Frontiers in molecular biosciences · 2026
Metabolic alterations and potential biomarkers in unstable angina investigated by lipidomic analysis.
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Feng Nan, Wang Linhe, Zhao Guoan, Zhou Yanlin
Аннотация
INTRODUCTION: Unstable angina (UA) represents a critical condition within the broader context of acute coronary syndromes, characterized by episodes of sudden chest pain resulting from insufficient blood flow to the myocardium. The pathophysiological mechanisms underlying UA remain complex and poorly understood, necessitating further investigation into the metabolic alterations associated with this condition. Identifying specific biomarkers for UA is crucial for improving diagnostic accuracy and facilitating timely therapeutic interventions.
METHODS: The present study was designed to elucidate the metabolic profile of UA by enrolling 60 patients diagnosed with UA, alongside 60 healthy controls. Participants were stratified into discovery and validation cohorts, with each group comprising 30 UA patients and 30 controls. The diagnosis of UA was performed in accordance with the 2020 European Society of Cardiology guidelines and the 2019 Chinese clinical pathway for UA management. Blood samples were meticulously collected following an overnight fast, processed, and subsequently stored at -80 °C. A comprehensive lipidomic analysis was executed utilizing ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), with compound identification referenced from the Metware Database. Data analysis involved advanced multivariate statistical techniques, including principal component analysis and orthogonal partial least squares-discriminant analysis.
RESULTS: The analysis revealed significant metabolic discrepancies between the UA and control groups, identifying a total of 193 differential metabolites, of which 67 were notably upregulated in the UA cohort. Pathway enrichment analysis highlighted critical alterations in glycerophospholipid metabolism and necroptosis pathways. Furthermore, the application of machine learning algorithms, specifically neural networks and random forests, enabled the identification of key metabolic biomarkers, including TxB3, LPC(16:0/0:0), and DL-Carnitine, which exhibited robust diagnostic potential.
CONCLUSION: In summary, our findings indicate that these metabolites may serve as promising candidate diagnostic biomarkers for UA, providing valuable insights into its pathophysiology.
METHODS: The present study was designed to elucidate the metabolic profile of UA by enrolling 60 patients diagnosed with UA, alongside 60 healthy controls. Participants were stratified into discovery and validation cohorts, with each group comprising 30 UA patients and 30 controls. The diagnosis of UA was performed in accordance with the 2020 European Society of Cardiology guidelines and the 2019 Chinese clinical pathway for UA management. Blood samples were meticulously collected following an overnight fast, processed, and subsequently stored at -80 °C. A comprehensive lipidomic analysis was executed utilizing ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), with compound identification referenced from the Metware Database. Data analysis involved advanced multivariate statistical techniques, including principal component analysis and orthogonal partial least squares-discriminant analysis.
RESULTS: The analysis revealed significant metabolic discrepancies between the UA and control groups, identifying a total of 193 differential metabolites, of which 67 were notably upregulated in the UA cohort. Pathway enrichment analysis highlighted critical alterations in glycerophospholipid metabolism and necroptosis pathways. Furthermore, the application of machine learning algorithms, specifically neural networks and random forests, enabled the identification of key metabolic biomarkers, including TxB3, LPC(16:0/0:0), and DL-Carnitine, which exhibited robust diagnostic potential.
CONCLUSION: In summary, our findings indicate that these metabolites may serve as promising candidate diagnostic biomarkers for UA, providing valuable insights into its pathophysiology.