Molecular genetics of myocardial infarction

Myocardial infarction (MI) is an important clinical problem because of its large contribution to mortality. The main causal and treatable risk factors for MI include hypertension, hypercholesterolemia or dyslipidemia, diabetes mellitus, and smoking. In addition to these risk factors, recent studies have shown the importance of genetic factors and interactions between multiple genes and environmental factors. Disease prevention is an important strategy for reducing the overall burden of MI, with the identification of markers for disease risk being key both for risk prediction and for potential intervention to lower the chance of future events. Although genetic linkage analyses of families and sib-pairs as well as candidate gene and genome-wide association studies have implicated several loci and candidate genes in predisposition to coronary heart disease (CHD) or MI, the genes that contribute to genetic susceptibility to these conditions remain to be identified definitively. In this review, we summarize both candidate loci for CHD or MI identified by linkage analyses and candidate genes examined by association studies. We also review in more detail studies that have revealed the association with MI or CHD of polymorphisms in MTHFR, LPL, and APOE by the candidate gene approach and those in LTA and at chromosomal region 9p21.3 by genome-wide scans. Such studies may provide insight into the function of implicated genes as well as into the role of genetic factors in the development of CHD and MI.     

Use of Systems Biology Approaches to Analysis of Genome-Wide Association Studies of Myocardial Infarction and Blood Cholesterol in the Nurses' Health Study and Health Professionals’ Follow-Up Study

With the advance of genome-wide association studies and newly identified SNP (single-nucleotide polymorphism) associations with complex disease, important discoveries have emerged focusing not only on individual genes but on disease-associated pathways and gene sets. The authors used prospective myocardial infarction case-control studies nested in the Nurses’ Health and Health Professionals Follow-Up Studies to investigate genetic variants associated with myocardial infarction or LDL, HDL, triglycerides, adiponectin and apolipoprotein B (apoB). Using these case-control studies to illustrate an integrative systems biology approach, the authors applied SNP set enrichment analysis to identify gene sets where expression SNPs representing genes from these sets show enrichment in their association with endpoints of interest. The authors also explored an aggregate score approach. While power limited one’s ability to detect significance for association of individual loci with myocardial infarction, the authors found significance for loci associated with LDL, HDL, apoB and triglycerides, replicating previous observations. Applying SNP set enrichment analysis and risk score methods, the authors also found significance for three gene sets and for aggregate scores associated with myocardial infarction as well as for loci-related to cardiovascular risk factors, supporting the use of these methods in practice.     

Genetic Insights into Cardiometabolic Risk Factors

Many biochemical traits are recognised as risk factors, which contribute to or predict the development of disease. Only a few are in widespread use, usually to assist with treatment decisions and motivate behavioural change. The greatest effort has gone into evaluation of risk factors for cardiovascular disease and/or diabetes, with substantial overlap as ‘cardiometabolic’ risk. Over the past few years many genome-wide association studies (GWAS) have sought to account for variation in risk factors, with the expectation that identifying relevant polymorphisms would improve our understanding or prediction of disease; others have taken the direct approach of genomic case-control studies for the corresponding diseases. Large GWAS have been published for coronary heart disease and Type 2 diabetes, and also for associated biomarkers or risk factors including body mass index, lipids, C-reactive protein, urate, liver function tests, glucose and insulin. Results are not encouraging for personal risk prediction based on genotyping, mainly because known risk loci only account for a small proportion of risk. Overlap of allelic associations between disease and marker, as found for low density lipoprotein cholesterol and heart disease, supports a causal association, but in other cases genetic studies have cast doubt on accepted risk factors. Some loci show unexpected effects on multiple markers or diseases. An intriguing feature of risk factors is the blurring of categories shown by the correlation between them and the genetic overlap between diseases previously thought of as distinct. GWAS can provide insight into relationships between risk factors, biomarkers and diseases, with potential for new approaches to disease classification.     

Candidate gene polymorphisms and the 9p21 locus in acute coronary syndromes

It is now widely accepted that the classic environmental risk factors for atherosclerosis only partly explain the incidence of coronary artery disease and the development of acute coronary syndromes. Therefore, genetic factors that vary among human populations seem to be involved in the clinical manifestations of such patients. Substantial data suggest that a significant proportion of genetic polymorphisms involved in endothelial function, inflammation, lipid metabolism, thrombosis and fibrinolysis are often present in patients with acute coronary syndromes. In particular, a common variant on chromosome 9p21 was recently identified to affect the risk of myocardial infarction. Here, we review the progress of candidate gene studies and genome-wide association studies in identifying the genetic bases of complex cardiovascular diseases such as acute coronary syndromes.     

Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia

Schizophrenia is a severe psychiatric disorder with strong heritability and marked heterogeneity in symptoms, course, and treatment response. There is strong interest in identifying genetic risk factors that can help to elucidate the pathophysiology and that might result in the development of improved treatments. Linkage and genome-wide association studies (GWASs) suggest that the genetic basis of schizophrenia is heterogeneous. However, it remains unclear whether the underlying genetic variants are mostly moderately rare and can be identified by the genotyping of variants observed in sequenced cases in large follow-up cohorts or whether they will typically be much rarer and therefore more effectively identified by gene-based methods that seek to combine candidate variants. Here, we consider 166 persons who have schizophrenia or schizoaffective disorder and who have had either their genomes or their exomes sequenced to high coverage. From these data, we selected 5,155 variants that were further evaluated in an independent cohort of 2,617 cases and 1,800 controls. No single variant showed a study-wide significant association in the initial or follow-up cohorts. However, we identified a number of case-specific variants, some of which might be real risk factors for schizophrenia, and these can be readily interrogated in other data sets. Our results indicate that schizophrenia risk is unlikely to be predominantly influenced by variants just outside the range detectable by GWASs. Rather, multiple rarer genetic variants must contribute substantially to the predisposition to schizophrenia, suggesting that both very large sample sizes and gene-based association tests will be required for securely identifying genetic risk factors.     

Prediction of individual genetic risk of complex disease

Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's genetic risk for a disease? In this review we consider the lessons from GWAS and the implications for genetic risk prediction to common disease. We conclude that with larger GWAS sample sizes or by combining studies, accurate prediction of genetic risk will be possible, even if the causal mutations or the mechanisms by which they affect susceptibility are unknown.     

Genetic Risk Score of NOS Gene Variants Associated with Myocardial Infarction Correlates with Coronary Incidence across Europe

Coronary artery disease (CAD) mortality and morbidity is present in the European continent in a four-fold gradient across populations, from the South (Spain and France) with the lowest CAD mortality, towards the North (Finland and UK). This observed gradient has not been fully explained by classical or single genetic risk factors, resulting in some cases in the so called Southern European or Mediterranean paradox. Here we approached population genetic risk estimates using genetic risk scores (GRS) constructed with single nucleotide polymorphisms (SNP) from nitric oxide synthases (NOS) genes. These SNPs appeared to be associated with myocardial infarction (MI) in 2165 cases and 2153 controls. The GRSs were computed in 34 general European populations. Although the contribution of these GRS was lower than 1% between cases and controls, the mean GRS per population was positively correlated with coronary incidence explaining 65–85% of the variation among populations (67% in women and 86% in men). This large contribution to CAD incidence variation among populations might be a result of colinearity with several other common genetic and environmental factors. These results are not consistent with the cardiovascular Mediterranean paradox for genetics and support a CAD genetic architecture mainly based on combinations of common genetic polymorphisms. Population genetic risk scores is a promising approach in public health interventions to develop lifestyle programs and prevent intermediate risk factors in certain subpopulations with specific genetic predisposition.     

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.     

Differential Associations of Depressive Symptom Dimensions with Cardio-Vascular Disease in the Community: Results from the Gutenberg Health Study

A current model suggested that the somatic symptom dimension accounts for the adverse effect of depression in patients with coronary heart disease (CHD). In order to test this model we sought to determine in a large population-based sample how symptom dimensions of depression are associated with CHD, biomarkers and traditional risk factors. The associations of cognitive and somatic symptom dimensions of depression with CHD, risk factors, endothelial function, and biomarkers of inflammation and myocardial stress were analyzed cross-sectionally in a sample of n = 5000 Mid-Europeans aged 35–74 years from the Gutenberg Health Study (GHS). Only the somatic symptom dimension of depression was associated with CHD, biomarkers (inflammation, vascular function) and cardio-vascular risk factors. When multivariable adjustment was applied by demographic and cardiovascular risk factors, the weak associations of the somatic symptom dimension with the biomarkers disappeared. However, the associations of the somatic symptom dimension with CHD, myocardial infarction, obesity, dyslipidemia and family history of myocardial infarction remained. Both dimensions of depression were independently associated with a previous diagnosis of depression and distressed personality (type D). Thus, our results partly confirm current models: Somatic, but not cognitive-affective symptom dimensions are responsible for the association between depression and CHD, inflammation, vascular function and cardiovascular risk factors in the general population. However, our findings challenge the assumptions that somatic depression might be due to inflammation or vascular dysfunction as consequence of progressed atherosclerotic disease. They rather emphasize a close interplay with life-style factors and with a family history of MI.     

环境和遗传因素与慢性代谢性疾病的人群研究

几十年来我国居民经历了快速的营养转型,与不健康的膳食和生活方式相关的＂致肥环境＂,以及遗传倾向是导致我国慢性代谢性疾病如代谢综合征和2型糖尿病快速流行的主要推手。然而,我国目前非常缺乏针对导致慢性代谢性疾病的主要遗传和环境危险因素而开展的系统研究。在过去若干年中,通过开展基于社区人群的流行病学研究,本课题组发现了多个与代谢性疾病相关的基因变异、环境因素和生物标记物。与此同时,通过对代谢综合征或2型糖尿病患者进行的营养干预,发现添加亚麻子或其衍生物木酚素、核桃,以及用糙米替代白米能不同程度地改善代谢综合征或血糖控制。总之,所有努力旨在增进对导致中国人代谢性疾病高易感性相关的病因和机制的理解,同时也希望为疾病的预测和预防提供新的思路和线索。     

Assessment of cardiovascular risk--PROCAM and new algorithms]

The absolute risk for myocardial infarction is best determined using algorithms or point schemes which have been developed on the basis of large prospective, epidemiological studies. In Germany, for instance, the PROCAM score developed in Münster is leading in this field. The role of new risk factors lies in the optimum risk prediction in patients with intermediary or high risk for myocardial infarction. It is not necessary to determine additional risk factors in low-risk-patients. At present there is however no conclusive epidemiological evidence so that the decision for investigating newer risk factors lies with the physician in charge. So far the target level for LDL cholesterol in patients with intermediate risk was set at 130 mg/dL, whereas in high risk patients LDL cholesterol should not be more than 100 mg/dL. The results of newer studies suggest that it might be appropriate to define a new category of patients with very high risk in whom the LDL cholesterol value should not exceed 70 mg/dL. This inference must however be substantiated by further studies before it becomes part of the general recommendations for treatment.     

Predicting loneliness with polygenic scores of social,psychological and psychiatric traits

Loneliness is a heritable trait that accompanies multiple disorders. The association between loneliness and mental health indices may partly be due to inherited biological factors. We constructed polygenic scores for 27 traits related to behavior, cognition and mental health and tested their prediction for self‐reported loneliness in a population‐based sample of 8798 Dutch individuals. Polygenic scores for major depressive disorder (MDD), schizophrenia and bipolar disorder were significantly associated with loneliness. Of the Big Five personality dimensions, polygenic scores for neuroticism and conscientiousness also significantly predicted loneliness, as did the polygenic scores for subjective well‐being, tiredness and self‐rated health. When including all polygenic scores simultaneously into one model, only 2 major depression polygenic scores remained as significant predictors of loneliness. When controlling only for these 2 MDD polygenic scores, only neuroticism and schizophrenia remain significant. The total variation explained by all polygenic scores collectively was 1.7%. The association between the propensity to feel lonely and the susceptibility to psychiatric disorders thus pointed to a shared genetic etiology. The predictive power of polygenic scores will increase as the power of the genome‐wide association studies on which they are based increases and may lead to clinically useful polygenic scores that can inform on the genetic predisposition to loneliness and mental health.     

Genetic predisposition to multiple myeloma

Genetic myeloma risk research relied on genome-wide association studies to identify 24 common but lowimpact germline predisposition alleles that account for an estimated one eighth of the heritable myeloma risk in Caucasians. Next-generation sequencing, particularly whole-exome sequencing, uncovered a handful of rare but high-impact myeloma risk loci that convey intriguing clues about etiology. The recent discovery of NCOA1 as a myeloma susceptibility gene in Han Chinese has set the stage for the more complete elucidation of the genetic myeloma risk across ethnic barriers. Validating individual myeloma risk loci at the functional level and integrating predisposition genes in genetic networks and biological pathways are important research tasks going forward. Candidate pathways that are currently emerging include plasma cell development, autophagy, telomere maintenance, and cell cycle regulation. An outstanding knowledge gap in the area of gene-environment interaction concerns the possibility that tumor-promoting effects of myeloma susceptibility alleles depend on specific environmental or occupational exposures. An implicit promise of myeloma risk research is the detection of new molecular targets for myeloma treatments and preventions. A related outcome is new biomarkers for patient stratification, prognostication, and development of individualized treatment plans.     

Risk Models to Predict Chronic Kidney Disease and Its Progression: A Systematic Review

Background

Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.

Methods and Findings

We systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias.

Conclusions

The development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored. Please see later in the article for the Editors'' Summary     

Association between dental health and acute myocardial infarction.

Known risk factors for coronary heart disease do not explain all of the clinical and epidemiological features of the disease. To examine the role of chronic bacterial infections as risk factors for the disease the association between poor dental health and acute myocardial infarction was investigated in two separate case-control studies of a total of 100 patients with acute myocardial infarction and 102 controls selected from the community at random. Dental health was graded by using two indexes, one of which was assessed blind. Based on these indexes dental health was significantly worse in patients with acute myocardial infarction than in controls. The association remained valid after adjustment for age, social class, smoking, serum lipid concentrations, and the presence of diabetes. Further prospective studies are required in different populations to confirm the association and to elucidate its nature.     

The Role of Genetics in Host Responses to Mucosal and Invasive Candidiasis

The development of mucosal and invasive candidiasis depends upon a variety of innate and acquired risk factors. The number of genes known to be important for immunity against candidiasis has been increasing. Studies of variants of these genes are facilitating our knowledge of host predisposition to infection. Insights gleaned from genetic variants identified in patients with primary immunodeficiency syndromes such as chronic mucocutaneous candidiasis have further aided in this process. This article reviews data from genomic association studies in patients with such syndromes and in broader patient populations. These studies are placed within the framework of our current understanding of antifungal host defenses.     

Cell-derived microparticles in atherosclerosis: biomarkers and targets for pharmacological modulation?

Cardiovascular diseases remain an important cause of morbi-mortality. Atherosclerosis, which predisposes to cardiovascular disorders such as myocardial infarction and stroke, develops silently over several decades. Identification of circulating biomarkers to evaluate cardiovascular event risk and pathology prognosis is of particular importance. Microparticles (MPs) are small vesicles released from cells upon apoptosis or activation. Microparticles are present in blood of healthy individuals. Studies showing a modification of their concentrations in patients with cardiovascular risk factors and after cardiovascular events identify MPs as potential biomarkers of disease. Moreover, the pathophysiological properties of MPs may contribute to atherosclerosis development. In addition, pharmacological compounds, used in the treatment of cardiovascular disease, can reduce plasma MP concentrations. Nevertheless, numerous issues remain to be solved before MP measurement can be applied as routine biological tests to improve cardiovascular risk prediction. In particular, prospective studies to identify the predictive values of MPs in pathologies such as cardiovascular diseases are needed to demonstrate whether MPs are useful biomarkers for the early detection of the disease and its progression.     

Power and Predictive Accuracy of Polygenic Risk Scores

Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.