Integrating multi-omics and machine learning strategies to explore the “gene-protein-metabolite” network in ischemic heart failure with Qi deficiency and blood stasis syndrome

Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study...

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Published in:Chinese medicine Vol. 20; no. 1; pp. 93 - 18
Main Authors: Wei, Jingjing, Wang, Aolong, Yu, Peng, Sun, Yang, Wu, Wenjun, Zhang, Yilin, Yu, Rui, Li, Bin, Zhu, Mingjun
Format: Journal Article
Language:English
Published: London BioMed Central 17.07.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Acetyl-L-carnitine
Acupuncture
Algorithms
Arachidonic acid
Artificial Intelligence in Traditional Chinese Medicine
Biology
Biomarkers
Biotechnology
Cardiovascular disease
Cell activation
China
Coagulation
Complementary & Alternative Medicine
Congestive heart failure
Data mining
Energy metabolism
Ethylenediaminetetraacetic acid
Genes
Genomes
Gluconeogenesis
Glycolysis
Health aspects
Heart failure
Hospitals
Hypoxia-inducible factor 1a
Inflammation
Informed consent
Ischemia
Ischemic heart failure
Learning algorithms
Learning strategies
Leukocytes (neutrophilic)
Machine learning
Medical diagnosis
Medical research
Medicine
Medicine & Public Health
Medicine, Experimental
Metabolites
Metabolomics
Molecular docking
Mortality
Multi-omics
N-Acetylaspartylglutamic acid
Oxidative phosphorylation
Phosphorylation
Proteins
Proteomics
RNA
RNA sequencing
Signal transduction
Taiwan
Traditional Chinese Medicine
United States
Yiqi Huoxue
United States
China
Taiwan
Beijing China
United States > US
Traditional Chinese medicine
Ischemic heart failure
Yiqi Huoxue
Multi-omics
Molecular docking
Machine learning
ISSN:1749-8546, 1749-8546
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Abstract Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach. Methods We enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive “gene-protein-metabolite” network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM). Results Patients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The “gene-protein-metabolite” network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1α, IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75). Conclusion This study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field. Graphical Abstract
AbstractList Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach.BACKGROUNDIschemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach.We enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive "gene-protein-metabolite" network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM).METHODSWe enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive "gene-protein-metabolite" network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM).Patients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The "gene-protein-metabolite" network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1α, IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75).RESULTSPatients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The "gene-protein-metabolite" network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1α, IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75).This study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field.CONCLUSIONThis study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field.
Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach. We enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive "gene-protein-metabolite" network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM). This study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field.
Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach. Methods We enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive “gene-protein-metabolite” network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM). Results Patients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The “gene-protein-metabolite” network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1α, IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75). Conclusion This study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field. Graphical Abstract
Abstract Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach. Methods We enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive “gene-protein-metabolite” network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM). Results Patients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The “gene-protein-metabolite” network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1α, IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75). Conclusion This study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field. Graphical Abstract
Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach. Methods We enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive "gene-protein-metabolite" network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM). Results Patients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The "gene-protein-metabolite" network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1[alpha], IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75). Conclusion This study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field. Graphical Keywords: Ischemic heart failure, Yiqi Huoxue, Multi-omics, Machine learning, Molecular docking, Traditional Chinese medicine
BackgroundIschemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach.MethodsWe enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive “gene-protein-metabolite” network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM).ResultsPatients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The “gene-protein-metabolite” network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1α, IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75).ConclusionThis study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field.
Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach. We enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive "gene-protein-metabolite" network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM). Patients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The "gene-protein-metabolite" network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1α, IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75). This study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field.
ArticleNumber 93
Audience Academic
Author Wei, Jingjing
Yu, Peng
Wang, Aolong
Zhu, Mingjun
Li, Bin
Zhang, Yilin
Sun, Yang
Wu, Wenjun
Yu, Rui
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  surname: Wei
  fullname: Wei, Jingjing
  organization: Department of Cardiovascular Disease, The First Affiliated Hospital of Henan University of Chinese Medicine
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  surname: Wang
  fullname: Wang, Aolong
  organization: Department of Cardiovascular Disease, The First Affiliated Hospital of Henan University of Chinese Medicine
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  givenname: Peng
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  organization: Department of Orthopedics, The First Affiliated Hospital of Henan University of Chinese Medicine
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  surname: Sun
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  organization: Department of Cardiovascular Disease, The First Affiliated Hospital of Henan University of Chinese Medicine
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  surname: Wu
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  organization: Department of Cardiovascular Disease, The First Affiliated Hospital of Henan University of Chinese Medicine
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/40671145$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1016_j_jtcms_2025_09_005
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Issue 1
Keywords Traditional Chinese medicine
Ischemic heart failure
Yiqi Huoxue
Multi-omics
Molecular docking
Machine learning
Language English
License 2025. The Author(s).
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Snippet Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to...
Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional...
Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to...
BackgroundIschemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to...
Abstract Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally....
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StartPage 93
SubjectTerms Acetyl-L-carnitine
Acupuncture
Algorithms
Arachidonic acid
Artificial Intelligence in Traditional Chinese Medicine
Biology
Biomarkers
Biotechnology
Cardiovascular disease
Cell activation
China
Coagulation
Complementary & Alternative Medicine
Congestive heart failure
Data mining
Energy metabolism
Ethylenediaminetetraacetic acid
Genes
Genomes
Gluconeogenesis
Glycolysis
Health aspects
Heart failure
Hospitals
Hypoxia-inducible factor 1a
Inflammation
Informed consent
Ischemia
Ischemic heart failure
Learning algorithms
Learning strategies
Leukocytes (neutrophilic)
Machine learning
Medical diagnosis
Medical research
Medicine
Medicine & Public Health
Medicine, Experimental
Metabolites
Metabolomics
Molecular docking
Mortality
Multi-omics
N-Acetylaspartylglutamic acid
Oxidative phosphorylation
Phosphorylation
Proteins
Proteomics
RNA
RNA sequencing
Signal transduction
Taiwan
Traditional Chinese Medicine
United States
Yiqi Huoxue
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Title Integrating multi-omics and machine learning strategies to explore the “gene-protein-metabolite” network in ischemic heart failure with Qi deficiency and blood stasis syndrome
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