Association between parental history and genetic risk scores for coronary heart disease prediction: The population-based CoLaus study

Prognostic Modelling Studies of Coronary Heart Disease—A Systematic Review of Conventional and Genetic Risk Factor Studies

This study aims to provide an overview of multivariable prognostic modelling studies developed for coronary heart disease (CHD) in the general population and to explore the optimal prognostic model by comparing the models’ performance. A systematic review was performed using Embase, PubMed, Cochrane, Web of Science, and Scopus databases until 30 November 2019. In this work, only prognostic studies describing conventional risk factors alone or a combination of conventional and genomic risk factors, being developmental and/or validation prognostic studies of a multivariable model, were included. A total of 4021 records were screened by titles and abstracts, and 72 articles were eligible. All the relevant studies were checked by comparing the discrimination, reclassification, and calibration measures. Most of the models were developed in the United States and Canada and targeted the general population. The models included a set of similar predictors, such as age, sex, smoking, cholesterol level, blood pressure, BMI, and diabetes mellitus. In this study, many articles were identified and screened for consistency and reliability using CHARM and GRIPS statements. However, the usefulness of most prognostic models was not demonstrated; only a limited number of these models supported clinical evidence. Unfortunately, substantial heterogeneity was recognized in the definition and outcome of CHD events. The inclusion of genetic risk scores in addition to conventional risk factors might help in predicting the incidence of CHDs; however, the generalizability of the existing prognostic models remains open. Validation studies for the existing developmental models are needed to ensure generalizability, improve the research quality, and increase the transparency of the study.

Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study

It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRS_Metabo ); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PL_Metabo ); validation of two comprehensive polygenic risk scores: GRS_Gola based on Metabochip data, and GRS_Khera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDI_rel ) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRS_Metabo and PL_Metabo , GRS_Gola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDI_rel : 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRS_Khera yielded slightly worse prediction results than GRS_Gola .

Genetic Risk Assessment for Atherosclerotic Cardiovascular Disease: A Guide for the General Cardiologist

Genetic testing for cardiovascular (CV) disease has had a profound impact on the diagnosis and evaluation of monogenic causes of CV disease, such as hypertrophic and familial cardiomyopathies, long QT syndrome, and familial hypercholesterolemia (FH). The success in genetic testing for monogenic diseases has prompted special interest in utilizing genetic information in the risk assessment of more common diseases such as atherosclerotic cardiovascular disease (ASCVD). Polygenic risk scores (PRS) have been developed to assess the risk of coronary artery disease (CAD) that now include millions of single-nucleotide polymorphisms (SNPs) that have been identified through genome-wide association studies (GWAS). While these PRS have demonstrated a strong association with CAD in large cross-sectional population studies, there remains intense debate regarding the added value that PRS contribute to existing clinical risk prediction models such as the pooled cohort equations (PCEs). In this review, we provide a brief background of genetic testing for monogenic drivers of CV disease and then focus on the recent developments in genetic risk assessment of ASCVD, including the use of PRS. We outline the genetic testing that is currently available to all cardiologists in the clinic and discuss the evolving sphere of specialized cardiovascular genetics programs (CVGPs) that integrate the expertise of cardiologists, geneticists, and genetic counselors. Finally, we review the possible implications that PRS and pharmacogenomic data may soon have on clinical practice in the care for patients with or at risk of developing ASCVD.

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.

"Vitamin D" as a profile marker for cardiovascular diseases

Objective

A growing body of research indicates that there exists a correlation between Vit D deficiency and cardiovascular diseases (CVD). In addition to being genetically determined, it is strongly influenced by lifestyle factors. In this study, Vit D and its interrelated factors have been studied as profile marker for identifying the risk of CVD in patients.

Methods

The present study includes comparison of a total 200 adults CVD patients with the healthy patients as control, by measuring their serum lipid levels and Vit D concentrations with other CVD risk factors.

Results

The average serum Vit D in CVD patients and controls are found to be 22.55±6.2 ng/ml and 37.62±3.2 ng/ml respectively, showing that 63% of CVD patients and 35% of controls are Vit D deficient. Serum lipids levels were considered as marker for patients having CVD which include high levels of total cholesterol, triglycerides, and low-density lipoprotein-cholesterol while low levels of high-density lipoproteins-cholesterol levels. Other risk factors like hypertension, lifestyle, smoking, dietary factors and nutritional status shows significantly correlation for CVD patients compared to controls.

Conclusion

Literature supports the relationship between lipid profile and Vit D level by using this as a profile marker for CVD patients. Our study also suggests the same that vitamin D can be used as profile marker for cardiovascular diseases.

Polygenic risk scores in coronary artery disease

PURPOSE OF REVIEW

Large genome-wide association studies (GWAS) have identified variants accounting for a substantial portion of the heritable risk for coronary artery disease (CAD). These studies have catalyzed drug discovery and generated the possibility of improved risk prediction and stratification. Here, we review the current state-of-the art in polygenic risk scores (PRSs) and look to the future, as these scores move towards clinical application.

RECENT FINDINGS

Over the last decade, multilocus PRSs for CAD have expanded to include millions of variants and demonstrated strong association with CAD outcomes, even when adjusted for traditional risk factors. Recently, PRSs have shown better prediction of CAD outcomes than any single traditional risk factor alone. Advances in statistical methods used to generate PRSs have improved their predictive ability and transferability between populations with varied ancestries. Initial clinical studies have also demonstrated the potential of genetic information to impact shared decision-making between patients and providers, leading to improved outcomes.

SUMMARY

PRSs can improve risk stratification for CAD especially in white/European populations and have the potential to alter routine clinical care. However, unlocking this potential will require additional research in PRSs in nonwhite populations and substantial investment in clinical implementation studies.

FADS3 is a Δ14Z sphingoid base desaturase that contributes to gender differences in the human plasma sphingolipidome

Sphingolipids (SLs) are structurally diverse lipids that are defined by the presence of a long-chain base (LCB) backbone. Typically, LCBs contain a single Δ4E double bond (DB) (mostly d18:1), whereas the dienic LCB sphingadienine (d18:2) contains a second DB at the Δ14Z position. The enzyme introducing the Δ14Z DB is unknown. We analyzed the LCB plasma profile in a gender-, age-, and BMI-matched subgroup of the CoLaus cohort (n = 658). Sphingadienine levels showed a significant association with gender, being on average ∼30% higher in females. A genome-wide association study (GWAS) revealed variants in the fatty acid desaturase 3 (FADS3) gene to be significantly associated with the plasma d18:2/d18:1 ratio (p = -log 7.9). Metabolic labeling assays, FADS3 overexpression and knockdown approaches, and plasma LCB profiling in FADS3-deficient mice confirmed that FADS3 is a bona fide LCB desaturase and required for the introduction of the Δ14Z double bond. Moreover, we showed that FADS3 is required for the conversion of the atypical cytotoxic 1-deoxysphinganine (1-deoxySA, m18:0) to 1-deoxysphingosine (1-deoxySO, m18:1). HEK293 cells overexpressing FADS3 were more resistant to m18:0 toxicity than WT cells. In summary, using a combination of metabolic profiling and GWAS, we identified FADS3 to be essential for forming Δ14Z DB containing LCBs, such as d18:2 and m18:1. Our results unravel FADS3 as a Δ14Z LCB desaturase, thereby disclosing the last missing enzyme of the SL de novo synthesis pathway.

Genetic Tools for Coronary Risk Assessment in Type 2 Diabetes: A Cohort Study From the ACCORD Clinical Trial

OBJECTIVE

We evaluated whether the increasing number of genetic loci for coronary artery disease (CAD) identified in the general population could be used to predict the risk of major CAD events (MCE) among participants with type 2 diabetes at high cardiovascular risk.

RESEARCH DESIGN AND METHODS

A weighted genetic risk score (GRS) derived from 204 variants representative of all the 160 CAD loci identified in the general population as of December 2017 was calculated in 5,360 and 1,931 white participants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Outcome Reduction With Initial Glargine Intervention (ORIGIN) studies, respectively. The association between GRS and MCE (combining fatal CAD events, nonfatal myocardial infarction, and unstable angina) was assessed by Cox proportional hazards regression.

RESULTS

The GRS was associated with MCE risk in both ACCORD and ORIGIN (hazard ratio [HR] per SD 1.27, 95% CI 1.18-1.37, P = 4 × 10-10, and HR per SD 1.35, 95% CI 1.16-1.58, P = 2 × 10-4, respectively). This association was independent from interventions tested in the trials and persisted, though attenuated, after adjustment for classic cardiovascular risk predictors. Adding the GRS to clinical predictors improved incident MCE risk classification (relative integrated discrimination improvement +8%, P = 7 × 10-4). The performance of this GRS was superior to that of GRS based on the smaller number of CAD loci available in previous years.

CONCLUSIONS

When combined into a GRS, CAD loci identified in the general population are associated with CAD also in type 2 diabetes. This GRS provides a significant improvement in the ability to correctly predict future MCE, which may increase further with the discovery of new CAD loci.

Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.

Weighted Multi-marker Genetic Risk Scores for Incident Coronary Heart Disease among Individuals of African, Latino and East-Asian Ancestry

We examined the clinical utility of two multi-locus genetic risk scores (GRSs) previously validated in Europeans among persons of African (AFR; n = 2,089), Latino (LAT; n = 4,349) and East-Asian (EA; n = 4,804) ancestry. We used data from the GERA cohort (30-79 years old, 68 to 73% female). We utilized two GRSs with 12 and 51 SNPs, respectively, and the Framingham Risk Score (FRS) to estimate 10-year CHD risk. After a median 8.7 years of follow-up, 450 incident CHD events were documented (95 in AFR, 316 in LAT and 39 EA, respectively). In a model adjusting for principal components and risk factors, tertile 3 vs. tertile 1 of GRS_12 was associated with 1.86 (95% CI, 1.15-3.01), 1.52 (95% CI, 1.02-2.25) and 1.19 (95% CI, 0.77-1.83) increased hazard of CHD in AFR, LAT and EA, respectively. Inclusion of the GRSs in models containing the FRS did not increase the C-statistic but resulted in net overall reclassification of 10% of AFR, 7% LAT and EA and in reclassification of 13% of AFR and EA as well as 10% LAT in the intermediate FRS risk subset. Our results support the usefulness of incorporating genetic information into risk assessment for primary prevention among minority subjects in the U.S.

Prevalence and determinants of polypharmacy in Switzerland: data from the CoLaus study

Background

Polypharmacy is a frequent condition, but its prevalence and determinants in the Swiss mid-aged population are unknown. We aimed to evaluate the prevalence and determinants of polypharmacy in a large Swiss mid-aged population-based sample.

Methods

Data from 4938 participants of the CoLaus study (53% women, age range 40–81 years) were collected between 2009 and 2012. Polypharmacy was defined by the regular use of five or more drugs.

Results

Polypharmacy was reported by 580 participants [11.8%, 95% confidence interval (10.9; 12.6)]. Participants on polypharmacy were significantly older (mean ± standard deviation: 66.0 ± 9.1 vs. 56.6 ± 10.1 years), more frequently obese (35.9% vs. 14.7%), of lower education (66.6% vs. 50.7%) and former smokers (46.7% vs. 36.4%) than participants not on polypharmacy. These findings were confirmed by multivariate analysis: odds ratio and (95% confidence interval) for age groups 50–64 and 65–81 relative to 40–49 years: 2.90 (2.04; 4.12) and 10.3 (7.26; 14.5), respectively, p for trend < 0.001; for low relative to high education: 1.56 (1.17; 2.07); for overweight and obese relative to normal weight participants: 2.09 (1.65; 2.66) and 4.38 (3.39; 5.66), respectively, p for trend < 0.001; for former and current relative to never smokers: 1.42 (1.14, 1.75) and 1.63 (1.25, 2.12), respectively, p for trend < 0.001.

Conclusion

One out of nine participants of our sample is on polypharmacy. Increasing age, body mass index, smoking and lower education independently increase the likelihood of being on polypharmacy.

Electronic supplementary material

The online version of this article (10.1186/s12913-017-2793-z) contains supplementary material, which is available to authorized users.

Prospective association of a genetic risk score with major adverse cardiovascular events in patients with coronary artery disease

Many susceptibility loci associated with coronary artery disease (CAD) have been identified using genome-wide association studies (GWAS). This study aimed to examine whether a composite of single nucleotide polymorphisms (SNPs) derived from GWAS could identify the risk of major adverse cardiovascular events (MACEs) in patients with established CAD. There were 1059 patients with CAD were included in the analysis. Of the participants, 686 were on statin treatment at the start of follow-up. A weighted genetic risk score (wGRS) was calculated as the sum of risk alleles multiplied by the hazard ratio for a particular SNP. In single variant analyses, rs579459, rs4420638, and rs2107595 were associated with an increased risk of MACE. A wGRS was further constructed to evaluate the cumulative effect of the 3 SNPs on the prognosis of CAD. The risk of MACE among patients with high and intermediate wGRS was 1.968- and 1.838-fold, respectively, higher than those with low wGRS. This effect was more evident in patients using lipid-lowering medication and with hypertension. Furthermore, the interaction analysis revealed that lipid-lowering medication and hypertension interacted with the genetic effect off wGRS on the risk of MACE in patients using lipid-lowering medication or with hypertension (Pinteraction < .001). We further analyzed the follow-up change in low-density lipoprotein cholesterol (LDL-C) level at 6 months after CAD disclosure and evaluated whether that was due to wGRS or statin use. The lowest reduction in LDL-C was observed in patients with high GRS who received statin treatment. Furthermore, LDL-C reduction of patients with intermediate wGRS was less than those with low wGRS in patients treated with statin. Taken together, a wGRS comprised of SNPs significantly predicts MACE in CAD patients receiving statin treatment and hypertension.

Polygenic risk score predicts prevalence of cardiovascular disease in patients with familial hypercholesterolemia

BACKGROUND

Although familial hypercholesterolemia (FH) is a severe monogenic disease, it has been shown that clinical risk factors and common genetic variants can modify cardiovascular disease (CVD) risk.

OBJECTIVE

The aim of the study was to evaluate the polygenic contribution to lipid traits and CVD in FH using genetic risk scores (GRSs).

METHODS

Among the 20,434 subjects attending the lipid clinic, we identified and included 725 individuals who carried an FH causing mutation in this retrospective cohort study. We evaluated the association of GRSs for several traits including coronary artery disease (CAD; GRS_CAD) as well as plasma concentrations of low-density lipoprotein cholesterol (LDL-C; GRS_LDL-C), high-density lipoprotein cholesterol (GRS_HDL-C) and triglycerides (GRS_TG).

RESULTS

A total of 32% (n = 231) of FH subjects presented a CVD event before their first visit. Patients in the highest GRS_LDL-C tertile presented an LDL-C 0.4 mmol/L (15.5 mg/dL) higher than the subjects in the lowest tertile (P = .01). The GRS_CAD was strongly associated with CVD events (odds ratio 1.80; 95% confidence interval 1.14-2.85; P = .01) even after adjustment for cardiovascular risk factors. Compared with subjects in the first tertile, those in the third GRS_CAD tertile had a significantly higher prevalence of events (40.9% vs 24.7%, P < .0001) and a significantly higher number of events (average 0.97 vs 0.57 [P = .0001] events per individual).

CONCLUSION

These results indicate that even in the context of a severe monogenic disease such as FH, common genetic variants can significantly modify the disease phenotype. The use of the 192-SNPs GRS_CAD may refine CVD risk prediction in FH patients and this could lead to a more personalized approach to therapy.

Utilizing nutritional genomics to tailor diets for the prevention of cardiovascular disease: a guide for upcoming studies and implementations

INTRODUCTION

Personalized diets based on an individual's genome to optimize the success of dietary intervention and reduce genetic cardiovascular disease (CVD) risk, is one of the challenges most frequently discussed in the scientific community. Areas covered: The authors gathered literature-based evidence on nutritional genomics and CVD phenotypes, our own results and research experience to provide a critical overview of the current situation of using nutritional genomics to tailor diets for CVD prevention and to propose guidelines for future studies and implementations. Expert commentary: Hundreds of studies on gene-diet interactions determining CVD intermediate (plasma lipids, hypertension, etc.) and final phenotypes (stroke, etc.) have furnished top-level scientific evidence for claiming that the genetic effect in cardiovascular risk is not deterministic, but can be modified by diet. However, despite the many results obtained, there are still gaps in practically applying a personalized diet design to specific genotypes. Hence, a better systemization and methodological improvement of new studies is required to obtain top-level evidence that will allow their application in the future precision nutrition/medicine. The authors propose several recommendations for tackling new approaches and applications.

Leveraging information from genetic risk scores of coronary atherosclerosis

PURPOSE OF REVIEW

Genome-wide association studies (GWAS) have identified ∼60 loci for coronary artery disease (CAD). Through genetic risk scores (GRSs), investigators are leveraging this genomic information to gain insights on both the fundamental mechanisms driving these associations as well as their utility in improving risk prediction.

RECENT FINDINGS

GRSs of CAD track with the earliest atherosclerosis lesions in the coronary including fatty streaks and uncomplicated raised lesions. In multiple cohort studies, they predict incident CAD events independent of all traditional and lifestyle risk factors. The incorporation of SNPs with suggestive but not genome-wide association in GWAS into GRSs often increases the strength of these associations. GRS may also predict recurrent events and identify patients most likely to respond to statins. The effect of the GRS on discrimination metrics remains modest but the minimal degree of improvement needed for clinical utility is unknown.

SUMMARY

Most novel loci for CAD identified through GWAS facilitate the formation of coronary atherosclerosis and stratify individuals based on their underlying burden of coronary atherosclerosis. GRSs may one day be routinely used in clinical practice to not only assess the risk of incident events but also to predict who will respond best to established prevention strategies.

ORMDL3 expression levels have no influence on the activity of serine palmitoyltransferase

ORMDL proteins are believed to be negative regulators of serine palmitoyltransferase (SPT), which catalyzes the first and rate limiting step in sphingolipid (SL) de novo synthesis. Several single-nucleotide polymorphisms (SNPs) that are close to the ORMDL3 locus have been reported to increase ORMDL3 expression and to be associated with an elevated risk for early childhood asthma; however, the direct effect of ORMDL3 expression on SPT activity and its link to asthma remains elusive. In this study, we investigated whether ORMDL3 expression is associated with changes in SPT activity and total SL levels. Ormdl3-knockout (Ormdl3^-/-) and transgenic (Ormdl3^Tg/wt) mice were generated to study the effect of ORMDL3 on total SL levels in plasma and tissues. Cellular SPT activity was measured in mouse embryonic fibroblasts from Ormdl3^-/- mice, as well as in HEK293 cells in which ORMDL3 was overexpressed and silenced. Furthermore, we analyzed the association of the reported ORMDL3 asthma SNPs with plasma sphingoid bases in a population-based cohort of 971 individuals. Total C₁₈-long chain bases were not significantly altered in the plasma and tissues of Ormdl3^-/- mice, whereas C₁₈-sphinganine showed a small and significant increase in plasma, lung, and liver tissues. Mouse embryonic fibroblast cells from Ormdl3^-/- mice did not show an altered SPT activity compared with Ormdl3^+/- and Ormdl3^+/+ mice. Overexpression or knockdown of ORMDL3 in HEK293 cells did not alter SPT activity; however, parallel knockdown of all 3 ORMDL isoforms increased enzyme activity significantly. A significant association of the annotated ORMDL3 asthma SNPs with plasma long-chain sphingoid base levels could not be confirmed. ORMDL3 expression levels seem not to be directly associated with changes in SPT activity. ORMDL3 might influence de novo sphingolipid metabolism downstream of SPT.-Zhakupova, A., Debeuf, N., Krols, M., Toussaint, W., Vanhoutte, L., Alecu, I., Kutalik, Z., Vollenweider, P., Ernst, D., von Eckardstein, A., Lambrecht, B. N., Janssens, S., Hornemann, T. ORMDL3 expression levels have no influence on the activity of serine palmitoyltransferase.