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.

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

Fig. 1

Protein subset predictive for the presence of a high-risk plaque. The importance plot (left panel) illustrates the relative importance of all 35 plasma proteins predictive for the presence of high-risk plaque. The spiderplot (right panel) depicts the 11 most important proteins in our machine learning model that differentiate between the presence (red) and absence of high-risk plaque (green). The axes of the spiderplot represent the standarized mean protein levels (scaled zero-mean unit-variance). Standaridized mean levels of MMP12, PLA2G7, TNFRSF10A, TRANCE, REN, TNFRSF13B, PRSS27, MEPE, and CD4 were higher in the high-risk plaque group compared to the non high-risk group. Conversely, TNFRSF10C and SERPINA7 levels were lower in the high-risk group. Abbrevations of protein names are defined in Supplementary Table 1.

Fig. 2

Diagnostic performance of the biomarker model versus the clinical model and the combined model for the presence of high-risk plaque. Receiver-operating characteristics curve with area under the curve (AUC) for the diagnostic performance of the clinical model (left), the biomarker model (middle) and the combined model (right) for the presence of high-risk coronary artery disease. The mean ROC curve for each model is depicted by the blue line. The grey shaded area represents the standard deviation of the curves. The clinical model was outperformed by both the biomarker model (p < 0·05) and the combined model (p < 0·05).

Fig. 3

Protein subset predictive for CCTA-defined absence of coronary atherosclerosis. Importance plot (left panel) illustrates the relative importance of all 34 plasma proteins predictive for CCTA-defined absence of coronary atherosclerosis. The spiderplot (right panel) depicts the 11 most important proteins in our machine learning model that differentiate between the presence (red) and absence of coronary atherosclerosis (green). The axis of the spiderplot represents the standardized mean protein levels (scaled zero-mean unit-variance). Standaridized mean levels of LEP and UMOD were higher in the absence of CAD group compared to the presence of CAD group. Conversely GDF-15, CCL24, CHIT1, REN, PLA2G7, MMP12, OPG, TNFRSF9, and MB were lower in patients with absence of CAD. Abbrevations of protein names are defined in Supplementary Table 1.

Fig. 4

Diagnostic performance of the biomarker model versus the clinical model and the combined model for the absence of coronary atherosclerosis. Receiver-operating characteristics curve with area under the curve (AUC) for the diagnostic performance of the clinical model (left), biomarker model (middle) and combined model (right) for the absence of coronary artery disease. The mean ROC curve for each model is depicted by the blue line. The grey shaded area represents the standard deviation of the curves. The clinical model was outperformed by both the biomarker model (p < 0·05) and the combined model (p < 0·05).