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- Document type:
- Article; Journal Article
- Author(s):
- Bom, Michiel J; Levin, Evgeni; Driessen, Roel S; Danad, Ibrahim; Van Kuijk, Cornelis C; van Rossum, Albert C; Narula, Jagat; Min, James K; Leipsic, Jonathon A; Belo Pereira, João P; Taylor, Charles A; Nieuwdorp, Max; Raijmakers, Pieter G; Koenig, Wolfgang; Groen, Albert K; Stroes, Erik S G; Knaapen, Paul
- Title:
- Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery disease.
- Abstract:
- BACKGROUND: Risk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA). METHODS: Patients with suspected CAD (n = 203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers. FINDINGS: A total of 196 patients with analyzable protein levels (n = 332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79 ± 0·01, outperforming prediction with generally available clinical characteristics (AUC = 0·65 ± 0·04, p < 0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC = 0·85 ± 0·05), again outperforming prediction with generally available characteristics (AUC = 0·70 ± 0·04, p < 0·05). INTERPRETATION: Using machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies. FUND: This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.
- Journal title abbreviation:
- EBioMedicine
- Year:
- 2019
- Journal volume:
- 39
- Pages contribution:
- 109-117
- Fulltext / DOI:
- doi:10.1016/j.ebiom.2018.12.033
- Pubmed ID:
- http://view.ncbi.nlm.nih.gov/pubmed/30587458
- TUM Institution:
- Klinik für Herz- und Kreislauferkrankungen im Erwachsenenalter (Prof. Schunkert)
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