ArticleComputational and mathematical methods in medicine2022
Identification of Serum Metabolomics Characteristics in Patients with Stable Angina Pectoris Using UHPLC-QE-MS.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Wheat bran oil ameliorates high-fat diet-induced obesity in rats with alterations in gut microbiota and liver metabolite profile.Nutrition & metabolism · 2024Article
- Retracted: Identification of Serum Metabolomics Characteristics in Patients with Stable Angina Pectoris Using UHPLC-QE-MS.Computational and mathematical methods in medicine · 2023Article
Corrections and comments
- Retraction · 2023-07-12Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Stable angina pectoris (SAP) is one of the main types of coronary heart disease (CHD). To improve treatment outcomes, more effective biomarkers are needed. Currently, studies on the metabolic characteristics of SAP are lacking. Here, we explored the serum metabolomic profile of SAP and identified potential biomarkers and related pathways to assist the clinical diagnosis and treatment of SAP. Method: Thirty patients with SAP patients and 30 healthy controls (CON) without stenosis were selected for this study. All patients underwent coronary angiography. The metabolites of the two groups' serum samples were investigated using UHPLC-QE-MS. Changes in serum metabolic profile were evaluated using multivariate statistical analysis and pathway analysis. Result: OPLS-DA analysis identified significant differences in the serum metabolic profiles between patients with SAP and CON. Twenty-five differential metabolites were identified between patients from SAP and CON groups, including choline, creatine, L-arginine, beta-guanidinopropionic acid, isopalmitic acid, xanthine, LysoPC (18 : 1), and LysoPC (20 : 3). Pathway analysis found that these differential metabolites were involved in energy metabolism, oxidative stress, purine metabolism, and other metabolic pathways. Conclusion: By comparing the serum metabolic profiles of SAP patients with a control group, we identified 25 potential biomarkers that could improve the clinical diagnosis and treatment of SAP.
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