Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease.

BACKGROUND: The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. METHODS: Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. RESULTS: HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. CONCLUSIONS: In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

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.

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 (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, 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 PRSCAD-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 (PRSCAD+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.

Sortilin as a Biomarker for Cardiovascular Disease Revisited.

Genetic variants in the genomic region containing SORT1 (encoding the protein sortilin) are strongly associated with cholesterol levels and the risk of coronary artery disease (CAD). Circulating sortilin has therefore been proposed as a potential biomarker for cardiovascular disease. Multiple studies have reported association between plasma sortilin levels and cardiovascular outcomes. However, the findings are not consistent across studies, and most studies have small sample sizes. The aim of this study was to evaluate sortilin as a biomarker for CAD in a well-characterized cohort with symptoms suggestive of CAD. In total, we enrolled 1,173 patients with suspected stable CAD referred to coronary computed tomography angiography. Sortilin was measured in plasma using two different technologies for quantifying circulating sortilin: a custom-made enzyme-linked immunosorbent assay (ELISA) and OLINK Cardiovascular Panel II. We found a relative poor correlation between the two methods (correlation coefficient = 0.21). In addition, genotyping and whole-genome sequencing were performed on all patients. By whole-genome regression analysis of sortilin levels measured with ELISA and OLINK, two independent cis protein quantitative trait loci (pQTL) on chromosome 1p13.3 were identified, with one of them being a well-established risk locus for CAD. Incorporating rare genetic variants from whole-genome sequence data did not identify any additional pQTLs for plasma sortilin. None of the traditional CAD risk factors, such as sex, age, smoking, and statin use, were associated with plasma sortilin levels. Furthermore, there was no association between circulating sortilin levels and coronary artery calcium score (CACS) or disease severity. Sortilin did not improve discrimination of obstructive CAD, when added to a clinical pretest probability (PTP) model for CAD. Overall, our results indicate that studies using different methodologies for measuring circulating sortilin should be compared with caution. In conclusion, the well-known SORT1 risk locus for CAD is linked to lower sortilin levels in circulation, measured with ELISA; however, the effect sizes are too small for sortilin to be a useful biomarker for CAD in a clinical setting of low- to intermediate-risk chest-pain patients.

Polygenic Risk Score-Enhanced Risk Stratification of Coronary Artery Disease in Patients With Stable Chest Pain.

BACKGROUND: Polygenic risk scores (PRSs) are associated with coronary artery disease (CAD), but the clinical potential of using PRSs at the single-patient level for risk stratification has yet to be established. We investigated whether adding a PRS to clinical risk factors (CRFs) improves risk stratification in patients referred to coronary computed tomography angiography on a suspicion of obstructive CAD. METHODS: In this prespecified diagnostic substudy of the Dan-NICAD trial (Danish study of Non-Invasive testing in Coronary Artery Disease), we included 1617 consecutive patients with stable chest symptoms and no history of CAD referred for coronary computed tomography angiography. CRFs used for risk stratification were age, sex, symptoms, prior or active smoking, antihypertensive treatment, lipid-lowering treatment, and diabetes. In addition, patients were genotyped, and their PRSs were calculated. All patients underwent coronary computed tomography angiography. Patients with a suspected ≥50% stenosis also underwent invasive coronary angiography with fractional flow reserve. A combined end point of obstructive CAD was defined as a visual invasive coronary angiography stenosis >90%, fractional flow reserve <0.80, or a quantitative coronary analysis stenosis >50% if fractional flow reserve measurements were not feasible. RESULTS: The PRS was associated with obstructive CAD independent of CRFs (adjusted odds ratio, 1.8 [95% CI, 1.5-2.2] per SD). The PRS had an area under the curve of 0.63 (0.59-0.68), which was similar to that for age and sex. Combining the PRS with CRFs led to a CRF+PRS model with area under the curve of 0.75 (0.71-0.79), which was 0.04 more than the CRF model (P=0.0029). By using pretest probability (pretest probability) cutoffs at 5% and 15%, a net reclassification improvement of 15.8% (P=3.1×10-4) was obtained, with a down-classification of risk in 24% of patients (211 of 862) in whom the pretest probability was 5% to 15% based on CRFs alone. CONCLUSIONS: Adding a PRS improved risk stratification of obstructive CAD beyond CRFs, suggesting a modest clinical potential of using PRSs to guide diagnostic testing in the contemporary clinical setting. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02264717.

Genetic Risk of Coronary Artery Disease, Features of Atherosclerosis, and Coronary Plaque Burden.

Background Polygenic risk scores (PRSs) based on risk variants from genome-wide association studies predict coronary artery disease (CAD) risk. However, it is unknown whether the PRS is associated with specific CAD characteristics. Methods and Results We consecutively included 1645 patients with suspected stable CAD undergoing coronary computed tomography angiography. A multilocus PRS was calculated as the weighted sum of CAD risk variants. Plaques were evaluated using an 18-segment model and characterized by stenosis severity and composition (soft [0%-19% calcified], mixed-soft [20%-49% calcified], mixed-calcified [50%-79% calcified], or calcified [≥80% calcified]). Coronary artery calcium score and segment stenosis score were used to characterize plaque burden. For each standard deviation increase in the PRS, coronary artery calcium score increased by 78% (P=4.1e-26) and segment stenosis score increased by 16% (P=2.4e-29) in the fully adjusted model. The PRS was associated with a higher prevalence of obstructive plaques (odds ratio [OR]: 1.78, P=5.6e-16), calcified (OR: 1.69, P=6.5e-17), mixed-calcified (OR: 1.67, P=7.3e-9), mixed-soft (OR: 1.45, P=1.6e-6), and soft plaques (OR: 1.49, P=2.5e-6), and a higher prevalence of plaque in each coronary vessel (all P<1.0e-4). However, when analyzing data on a plaque level (3007 segments with plaque in 849 patients) the PRS was not associated with stenosis severity, plaque composition, or localization (all P>0.05). Conclusions Our results suggest that polygenic risk based on large genome-wide association studies increases CAD risk through an increased burden of coronary atherosclerosis rather than promoting specific plaque features. Clinical Trial Registration URL: https://www.clinicaltrials.gov. Unique identifier: NCT02264717.