ArticleScientific reports2016
Analysis of Serum Metabolites to Diagnose Bicuspid Aortic Valve.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Resolving Early Targets and Metabolomic Profile of Congenital Heart Disease Through Tandem Mass Spectrometry Screening in Neonates.Journal of the American Heart Association · 2025Article
- Biomarkers for congenital ventricular outflow tract malformations based on maternal serum lipid metabolomics analysis.BMC pregnancy and childbirth · 2024Article
- Metabolic profiling reveals circulating biomarkers associated with incident and prevalent Parkinson's disease.NPJ Parkinson's disease · 2024Article
- High-Throughput Metabolomics Applications in Pathogenesis and Diagnosis of Valvular Heart Disease.Reviews in cardiovascular medicine · 2023Review
- Metabolomics: A New Tool in Our Understanding of Congenital Heart Disease.Children (Basel, Switzerland) · 2022Review
- Biomarkers for isolated congenital heart disease based on maternal amniotic fluid metabolomics analysis.BMC cardiovascular disorders · 2022Article
- Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease.Molecules (Basel, Switzerland) · 2021Article
- Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis.Circulation research · 2021Review
- Plasma Metabolomic Profiling Associates Bicuspid Aortic Valve Disease and Ascending Aortic Dilation with a Decrease in Antioxidant Capacity.Journal of clinical medicine · 2020Article
- Disturbed energy and amino acid metabolism with their diagnostic potential in mitral valve disease revealed by untargeted plasma metabolic profiling.Metabolomics : Official journal of the Metabolomic Society · 2019Article
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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