Analysis of Serum Metabolites to Diagnose Bicuspid Aortic Valve.
Analysis of Serum Metabolites to Diagnose Bicuspid Aortic Valve.
复制标题
分析血清代谢物诊断二尖瓣主动脉瓣。
DOI：
10.1038/srep37023
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发表时间：
2016-11-15
影响因子：
4.6
通讯作者：
Wei L
中科院分区：
文献类型：
--
作者：
Wang W;Maimaiti A;Zhao Y;Zhang L;Tao H;Nian H;Xia L;Kong B;Wang C;Liu M;Wei L
二叶式主动脉瓣（BAV）是最常见的先天性心脏病。本研究旨在构建一个基于代谢谱的诊断模型，作为BAV筛查的非侵入性工具。从估计组和验证组制备血清样品，每个组由30名BAV患者和20名健康个体组成，并通过液相色谱-质谱法（LC-MS）进行分析。总共检测到2213种代谢物，其中41种被认为是不同的。使用甘油单酯（MG）（18：2）和甘油磷酸-N-油酰乙醇胺（GNOE）的浓度水平构建了预测估计组中BAV的模型。建立了一个新的模型Zhongshan（ZS）来放大BAV与两种代谢产物之间的关联。ZS用于BAV预测的曲线下面积（AUC）为0.900（0.782-0.967），当应用于估计组时上级所有单一代谢物模型。使用优化的截止值（-0.1634），ZS模型对验证组的敏感性评分为76.7%，特异性评分为90.0%，阳性预测值为80%，阴性预测值为85.0%。这些结果支持使用基于血清的代谢组学分析方法作为大群体中BAV筛查的补充工具。
Bicuspid aortic valve (BAV) is the most common congenital heart disease. The current study aims to construct a diagnostic model based on metabolic profiling as a non-invasive tool for BAV screening. Blood serum samples were prepared from an estimation group and a validation group, each consisting of 30 BAV patients and 20 healthy individuals, and analyzed by liquid chromatography-mass spectrometry (LC-MS). In total, 2213 metabolites were detected and 41 were considered different. A model for predicting BAV in the estimation group was constructed using the concentration levels of monoglyceride (MG) (18:2) and glycerophospho-N-oleoyl ethanolamine (GNOE). A novel model named Zhongshan (ZS) was developed to amplify the association between BAV and the two metabolites. The area under curve (AUC) of ZS for BAV prediction was 0.900 (0.782–0.967) and was superior to all single-metabolite models when applied to the estimation group. Using optimized cutoff (−0.1634), ZS model had a sensitivity score of 76.7%, specificity score of 90.0%, positive predictive value of 80% and negative predictive value of 85.0% for the validation group. These results support the use of serum-based metabolomics profiling method as a complementary tool for BAV screening in large populations.
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影响因子：
24
作者：
Cheng, Mei-Ling;Wang, Chao-Hung;Yang, Ning-I
通讯作者：
Yang, Ning-I
影响因子：
64.8
作者：
Durst R;Sauls K;Peal DS;deVlaming A;Toomer K;Leyne M;Salani M;Talkowski ME;Brand H;Perrocheau M;Simpson C;Jett C;Stone MR;Charles F;Chiang C;Lynch SN;Bouatia-Naji N;Delling FN;Freed LA;Tribouilloy C;Le Tourneau T;LeMarec H;Fernandez-Friera L;Solis J;Trujillano D;Ossowski S;Estivill X;Dina C;Bruneval P;Chester A;Schott JJ;Irvine KD;Mao Y;Wessels A;Motiwala T;Puceat M;Tsukasaki Y;Menick DR;Kasiganesan H;Nie X;Broome AM;Williams K;Johnson A;Markwald RR;Jeunemaitre X;Hagege A;Levine RA;Milan DJ;Norris RA;Slaugenhaupt SA
通讯作者：
Slaugenhaupt SA
慢性心力衰竭患者诊断血浆生物标志物的代谢组学鉴定
影响因子：
--
作者：
Wang, Juan;Li, Zhongfeng;Wang, Wei
通讯作者：
Wang, Wei
影响因子：
64.8
作者：
Garg, V;Muth, AN;Srivastava, D
通讯作者：
Srivastava, D
影响因子：
7.3
作者：
Sun, Y.;Alexander, S. P. H.;Bennett, A. J.
通讯作者：
Bennett, A. J.