基于非靶向代谢组学对缺血性卒中代谢变化及潜在生物标志物的分析研究  

An analysis of metabolic changes and potential biomarkers in ischemic stroke based on untargeted metabolomics

作　　者：王云宇 李亚奇 赵田 韩丽媛 李永安[3] 钱庆增 Wang Yunyu;Li Yaqi;Zhao Tian;Han Liyuan;Li Yongan;Qian Qingzeng(School of Public Health,North China University of Science and Technology,Tangshan,Hebei 063210,China;不详)

机构地区：[1]华北理工大学公共卫生学院,唐山063210 [2]国科宁波生命与健康产业研究院心脑血管流行病学及转化医学中心,中国科学院大学宁波市第二医院临床流行病学研究室 [3]苏州市相城人民医院神经内科

出　　处：《中国脑血管病杂志》2025年第3期199-209,共11页Chinese Journal of Cerebrovascular Diseases

基　　金：河北省自然科学基金(H2021209060)。

摘　　要：目的通过代谢组学分析,探索缺血性卒中(IS)患者血清代谢物及代谢途径的变化,筛选可靠的IS血清代谢标志物。方法前瞻性连续纳入2022年12月1日至2023年12月31日于苏州市相城人民医院神经内科就诊的IS患者作为IS组,并招募同期的健康体检者,经年龄、性别1∶1匹配后作为对照组。收集所有参与者入组时的年龄、性别、身高、体质量指数及血压等基线资料。采集所有参与者禁食8 h后的清晨空腹血样本,完成相关血液指标检测,包括血糖、总胆红素、血清肌酐、尿素氮、总胆固醇、三酰甘油、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇。提取两组参与者的血清代谢物,通过超高效液相色谱和串联质谱分离和检测代谢物。将代谢组学分析产生的数据导入Simca-p软件进行分析,通过无监督主成分分析(PCA)和正交偏最小二乘法判别分析(OPLS-DA)判断代谢组学数据的分离程度及实验稳定性。通过Simca-p软件计算出所有代谢物的变量投影影响(VIP)值,以代谢物的VIP值、IS组与对照组代谢物非参数检验的P值和倍数变化(FC)筛选差异代谢物,差异代谢物筛选标准为VIP值≥1.0、FC≥2.0或≤0.5、P<0.05。将筛选出的代谢物名称或化学式通过人类代谢组数据库(HMDB;https://hmdb.ca/)和PubChem数据库(https://pubchem.ncbi.nlm.nih.gov/)进行比对,确定代谢物名称及来源,并排除外源性代谢物(药物来源代谢物)。通过代谢组学数据分析和解释的综合网络应用程序MetaboAnalyst 6.0(http://www.metaboanalyst.ca)对差异代谢物行京都基因与基因组百科全书(KEGG)数据比对后行代谢通路富集分析。使用Python构建机器学习模型。采用最小绝对收缩和选择算子(LASSO)回归和随机森林算法筛选能够有效区分IS患者与对照者的差异代谢物。将上述两种算法共同筛选出的差异代谢物纳入极限梯度提升(XGBoost)算法,构建诊断模型。通过受�Objective To investigate serum metabolites and metabolic pathways alterations in patients with ischemic stroke(IS)through metabolomic analysis,and to identify reliable serum metabolic biomarkers for IS diagnosis.Methods This prospective study enrolled patients with IS admitted to the Department of Neurology at Xiangcheng People′s Hospital of Suzhou from December 1,2022 to December 31,2023.Age-and sex-matched healthy individuals were recruited as controls during the same period.Baseline characteristics were collected,including age,sex,height,body mass index,and blood pressure.Venous blood samples were obtained after an 8 h fast for biochemical analysis of blood glucose,total bilirubin,serum creatinine,urea nitrogen,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol.Serum metabolites of both groups were extracted and analyzed using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry.Metabolomic data were processed using Simca-p software for unsupervised principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA)to evaluate group separation and experimental stability.Differential metabolites were defined by variable importance in projection(VIP)≥1.0,fold change(FC)≥2.0 or≤0.5,and P<0.05.Drug-derived exogenous metabolites were excluded by cross-referencing the Human Metabolome Database(HMDB,https://hmdb.ca/)and PubChem(https://pubchem.ncbi.nlm.nih.gov/).MetaboAnalyst 6.0(http://www.metaboanalyst.ca),a comprehensive web-based tool for metabolomic data analysis,was employed to map differential metabolites to the Kyoto encyclopedia of genes and genomes(KEGG)databased and to perform pathway enrichment analysis.Machine learning models were developed using Python.Least absolute shrinkage and selection operator(LASSO)regression and random forest(RF)algorithms were employed to identify diagnostic biomarkers capable of effectively distinguishing IS patients from controls.Metabolites identif

关 键 词：缺血性卒中 代谢组学 血清 生物标志物 诊断模型 

分 类 号：R743.3[医药卫生—神经病学与精神病学]