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. 2020 Nov 1;41(41):3998-4007.
doi: 10.1093/eurheartj/ehaa648.

Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention

Renate M Hoogeveen  1 , João P Belo Pereira  1 , Nick S Nurmohamed  1   2 , Veronica Zampoleri  3 , Michiel J Bom  2 , Andrea Baragetti  3 , S Matthijs Boekholdt  4 , Paul Knaapen  2 , Kay-Tee Khaw  5 , Nicholas J Wareham  6 , Albert K Groen  1 , Alberico L Catapano  3   7 , Wolfgang Koenig  8   9   10 , Evgeni Levin  1   11 , Erik S G Stroes  1
Affiliations

Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention

Renate M Hoogeveen et al. Eur Heart J. .

Abstract

Aims: In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort.

Methods and results: Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001).

Conclusion: In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention.

Keywords: Cardiovascular event risk; Clinical risk score; Machine learning; Prediction; Proteomics; Targeted proteomics.

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Figures

Graphical Abstract
Graphical Abstract
Figure 1
Figure 1
Machine learning workflow of model construction and validation. AHT med, antihypertensive medication; BMI, body mass index; CV, cardiovascular; EPIC, European Prospective Investigation; HDL-C, high-density lipoprotein cholesterol; PLIC, Progressione della Lesione Intimale Carotidea; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides.
Figure 2
Figure 2
Importance plot of proteins. Relative importance of 50 proteins predictive in derivation cohort.
Figure 3
Figure 3
Receiver operating characteristics of prediction models. (A) Prediction of events with protein, clinical risk, and combined model in derivation cohort. (B) Short-term prediction (<3 years) of events with protein, clinical risk, and combined model in derivation cohort. (C) Prediction of events with protein, clinical risk, and combined model in validation cohort. AUC, area under the curve; ROC, receiver operating characteristic.
Take-home figure
Take-home figure
Derivation and validation of a plasma proteomic model improves cardiovascular risk prediction in a primary prevention setting, demonstrating the potential of a proteomics panel to further refine risk assessment. CV, cardiovascular; NPX, Normalized Protein eXpression; PEA, proximity extension assay.
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See this image and copyright information in PMC

Comment in

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