Association of a genetic risk score with prevalent and incident myocardial infarction in subjects undergoing coronary angiography

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Comparison of Genetic Susceptibility to Coronary Heart Disease in the Hungarian Populations: Risk Prediction Models for Coronary Heart Disease

Background and Aim: It was evaluated whether the integration of genetic risk scores (GRS-unweighted, wGRS-weighted) into conventional risk factor (CRF) models for coronary heart disease or acute myocardial infarction (CHD/AMI) could improve the predictive ability of the models. Methods: Subjects and data collected in a previous survey were used to perform regression and ROC curve analyses as well as to examine the role of genetic components. Thirty SNPs were selected, and genotype and phenotype data were available for 558 participants (general: N = 279 and Roma: N = 279). Results: The mean GRS (27.27 ± 3.43 vs. 26.68 ± 3.51, p = 0.046) and wGRS (3.52 ± 0.68 vs. 3.33 ± 0.62, p = 0.001) were significantly higher in the general population. The addition of the wGRS to the CRF model yielded the strongest improvement in discrimination among Roma (from 0.8616 to 0.8674), while the addition of GRS to the CRF model yielded the strongest improvement in discrimination in the general population (from 0.8149 to 0.8160). In addition to that, the Roma individuals were likely to develop CHD/AMI at a younger age than subjects in the general population. Conclusions: The combination of the CRFs and genetic components improved the model's performance and predicted AMI/CHD better than CRFs alone.

Advances and Applications of Polygenic Scores for Coronary Artery Disease

Polygenic scores quantify inherited risk by integrating information from many common sites of DNA variation into a single number. Rapid increases in the scale of genetic association studies and new statistical algorithms have enabled development of polygenic scores that meaningfully measure-as early as birth-risk of coronary artery disease. These newer-generation polygenic scores identify up to 8% of the population with triple the normal risk based on genetic variation alone, and these individuals cannot be identified on the basis of family history or clinical risk factors alone. For those identified with increased genetic risk, evidence supports risk reduction with at least two interventions, adherence to a healthy lifestyle and cholesterol-lowering therapies, that can substantially reduce risk. Alongside considerable enthusiasm for the potential of polygenic risk estimation to enable a new era of preventive clinical medicine is recognition of a need for ongoing research into how best to ensure equitable performance across diverse ancestries, how and in whom to assess the scores in clinical practice, as well as randomized trials to confirm clinical utility.

Common genetic variants do not predict recurrent events in coronary heart disease patients

Background

It is unclear whether genetic variants identified from single nucleotide polymorphisms (SNPs) strongly associated with coronary heart disease (CHD) in genome-wide association studies (GWAS), or a genetic risk score (GRS) derived from them, can help stratify risk of recurrent events in patients with CHD.

Methods

Study subjects were enrolled at the close-out of the LIPID randomised controlled trial of pravastatin vs placebo. Entry to the trial had required a history of acute coronary syndrome 3–36 months previously, and patients were in the trial for a mean of 36 months. Patients who consented to a blood sample were genotyped with a custom designed array chip with SNPs chosen from known CHD-associated loci identified in previous GWAS. We evaluated outcomes in these patients over the following 10 years.

Results

Over the 10-year follow-up of the cohort of 4932 patients, 1558 deaths, 898 cardiovascular deaths, 727 CHD deaths and 375 cancer deaths occurred. There were no significant associations between individual SNPs and outcomes before or after adjustment for confounding variables and for multiple testing. A previously validated 27 SNP GRS derived from SNPs with the strongest associations with CHD also did not show any independent association with recurrent major cardiovascular events.

Conclusions

Genetic variants based on individual single nucleotide polymorphisms strongly associated with coronary heart disease in genome wide association studies or an abbreviated genetic risk score derived from them did not help risk profiling in this well-characterised cohort with 10-year follow-up. Other approaches will be needed to incorporate genetic profiling into clinically relevant stratification of long-term risk of recurrent events in CHD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02520-0.

Genetic predisposition to coronary artery disease is predictive of recurrent events: a Chinese prospective cohort study

Evidence of the effects of genetic risk score (GRS) on secondary prevention is scarce and mixed. We investigated whether coronary artery disease (CAD) susceptible loci can be used to predict the risk of major adverse cardiovascular events (MACEs) in a cohort with acute coronary syndromes (ACSs). A total of 1667 patients hospitalized with ACS were enrolled and prospectively followed for a median of 2 years. We constructed a weighted GRS comprising 79 CAD risk variants and investigated the association between GRS and MACE using a multivariable cox proportional hazard regression model. The incremental value of adding GRS into the prediction model was assessed by integrated discrimination improvement (IDI) and decision curve analysis (DCA). In the age- and sex-adjusted model, each increase in standard deviation in the GRS was associated with a 33% increased risk of MACE (hazard ratio: 1.33; 95% confidence interval: 1.10-1.61; P = 0.003), with this association not attenuating after further adjustment for traditional cardiovascular risk factors. The addition of GRS to a prediction model of seven clinical risk factors and EPICOR prognostic model slightly improved risk stratification for MACE as calculated by IDI (+1.7%, P = 0.006; +0.3%, P = 0.024, respectively). DCA demonstrated positive net benefits by adding GRS to other models. GRS was associated with MACE after multivariable adjustment in a cohort comprising Chinese ACS patients. Future studies are needed to validate our results and further evaluate the predictive value of GRS in secondary prevention.

Polygenic Risk Scores to Identify CVD Risk and Tailor Therapy: Hope or Hype?

PURPOSE OF REVIEW

The purpose of this review is to understand the conceptual basis and implications of polygenic risk scores (PRS) in assessing risk of future coronary artery disease (CAD).

RECENT FINDINGS

Genetic information from the USA and beyond has been pooled together to create population-based biobanks, composed of millions of genotyped individuals, which have helped further our understanding of the relationship between single nucleotide polymorphisms (SNPs) and CAD. Contemporary PRS composed of millions of SNPs now serve as the gold standard and have been evaluated in several cohort studies to predict risk of CAD and potentially help guide pharmacotherapy. The development of PRS has enhanced our understanding of the relationship between genes and disease, thereby facilitating CAD risk prediction. While certain constraints currently limit their utility in clinical practice, further refinement of this tool will enable clinicians to more fully understand genetic risk and improve preventive care.

Genetic information improves the prediction of major adverse cardiovascular events in the GENEMACOR population

The inclusion of a genetic risk score (GRS) can modify the risk prediction of coronary artery disease (CAD), providing an advantage over the use of traditional models. The predictive value of the genetic information on the recurrence of major adverse cardiovascular events (MACE) remains controversial. A total of 33 genetic variants previously associated with CAD were genotyped in 1587 CAD patients from the GENEMACOR study. Of these, 18 variants presented an hazard ratio >1, so they were selected to construct a weighted GRS (wGRS). MACE discrimination and reclassification were evaluated by C-Statistic, Net Reclassification Index and Integrated Discrimination Improvement methodologies. After the addition of wGRS to traditional predictors, the C-index increased from 0.566 to 0.572 (p=0.0003). Subsequently, adding wGRS to traditional plus clinical risk factors, this model slightly improved from 0.620 to 0.622 but with statistical significance (p=0.004). NRI showed that 17.9% of the cohort was better reclassified when the primary model was associated with wGRS. The Kaplan-Meier estimator showed that, at 15-year follow-up, the group with a higher number of risk alleles had a significantly higher MACE occurrence (p=0.011). In CAD patients, wGRS improved MACE risk prediction, discrimination and reclassification over the conventional factors, providing better cost-effective therapeutic strategies.

Genetic Risk Score for Coronary Heart Disease: Review

The present review deals with the stages of creation, methods of calculation, and the use of a genetic risk score for coronary heart disease in various populations. The concept of risk factors is generally recognized on the basis of the results of epidemiological studies in the 20th century; according to this concept, the high prevalence of diseases of the circulatory system is due to lifestyle characteristics and associated risk factors. An important and relevant task for the healthcare system is to identify the population segments most susceptible to cardiovascular diseases (CVDs). The level of individual risk of an unfavorable cardiovascular prognosis is determined by genetic factors in addition to lifestyle factors.

Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk: Clinical Perspectives and Basic Implications

An individual's susceptibility to atherosclerotic cardiovascular disease is influenced by numerous clinical and lifestyle factors, motivating the multifaceted approaches currently endorsed for primary and secondary cardiovascular disease prevention. With growing knowledge of the genetic basis of atherosclerotic cardiovascular disease-in particular, coronary artery disease-and its contribution to disease pathogenesis, there is increased interest in understanding the potential clinical utility of a genetic predictor that might further refine the assessment and management of atherosclerotic cardiovascular disease risk. Rapid scientific and technological advances have enabled widespread genotyping efforts and dynamic research in the field of coronary artery disease genetic risk prediction. In this review, we describe how genomic analyses of coronary artery disease have been leveraged to create polygenic risk scores. We then discuss evaluations of the clinical utility of these scores, pertinent mechanistic insights gleaned, and practical considerations relevant to the implementation of polygenic risk scores in the health care setting.

Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations

Motivation

Metabolomics data generated from liquid chromatography-mass spectrometry platforms often contain missing values. Existing imputation methods do not consider underlying feature relations and the metabolic network information. As a result, the imputation results may not be optimal.

Results

We proposed an imputation algorithm that incorporates the existing metabolic network, adduct ion relations even for unknown compounds, as well as linear and nonlinear associations between feature intensities to build a feature-level network. The algorithm uses support vector regression for missing value imputation based on features in the neighborhood on the network. We compared our proposed method with methods being widely used. As judged by the normalized root mean squared error in real data-based simulations, our proposed methods can achieve better accuracy.

Availability and implementation

The R package is available at http://web1.sph.emory.edu/users/tyu8/MINMA.

Contact

jiankang@umich.edu or tianwei.yu@emory.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction

Currently, genetic risk score (GRS) model has been a widely used method to evaluate the genetic effect of cancer risk prediction, but seldom studies investigated their discriminatory power, especially for colorectal cancer (CRC) risk prediction. In this study, we applied both simulation and real data to comprehensively compare the discriminability of different GRS models. The GRS models were fitted by logistic regression with three scenarios, including simple count GRS (SC-GRS), logistic regression weighted GRS (LR-GRS, including DL-GRS and OR-GRS) and explained variance weighted GRS (EV-GRS, including EV_DL-GRS and EV_OR-GRS) models. The model performance was evaluated by receiver operating characteristic (ROC) curves and area under curves (AUC) metric, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In real data analysis, as DL-GRS and EV_DL-GRS models were carried with serious over-fitting, the other three models were kept for further comparison. Compared to unweighted SC-GRS model, reclassification was significantly decreased in OR-GRS model (NRI = -0.082, IDI = -0.002, P < 0.05), while EV_OR-GRS model showed negative NRI and IDI (NRI = -0.077, IDI = -5.54E-04, P < 0.05) compared to OR-GRS model. Besides, traditional model with smoking status (AUC = 0.523) performed lower discriminability compared to the combined model (AUC = 0.607) including genetic (i.e., SC-GRS) and smoking factors. Similarly, the findings from simulation were all consistent to real data results. It is plausible that SC-GRS model could be optimal for predicting genetic risk of CRC. Moreover, the addition of more significant genetic variants to traditional model could further improve predictive power on CRC risk prediction.

Methods for Polygenic Traits

An important aspect of public health is disease prediction and health promotion through better targeting of preventive strategies. Well-targeted preventive strategies will eventually decrease burden of diseases and thus precise prediction plays a crucial role in public health. Many investigators put efforts into finding models that improve prediction using known risk factors of diseases. Recently with the overwhelming load of genetic loci discovered for complex diseases through genome-wide association studies (GWAS), much of attention has been focused on the role of these genetic loci to improve prediction models. Genetic loci in solo explain little variance of diseases. It is thus necessary to create new genetic parameters that combine the effect of as many genetic loci as possible. Such new parameters aim to better distinguish individuals who will develop a disease from those who will not. In this chapter, various polygenic methods that use multiple genetic loci to directly or indirectly improve precision of genetic prediction are discussed.

Genetic Risk Prediction for Primary and Secondary Prevention of Atherosclerotic Cardiovascular Disease: an Update

PURPOSE OF REVIEW

This review aims to summarize the research on genetic risk scores and their ability to improve risk prediction in both a primary and a secondary prevention population.

RECENT FINDINGS

Several groups have examined the role of genetic scores in different patient populations. Recent studies have capitalized on the growing number of identified genetic variants to construct polygenic risk scores that include hundreds and sometimes thousands of SNPs. Also, recent studies have demonstrated that individuals with high genetic risk scores can attenuate their risk with lifestyle modifications and with statins, for which the benefit of treatment may be greater in those at highest genetic risk. Genetic risk scores when added to existing clinical models appear to improve risk prediction, particularly in the setting of incident cardiovascular disease and may provide actionable information to optimize prevention early in life. Future research will need to establish how to best use this genetic risk information either as a means to further individualize treatment decisions or to better identify high-risk populations.

Cohort profile: the Emory Cardiovascular Biobank (EmCAB)

PURPOSE

The Emory Cardiovascular Biobank (EmCAB) is an ongoing prospective registry of patients undergoing cardiac catheterisation, which was established to identify novel factors associated with the pathobiological process and treatment of cardiovascular disease.

PARTICIPANTS

Individuals aged 18 years and older undergoing cardiac catheterisation at three Emory Healthcare sites in Atlanta are asked to participate in this prospective registry. Around 95% agree to participate. Around 7000 unique patients have been enrolled. The current data set contains detailed phenotyping, patient outcomes, genomics, protein biomarkers, regenerative markers, transcriptomic analysis, metabolomics profiling and longitudinal follow-up for adverse cardiovascular outcomes.

FINDINGS TO DATE

Thus far, the EmCAB has approximately 3000 major cardiovascular events. About 48% of the EmCAB participants have more than 5 years of follow-up. It is a great resource for discovery of novel predictive factors for cardiovascular disease outcomes, including genomics, transcriptomics, protein biomarkers, oxidative stress markers and circulating progenitor cells. Several circulating inflammatory markers have shown to improve risk prediction metrics beyond standard risk factors.

FUTURE PLANS

Future integrative -omics analyses will provide the cardiovascular research community opportunities for subsequent mechanistic confirmation studies, which will promote the development of effective personalised therapy that leads to clinical care tailored to the individual patient.

The Genetics of Ischemic Heart Disease: From Current Knowledge to Clinical Implications

Ischemic heart disease continues to cause high morbidity and mortality. Its prevalence is expected to increase due to population aging, and its prevention is a major goal of health policies. The risk of developing ischemic heart disease is related to a complex interplay between genetic, environmental, and lifestyle factors. In the last decade, considerable progress has been made in knowledge of the genetic architecture of this disease. This narrative review provides an overview of current knowledge of the genetics of ischemic heart disease and of its translation to clinical practice: identification of new therapeutic targets, assessment of the causal relationship between biomarkers and disease, improved risk prediction, and identification of responders and nonresponders to specific drugs (pharmacogenomics).

A genetic risk score predicts cardiovascular events in patients with stable coronary artery disease

BACKGROUND

Genetic risk scores (GRSs) may predict cardiovascular risk in community-based populations. However, studies investigating the association with recurrent cardiovascular events in patients with established coronary artery disease (CAD) are conflicting.

METHODS

We genotyped 879 patients with high-risk stable CAD and created a GRS based on 45 single nucleotide polymorphisms previously reported to be associated with CAD in genome-wide association studies. Patients were categorised into high or low GRS according to the median GRS and followed for recurrent cardiovascular events using national Danish registries. The primary endpoint was a composite of myocardial infarction, coronary revascularisation, and cardiovascular death.

RESULTS

Median (interquartile range) follow-up time was 2.8 (2.4-3.8)years. The cumulative incidence proportions of the primary endpoint at 1 and 3years were 6.4% and 11.5% in high-GRS patients vs. 2.5% and 7.3% in low-GRS patients. The corresponding relative risks were 2.56 (95% confidence interval (CI) 1.29-5.07), and 1.57 (95% CI 1.02-2.44). The adjusted hazard ratio (HR) of the primary endpoint was 1.50 (95% CI 1.00-2.25). The most pronounced effect of a high GRS was observed on coronary revascularisations (adjusted HR 2.10 [95% CI 1.08-4.07]). Risks of cardiovascular death (adjusted HR 1.07 [95% CI 0.46-2.48]) and all-cause death (adjusted HR 1.15 [95% CI 0.65-2.03]) were unaffected.

CONCLUSIONS

A GRS predicts recurrent cardiovascular events in high-risk stable CAD patients. The observed effect was mainly driven by coronary revascularisations.

Association Between Family History, a Genetic Risk Score, and Severity of Coronary Artery Disease in Patients With Premature Acute Coronary Syndromes

Objective—

A genetic risk score (GRS) for coronary artery disease has recently been shown to be independent of family history (FHx) in predicting future cardiovascular events. We sought to determine whether the presence of these risk factors, either individually or together, was associated with a higher burden of angiographic coronary artery disease.

Approach and Results—

We included 763 patients with premature acute coronary syndrome (median age, 50 [46–53] years; 30.8% women) with at least 1 major epicardial vessel stenosis enrolled in the Gender and Sex Determinants of Cardiovascular Disease From Bench to Beyond in Premature Acute Coronary Syndrome (GENESIS-PRAXY) study, a multicentre prospective cohort study of premature patients with acute coronary syndrome (aged ≤55 years). The prevalence of multivessel disease (ie, ≥2 vessels with >50% stenosis) in individuals with FHx was 49.7% as compared with 37.9% in those without FHx ( P <0.01 for comparison). In adjusted models for age, sex, traditional risk factors, and GRS, FHx was associated with a higher prevalence of 3-vessel disease (odds ratio [OR], 1.42; 95% confidence interval, 0.91–2.21; P =0.12 for 2-vessel disease and OR, 2.26; 95% confidence interval, 1.29–3.95; P =0.005 for 3-vessel disease). Individuals with a high GRS were also more likely to have multivessel disease (OR, 1.41; 95% confidence interval, 1.01–1.99; P =0.047) after adjustment for traditional risk factors, including FHx. Individuals with both a FHx and a high GRS as compared with those with neither had the highest ORs for multivessel disease (adjusted OR, 2.14; 95% confidence interval, 1.24–3.69; P =0.0064).

Conclusions—

In patients with premature acute coronary syndrome, the presence of either a high GRS or FHx is associated with greater severity of coronary artery disease at angiography. Whether preventive strategies targeted to genetically predisposed individuals will reduce the burden of early acute coronary syndrome warrants further study.

Local false discovery rate estimation using feature reliability in LC/MS metabolomics data

False discovery rate (FDR) control is an important tool of statistical inference in feature selection. In mass spectrometry-based metabolomics data, features can be measured at different levels of reliability and false features are often detected in untargeted metabolite profiling as chemical and/or bioinformatics noise. The traditional false discovery rate methods treat all features equally, which can cause substantial loss of statistical power to detect differentially expressed features. We propose a reliability index for mass spectrometry-based metabolomics data with repeated measurements, which is quantified using a composite measure. We then present a new method to estimate the local false discovery rate (lfdr) that incorporates feature reliability. In simulations, our proposed method achieved better balance between sensitivity and controlling false discovery, as compared to traditional lfdr estimation. We applied our method to a real metabolomics dataset and were able to detect more differentially expressed metabolites that were biologically meaningful.

Current Applications of Genetic Risk Scores to Cardiovascular Outcomes and Subclinical Phenotypes

Genetic risk scores are a useful tool for examining the cumulative predictive ability of genetic variation on cardiovascular disease. Important considerations for creating genetic risk scores include the choice of genetic variants, weighting, and comparability across ethnicities. Genetic risk scores that use information from genome-wide meta-analyses can successfully predict cardiovascular outcomes and subclinical phenotypes, yet there is limited clinical utility of these scores beyond traditional cardiovascular risk factors in many populations. Novel uses of genetic risk scores include evaluating the genetic contribution of specific intermediate traits or risk factors to cardiovascular disease, risk prediction in high-risk populations, gene-by-environment interaction studies, and Mendelian randomization studies. Though questions remain about the ultimate clinical utility of the genetic risk score, further investigation in high-risk populations and new ways to combine genetic risk scores with traditional risk factors may prove to be fruitful.

Prospective association of a genetic risk score and lifestyle intervention with cardiovascular morbidity and mortality among individuals with type 2 diabetes: the Look AHEAD randomised controlled trial

AIMS/HYPOTHESIS

Both obesity and genetics contribute to cardiovascular disease (CVD). We examined whether a genetic risk score (GRS) prospectively predicted cardiovascular morbidity and mortality among overweight/obese individuals with type 2 diabetes and whether behavioural weight loss could diminish this association.

METHODS

Look AHEAD (Action for Health in Diabetes) is a randomised controlled trial to determine the effects of intensive lifestyle intervention (ILI), including weight loss and physical activity, relative to diabetes support and education, on cardiovascular outcomes among overweight/obese individuals with type 2 diabetes. Of the participants, 4,016 provided consent for genetic analyses and had DNA samples passing quality control procedures. These secondary data analyses focused on whether a GRS derived from 153 single nucleotide polymorphisms (SNPs) associated with coronary artery disease in the most recent genome-wide association study predicted cardiovascular morbidity and mortality over a median of 9.6 years of follow-up, and whether ILI would diminish this association.

RESULTS

The GRS significantly predicted the primary composite endpoint of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, or hospitalisation for angina in the full sample (HR, 95% CI per 1 SD increase in GRS: 1.19 [1.10, 1.28]) and among individuals with no known history of CVD at baseline (HR 1.18 [95% CI 1.07, 1.30]). In no case did ILI significantly alter this association.

CONCLUSIONS/INTERPRETATION

A GRS comprised of SNPs significantly predicts cardiovascular morbidity and mortality over 9.6 years of follow-up in Look AHEAD. Lifestyle intervention did not alter the genetic association.

CLINICAL TRIAL REGISTRATION

NCT00017953; NCT01270763.

Common sequence variants associated with coronary artery disease correlate with the extent of coronary atherosclerosis

OBJECTIVE

Single-nucleotide polymorphisms predisposing to coronary artery disease (CAD) have been shown to predict cardiovascular risk in healthy individuals when combined into a genetic risk score (GRS). We examined whether the cumulative burden of known genetic risk variants associated with risk of CAD influences the development and progression of coronary atherosclerosis.

APPROACH AND RESULTS

We investigated the combined effects of all known CAD variants in a cross-sectional study of 8622 Icelandic patients with angiographically significant CAD (≥ 50% diameter stenosis). We constructed a GRS based on 50 CAD variants and tested for association with the number of diseased coronary arteries on angiography. In models adjusted for traditional cardiovascular risk factors, the GRS associated significantly with CAD extent (difference per SD increase in GRS, 0.076; P=7.3 × 10(-17)). When compared with the bottom GRS quintile, patients in the top GRS quintile were roughly 1.67× more likely to have multivessel disease (odds ratio, 1.67; 95% confidence interval, 1.45-1.94). The GRS significantly improved prediction of multivessel disease over traditional cardiovascular risk factors (χ(2) likelihood ratio 48.1; P<0.0001) and modestly improved discrimination, as estimated by the C-statistic (without GRS versus with GRS, 64.0% versus 64.8%) and the integrated discrimination improvement (0.52%). Furthermore, the GRS associated with an earlier age at diagnosis of angiographic CAD. These findings were replicated in an independent sample from the Emory Biobank study (n=1853).

CONCLUSIONS

When combined into a single GRS, known genetic risk variants for CAD contribute significantly to the extent of coronary atherosclerosis in patients with significant angiographic disease.

Traditional risk factors and a Genetic Risk Score are associated with age of first acute coronary syndrome

OBJECTIVE

To examine the association between traditional risk factors (TRF) and a Genetic Risk Score (GRS) with age of first acute coronary syndrome (ACS). Early onset ACS may occur due to a high burden of TRFs or to genetic factors that accelerate atherosclerosis. Whether recently discovered genetic variants for ACS are more prevalent at earlier age of first ACS remains unknown.

METHODS

To construct a multilocus GRS, participants were genotyped for 30 single nucleotide polymorphisms (SNP) identified from prior genome-wide association studies. Linear regression models were fit to estimate the association between TRFs and GRS with age of first ACS.

RESULTS

We included 460 participants with a first ACS enrolled in the Recurrence and Inflammation in the Acute Coronary Syndromes (RISCA) cohort. Several TRFs were associated (all p<0.05) with earlier age of first ACS: male sex (6.9 years earlier (95% CI 4.1 to 9.7)), current cigarette smoking (8.1 years (95% CI 6.1 to 10.0)), overweight (Body Mass Index, BMI >25) and obesity (BMI>30) (5.2 years (95% CI 2.6 to 7.9)). In women, hormone replacement therapy was also associated with earlier age of first ACS (4.8 years earlier (95% CI 0.3 to 8.4)). After multivariable adjustment for TRFs, a 1 SD increment in the GRS was associated with a 1.0 (95% CI 0.1 to 2.0) year earlier age of first ACS.

CONCLUSIONS

Among individuals with a first ACS, a GRS composed of 30 SNPs is associated with younger age of presentation. Although genetic predisposition modestly contributes to earlier ACS, a heavy burden of TRF is associated with markedly earlier ACS.

Genetic variants at chromosome 9p21 and risk of first versus subsequent coronary heart disease events: a systematic review and meta-analysis

Objectives

The purpose of this analysis was to compare the association between variants at the chromosome 9p21 locus (Ch9p21) and risk of first versus subsequent coronary heart disease (CHD) events through systematic review and meta-analysis.

Background

Ch9p21 is a recognized risk factor for a first CHD event. However, its association with risk of subsequent events in patients with established CHD is less clear.

Methods

We searched PubMed and EMBASE for prospective studies reporting association of Ch9p21 with incident CHD events and extracted information on cohort type (individuals without prior CHD or individuals with established CHD) and effect estimates for risk of events.

Results

We identified 31 cohorts reporting on 193,372 individuals. Among the 16 cohorts of individuals without prior CHD (n = 168,209), there were 15,664 first CHD events. Ch9p21 was associated with a pooled hazard ratio (HR) of a first event of 1.19 (95% confidence interval: 1.17 to 1.22) per risk allele. In individuals with established CHD (n = 25,163), there were 4,436 subsequent events providing >99% and 91% power to detect a per-allele HR of 1.19 or 1.10, respectively. The pooled HR for subsequent events was 1.01 (95% confidence interval: 0.97 to 1.06) per risk allele. There was strong evidence of heterogeneity between the effect estimates for first and subsequent events (p value for heterogeneity = 5.6 × 10⁻¹¹). We found no evidence for biases to account for these findings.

Conclusions

Ch9p21 shows differential association with risk of first versus subsequent CHD events. This has implications for genetic risk prediction in patients with established CHD and for mechanistic understanding of how Ch9p21 influences risk of CHD.

Pathogenesis of coronary artery disease: focus on genetic risk factors and identification of genetic variants

Coronary artery disease (CAD) is the leading cause of death and disability worldwide, and its prevalence is expected to increase in the coming years. CAD events are caused by the interplay of genetic and environmental factors, the effects of which are mainly mediated through cardiovascular risk factors. The techniques used to study the genetic basis of these diseases have evolved from linkage studies to candidate gene studies and genome-wide association studies. Linkage studies have been able to identify genetic variants associated with monogenic diseases, whereas genome-wide association studies have been more successful in determining genetic variants associated with complex diseases. Currently, genome-wide association studies have identified approximately 40 loci that explain 6% of the heritability of CAD. The application of this knowledge to clinical practice is challenging, but can be achieved using various strategies, such as genetic variants to identify new therapeutic targets, personal genetic information to improve disease risk prediction, and pharmacogenomics. The main aim of this narrative review is to provide a general overview of our current understanding of the genetics of coronary artery disease and its potential clinical utility.

Relationship between C-reactive protein and kidney stone prevalence

PURPOSE

We evaluated the relationship between serum C-reactive protein and the lifetime kidney stone prevalence.

MATERIALS AND METHODS

We performed a cross-sectional study of participants from the Continuous National Health and Nutrition Examination Survey (NHANES) from 2007 to 2008 and 2008 to 2009. Data were available on 11,033 participants.

RESULTS

On univariate analysis we noted a strong correlation between C-reactive protein quintile and kidney stone history. After adjusting for known confounders multivariate logistic regression revealed a significant relationship between C-reactive protein and the lifetime prevalence of kidney stones in younger individuals (age 20 to 39 years, p for trend = 0.002). In individuals 20 to 39 years old the lifetime prevalence of kidney stones increased with increasing C-reactive protein quintile (p = 0.002 for trend), specifically, those in the third quintile (OR 3.86, 95% CI 1.07-13.88, p = 0.04) and the fifth quintile (OR 3.85, 95% CI 1.46-10.17, p = 0.009). The fourth quintile of C-reactive protein approached statistical significance (OR 2.56, 95% CI 0.96-6.81, p = 0.059). The relationship between C-reactive protein and kidney stone history was not significant in the older age groups (40 to 59 and 60 years or greater).

CONCLUSIONS

There exists a significant relationship between serum C-reactive protein and self-reported kidney stones in younger individuals. This may shed light on potential mechanisms of stone formation in this age group and help gain a better understanding of stone risk mediators. Further studies are needed to understand the mechanisms underlying these epidemiological findings.