Genetic profiling using genome-wide significant coronary artery disease risk variants does not improve the prediction of subclinical atherosclerosis: the Cardiovascular Risk in Young Finns Study, the Bogalusa Heart Study and the Health 2000 Survey--a meta-analysis of three independent studies

Common atherosclerosis genetic risk factors and subclinical atherosclerosis in rheumatoid arthritis: the relevance of disease duration

Rheumatoid arthritis (RA) is a common systemic autoimmune disease characterized by increased cardiovascular morbidity. Several previous studies assessed associations between common atherosclerotic genetic risk factors and subclinical atherosclerosis (SA) in RA patients, yet most of them gave negative results. We undertook a cross-sectional study to evaluate the association between previously reported SNPs and subclinical atherosclerosis in a cohort of Polish RA patients. 29 SNPs associated with atherosclerosis in general population were genotyped in 289 RA patients: 116 patients with SA (increased carotid intima-media thickness and/or presence of carotid plaque) and 173 patients without SA. To assess the cumulative effect of SNPs we calculated 3 weighted genetic risk scores: GRS_IMT, GRS_CP and GRS_CAD, comprising intima-media thickness-associated SNPs, carotid plaque-associated SNPs and coronary artery disease-associated SNPs, respectively. None of the SNPs showed a significant association with SA. However, we found an association between SA and GRS_IMT. Interestingly, this association was limited to patients with short disease duration (P = 0.00004 vs. P > 0.5, for comparison of GRS_IMT among patients within the 1st quartile of disease duration vs. others, respectively). Patients within the 1st quartile of disease duration were more frequently disease modifying anti-rheumatic drugs (DMARDs)-naïve and less frequently treated with biologics. Our study suggests that in patients with early RA subclinical atherosclerosis may be driven by similar genetic factors as in general population, while in long-lasting disease, the role common genetic risk factors may decrease. Possibly, this effect may be due to the influence of DMARDs.

Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study

An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.

The MUC5B promoter polymorphism is associated with specific interstitial lung abnormality subtypes

The MUC5B promoter polymorphism (rs35705950) has been associated with interstitial lung abnormalities (ILA) in white participants from the general population; whether these findings are replicated and influenced by the ILA subtype is not known. We evaluated the associations between the MUC5B genotype and ILA in cohorts with extensive imaging characterisation.We performed ILA phenotyping and MUC5B promoter genotyping in 5308 and 9292 participants from the AGES-Reykjavik and COPDGene cohorts, respectively.We found that ILA was present in 7% of participants from the AGES-Reykjavik, 8% of non-Hispanic white participants from COPDGene and 7% of African-American participants from COPDGene. Although the MUC5B genotype was strongly associated (after correction for multiple testing) with ILA (OR 2.1, 95% CI 1.8-2.4, p=1×10^-26), there was evidence of significant heterogeneity between cohorts (I²=81%). When narrowed to specific radiologic subtypes, (e.g. subpleural ILA), the MUC5B genotype remained strongly associated (OR 2.6, 95% CI 2.2-3.1, p=1×10^-30) with minimal heterogeneity (I²=0%). Although there was no evidence that the MUC5B genotype influenced survival, there was evidence that MUC5B genotype improved risk prediction for possible usual interstitial pneumonia (UIP) or a UIP pattern in non-Hispanic white populations.The MUC5B promoter polymorphism is strongly associated with ILA and specific radiologic subtypes of ILA, with varying degrees of heterogeneity in the underlying populations.

Genetic risk analysis of coronary artery disease in Pakistani subjects using a genetic risk score of 21 variants

BACKGROUND AND AIMS

Conventional coronary artery disease (CAD) risk factors like age, gender, blood lipids, hypertension and smoking have been the basis of CAD risk prediction algorithms, but provide only modest discrimination. Genetic risk score (GRS) may provide improved discrimination over and above conventional risk factors. Here we analyzed the genetic risk of CAD in subjects from Pakistan, using a GRS of 21 variants in 18 genes and examined whether the GRS is associated with blood lipid levels.

METHODS

625 (405 cases and 220 controls) subjects were genotyped for variants, NOS3 rs1799983, SMAD3 rs17228212, APOB rs1042031, LPA rs3798220, LPA rs10455872, SORT1 rs646776, APOE rs429358, GLUL rs10911021, FTO rs9939609, MIA3 rs17465637, CDKN2Ars10757274, DAB2IP rs7025486, CXCL12 rs1746048, ACE rs4341, APOA5 rs662799, CETP rs708272, MRAS rs9818870, LPL rs328, LPL rs1801177, PCSK9 rs11591147 and APOE rs7412 by TaqMan and KASPar allele discrimination techniques.

RESULTS

Individually, the single SNPs were not associated with CAD except APOB rs1042031 and FTO rs993969 (p = 0.01 and 0.009 respectively). However, the combined GRS of 21 SNPs was significantly higher in cases than controls (19.37 ± 2.56 vs. 18.47 ± 2.45, p = 2.9 × 10^-5), and compared to the bottom quintile, CAD risk in the top quintile of the GRS was 2.96 (95% CI 1.71-5.13). Atherogenic blood lipids showed significant positive association with GRS.

CONCLUSIONS

The GRS was quantitatively associated with CAD risk and showed association with blood lipid levels, suggesting that the mechanism of these variants is likely to be, in part at least, through creating an atherogenic lipid profile in subjects carrying high numbers of risk alleles.

Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives

Reliable risk assessment of frequent, but treatable diseases and disorders has considerable clinical and socio-economic relevance. However, as these conditions usually originate from a complex interplay between genetic and environmental factors, precise prediction remains a considerable challenge. The current progress in genotyping technology has resulted in a substantial increase of knowledge regarding the genetic basis of such diseases and disorders. Consequently, common genetic risk variants are increasingly being included in epidemiological models to improve risk prediction. This work reviews recent high-quality publications targeting the prediction of common complex diseases. To be included in this review, articles had to report both, numerical measures of prediction performance based on traditional (non-genetic) risk factors, as well as measures of prediction performance when adding common genetic variants to the model. Systematic PubMed-based search finally identified 55 eligible studies. These studies were compared with respect to the chosen approach and methodology as well as results and clinical impact. Phenotypes analysed included tumours, diabetes mellitus, and cardiovascular diseases. All studies applied one or more statistical measures reporting on calibration, discrimination, or reclassification to quantify the benefit of including SNPs, but differed substantially regarding the methodological details that were reported. Several examples for improved risk assessments by considering disease-related SNPs were identified. Although the add-on benefit of including SNP genotyping data was mostly moderate, the strategy can be of clinical relevance and may, when being paralleled by an even deeper understanding of disease-related genetics, further explain the development of enhanced predictive and diagnostic strategies for complex diseases.

Potential Diagnostic and Prognostic Biomarkers of Epigenetic Drift within the Cardiovascular Compartment

Biomarkers encompass a wide range of different measurable indicators, representing a tangible link to physiological changes occurring within the body. Accessibility, sensitivity, and specificity are significant factors in biomarker suitability. New biomarkers continue to be discovered, and questions over appropriate selection and assessment of their usefulness remain. If traditional markers of inflammation are not sufficiently robust in their specificity, then perhaps alternative means of detection may provide more information. Epigenetic drift (epigenetic modifications as they occur as a direct function with age), and its ancillary elements, including platelets, secreted microvesicles (MVs), and microRNA (miRNA), may hold enormous predictive potential. The majority of epigenetic drift observed in blood is independent of variations in blood cell composition, addressing concerns affecting traditional blood-based biomarker efficacy. MVs are found in plasma and other biological fluids in healthy individuals. Altered MV/miRNA profiles may also be found in individuals with various diseases. Platelets are also highly reflective of physiological and lifestyle changes, making them extremely sensitive biomarkers of human health. Platelets release increased levels of MVs in response to various stimuli and under a plethora of disease states, which demonstrate a functional effect on other cell types.

A comparison of genomic profiles of complex diseases under different models

Background

Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task.

Results

We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium.

Conclusions

In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0157-2) contains supplementary material, which is available to authorized users.

Association Between Coronary Artery Disease Genetic Variants and Subclinical Atherosclerosis: An Association Study and Meta-analysis

INTRODUCTION AND OBJECTIVES

Recent studies have identified several genetic variants associated with coronary artery disease. Some of these genetic variants are not associated with classical cardiovascular risk factors and the mechanism of such associations is unclear. The aim of the study was to determine whether these genetic variants are related to subclinical atherosclerosis measured by carotid intima media thickness, carotid stiffness, and ankle brachial index.

METHODS

A cross-sectional study nested in the follow-up of the REGICOR cohort was undertaken. The study included 2667 individuals. Subclinical atherosclerosis measurements were performed with standardized methods. Nine genetic variants were genotyped to assess associations with subclinical atherosclerosis, individually and in a weighted genetic risk score. A systematic review and meta-analysis of previous studies that analyzed these associations was undertaken.

RESULTS

Neither the selected genetic variants nor the genetic risk score were significantly associated with subclinical atherosclerosis. In the meta-analysis, the rs1746048 (CXCL12; n = 10581) risk allele was directly associated with carotid intima-media thickness (β = 0.008; 95% confidence interval, 0.001-0.015), whereas the rs6725887 (WDR12; n = 7801) risk allele was inversely associated with this thickness (β = -0.013; 95% confidence interval, -0.024 to -0.003).

CONCLUSIONS

The analyzed genetic variants seem to mediate their association with coronary artery disease through different mechanisms. Our results generate the hypothesis that the CXCL12 variant appears to influence coronary artery disease risk through arterial remodeling and thickening, whereas the WDR12 risk variant could be related to higher plaque vulnerability.

Ethnicity and Smoking-Associated DNA Methylation Changes at HIV Co-Receptor GPR15

Smoking is associated with poorer health outcomes for both African and European Americans. In order to better understand whether ethnic-specific genetic variation may underlie some of these differences, we compared the smoking-associated genome-wide methylation signatures of African Americans with those of European Americans, and followed up this analysis with a focused examination of the most ethnically divergent locus, cg19859270, at the GPR15 gene. We examined the association of methylation at this locus to the rs2230344 SNP and GPR15 gene and protein expression. Consistent with prior analyses, AHRR residue cg05575921 was the most differentially methylated residue in both African Americans and European Americans. However, the second most differentially methylated locus in African Americans, cg19859270, was only modestly differentially methylated in European Americans. Interrogation of the methylation status of this CpG residue found in GPR15, a chemokine receptor involved in HIV pathogenesis, showed a significant interaction of ethnicity with smoking as well as a marginal effect of genotype at rs2230344, a neighboring non-synonymous SNP, but only among African Americans. Gene and protein expression analyses showed that demethylation at cg19859270 was associated with an increase in both mRNA and protein levels. Since GPR15 is involved in the early stages of viral replication for some HIV-1 and HIV-2 isolates, and the prevalence of HIV is increased in African Americans and smokers, these data support a possible role for GPR15 in the ethnically dependent differential prevalence of HIV.

RYR3 gene variants in subclinical atherosclerosis among HIV-infected women in the Women's Interagency HIV Study (WIHS)

BACKGROUND

Single nucleotide polymorphisms (SNPs) in the Ryanodine receptor 3 (RYR3) gene are associated with common carotid intima media thickness (CCA cIMT) in HIV-infected men. We evaluated SNPs in the RYR3 gene among HIV-infected women participating in Women's Interagency HIV Study (WIHS).

METHODS

CCA cIMT was measured using B-mode ultrasound and the 838 SNPs in the RYR3 gene region were genotyped using the Illumina HumanOmni2.5-quad beadchip. The CCA cIMT genetic association was assessed using linear regression analyses among 1213 women and also separately among White (n=139), Black (n=720) and Hispanic (n=354) women after adjusting for confounders. A summary measure of pooled association was estimated using a meta-analytic approach by combining the effect estimates from the three races. Haploblocks were inferred using Gabriel's method and haplotype association analyses were conducted among the three races separately.

RESULTS

SNP rs62012610 was associated with CCA cIMT among the Hispanics (p=4.41×10(-5)), rs11856930 among Whites (p=5.62×10(-4)), and rs2572204 among Blacks (p=2.45×10(-3)). Meta-analysis revealed several associations of SNPs in the same direction and of similar magnitude, particularly among Blacks and Hispanics. Additionally, several haplotypes within three haploblocks containing SNPs previously related with CCA cIMT were also associated in Whites and Hispanics.

DISCUSSION

Consistent with previous research among HIV-infected men, SNPs within the RYR3 region were associated with subclinical atherosclerosis among HIV-infected women. Allelic heterogeneity observed across the three races suggests that the contribution of the RYR3 gene to CCA cIMT is complex, and warrants future studies to better understand regional SNP function.

Predicting stroke through genetic risk functions: the CHARGE Risk Score Project

BACKGROUND AND PURPOSE

Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors.

METHODS

The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke.

RESULTS

In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)).

CONCLUSIONS

The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

Introduction to personalized medicine in diabetes mellitus

The world is facing an epidemic rise in diabetes mellitus (DM) incidence, which is challenging health funders, health systems, clinicians, and patients to understand and respond to a flood of research and knowledge. Evidence-based guidelines provide uniform management recommendations for "average" patients that rarely take into account individual variation in susceptibility to DM, to its complications, and responses to pharmacological and lifestyle interventions. Personalized medicine combines bioinformatics with genomic, proteomic, metabolomic, pharmacogenomic ("omics") and other new technologies to explore pathophysiology and to characterize more precisely an individual's risk for disease, as well as response to interventions. In this review we will introduce readers to personalized medicine as applied to DM, in particular the use of clinical, genetic, metabolic, and other markers of risk for DM and its chronic microvascular and macrovascular complications, as well as insights into variations in response to and tolerance of commonly used medications, dietary changes, and exercise. These advances in "omic" information and techniques also provide clues to potential pathophysiological mechanisms underlying DM and its complications.

Main findings from the prospective Cardiovascular Risk in Young Finns Study

PURPOSE OF REVIEW

To provide a comprehensive overview on the main findings from the Cardiovascular Risk in Young Finns Study. This prospective multicenter study initiated in 1980 (N = 3596, baseline age 3-18 years) has followed up study participants over 30 years to investigate childhood risk factors for cardiometabolic outcomes in adulthood.

RECENT FINDINGS

Childhood BMI, socioeconomic status, parental risk factor status, as well as genetic polymorphisms are independent predictors of adult obesity, hypertension, and dyslipidemia. Results from the Young Finns Study and other follow-up studies have shown that conventional childhood risk factors, such as dyslipidemia, obesity, elevated blood pressure and smoking, are predictive of subclinical atherosclerosis in young adults. Recent findings suggest that childhood lifestyle (diet, physical activity) is associated with subclinical atherosclerosis and its progression in adulthood. Concerning the timing of risk factor measurements, they seem to be predictive of adult atherosclerosis from the age of 9 onwards. From a clinical point of view, a recent observation suggesting that the adverse cardiometabolic effects of childhood overweight/obesity are reversed among those who become nonobese adults, provides optimism during the days of obesity epidemic.

SUMMARY

Current data suggest that childhood risk factors are associated with higher risk of subclinical atherosclerosis in adulthood. Future studies among aging cohorts followed since childhood will provide data on their influence on clinical cardiovascular outcomes.

Update in cardiology: Vascular risk and cardiac rehabilitation

Atherosclerotic cardiovascular disease remains the major cause of premature death in developed and developing countries. Nevertheless, surveys show that most patients still do not achieve the lifestyles, risk factor levels, and therapeutic targets recommended in primary and secondary prevention. The present update reflects the most recent novelties in risk classification and estimation of risk and documents the latest changes in fields such as smoking, diet and nutrition, physical activity, lipids, hypertension, diabetes, and cardiovascular rehabilitation, based on experimental trials and population-based observational studies.