Renate M. Hoogeveen, Netherlands

Amsterdam UMC Department of Vascular Medicine

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Improved cardiovascular risk prediction using plasma proteomics in primary prevention

Session Type
Rapid Fire
Date
07.10.2020, Wednesday
Session Time
12:00 - 13:00
Lecture Time
12:00 - 12:05

Abstract

Background and Aims

In the era of personalized medicine, it is of utmost importance to be able to identify subjects at highest cardiovascular risk. To date, single biomarkers have failed to markedly improve estimation of cardiovascular risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In this 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 EPIC-Norfolk study, followed by validation in the PLIC cohort.

Methods

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 algorithm and a model combining both.

Results

In the derivation cohort we defined a panel of 50 proteins, which outperformed the clinical risk algorithm in prediction of myocardial infarction (AUC 0.754 vs 0.722;p<0.0001) during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.680 using the clinical risk algorithm and an AUC of 0.803 for the protein model(p<0.0001). The predictive value of the protein panel was confirmed in the validation cohort.

Conclusions

In primary prevention setting, a protein-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 in order to address the value for future clinical implementation in CV-prevention.

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