Cholesterol lowering depletes atherosclerotic lesions of smooth muscle cell-derived fibromyocytes and chondromyocytes

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Diagnostic Yield of Genetic Testing in Young Patients With Atrioventricular Block of Unknown Cause

BACKGROUND: The cause of atrioventricular block (AVB) remains unknown in approximately half of young patients with the diagnosis. Although variants in several genes associated with cardiac conduction diseases have been identified, the contribution of genetic variants in younger patients with AVB is unknown. METHODS AND RESULTS: Using the Danish Pacemaker and Implantable Cardioverter Defibrillator (ICD) Registry, we identified all patients younger than 50 years receiving a pacemaker because of AVB in Denmark in the period from January 1, 1996 to December 31, 2015. From medical records, we identified patients with unknown cause of AVB at time of pacemaker implantation. These patients were invited to a genetic screening using a panel of 102 genes associated with inherited cardiac diseases. We identified 471 living patients with AVB of unknown cause, of whom 226 (48%) accepted participation. Median age at the time of pacemaker implantation was 39 years (interquartile range, 32–45 years), and 123 (54%) were men. We found pathogenic or likely pathogenic variants in genes associated with or possibly associated with AVB in 12 patients (5%). Most variants were found in the LMNA gene (n=5). LMNA variant carriers all had a family history of either AVB and/or sudden cardiac death. CONCLUSIONS: In young patients with AVB of unknown cause, we found a possible genetic cause in 1 out of 20 participating patients. Variants in the LMNA gene were most common and associated with a family history of AVB and/or sudden cardiac death, suggesting that genetic testing should be a part of the diagnostic workup in these patients to stratify risk and screen family members

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 (GPS Mult). 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 GPS Mult 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 GPS Mult. Combining CRFs with GPS Mult 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 + GPS Mult (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).</p

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

BackgroundPatients 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).MethodsGenotyping 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.ResultsPrediction 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&lt;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 &gt;15% pretest probability patients.ConclusionsFor 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.REGISTRATIONURL: https://www.clinicaltrials.gov; Unique identifier: NCT02264717Background: 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&lt;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 &gt;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.</p