矫正(FDR<0.2), 控制假阳性率。反向MR分析以瘢痕疙瘩为暴露因素, 将前述MR分析筛选得到的血液代谢物作为结局变量进行效应分析和敏感性分析。使用R 4.3.2软件及其中的TwoSampleMR程序包对数据进行分析, MR分析的因果效应值用比值比(OR)和95%CI表示, P<0.05为差异有统计学意义, 即潜在因果效应的证据显著。构建森林图、漏斗图、散点图对MR分析、敏感性分析结果进行可视化展示。结果从GWAS数据库中获得1 400种血液代谢物, 共有34 843个SNPs, 均符合遗传变异与暴露因素密切相关的假设;瘢痕疙瘩数据集共获得24 197 210个SNPs。IVW分析发现1种血液代谢物琥珀酰牛磺酸(16∶1n-7)有28个SNPs与瘢痕疙瘩具有因果关系(OR=1.13, 95%CI 1.06~1.19, P<0.001, FDR=0.07);MR-Egger回归法(OR=1.11, 95%CI 1.04~1.19, P=0.005)、加权中位数法(OR=1.11, 95%CI 1.02~1.20, P=0.014)和加权模型法(OR=1.12, 95%CI 1.04~1.20, P=0.004)分析结果也显示, 琥珀酰牛磺酸(16∶1n-7)是瘢痕疙瘩疾病的风险因素;仅简单模型法结果显示琥珀酰牛磺酸(16∶1n-7)与瘢痕疙瘩疾病的因果关系不明显(OR=1.10, 95%CI 0.85~1.41, P=0.485)。MR整体分析结果表明, 琥珀酰牛磺酸(16∶1n-7)与瘢痕疙瘩有显著的正向因果关系, 即琥珀酰牛磺酸(16∶1n-7)水平升高, 瘢痕疙瘩患病风险增高。CochranQ检验(Q=26.98, P=0.465)、MR-Egger回归检验(Q=26.65, P=0.428)、MR-Egger截距检测(P=0.574)、MR-PRESSO法检验(P=0.569)结果均显示, SNPs之间不存在异质性及水平多效性(P>0.05);留一法检验证实, 单个SNPs对整体结果没有显著影响, 说明结果具有可靠性和稳定性。而反向MR分析提示瘢痕疙瘩对琥珀酰牛磺酸(16∶1n-7)不存在因果关系(IVW:OR=0.98, 95%CI 0.93~1.04, P=0.490)。结论血液代谢物琥珀酰牛磺酸(16∶1n-7)与瘢痕疙瘩存在显著的正向因果关系, 琥珀酰牛磺酸(16∶1n-7)是瘢痕疙瘩疾病的风险因素。
Objective:To explore the causal relationship between blood metabolites and keloids.Methods:The study was a two-sample bidirectional Mendelian randomization(MR)analysis-based study.Data on blood metabolites were collected from 7824 adult volunteers and 8299 participants,alongside information related to 481912 keloid patients obtained from the genome-wide association studies(GWAS)database.Single nucleotide polymorphisms(SNPs)significantly associated with blood metabolites and keloids were screened for inclusion as instrumental variables in the MR analysis by setting a significance threshold of P<1.0×10^(-5),chain imbalance analysis[r^(2)=0.001,kilobase pairs(kb)=10000],and the F statistic(F≥10).Five methods of MR analysis were employed:inverse variance weighting(IVW)as the primary method,and MR-Egger regression,weighted median,simple modeling,and weighted modeling as auxiliary methods.These analyses aimed to determine the causal relationship between blood metabolites(exposure factors)and keloids(outcome variables).Sensitivity analyses were performed on eligible blood metabolite SNPs to assess the reliability and stability of the findings.Heterogeneity was assessed by Cochran Q-test and MR-Egger regression test.MR Egger intercept test was utilized to rule out horizontal pleiotropy,while the leave-one-out test determined whether the presence of a single SNP significantly affected the results of the MR analyses.The MR-PRESSO method was used to identify outliers among SNPs,which were corrected by false discovery rate(FDR)(FDR<0.2)to control the false positive rate.Reverse MR analysis was performed with keloid as the exposure factor,using the blood metabolites identified in the previous MR analysis as outcome variables for effect and sensitivity analyses.The data were analyzed using R version 4.3.2 and the TwoSampleMR package,with the causal effect values of the MR analysis expressed as odds ratio(OR)and 95%confidence intervals(CI).A P-value<0.05 was considered statistically significant,indicating substantial evidence of a potential causal effect.Forest plots,funnel plots,and scatter plots were constructed to visualize the results of the MR analysis and sensitivity analyses.Results:A total of 1400 blood metabolites with 34843 SNPs were obtained from the GWAS database,all of which support the hypothesis that genetic variants were closely associated with exposure factors.Additionally,24197210 SNPs were obtained from the keloid dataset.The IVW analysis revealed that one blood metabolite,succinyl taurine(16∶1n-7)with 28 SNPs,was linked to keloid disease,demonstrating a causal relationship(O R=1.13,95%CI 1.06-1.19,P<0.001,FDR=0.07).Further analyses using the MR-Egger regression method(OR=1.11,95%CI 1.04-1.19,P=0.005),the weighted median method(OR=1.11,95%CI 1.02-1.20,P=0.014)and weighted modeling method(OR=1.12,95%CI 1.04-1.20,P=0.004)also indicated that succinyl taurine(16∶1n-7)was a risk factor for keloid disease.However,the results from the simple modeling method only showed that the causal relationship between succinyl taurine(16∶1n-7)and keloid disease was not significant(OR=1.10,95%CI 0.85-1.41,P=0.485).MR overall analysis showed a significant positive causal relationship between succinyl taurine(16∶1n-7)and keloid,suggesting that elevated levels of succinyl taurine(16∶1n-7)were associated with an increased risk of keloid disease.The Cochran Q-test(Q=26.98,P=0.465),MR-Egger regression test(Q=26.65,P=0.428),MR-Egger intercept test(P=0.574),and MR-PRESSO composite test(P=0.569)indicated no heterogeneity or horizontal pleiotropy among SNPs(P>0.05).The leave-one-out test confirmed that individual SNPs did not have a significant effect on the overall results,indicating the reliability and stability of the findings.The inverse MR analysis suggested that there was no causal relationship between keloid on succinyl taurine(16∶1n-7)(IVW:OR=0.98,95%CI 0.93-1.04,P=0.490).Conclusion:There is a significant positive causal relationship between the blood metabolite succinyl taurine(16∶1n-7)and keloids,and succinyl taurine(16∶1n-7)is a risk factor for keloid disease.
作者
陈庆泳
林立强
吕怀庆
王冬青
Chen Qingyong;Lin Liqiang;Lyu Huaiqing;Wang Dongqing(Department of Otorhinolaryngology Head and Neck Surgery,the Second Clinical Medical College of Binzhou Medical University,Yantai 264003,China;Department of Otorhinolaryngology Head and Neck Surgery,Linyi City People’s Hospital,Linyi 276003,China)
出处
《中华整形外科杂志》
CSCD
北大核心
2024年第12期1331-1340,共10页
Chinese Journal of Plastic Surgery
