应用代谢组学方法构建原发性肾病综合征诊断模型  

Application of metabolomics in establishing primary nephrotic syndrome diagnosis model

作　　者：张晓波[1] 李鞠[2] 乔善磊[1] 夏彦恺[3] 唐凤英[3] 

机构地区：[1]南京医科大学附属淮安第一医院肾内科,江苏省淮安市223300 [2]南京医科大学附属淮安第一医院风湿科,江苏省淮安市223300 [3]南京医科大学公共卫生学院

出　　处：《中华肾脏病杂志》2016年第5期334-338,共5页Chinese Journal of Nephrology

基　　金：国家自然科学基金青年科学基金项目（81102684）

摘　　要：目的应用代谢组学的方法分析原发性肾病综合征（ primary nephrotie syndrome,PNS）患者血清的代谢物特征并构建疾病血清代谢物诊断模型，寻找与疾病相关的代谢通路。方法选取2010年12月至2012年4月在南京医科大学附属淮安第一医院就诊的30例PNS患者。应用液相色谱质谱联用技术（LC—MS）方法对PNS患者和健康志愿者（n=30）血清样本进行代谢物无靶标检测，采集代谢物指纹图谱，结合模式识别分析方法构建PNS诊断模型，并进行MetPA代谢通路分析。结果主成分分析法（PCA）、非监督的偏最小二乘法-判别分析（PLS-DA）均显著区分PNS组与健康对照组，PLS-DA模型的预测率Q^2=0．300，解释性良好（R2）（=0．581，R^2Y=0．452）。与健康对照组比较，PNS患者血清胆甾烷3，7，12，15醇、二酰甘油、植物鞘氨醇和色氨酸均降低，鞘磷脂、精氨酸和谷氨酸均增加（均VIP〉1且P〈0．05）；PNS患者血清代谢紊乱通路主要包括鞘脂类代谢、精氨酸脯氨酸代谢、亚油酸代谢、嘧啶代谢（影响因子，0．10，均P〈0．05）。结论代谢组学技术结合模式识别分析方法有望成为临床诊断PNS及病情监测的新方法。Objective To establish diagnosis model and explore related metabolic pathways by analyzing the serum metabolic profile of patients with primary nephrotic syndrome （PNS） through metabolomics. Methods Thirty PNS patients hospitalized in Huai/an First People＇s Hospital between December 2010 and April 2012 were enrolled. High performance liquid chromatography-mass spectrometry （LC- MS） was employed to detect metabolites in the serum of 30 PNS patients and 30 healthy controls. Metabolic fingerprint profiling and multivariate pattern recognition analysis were combined to establish disease-specific metabolic diagnosis model, and metabolic pathway analysis was performed. Results PNS group and control group could be well separated by principal component analysis （PCA） model as well as partial least-squares discriminant analysis （PLS-DA） model with Q2 of 0.300. There was well interpretation in PLA- DA model （R2X=0.581, R2Y=0.452）. Compared with healthy controls, PNS patients had decreased cholestane 3, 7, 12, 15 alcohol, acyl glycerine, phytosphingosine and tryptophan, and increased sphingomyelin, arginine and glutamic acid （all VIP 〉 1, P〈 0.05）. The metabolic disorders pathways of PNS patients included sphingolipid metabolism, arginine and proline metabolism, linoleic acid metabolism and pyrimidine metabolism （all impact 〉0.10 and P 〈 0.05）. Conclusions Metabolomics combined with multivariate pattern recognition analysis may be a new tool for diagnosis and monitoring of PNS.

关 键 词：肾病综合征 诊断技术和方法 代谢组学 

分 类 号：R692[医药卫生—泌尿科学]