TY - JOUR
T1 - 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
AU - Møller, Peter Loof
AU - Rohde, Palle Duun
AU - Nørtoft Dahl, Jonathan
AU - Dupont Rasmussen, Laust
AU - Schmidt, Samuel Emil
AU - Nissen, Louise
AU - McGilligan, Victoria
AU - F. Bentzon, Jacob
AU - F. Gudbjartsson, Daniel
AU - Stefansson, Kari
AU - Holm, Hilma
AU - Winther, Simon
AU - Bøttcher, Morten
AU - Nyegaard, Mette
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
KW - angiography
KW - area under the curve
KW - coronary artery disease
KW - genotype
KW - proteomics
UR - http://www.scopus.com/inward/record.url?scp=85174750933&partnerID=8YFLogxK
U2 - 10.1161/CIRCGEN.123.004053
DO - 10.1161/CIRCGEN.123.004053
M3 - Journal article
SN - 2574-8300
VL - 16
SP - 442
EP - 451
JO - CIRCULATION-GENOMIC AND PRECISION MEDICINE
JF - CIRCULATION-GENOMIC AND PRECISION MEDICINE
IS - 5
ER -