ArticleScientific reports2023
Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Advances in postmortem interval estimation: A systematic review of machine learning and metabolomics across various tissue types.Forensic science, medicine, and pathology · 2025Pooled it
- A low-protein diet supplemented with an α-ketoacid-diet modulates intestinal flora and urinary metabolism in diabetic nephropathy mice.Nutrition research and practice · 2026Article
- Plasma-based Raman spectroscopy for early detection of acute myocardial infarction in murine models.Scientific reports · 2025Article
- Recent progress in metabolomic analysis of acute coronary syndrome: a narrative review.Cardiovascular diagnosis and therapy · 2025Review
- Urinary metabolomics reveals myo-inositol and (E)-Monocrotophos associate with prognosis in ACS comorbid with T2DM and preserved renal function.Scientific reports · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Urinary metabolomics analysis based on LC-MS for the diagnosis and monitoring of acute coronary syndrome.Frontiers in molecular biosciences · 2025Article
- Multi-Omics Research on Angina Pectoris: A Novel Perspective.Aging and disease · 2024Review
- Identification of novel hypertension biomarkers using explainable AI and metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Recent advances in cardiovascular disease research driven by metabolomics technologies in the context of systems biology.npj metabolic health and disease · 2024Review
- Exploring the role of CBLB in acute myocardial infarction: transcriptomic, microbiomic, and metabolomic analyses.Journal of translational medicine · 2024Article
- Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
Abstract
A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.
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