将多基因和蛋白质组风险评分与临床危险因素相结合，以提高对新发胸痛患者无冠状动脉疾病的诊断性能。

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.

机构信息

Department of Biomedicine (P.L.M., M.N.), Aarhus University.

Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.).

出版信息

Circ Genom Precis Med. 2023 Oct;16(5):442-451. doi: 10.1161/CIRCGEN.123.004053. Epub 2023 Sep 27.

DOI:10.1161/CIRCGEN.123.004053

PMID:37753640

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) was calculated. The prediction was performed using combinations of PRS, 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-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; =0.13). Optimal predictive ability was achieved by the full model (PRS+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (<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.

摘要

背景

新发胸痛患者，因疑似冠心病（CAD）而接受评估，常因不存在 CAD 而进行大量无临床意义的检查。本研究旨在探讨在加入 PROMISE（前瞻性多中心影像学评估胸痛研究）最小风险评分（PMRS）后，多基因风险评分和靶向蛋白质组学是否能改善疑似 CAD 患者中 CAD 缺失的预测。

方法

对 1440 例因疑似 CAD 而行冠状动脉计算机断层扫描血管造影术的患者进行基因分型和靶向血浆蛋白质组学（368 种蛋白）检测。根据个体基因型，计算 CAD 的多基因风险评分（PRS）。使用稳定性选择和机器学习的模型，以 PRS、蛋白和 PMRS 作为特征，组合进行预测。

结果

CAD 缺失的预测结果显示，PRS 模型的曲线下面积为 0.64±0.03，蛋白质组学模型为 0.58±0.03，PMRS 模型为 0.76±0.02。遗传和蛋白质组学风险评分之间无显著相关性（Pearson 相关系数，-0.04；=0.13）。最佳预测能力由全模型（PRS+蛋白+PMRS）获得，CAD 缺失的曲线下面积为 0.80±0.02，明显优于 PMRS 模型（<0.001）。为了重新分类的目的，全模型可将 5%至 15%的低至中危患者的 49%（324/661）下分类，>15%的高风险患者的 18%（113/611）下分类。