Prediction of coronary artery disease using urinary proteomics.


Journal

European journal of preventive cardiology
ISSN: 2047-4881
Titre abrégé: Eur J Prev Cardiol
Pays: England
ID NLM: 101564430

Informations de publication

Date de publication:
10 10 2023
Historique:
received: 12 01 2023
revised: 13 03 2023
accepted: 20 03 2023
medline: 23 10 2023
pubmed: 22 3 2023
entrez: 21 3 2023
Statut: ppublish

Résumé

Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD. Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly). A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention. A biomarker that can predict coronary artery disease (CAD) is urgently in need. We developed and validated a urinary proteomic classifier for the prediction of CAD. The proteomic classifier involved in atherosclerosis improved the risk reclassification on top of the clinical risk score.

Autres résumés

Type: plain-language-summary (eng)
A biomarker that can predict coronary artery disease (CAD) is urgently in need. We developed and validated a urinary proteomic classifier for the prediction of CAD. The proteomic classifier involved in atherosclerosis improved the risk reclassification on top of the clinical risk score.

Identifiants

pubmed: 36943304
pii: 7082480
doi: 10.1093/eurjpc/zwad087
doi:

Substances chimiques

Proteome 0
Biomarkers 0
Peptides 0
Collagen 9007-34-5

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1537-1546

Commentaires et corrections

Type : CommentIn

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.

Déclaration de conflit d'intérêts

Conflict of interest: The authors have no conflicts of interest to declare.

Auteurs

Dongmei Wei (D)

Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium.

Jesus D Melgarejo (JD)

Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium.

Lucas Van Aelst (L)

Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.

Thomas Vanassche (T)

Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.

Peter Verhamme (P)

Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.

Stefan Janssens (S)

Division of Cardiology, University Hospitals Leuven, University of Leuven, Herestraat 49, 3000 Leuven, Belgium.

Karlheinz Peter (K)

Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne VIC 3004, Australia.
Department of Cardiology, The Alfred Hospital, 55 Commercial Rd, Melbourne VIC 3004, Australia.

Zhen-Yu Zhang (ZY)

Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium.

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Classifications MeSH