ArticleFrontiers in endocrinology2023
Untargeted metabolomic profiling of sepsis-induced cardiac dysfunction.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Plasma metabolomic signatures in patients with multidrug-resistant bacterial sepsis.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- A Metabolic Signature Specific to the Patients with Type 2 Diabetes and its Association with the Pathogenesis of Diabetic-Foot Syndrome.Journal of cardiovascular translational research · 2025Article
- Kynurenic Acid Is a Predictive Prognostic Metabolic Marker in ST-Elevation Myocardial Infarction.Cardiovascular therapeutics · 2025Article
- Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification.Molecules (Basel, Switzerland) · 2024Review
- Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review.Metabolites · 2024Review
- Article
- Characteristics of Metabolites in the Development of Atherosclerosis in Tibetan Minipigs Determined Using Untargeted Metabolomics.Nutrients · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Sepsis is a life-threatening condition secondary to infection that evolves into a dysregulated host response and is associated with acute organ dysfunction. Sepsis-induced cardiac dysfunction is one of the most complex organ failures to characterize. This study performed comprehensive metabolomic profiling that distinguished between septic patients with and without cardiac dysfunction. Method: Plasma samples collected from 80 septic patients were analysed by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyse the metabolic model between septic patients with and without cardiac dysfunction. The screening criteria for potential candidate metabolites were as follows: variable importance in the projection (VIP) >1, Results: Two metabolite markers, kynurenic acid and gluconolactone, could distinguish the cardiac dysfunction group from the normal cardiac function group. Two metabolites, kynurenic acid and galactitol, could distinguish survivors and non-survivors in the subgroup analysis. Kynurenic acid is a common differential metabolite that could be used as a candidate for both diagnosis and prognosis for septic patients with cardiac dysfunction. The main associated pathways were amino acid metabolism, glucose metabolism and bile acid metabolism. Conclusion: Metabolomic technology could be a promising approach for identifying diagnostic and prognostic biomarkers of sepsis-induced cardiac dysfunction.
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