Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events

Hannes Helgason1,2, Thjodbjorg Eiriksdottir1, Magnus O Ulfarsson1,2

  • 1deCODE genetics/Amgen, Inc, Reykjavik, Iceland.

JAMA
|August 22, 2023
PubMed

Insights

A novel protein risk score shows promise for assessing atherosclerotic cardiovascular disease (ASCVD) risk. This score, when combined with existing clinical factors and polygenic risk scores, modestly improves risk prediction accuracy.

Area of Science:

  • Cardiovascular Medicine
  • Proteomics
  • Genetics

Background:

  • Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of mortality worldwide.
  • Current risk assessment models rely on clinical factors and polygenic risk scores, but their predictive power can be limited.
  • The utility of protein biomarkers in plasma for enhancing ASCVD risk prediction is an area of active investigation.

Purpose of the Study:

  • To develop and validate protein risk scores for predicting ASCVD events.
  • To compare the performance of protein risk scores against traditional clinical risk factors and polygenic risk scores.
  • To assess the additive value of protein risk scores in both primary and secondary prevention populations.

Main Methods:

  • Retrospective analysis of a primary event population (13,540 individuals) in Iceland with proteomics data.
  • Analysis of a secondary event population from a clinical trial (6,791 individuals) with stable ASCVD.
  • Development of protein risk scores using 4963 plasma protein levels; comparison with polygenic risk scores and clinical factors (age, sex, statin use, hypertension, diabetes, BMI, smoking).

Main Results:

  • The protein risk score demonstrated significant association with ASCVD events in both primary (HR 1.93/SD) and secondary (HR 1.62/SD) populations.
  • Addition of the protein risk score to clinical risk factor models significantly improved discrimination (increased C-index by 0.014-0.022).
  • The protein risk score also showed significant associations across different ancestries in the secondary population.

Conclusions:

  • A protein risk score derived from plasma proteomics is a significant predictor of ASCVD events.
  • Integrating protein risk scores with clinical and polygenic risk scores offers a modest but statistically significant improvement in risk prediction.
  • These findings suggest protein risk scores could be a valuable addition to current ASCVD risk assessment strategies.
Abstract

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