Analysis of Serum Metabolites to Diagnose Bicuspid Aortic Valve

  • Sci Rep. 2016 Nov 15:6:37023. doi: 10.1038/srep37023.
Wenshuo Wang  1 ,  Aikebaier Maimaiti  1 ,  Yun Zhao  1 ,  Lingfei Zhang  2 ,  Hongyue Tao  3 ,  Hui Nian  1 ,  Limin Xia  1 ,  Biao Kong  4 ,  Chunsheng Wang  1 ,  Mofang Liu  2 ,  Lai Wei  1
Affiliations
  • 1. Department of Cardiac Surgery, Zhongshan Hospital, Fudan University. 200032, Shanghai, China.
  • 2. State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, China.
  • 3. Department of Radiology, Huashan Hospital, Fudan University. 200040, Shanghai, China.
  • 4. Department of Chemistry, Laboratory of Advanced Materials, Fudan University, 200433, Shanghai, China.
PMID: 27845433 DOI: 10.1038/srep37023
Abstract

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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