Predictive value of targeted proteomics for coronary plaque morphology in patients with suspected coronary artery disease

EBioMedicine. 2019 Jan;39:109-117. doi: 10.1016/j.ebiom.2018.12.033. Epub 2018 Dec 23.

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.

Keywords: Biomarkers; Coronary artery disease; Coronary computed tomography angiography; Proteomics; Risk assessment.

MeSH terms

  • Aged
  • Area Under Curve
  • Biomarkers / blood*
  • Computed Tomography Angiography
  • Coronary Artery Disease / blood
  • Coronary Artery Disease / diagnostic imaging*
  • Coronary Artery Disease / metabolism
  • Female
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Proteomics / methods*
  • Risk Assessment

Substances

  • Biomarkers