ArticleBMC cardiovascular disorders2024
Analysis of differential metabolites in serum metabolomics of patients with aortic dissection.
Article in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 5 citations in OpenAlex.
- Integrated multi-omics analysis reveals a gut microbiota-tryptophan metabolism axis contributes to sex differences in a β-aminopropionitrile-induced aortic dissection mouse model.Biology of sex differences · 2026Article
- Efficacy of multimodal rehabilitation strategies on gastrointestinal function recovery in postoperative aortic dissection patients: a narrative review.Frontiers in physiology · 2025Review
- An Integrated Approach Based on Clinical Data Combined with Metabolites and Biomarkers for the Assessment of Post-Operative Complications after Cardiac Surgery.Journal of clinical medicine · 2024Article
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8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundPathogenesis and diagnostic biomarkers of aortic dissection (AD) can be categorized through the analysis of differential metabolites in serum. Analysis of differential metabolites in serum provides new methods for exploring the early diagnosis and treatment of aortic dissection.
objectivesThis study examined affected metabolic pathways to assess the diagnostic value of metabolomics biomarkers in clients with AD.
methodThe serum from 30 patients with AD and 30 healthy people was collected. The most diagnostic metabolite markers were determined using metabolomic analysis and related metabolic pathways were explored.
resultsIn total, 71 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism and four different metabolites considered of most diagnostic value including N2-gamma-glutamylglutamine, PC(phocholines) (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine, and taurine. These four predictive metabolic biomarkers accurately classified AD patient and healthy control (HC) samples with an area under the curve (AUC) of 0.9875. Based on the value of the four different metabolites, a formula was created to calculate the risk of aortic dissection. Risk score = (N2-gamma-glutamylglutamine × -0.684) + (PC (20:4(5Z,8Z,11Z,14Z)/15:0) × 0.427) + (propionyl carnitine × 0.523) + (taurine × -1.242). An additional metabolic pathways model related to aortic dissection was explored.
conclusionMetabolomics can assist in investigating the metabolic disorders associated with AD and facilitate a more in-depth search for potential metabolic biomarkers.
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