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Assessment of the value of a genetic risk score in improving the estimation of coronary risk.

ABSTRACT: The American Heart Association has established criteria for the evaluation of novel markers of cardiovascular risk. In accordance with these criteria, we assessed the association between a multi-locus genetic risk score (GRS) and incident coronary heart disease (CHD), and evaluated whether this GRS improves the predictive capacity of the Framingham risk function.Using eight genetic variants associated with CHD but not with classical cardiovascular risk factors (CVRFs), we generated a multi-locus GRS, and found it to be linearly associated with CHD in two population based cohorts: The REGICOR Study (n=2351) and The Framingham Heart Study (n=3537) (meta-analyzed HR [95%CI]: ~1.13 [1.01-1.27], per unit). Inclusion of the GRS in the Framingham risk function improved its discriminative capacity in the Framingham sample (c-statistic: 72.81 vs.72.37, p=0.042) but not in the REGICOR sample. According to both the net reclassification improvement (NRI) index and the integrated discrimination index (IDI), the GRS improved re-classification among individuals with intermediate coronary risk (meta-analysis NRI [95%CI]: 17.44 [8.04; 26.83]), but not overall.A multi-locus GRS based on genetic variants unrelated to CVRFs was associated with a linear increase in risk of CHD events in two distinct populations. This GRS improves risk reclassification particularly in the population at intermediate coronary risk. These results indicate the potential value of the inclusion of genetic information in classical functions for risk assessment in the intermediate risk population group.

SUBMITTER: Lluis-Ganella C

PROVIDER: S-EPMC3697002 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Assessment of the value of a genetic risk score in improving the estimation of coronary risk.

Lluis-Ganella Carla C Subirana Isaac I Lucas Gavin G Tomás Marta M Muñoz Daniel D Sentí Mariano M Salas Eduardo E Sala Joan J Ramos Rafel R Ordovas Jose M JM Marrugat Jaume J Elosua Roberto R

Atherosclerosis 20120330 2

<h4>Background</h4>The American Heart Association has established criteria for the evaluation of novel markers of cardiovascular risk. In accordance with these criteria, we assessed the association between a multi-locus genetic risk score (GRS) and incident coronary heart disease (CHD), and evaluated whether this GRS improves the predictive capacity of the Framingham risk function.<h4>Methods and results</h4>Using eight genetic variants associated with CHD but not with classical cardiovascular r ...[more]

PMID: 22521901

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Project description:BackgroundCoronary artery disease (CAD) remains one of the leading causes of mortality worldwide and is associated with multiple inherited and environmental risk factors. This study is designed to identify, design, and develop a panel of genetic markers that combined with clinical and angiographic information, will facilitate the creation of a personalized risk prediction algorithm (GEnetic Syntax Score-GESS). GESS score could be a reliable tool for predicting cardiovascular risk for future adverse events and for guiding therapeutic strategies.MethodsGESS (ClinicalTrials.gov Identifier: NCT03150680) is a prospective, non-interventional clinical study designed to enroll 1080 consecutive patients with no prior history of coronary revascularization procedure, who undergo scheduled or emergency coronary angiography in AHEPA, University General Hospital of Thessaloniki. Next generation sequencing (NGS) technology will be used to genotype specific single-nucleotide polymorphisms (SNPs) across the genome of study participants, which were identified as clinically relevant to CAD after extensive bioinformatic analysis of literature-based SNPs. Enrichment analyses of Gene Ontology-Molecular Function, Reactome Pathways and Disease Ontology terms were also performed to identify the top 15 statistically significant terms and pathways. Furthermore, the SYNTAX score will be calculated for the assessment of CAD severity of all patients based on their angiographic findings. All patients will be followed-up for one-year, in order to record any major adverse cardiovascular events.DiscussionA group of 228 SNPs was identified through bioinformatic and pharmacogenomic analysis to be involved in CAD through a wide range of pathways and was correlated with various laboratory and clinical parameters, along with the patients' response to clopidogrel and statin therapy. The annotation of these SNPs revealed 127 genes being affected by the presence of one or more SNPs. The first patient was enrolled in the study in February 2019 and enrollment is expected to be completed until June 2021. Hence, GESS is the first trial to date aspiring to develop a novel risk prediction algorithm, the GEnetic Syntax Score, able to identify patients at high risk for complex CAD based on their molecular signature profile and ultimately promote pharmacogenomics and precision medicine in routine clinical settings. Trial registration GESS trial registration: ClinicalTrials.gov Number: NCT03150680. Registered 12 May 2017- Prospectively registered, https://clinicaltrials.gov/ct2/show/NCT03150680 .

Project description:AimsInsulin resistance associates with development of metabolic syndrome and risk of cardiovascular disease. The link between insulin resistance and cardiovascular disease is complex and multifactorial. Confirming the genetic link between insulin resistance, type 2 diabetes, and coronary artery disease, as well as the extent of coronary artery disease, is important and may provide better risk stratification for patients at risk. We investigated whether a genetic risk score of 53 single nucleotide polymorphisms known to be associated with insulin resistance phenotypes was associated with diabetes and burden of coronary artery disease.Methods and resultsWe genotyped patients with a coronary angiography performed in the capital region of Denmark from 2010-2014 and constructed a genetic risk score of the 53 single nucleotide polymorphisms. Logistic regression using quartiles of the genetic risk score was performed to determine associations with diabetes and coronary artery disease. Associations with the extent of coronary artery disease, defined as one-, two- or three-vessel coronary artery disease, was determined by multinomial logistic regression. We identified 4,963 patients, of which 17% had diabetes and 55% had significant coronary artery disease. Of the latter, 27%, 14% and 14% had one, two or three-vessel coronary artery disease, respectively. No significant increased risk of diabetes was identified comparing the highest genetic risk score quartile with the lowest. An increased risk of coronary artery disease was found for patients with the highest genetic risk score quartile in both unadjusted and adjusted analyses, OR 1.21 (95% CI: 1.03, 1.42, p = 0.02) and 1.25 (95% CI 1.06, 1.48, p<0.01), respectively. In the adjusted multinomial logistic regression, patients in the highest genetic risk score quartile were more likely to develop three-vessel coronary artery disease compared with patients in the lowest genetic risk score quartile, OR 1.41 (95% CI: 1.10, 1.82, p<0.01).ConclusionsAmong patients referred for coronary angiography, only a strong genetic predisposition to insulin resistance was associated with risk of coronary artery disease and with a greater disease burden.

Project description:BackgroundPrecision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD.MethodsWe analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (Str) and internal validation (Sv) sets [Str (1): Sv (1): 50:50; Str (2): Sv (2): 60:40; Str (3): Sv (3): 70:30; Str (4): Sv (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from Str to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (Mecg), CAC alone (Mcac) and a combination of eRiS and CAC (Mecg+cac) for MACE prediction. A nomogram (Mnom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of Mecg, Mcac, Mecg+cac and Mnom in predicting CV disease-free survival in ASCVD.FindingsOver a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (Str). The Mecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all Sv). The Mecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that Mecg+cac was superior to Mecg (p = 1.8e-10) or Mcac (p < 2.2e-16) alone. The Mnom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, Mnom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables.ConclusionThe use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.

Project description:BackgroundImprecise interpretation of coronary angiograms was reported and resulted in inappropriate revascularization. Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score is a comprehensive system to evaluate the complexity of the overall lesions. We hypothesized that a real-time SYNTAX score feedback from image analysts may rectify the mis-estimation and improve revascularization appropriateness in patients with stable coronary artery disease (CAD).MethodsIn this single-center, historical control study, patients with stable CAD with coronary lesion stenosis ≥50% were consecutively recruited. During the control period, SYNTAX scores were calculated by treating cardiologists. During the intervention period, SYNTAX scores were calculated by image analysts immediately after coronary angiography and were provided to cardiologists in real-time to aid decision-making. The primary outcome was revascularization deemed inappropriate by Chinese appropriate use criteria for coronary revascularization.ResultsA total of 3245 patients were enrolled and assigned to the control group (08/2016-03/2017, n = 1525) or the intervention group (03/2017-09/2017, n = 1720). For SYNTAX score tertiles, 17.9% patients were overestimated and 4.3% were underestimated by cardiologists in the control group. After adjustment, inappropriate revascularization significantly decreased in the intervention group compared with the control group (adjusted odds ratio [OR]: 0.83; 95% confidence interval [CI]: 0.73-0.95; P = 0.007). Both inappropriate percutaneous coronary intervention (adjusted OR: 0.82; 95% CI: 0.74-0.92; P < 0.001) and percutaneous coronary intervention utilization (adjusted OR: 0.88; 95% CI: 0.79-0.98; P = 0.016) decreased significantly in the intervention group. There was no significant difference in 1-year adverse cardiac events between the control group and the intervention group.ConclusionsReal-time SYNTAX score feedback significantly reduced inappropriate coronary revascularization in stable patients with CAD.Clinical trial registrationNos. NCT03068858 and NCT02880605; https://www.clinicaltrials.gov.

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Assessment of the value of a genetic risk score in improving the estimation of coronary risk.

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Assessment of the value of a genetic risk score in improving the estimation of coronary risk.

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