Role of serum cytokines in the prediction of heart failure in patients with coronary artery disease
Open accessAbstract
Aims: Coronary artery disease (CAD) is the most common cause of heart failure (HF). This study aimed to identify cytokine biomarkers for predicting HF in patients with CAD. Methods and results: Twelve patients with CAD without HF (CAD-non HF), 12 patients with CAD complicated with HF (CAD-HF), and 12 healthy controls were enrolled for Human Cytokine Antibody Array, which were used as the training dataset. Then, differentially expressed cytokines among the different groups were identified, and crucial characteristic proteins related to CAD-HF were screened using a combination of the least absolute shrinkage and selection operator, recursive feature elimination, and random forest methods. A support vector machine (SVM) diagnostic model was constructed based on crucial characteristic proteins, followed by receiver operating characteristic curve analysis. Finally, two validation datasets, GSE20681 and GSE59867, were downloaded to verify the diagnostic performance of the SVM model and expression of crucial proteins, as well as enzyme-linked immunosorbent assay was also used to verify the levels of crucial proteins in blood samples. In total, 12 differentially expressed proteins were overlapped in the three comparison groups, and then four optimal characteristic proteins were identified, including VEGFR2, FLRG, IL-23, and FGF-21. After that, the area under the receiver operating characteristic curve of the constructed SVM classification model for the training dataset was 0.944. The accuracy of the SVM classification model was validated using the GSE20681 and GSE59867 datasets, with area under the receiver operating characteristic curve values of 0.773 and 0.745, respectively. The expression trends of the four crucial proteins in the training dataset were consistent with those in the validation dataset and those determined by enzyme-linked immunosorbent assay. Conclusions: The combination of VEGFR2, FLRG, IL-23, and FGF-21 can be used as a candidate biomarker for the prediction and prevention of HF in patients with CAD.
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Hou et al. [149] achieved an AUC of 0.944.
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From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [149]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
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Self-citation
- Authors
- Hou, Q., Sun, Z., Zhao, L., Liu, Y., Zhang, J., Huang, J., Luo, Y., Xiao, Y., Hu, Z., & Shen, A.
- Affiliations
- Third Affiliated Hospital of Southern Medical University (CN)
- Corresponding author
- Hu, Zhaoting
- Shen, Anna
- Venue
- ESC Heart Failure
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-08-22
- Volume
- 10
- Issue
- 5
- Pages
- 3102-3113
- ISSN
- 2055-5822 (ISSN-L)
- PMC
- PMC10567644
- OpenAlex
- W4386084816
- Cited by
- 5
- References
- 47
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 1
- FWCI
- 0.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ESC Heart Failure (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Fibrosis and Remodeling
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- heart failure, coronary artery disease, serum cytokines, cytokine biomarkers, heart failure prediction, VEGFR2, IL-23, FGF21, support vector machine classification, differentially expressed proteins, blood biomarkers
- MeSH terms
- Aged, Coronary Artery Disease, Enzyme-Linked Immunosorbent Assay, Female, Heart Failure, Humans, Male, Middle Aged, Prognosis, ROC Curve, Biomarkers, Cytokines, Support Vector Machine
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