Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With De Novo Chest Pain

Peter Loof Møller, Palle Duun Rohde, Jonathan Nørtoft Dahl, Laust Dupont Rasmussen, Samuel Emil Schmidt, Louise Nissen, Victoria McGilligan, Jacob F. Bentzon, Daniel F. Gudbjartsson, Kari Stefansson, Hilma Holm, Simon Winther, Morten Bøttcher, Mette Nyegaard

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Abstract

Background: Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS).
Methods: Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRS CAD ) was calculated. The prediction was performed using combinations of PRS CAD , proteins, and PMRS as features in models using stability selection and machine learning.
Results: Prediction of absence of CAD yielded an area under the curve of PRS CAD -model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, −0.04; P =0.13). Optimal predictive ability was achieved by the full model (PRS CAD +protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone ( P <0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients.
Conclusions: For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02264717
Original languageEnglish
Pages (from-to)442-451
Number of pages10
JournalCirculation: Genomic and Precision Medicine
Volume16
Issue number5
Early online date27 Sept 2023
DOIs
Publication statusPublished (in print/issue) - 27 Sept 2023

Bibliographical note

Funding Information:
This study was supported by the Karen Elise Jensen foundation. Dr Winther was supported by the Novo Nordisk Foundation Clinical Emerging Investigator grant (NNF21OC0066981). Dr Nyegaard was supported by the Novo Nordisk Foundation Start Package grants for faculty recruitment (NNF21OC0071050). Dr McGilligan was supported by funding from the European Union Regional Development Fund (ERDF) EU Sustainable Competitiveness Program for Northern Ireland; Northern Ireland Public Health Agency (HSC R&D).

Publisher Copyright:
© 2023 American Heart Association, Inc.

Keywords

  • General Medicine
  • coronary artery disease
  • proteomics
  • area under the curve
  • angiography
  • genotype

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