Genomics of cardiovascular disease

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

Novel molecular mechanisms of doxorubicin cardiotoxicity: latest leading-edge advances and clinical implications

Doxorubicin (Dox) is among the most widely used cancer chemotherapeutic drugs. The clinical use of Dox is, however, limited due to its cardiotoxicity. Studies over the past several decades have suggested various mechanisms of Dox-induced cardiotoxicity (DIC). Among them are oxidative stress, topoisomerase inhibition, and mitochondrial damage. Several novel molecular targets and signaling pathways underlying DIC have emerged over the past few years. The most notable advances include discovery of ferroptosis as a major form of cell death in Dox cytotoxicity, and elucidation of the involvement of cardiogenetics and regulatory RNAs as well as multiple other targets in DIC. In this review, we discuss these advances, focusing on latest cutting-edge research discoveries from mechanistic studies reported in influential journals rather than surveying all research studies available in the literature.

Current RNA strategies in treating cardiovascular diseases

Cardiovascular disease (CVD) continues to impose a significant global health burden, necessitating the exploration of innovative treatment strategies. Ribonucleic acid (RNA)-based therapeutics have emerged as a promising avenue to address the complex molecular mechanisms underlying CVD pathogenesis. We present a comprehensive review of the current state of RNA therapeutics in the context of CVD, focusing on the diverse modalities that bring about transient or permanent modifications by targeting the different stages of the molecular biology central dogma. Considering the immense potential of RNA therapeutics, we have identified common gene targets that could serve as potential interventions for prevalent Mendelian CVD caused by single gene mutations, as well as acquired CVDs developed over time due to various factors. These gene targets offer opportunities to develop RNA-based treatments tailored to specific genetic and molecular pathways, presenting a novel and precise approach to address the complex pathogenesis of both types of cardiovascular conditions. Additionally, we discuss the challenges and opportunities associated with delivery strategies to achieve targeted delivery of RNA therapeutics to the cardiovascular system. This review highlights the immense potential of RNA-based interventions as a novel and precise approach to combat CVD, paving the way for future advancements in cardiovascular therapeutics.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.

Interplay between acute coronary syndromes and cancer: insights into pathophysiology, diagnostic challenges, and treatment options

Acute coronary syndromes (ACS) and cancer are among the leading causes of death worldwide. In recent years, increasing evidence has suggested an interplay between these 2 conditions. This article reviews the pathophysiology, diagnostic challenges, and treatment options for ACS and cancer. The interplay between these conditions may be due to environmental, genetic, and metabolic factors. For example, smoking, hypertension, and obesity are risk factors for both ACS and cancer. Diagnosis of ACS and cancer can be challenging because the symptoms of these conditions often overlap. For example, chest pain can be a symptom of both ACS and a neoplasms. Treatment options for ACS and cancer are varied and depend on the type of disease, stage of disease, and individual patient factors. Treatment for ACS may include angioplasty, stenting, or medication therapy. Treatment for cancer may include surgical removal of the tumour, radiation therapy, or chemotherapy.

Gene-environment interactions that influence CVD, lipid traits, obesity, diabetes, and hypertension appear to be able to influence gene therapy

Most mind boggling diseases are accepted to be impacted by both genetic and environmental elements. As of late, there has been a flood in the improvement of different methodologies, concentrate on plans, and measurable and logical techniques to examine gene-environment cooperations (G × Es) in enormous scope studies including human populaces. The many-sided exchange between genetic elements and environmental openings has long charmed the consideration of clinicians and researchers looking to grasp the complicated starting points of diseases. While single variables can add to disease, the blend of genetic variations and environmental openings frequently decides disease risk. The fundamental point of this paper is to talk about the Gene-Environment Associations That Impact CVD, Lipid Characteristics, Obesity, Diabetes, and Hypertension Have all the earmarks of being Ready to Impact Gene Therapy. This survey paper investigates the meaning of gene-environment collaborations (G × E) in disease advancement. The intricacy of genetic and environmental communications in disease causation is explained, underlining the multifactorial idea of many circumstances. The job of gene-environment cooperations in cardiovascular disease, lipid digestion, diabetes, obesity, and hypertension is investigated. This audit fixates on Gene by Environment (G × E) collaborations, investigating their importance in disease etiology.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Discovery of Transacting Long Noncoding RNAs That Regulate Smooth Muscle Cell Phenotype

BACKGROUND

Smooth muscle cells (SMC), the major cell type in atherosclerotic plaques, are vital in coronary artery diseases (CADs). SMC phenotypic transition, which leads to the formation of various cell types in atherosclerotic plaques, is regulated by a network of genetic and epigenetic mechanisms and governs the risk of disease. The involvement of long noncoding RNAs (lncRNAs) has been increasingly identified in cardiovascular disease. However, SMC lncRNAs have not been comprehensively characterized, and their regulatory role in SMC state transition remains unknown.

METHODS

A discovery pipeline was constructed and applied to deeply strand-specific RNA sequencing from perturbed human coronary artery SMC with different disease-related stimuli, to allow for the detection of novel lncRNAs. The functional relevance of a select few novel lncRNAs were verified in vitro.

RESULTS

We identified 4579 known and 13 655 de novo lncRNAs in human coronary artery SMC. Consistent with previous long noncoding RNA studies, these lncRNAs overall have fewer exons, are shorter in length than protein-coding genes (pcGenes), and have relatively low expression level. Genomic location of these long noncoding RNA is disproportionately enriched near CAD-related TFs (transcription factors), genetic loci, and gene regulators of SMC identity, suggesting the importance of their function in disease. Two de novo lncRNAs, ZIPPOR (ZEB-interacting suppressor) and TNS1-AS2 (TNS1-antisense 2), were identified by our screen. Combining transcriptional data and in silico modeling along with in vitro validation, we identified CAD gene ZEB2 as a target through which these lncRNAs exert their function in SMC phenotypic transition.

CONCLUSIONS

Expression of a large and diverse set of lncRNAs in human coronary artery SMC are highly dynamic in response to CAD-related stimuli. The dynamic changes in expression of these lncRNAs correspond to alterations in transcriptional programs that are relevant to CAD, suggesting a critical role for lncRNAs in SMC phenotypic transition and human atherosclerotic disease.

Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine

Cardiovascular disease (CVD) is the leading cause of mortality and loss of disability adjusted life years (DALYs) globally. CVDs like Heart Failure (HF) and Atrial Fibrillation (AF) are associated with physical effects on the heart muscles. As a result of the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. Rightful application of artificial intelligence (AI) and machine learning (ML) approaches can lead to new insights into CVDs for providing better personalized treatments with predictive analysis and deep phenotyping. In this study we focused on implementing AI/ML techniques on RNA-seq driven gene-expression data to investigate genes associated with HF, AF, and other CVDs, and predict disease with high accuracy. The study involved generating RNA-seq data derived from the serum of consented CVD patients. Next, we processed the sequenced data using our RNA-seq pipeline and applied GVViZ for gene-disease data annotation and expression analysis. To achieve our research objectives, we developed a new Findable, Accessible, Intelligent, and Reproducible (FAIR) approach that includes a five-level biostatistical evaluation, primarily based on the Random Forest (RF) algorithm. During our AI/ML analysis, we have fitted, trained, and implemented our model to classify and distinguish high-risk CVD patients based on their age, gender, and race. With the successful execution of our model, we predicted the association of highly significant HF, AF, and other CVDs genes with demographic variables.

Clinical Cancer and Direct-to-Consumer Genetic Test Result-Sharing Behavior: Findings from HINTS 2020

INTRODUCTION

Sharing genetic test results with different stakeholders such as family members, healthcare providers and genetic counselors (HCP/GCs), spouses/partners, and friends is a health behavior of clinical importance in genomic medicine.

METHODS

Using nationally representative population-based data collected from the Health Information National Trends Survey (HINTS 5, cycle 4), we identified the prevalence and factors associated with genetic test result-sharing behavior for high-risk cancer tests, genetic health risk tests, and ancestry tests within four groups: HCP/GCs, first-degree relatives (FDRs), spouse/partner, and friend/other.

RESULTS

Overall, 68.4% of those who underwent high-risk cancer genetic testing shared their results with FDRs, whereas 89.9% shared with HCP/GCs. In adjusted multivariable analyses, women were nine times more likely than men to share (p = 0.006), and those with a personal history of cancer were less likely to share with HCP/GCs (OR = 0.025, p ≤ 0.001). Of those tested for genetic health risk, 66.5% shared with HCP/GCs, 38.7% with FDRs, 66.6% with a spouse/partner, 12.8% with a friend, and 14.1% did not share results with anyone. Of those who underwent ancestry testing, very few shared results with HCP/GCs (2.6%), whereas modest sharing was reported with FDRs, spouses/partners, and friends.

DISCUSSION

These data add empirical evidence about the population prevalence of genetic information sharing and serve as a metric for public engagement with genetic testing.

Analysis of Anti-Arrhythmic Impacts of Crocin through Estimation of Expression of Cx43 in Myocardial Infarction Using a Rat Animal Model

Arrhythmia is an important cause of death after myocardial infarction (MI). Different substances have been evaluated for their anti-arrhythmic effect in MI. This study was performed to evaluate the anti-arrhythmic impacts of crocin in an MI animal model (rat) by estimation of the expression of connexin 43 (Cx43). Fifty male Sprague-Dawley rats were grouped into 5 groups, each composed of 10 rats. The first group was regarded as the normal control group and the second one was considered as the MI group, which was caused by ligation of the left anterior descending artery. The other three groups received crocin 50 or 10 mg/kg/day or metoprolol 100 mg/kg/day for 1 week, following ligation of the left anterior descending artery. Evaluated outcomes were cardiac Cx43 expression, arrhythmia incidence, histological findings, and myocyte resting potential. Crocin-treated MI groups showed a significantly lower arrhythmia score than the non-treated MI group, 10 mg/kg/day (1.85 ± 0.55, p < 0.01) and 50 mg/kg/day (1.70 ± 0.33, p < 0.01). Groups that received crocin 10 mg/kg/day (66.30 ± 2.59, p < 0.01), crocin 50 mg/kg/day (68.10 ± 2.43, p < 0.01), and metoprolol 100 mg/kg/day (-63.54 ± 0.63 mV, p < 0.01) significantly prevented depolarization in comparison with the non-treated MI group. Expression of Cx43 mRNA in crocin 10 mg/kg/day (1.54 ± 0.24, p < 0.01), crocin 50 mg/kg/day (1.73 ± 0.09, p < 0.01), and metoprolol 100 mg/kg/day (1.75 ± 0.14, p < 0.01) treatment groups was significantly higher in comparison with the non-treated MI group. Crocin showed a preventive effect on the arrhythmogenic impact of MI in an experimental model of ischemic injury through an increase in expression of Cx43.

Coronary artery disease risk factors affected by RNA modification-related genetic variants

Background

Single nucleotide polymorphisms that affect RNA modification (RNAm-SNPs) may have functional roles in coronary artery disease (CAD). The aim of this study was to identify RNAm-SNPs in CAD susceptibility loci and highlight potential risk factors.

Methods

CAD-associated RNAm-SNPs were identified in the CARDIoGRAMplusC4D and UK Biobank genome-wide association studies. Gene expression and circulating protein levels affected by the RNAm-SNPs were identified by QTL analyses. Cell experiments and Mendelian randomization (MR) methods were applied to test whether the gene expression levels were associated with CAD.

Results

We identified 81 RNAm-SNPs that were associated with CAD or acute myocardial infarction (AMI), including m6A-, m1A-, m5C-, A-to-I- and m7G-related SNPs. The m6A-SNPs rs3739998 in JCAD, rs148172130 in RPL14 and rs12190287 in TCF21 and the m7G-SNP rs186643756 in PVT1 were genome-wide significant. The RNAm-SNPs were associated with gene expression (e.g., MRAS, DHX36, TCF21, JCAD and SH2B3), and the expression levels were associated with CAD. Differential m6A methylation and differential expression in FTO-overexpressing human aorta smooth muscle cells and peripheral blood mononuclear cells of CAD patients and controls were detected. The RNAm-SNPs were associated with circulating levels of proteins with specific biological functions, such as blood coagulation, and the proteins (e.g., cardiotrophin-1) were confirmed to be associated with CAD and AMI in MR analyses.

Conclusion

The present study identified RNAm-SNPs in CAD susceptibility genes, gene expression and circulating proteins as risk factors for CAD and suggested that RNA modification may play a role in the pathogenesis of CAD.

Validation of the Ayurvedic construct, Rasadhatudushti, in adults at risk of cardiovascular diseases - A mixed-method study

Background

Rasadhatudushti (RD), the deranged state of Rasadhatu, is a construct in Ayurveda mentioned as the cause of diseases affecting the circulatory channels and the heart, collectively called cardiovascular diseases (CVD). It is a morbid condition generic to some other disorders, hence is non-specific to CVD. It was observed that RD was present in majority of acute coronary artery disease in a cross-sectional, descriptive study in 250 patients stabilized after an acute episode.

Objectives

To validate the available scale for assessing RD in the context of CVD risk.

To study the prevalence of RD in participants at risk of cardiovascular diseases.

Method

In the first part, validation of the scale for assessing RD, as mentioned in the texts, was done through standard steps for scale validation in the context of CVD risk. Psychometric analysis was done after administering the draft scale of 39 items in 218 participants above the age of 40 years who were not yet diagnosed with overt CVD conditions. Construct validation was done by comparing mean score of Framingham global risk score in high and low RD scores and comparing the reduction in CVD risk score assessed by Qrisk®2–2017 by life style modification and a conventional RD correction as add on. Second part was a cross sectional survey study to estimate the prevalence of RD in a specific population vulnerable to CVD. This was done in a sample of 160 sedentary government employees of Thiruvananthapuram District, Kerala, aged above 40, using the validated RD assessment scale.

Result

–The final scale to assess RD, ‘RAS-RCVD’, with 25 symptoms was found to have concurrent validity using WHO/ISH risk prediction as the reference standard. Framingham global risk score also showed significant but low positive correlation with eight as the cut off for RD score. The reduction in mean (SD) Qrisk score was 2.53 (3.22) in the trial group receiving RD correction drug and 0.30 (3.43) in the control with statistical significance (p < 0.05). The prevalence of RD as assessed by RD score above the cut off in the second part of the study was 49.4%. The prevalence of RD was significantly high in participants with moderate to high risk for CVD (61.3%).

Conclusion

– The construct RD was observed to be valid in pre-clinical states of CVD. There was a high prevalence of this morbid construct in moderate to high-risk individuals. Ayurvedic CVD prevention strategies need to target on correction of RD along with individual risk factor management.

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.

Inframe insertion and splice site variants in MFGE8 associate with protection against coronary atherosclerosis

Cardiovascular diseases are the leading cause of premature death and disability worldwide, with both genetic and environmental determinants. While genome-wide association studies have identified multiple genetic loci associated with cardiovascular diseases, exact genes driving these associations remain mostly uncovered. Due to Finland’s population history, many deleterious and high-impact variants are enriched in the Finnish population giving a possibility to find genetic associations for protein-truncating variants that likely tie the association to a gene and that would not be detected elsewhere. In a large Finnish biobank study FinnGen, we identified an association between an inframe insertion rs534125149 in MFGE8 (encoding lactadherin) and protection against coronary atherosclerosis. This variant is highly enriched in Finland, and the protective association was replicated in meta-analysis of BioBank Japan and Estonian biobank. Additionally, we identified a protective association between splice acceptor variant rs201988637 in MFGE8 and coronary atherosclerosis, independent of the rs534125149, with no significant risk-increasing associations. This variant was also associated with lower pulse pressure, pointing towards a function of MFGE8 in arterial aging also in humans in addition to previous evidence in mice. In conclusion, our results suggest that inhibiting the production of lactadherin could lower the risk for coronary heart disease substantially.

A genome-wide association study identifies MFGE8 as protective against coronary atherosclerosis in European and East Asian populations.

MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data

Background

Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each ‘omic’ type.

Results

Robustness was assessed over simulations based on available single-cell simulation methods, and applicability was assessed through an example using CITE-seq data to detect genes associated with leukemia. We find that MOSCATO performs favorably in selecting network features while also shown to be applicable to real multimodal single-cell data.

Conclusions

MOSCATO is a useful analytical technique for supervised feature selection in multimodal single-cell data. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08759-3).

The Need for Individualized Risk Assessment in Cardiovascular Disease

Cardiovascular disease remains the leading cause of death in the era of modern medicine despite major advancements in this field. Current available clinical surrogate markers and blood tests do not adequately predict individual risk of cardiovascular disease. A more precise and sophisticated tool that can reliably predict the thrombosis and bleeding risks at an individual level is required in order for clinicians to confidently recommend early interventions with a favorable risk–benefit profile. Critical to the development of this tool is the assessment and understanding of Virchow’s triad and its complex interactions between hypercoagulability, endothelial dysfunction and vessel flow, a fundamental concept to the development of thrombosis. This review explores the pathophysiology of cardiovascular disease stemming from the triad of factors and how individualized risk assessment can be improved through the multimodal use of tools such as global coagulation assays, endothelial biomarkers and vessel flow assessment.

The Challenge of Genetic Variants of Uncertain Clinical Significance : A Narrative Review

Genomic tests expand diagnostic and screening opportunities but also identify genetic variants of uncertain clinical significance (VUSs). Only a minority of VUSs are likely to prove pathogenic when later reassessed, but resolution of the uncertainty is rarely timely. That uncertainty adds complexity to clinical decision making and can result in harms and costs to patients and the health care system, including the time-consuming analysis required to interpret a VUS and the potential for unnecessary treatment and adverse psychological effects. Current efforts to improve variant interpretation will help reduce the scope of the problem, but the high prevalence of rare and novel variants in the human genome points to VUSs as an ongoing challenge. Additional strategies can help mitigate the potential harms of VUSs, including testing protocols that limit identification or reporting of VUSs, subclassification of VUSs according to the likelihood of pathogenicity, routine family-based evaluation of variants, and enhanced counseling efforts. All involve tradeoffs, and the appropriate balance of measures is likely to vary for different test uses and clinical settings. Cross-specialty deliberation and public input could contribute to systematic and broadly supported policies for managing VUSs.

The angiotensin-converting enzyme gene insertion/deletion polymorphism interacts with fear of falling in relation to stepping speed in community-dwelling older adults

BACKGROUND

Despite the association of genetic factors with falls, balance, and lower extremity functioning, interaction of the angiotensin-converting enzyme (ACE) gene insertion/deletion (I/D) polymorphism with fear of falling (FOF) in relation to stepping performance has, to the best of our knowledge, not been investigated in older adults.

OBJECTIVE

The purpose of this study was to examine the interaction effects of the ACE I/D polymorphism with FOF in relation to stepping performance in older adults.

METHODS

Eighty-eight community-dwelling adults 60 years or older participated in a cross-sectional observational study. Participants completed tests of rapid and distance stepping, and self-reported FOF (yes/no). Participants provided saliva for ACE genotyping. General linear models evaluated ACE genotype × FOF interaction effects in relation to stepping performance. The α level was set at 0.05.

RESULTS

The ACE I/D polymorphism exhibited significant interaction effects (p for interactions 0.002 ≤ p ≤ .04) with FOF in relation to stepping speed. Relationships between FOF and stepping speed varied among ACE genotypes. The insertion/insertion (II) genotype was significantly associated (p = .01) with slow stepping in individuals with, but not without FOF (p > .05).

CONCLUSION

Variation in relationships between FOF and stepping speed among ACE genotypes suggests a role for the ACE I/D polymorphism in modifying relationships between FOF and stepping speed in older adults. The association of the ACE II genotype with slow stepping performance in individuals with, but not without FOF, suggests that older adults with the ACE II genotype and FOF may be at increased risk for poor stepping performance and associated functional declines.

Clinical Phenotypes of Cardiovascular and Heart Failure Diseases Can Be Reversed? The Holistic Principle of Systems Biology in Multifaceted Heart Diseases

Recent advances in cardiology and biological sciences have improved quality of life in patients with complex cardiovascular diseases (CVDs) or heart failure (HF). Regardless of medical progress, complex cardiac diseases continue to have a prolonged clinical course with high morbidity and mortality. Interventional coronary techniques together with drug therapy improve quality and future prospects of life, but do not reverse the course of the atherosclerotic process that remains relentlessly progressive. The probability of CVDs and HF phenotypes to reverse can be supported by the advances made on the medical holistic principle of systems biology (SB) and on artificial intelligence (AI). Studies on clinical phenotypes reversal should be based on the research performed in large populations of patients following gathering and analyzing large amounts of relative data that embrace the concept of complexity. To decipher the complexity conundrum, a multiomics approach is needed with network analysis of the biological data. Only by understanding the complexity of chronic heart diseases and explaining the interrelationship between different interconnected biological networks can the probability for clinical phenotypes reversal be increased.

EARLY CONSIDERATIONS OF GENETICS IN APHASIA REHABILITATION: A NARRATIVE REVIEW

Background

Early investigations linking language and genetics were focused on the evolution of human communication in populations with developmental speech and language disorders. Recently, studies suggest that genes may also modulate recovery from post-stroke aphasia.

Aims

Our goal is to review current literature related to the influence of genetics on post-stroke recovery, and the implications for aphasia rehabilitation. We describe candidate genes implicated by empirical findings and address additional clinical considerations.

Main Contribution

We describe existing evidence and mechanisms supporting future investigations into how genetic factors may modulate aphasia recovery and propose that two candidate genes, brain derived neurotrophic factor (BDNF) and apolipoprotein E (APOE), may be important considerations for future research assessing response to aphasia treatment. Evidence suggests that BDNF is important for learning, memory, and neuroplasticity. APOE influences cognitive functioning and memory in older individuals and has also been implicated in neural repair. Moreover, recent data suggest an interaction between specific alleles of the BDNF and APOE genes in influencing episodic memory.

Conclusions

Genetic influences on recovery from aphasia have been largely unexplored in the literature despite evidence that genetic factors influence behaviour and recovery from brain injury. As researchers continue to explore prognostic factors that may influence response to aphasia treatment, it is time for genetic factors to be considered as a source of variability. As the field moves in the direction of personalized medicine, eventually allied health professionals may utilize genetic profiles to inform treatment decisions and education for patients and care partners.

Machine Learning Revealed Ferroptosis Features and a Novel Ferroptosis-Based Classification for Diagnosis in Acute Myocardial Infarction

Acute myocardial infarction (AMI) is a leading cause of death and disability worldwide. Early diagnosis of AMI and interventional treatment can significantly reduce myocardial damage. However, owing to limitations in sensitivity and specificity, existing myocardial markers are not efficient for early identification of AMI. Transcriptome-wide association studies (TWASs) have shown excellent performance in identifying significant gene–trait associations and several cardiovascular diseases (CVDs). Furthermore, ferroptosis is a major driver of ischaemic injury in the heart. However, its specific regulatory mechanisms remain unclear. In this study, we screened three Gene Expression Omnibus (GEO) datasets of peripheral blood samples to assess the efficiency of ferroptosis-related genes (FRGs) for early diagnosis of AMI. To the best of our knowledge, for the first time, TWAS and mRNA expression data were integrated in this study to identify 11 FRGs specifically expressed in the peripheral blood of patients with AMI. Subsequently, using multiple machine learning algorithms, an optimal prediction model for AMI was constructed, which demonstrated satisfactory diagnostic efficiency in the training cohort (area under the curve (AUC) = 0.794) and two external validation cohorts (AUC = 0.745 and 0.711). Our study suggests that FRGs are involved in the progression of AMI, thus providing a new direction for early diagnosis, and offers potential molecular targets for optimal treatment of AMI.

Associations between empirically derived dietary patterns and cardiovascular risk factors among older adult men

Cardiovascular disease (CVD) remains the leading cause of death globally, and epidemiological studies have suggested a link between diet and cardiometabolic risk. Currently, the prevalence of CVD is rapidly increasing with an aging population and continues to contribute to the growing economic and public health burden. However, there is limited evidence available regarding dietary patterns and cardiometabolic risk factors in older adults. We conducted a cross-sectional study to assess dietary patterns and cardiometabolic risk factors in males ≥60 years. Factor analysis identified a "healthy" diet and an "unhealthy" diet as the two primary dietary patterns. Multivariable logistic regression was used for estimating the associations of identified dietary patterns and cardiometabolic risk factors including anthropometric measures, blood pressure, glycemic biomarkers, lipid profile, and inflammatory biomarkers. A healthy dietary pattern was significantly associated with decreased odds of high serum fasting blood sugar (FBS) (OR: 0.32; 95% CI: 0.15-0.67; P_trend=0.002), but increased odds of high serum low-density lipoprotein cholesterol (LDL-C) (OR: 1.82; 95% CI: 1.02-3.24; P_trend=0.04). In comparison, an unhealthy diet was associated with increased odds of obesity (OR: 2.33; 95% CI: 1.31-4.15; P_trend=0.004) and high LDL-C (OR: 2.00; 95% CI: 1.10-2.61; P_trend=0.02). Thus, in older adults, adherence to an unhealthy dietary pattern has a significant impact on clinically relevant risk factors for cardiometabolic risk.

Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure

Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual’s quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.

Deciphering pathogenicity of variants of uncertain significance with CRISPR-edited iPSCs

Genetic variants play an important role in conferring risk for cardiovascular diseases (CVDs). With the rapid development of next-generation sequencing (NGS), thousands of genetic variants associated with CVDs have been identified by genome-wide association studies (GWAS), but the function of more than 40% of genetic variants is still unknown. This gap of knowledge is a barrier to the clinical application of the genetic information. However, determining the pathogenicity of a variant of uncertain significance (VUS) is challenging due to the lack of suitable model systems and accessible technologies. By combining clustered regularly interspaced short palindromic repeats (CRISPR) and human induced pluripotent stem cells (iPSCs), unprecedented advances are now possible in determining the pathogenicity of VUS in CVDs. Here, we summarize recent progress and new strategies in deciphering pathogenic variants for CVDs using CRISPR-edited human iPSCs.

Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

For a nonparametric Behrens-Fisher problem, a directional-sum test is proposed based on division-combination strategy. A one-layer wild bootstrap procedure is given to calculate its statistical significance. We conduct simulation studies with data generated from lognormal, t and Laplace distributions to show that the proposed test can control the type I error rates properly and is more powerful than the existing rank-sum and maximum-type tests under most of the considered scenarios. Applications to the dietary intervention trial further show the performance of the proposed test.

Association of dipeptidyl peptidase IV polymorphism, serum lipid profile, and coronary artery stenosis in patients with coronary artery disease and type 2 diabetes

Cardiovascular disease (CAD) is a devastating illness, but to date there are limited means of predicting a person's coronary stenosis severity and their prognosis. The study was performed to investigate the relationship between dipeptidyl peptidase 4(DPP4) gene polymorphisms and serum lipid profiles, as well as the severity of coronary artery stenosis in patients with CAD and type 2 diabetes (T2DM) for the first time.Herein, 201 patients with CAD and T2DM were enrolled in the Department of Cardiology, Shandong Provincial Qianfoshan Hospital. DPP4 rs3788979 and rs7608798 single nucleotide polymorphisms (SNPs) were genotyped. The general information of all patients was collected, and the associations between DPP4 SNPs and lipid profiles were detected. At the same time, association between SNP polymorphisms and the degree of coronary artery stenosis were analyzed.There was a significant difference in apolipoprotein B (ApoB) levels (P = .011) for the rs3788979 polymorphism, while no difference was identified in other blood lipids or with other mutations. SNP mutation of A to G in rs3788979 was associated with a reduced percentage of severe coronary artery stenosis in female patients (P = .023) as well as those with nosmoking (P = .030), nodrinking (P = 0.007), and nocardiovascular family history (P = 0.015).G allele of rs3788979 is associated with a reduced ApoB level. Besides, we suggest that G allele in rs3788979 may have a cardioprotective effect and prove to be a useful and specific measure when predicting a patient's coronary stenosis severity if diagnosed with CAD and T2DM.

Integration of summary data from GWAS and eQTL studies identified novel risk genes for coronary artery disease

Several genetic loci have been reported to be significantly associated with coronary artery disease (CAD) by multiple genome-wide association studies (GWAS). Nevertheless, the biological and functional effects of these genetic variants on CAD remain largely equivocal. In the current study, we performed an integrative genomics analysis by integrating large-scale GWAS data (N = 459,534) and 2 independent expression quantitative trait loci (eQTL) datasets (N = 1890) to determine whether CAD-associated risk single nucleotide polymorphisms (SNPs) exert regulatory effects on gene expression. By using Sherlock Bayesian, MAGMA gene-based, multidimensional scaling (MDS), functional enrichment, and in silico permutation analyses for independent technical and biological replications, we highlighted 4 susceptible genes (CHCHD1, TUBG1, LY6G6C, and MRPS17) associated with CAD risk. Based on the protein-protein interaction (PPI) network analysis, these 4 genes were found to interact with each other. We detected a remarkably altered co-expression pattern among these 4 genes between CAD patients and controls. In addition, 3 genes of CHCHD1 (P = .0013), TUBG1 (P = .004), and LY6G6C (P = .038) showed significantly different expressions between CAD patients and controls. Together, we provide evidence to support that these identified genes such as CHCHD1 and TUBG1 are indicative factors of CAD.

Targets of Vitamin C With Therapeutic Potential for Cardiovascular Disease and Underlying Mechanisms: A Study of Network Pharmacology

Vitamin C (ascorbic acid) is a nutrient used to treat cardiovascular disease (CVD). However, the pharmacological targets of vitamin C and the mechanisms underlying the therapeutic effects of vitamin C on CVD remain to be elucidated. In this study, we used network pharmacology approach to investigate the pharmacological mechanisms of vitamin C for the treatment of CVD. The core targets, major hubs, enriched biological processes, and key signaling pathways were identified. A protein-protein interaction network and an interaction diagram of core target-related pathways were constructed. Three core targets were identified, including phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform, signal transducer and activator of transcription-3 (STAT3), and prothrombin. The GO and KEGG analyses identified top 20 enriched biological processes and signaling pathways involved in the therapeutic effects of vitamin C on CVD. The JAK-STAT, STAT, PD1, EGFR, FoxO, and chemokines signaling pathways may be highly involved in the protective effects of vitamin C against CVD. In conclusion, our bioinformatics analyses provided evidence on the possible therapeutic mechanisms of vitamin C in CVD treatment, which may contribute to the development of novel drugs for CVD.

Three novel ATG16L1 mutations in a patient with acute myocardial infarction and coronary artery ectasia: A case report

INTRODUCTION

Acute myocardial infarction (AMI) is a specific type of coronary artery disease (CAD) caused by the rupture of coronary atherosclerotic plaques. Coronary artery ectasia (CAE) is a rare phenotype of cardiovascular disease that may promote thrombosis and inflammatory responses leading to myocardial infarction due to abnormal dilatation of blood vessels and coronary blood flow disorders. It is a complicated disease and shows interaction between genetic and environmental factors.

PATIENT CONCERNS

A 34-year-old male patient was admitted to our hospital on May 12, 2016, with complaints of chest pain for 1 hour duration.

DIAGNOSIS

Coronary angiography through the emergency medical service (EMS) system showed 100% occlusion at the first turning point of the right coronary artery (RCA), along with tumor-like expansion of the proximal segment of the RCA and the end of the left main (LM) artery. The patient was diagnosed with AMI and CAE. Three-point mutations in the ATG16L1 gene were identified by direct sequencing.

INTERVENTIONS

After admission, the patient underwent emergency green channel coronary angiography and percutaneous coronary intervention (PCI) to assess and unblock the stenosis and occlusion of the RCA lumen, but no stenting was performed because the catheter could not pass the second inflection point of the RCA. Aspirin enteric-coated tablets, clopidogrel sulfate tablets, tirofiban hydrochloride, and low molecular weight heparin calcium were given as anticoagulant and antiplatelet therapy. Atorvastatin calcium tablets were used to regulate blood lipid levels. Perindopril and spironolactone were used to inhibit the renin-angiotensin-aldosterone system (RAAS) to reverse myocardial remodeling. Acetylcholinesterase inhibitors (ACEI) and beta blockers were administered to resist ventricular remodeling and improve cardiac function and prognosis after the patient's blood pressure and heart rhythm were stabilized.

OUTCOMES

After active rescue treatment, the patient recovered and was discharged. A coronary angiogram performed 2 years later showed that the RCA blood flow was restored, and the patient had recovered well.

CONCLUSION

Three-point mutations in the ATG16L1 gene were identified in a patient with AMI and CAE, which extended the mutation spectrum of the ATG16L1 gene. Hence, the etiology of coronary artery aneurysmal dilatation is worthy of further investigation.

Personalized Nutrition and -Omics

With change in global concern toward food quality over food quantity, consumer concern and choice of healthy food has become a matter of prime importance. It gave rise to concept of “personalized or precision nutrition”. The theory behind personalization of nutrition is supported by multiple factors including advances in food analytics, nutrition based diseases and public health programs, increasing use of information technology in nutrition science, concept of gene-diet interaction and growing consumer capacity or concern by better and healthy foods. The advances in “omics” tools and related analytical techniques have resulted into tremendous scope of their application in nutrition science. As a consequence, a better understanding of underlying interaction between diet and individual is expected with addressing of key challenges for successful implementation of this science. In this chapter, the above aspects are discussed to get an insight into driving factors for increasing concern in personalized nutrition.

The Human Explanted Heart Program: A translational bridge for cardiovascular medicine

The progression of cardiovascular research is often impeded by the lack of reliable disease models that fully recapitulate the pathogenesis in humans. These limitations apply to both in vitro models such as cell-based cultures and in vivo animal models which invariably are limited to simulate the complexity of cardiovascular disease in humans. Implementing human heart tissue in cardiovascular research complements our research strategy using preclinical models. We established the Human Explanted Heart Program (HELP) which integrates clinical, tissue and molecular phenotyping thereby providing a comprehensive evaluation into human heart disease. Our collection and storage of biospecimens allow them to retain key pathogenic findings while providing novel insights into human heart failure. The use of human non-failing control explanted hearts provides a valuable comparison group for the diseased explanted hearts. Using HELP we have been able to create a tissue repository which have been used for genetic, molecular, cellular, and histological studies. This review describes the process of collection and use of explanted human heart specimens encompassing a spectrum of pediatric and adult heart diseases, while highlighting the role of these invaluable specimens in translational research. Furthermore, we highlight the efficient procurement and bio-preservation approaches ensuring analytical quality of heart specimens acquired in the context of heart donation and transplantation.

Diabetes and cardiovascular disease: inter-relation of risk factors and treatment

Background

The diabetes mellitus prevalence is still advancing and increasingly becoming one of the globally most severe and expensive chronic illnesses. The strong correlation between diabetes as well as the most prominent reason for diabetes and death in diabetic patients is cardiovascular disorders. Health conditions like dyslipidemia, hypertension, obesity, and other factors of risk like the risk of cardiovascular are frequent in diabetic persons and raise the likelihood of heart attacks.

Main text

In particular, several researchers have found diabetes mellitus-related biochemical pathways that raise the likelihood of cardiovascular disorder in people with diabetes individually. This review describes diabetes-cardiovascular disorder relationships, explores potential therapeutic mechanisms, addresses existing treatment, care, and describes the directions for the future for study.

Conclusion

Thus, in individuals with diabetes, it is important to concentrate on cardiovascular threat variables to reduce the illness’s lasting cardiovascular complications. Further work to enhance knowledge of the disease state and its impact on cardiovascular function is required to boost medical treatment and cardiovascular disorders result in people with diabetes.

Personalised medicine in hypercholesterolaemia: the role of pharmacogenetics in statin therapy

Statins are the first-line choice in Lipid-lowering therapy to reduce cardiovascular risk. In a continuous attempt to optimise treatment success, there is a need for additional research on genes and related molecular pathways that can determine the efficacy and toxicity of lipid-lowering drugs. Several variations within genes associated with lipid metabolism, including those involved in uptake, distribution and metabolism of statins have been reported. The purpose of this study was to evaluate the effect of genetic variations in the key genes responsible for statins' metabolism and their role in personalised medicine and pharmacogenetic testing (PGx) in patients treated with such drugs. Genetic assessment for specific known SNPs within the most known genes such as ABCG2, SLCO1B1, CYP3A4, and HMGCR, appears likely to predict the efficacy of statin therapy and prevent their side effects but does not necessarily reduce the risk of cardiovascular events. Key Messages Hypercholesterolaemia patients show different response to statin therapy. Several variations within genes associated with statin metabolism have been investigated. Genetic assessment for specific known SNPs within the most known genes may improve the efficacy of statins treatment and prevent their side effects.

Genetic loci associated with prevalent and incident myocardial infarction and coronary heart disease in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium

BACKGROUND

Genome-wide association studies have identified multiple genomic loci associated with coronary artery disease, but most are common variants in non-coding regions that provide limited information on causal genes and etiology of the disease. To overcome the limited scope that common variants provide, we focused our investigation on low-frequency and rare sequence variations primarily residing in coding regions of the genome.

METHODS AND RESULTS

Using samples of individuals of European ancestry from ten cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, both cross-sectional and prospective analyses were conducted to examine associations between genetic variants and myocardial infarction (MI), coronary heart disease (CHD), and all-cause mortality following these events. For prevalent events, a total of 27,349 participants of European ancestry, including 1831 prevalent MI cases and 2518 prevalent CHD cases were used. For incident cases, a total of 55,736 participants of European ancestry were included (3,031 incident MI cases and 5,425 incident CHD cases). There were 1,860 all-cause deaths among the 3,751 MI and CHD cases from six cohorts that contributed to the analysis of all-cause mortality. Single variant and gene-based analyses were performed separately in each cohort and then meta-analyzed for each outcome. A low-frequency intronic variant (rs988583) in PLCL1 was significantly associated with prevalent MI (OR = 1.80, 95% confidence interval: 1.43, 2.27; P = 7.12 × 10-7). We conducted gene-based burden tests for genes with a cumulative minor allele count (cMAC) ≥ 5 and variants with minor allele frequency (MAF) < 5%. TMPRSS5 and LDLRAD1 were significantly associated with prevalent MI and CHD, respectively, and RC3H2 and ANGPTL4 were significantly associated with incident MI and CHD, respectively. No loci were significantly associated with all-cause mortality following a MI or CHD event.

CONCLUSION

This study identified one known locus (ANGPTL4) and four new loci (PLCL1, RC3H2, TMPRSS5, and LDLRAD1) associated with cardiovascular disease risk that warrant further investigation.

The Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project: defining a healthy volunteer cohort

The integration of comprehensive genomic and phenotypic data from diverse ethnic populations offers unprecedented opportunities toward advancements in precision medicine and novel diagnostic technologies. Current reference genomic databases are not representative of the global human population, making variant interpretation challenging, especially in underrepresented populations, such as the North African population. To address this, the Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project launched a study comprising 1000 individuals free of cardiovascular disease (CVD). Here, we present the first 391 Egyptian healthy volunteers recruited to establish a pilot phenotyped control cohort. All individuals underwent detailed clinical investigation, including cardiac magnetic resonance imaging (MRI), and were sequenced using a targeted panel of 174 genes with reported roles in inherited cardiac conditions. We identified 1262 variants in 27 cardiomyopathy genes of which 15.1% were not captured in current global and regional genetic reference databases (here: gnomAD and Great Middle Eastern Variome). The ECCO-GEN project aims at defining the genetic landscape of an understudied population and providing individual-level genetic and phenotypic data to support future studies in CVD and population genetics.

Role of m6A RNA methylation in cardiovascular disease (Review)

N⁶-methyladenosine (m⁶A) is the most prevalent and abundant type of internal post-transcriptional RNA modification in eukaryotic cells. Multiple types of RNA, including mRNAs, rRNAs, tRNAs, long non-coding RNAs and microRNAs, are involved in m⁶A methylation. The biological function of m⁶A modification is dynamically and reversibly mediated by methyltransferases (writers), demethylases (erasers) and m⁶A binding proteins (readers). The methyltransferase complex is responsible for the catalyzation of m⁶A modification and is typically made up of methyltransferase-like (METTL)3, METTL14 and Wilms tumor 1-associated protein. Erasers remove methylation by fat mass and obesity-associated protein and ALKB homolog 5. Readers play a role through the recognition of m⁶A-modified targeted RNA. The YT521-B homology domain family, heterogeneous nuclear ribonucleoprotein and insulin-like growth factor 2 mRNA-binding protein serve as m⁶A readers. The m⁶A methylation on transcripts plays a pivotal role in the regulation of downstream molecular events and biological functions, such as RNA splicing, transport, stability and translatability at the post-transcriptional level. The dysregulation of m⁶A modification is associated with cancer, drug resistance, virus replication and the pluripotency of embryonic stem cells. Recently, a number of studies have identified aberrant m⁶A methylation in cardiovascular diseases (CVDs), including cardiac hypertrophy, heart failure, arterial aneurysm, vascular calcification and pulmonary hypertension. The aim of the present review article was to summarize the recent research progress on the role of m⁶A modification in CVD and give a brief perspective on its prospective applications in CVD.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Significant association between rs28362491 polymorphism in NF-κB1 gene and coronary artery disease: a meta-analysis

BACKGROUND

The association of rs28362491 polymorphism in NF-κB1 gene and coronary artery disease (CAD) risk was reported in several studies with inconsistent outcomes. This study aimed to comprehensively collect and synthesize the existing evidence to appraise whether rs28362491 was correlated to CAD susceptibility.

METHODS

Databases of Web of Science, EMBASE, PubMed, Wanfang, and CNKI were retrieved from inception to August 1, 2019 without any restriction on language. The strengths of association between rs28362491 polymorphism and CAD were presented as odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Thirteen case-control studies with 17 individual cohorts containing 9378 cases and 10,738 controls were incorporated into this meta-analysis. The findings indicated that rs28362491 polymorphism was significantly correlated to CAD risk in five genetic models: D vs. I, OR = 1.16, 95%CI 1.11-1.21, P<0.01; DD vs. II, OR = 1.37, 95%CI 1.25-1.49, P<0.01; DI vs. II, OR = 1.11, 95%CI 1.05-1.18, P<0.01; DD + DI vs. II, OR = 1.17, 95%CI 1.11-1.24, P<0.01; DD vs. DI + II, OR = 1.29, 95%CI 1.15-1.43, P<0.01. After stratification by ethnicity and gender, significant association still existed between rs28362491 and CAD, especially in the dominant model.

CONCLUSIONS

The findings suggest that the mutant D allele in rs28362491 locus may increase the risk of CAD, and carriers of D allele appear to be more susceptible to CAD.

A single-cell transcriptomic landscape of primate arterial aging

Our understanding of how aging affects the cellular and molecular components of the vasculature and contributes to cardiovascular diseases is still limited. Here we report a single-cell transcriptomic survey of aortas and coronary arteries in young and old cynomolgus monkeys. Our data define the molecular signatures of specialized arteries and identify eight markers discriminating aortic and coronary vasculatures. Gene network analyses characterize transcriptional landmarks that regulate vascular senility and position FOXO3A, a longevity-associated transcription factor, as a master regulator gene that is downregulated in six subtypes of monkey vascular cells during aging. Targeted inactivation of FOXO3A in human vascular endothelial cells recapitulates the major phenotypic defects observed in aged monkey arteries, verifying FOXO3A loss as a key driver for arterial endothelial aging. Our study provides a critical resource for understanding the principles underlying primate arterial aging and contributes important clues to future treatment of age-associated vascular disorders.

High-frequency metabolite profiling and the incidence of recurrent cardiac events in patients with post-acute coronary syndrome

Purpose: The aim of this study was to study temporal changes in metabolite profiles in patients with post-acute coronary syndrome (ACS), in particular prior to the development of recurrent ACS (reACS).Methods: BIOMArCS (BIOMarker study to identify the Acute risk of a Coronary Syndrome) is a prospective study including patients admitted for ACS, who underwent high-frequency blood sampling during 1-year follow-up. Within BIOMArCS, we performed a nested case-cohort analysis of 158 patients (28 cases of reACS). We determined 151 metabolites by nuclear magnetic resonance in seven (median) blood samples per patient. Temporal evolution of the metabolites and their relation with reACS was assessed by joint modelling. Results are reported as adjusted (for clinical factors) hazard ratios (aHRs).Results: Median age was 64 (25th-75th percentiles; 56-72) years and 78% were men. After multiple testing correction (p < 0.001), high concentrations of extremely large very low density lipoprotein (VLDL) particles (aHR 1.60/SD increase; 95%CI 1.25-2.08), very large VLDL particles (aHR 1.60/SD increase; 95%CI 1.25-2.08) and large VLDL particles (aHR 1.56/SD increase; 95%CI 1.22-2.05) were significantly associated with reACS. Moreover, these longitudinal particle concentrations showed a steady increase over time prior to reACS. Among the other metabolites, no significant associations were observed.Conclusion: Post-ACS patients with persistent high concentrations of extremely large, very large and large VLDL particles have increased risk of reACS within 1 year.

Novel Point Mutations in Mitochondrial MT-CO2 Gene May Be Risk Factors for Coronary Artery Disease

A wide range of genetic and environmental interactions are involved in the development of coronary artery disease (CAD). Considerable evidence suggests that mitochondrial DNA mutations are associated with heart failure. In this work, we examined the possible mutations in hotspot mitochondrial genes and their association with Iranian patients with coronary artery disease. In this case-control study, nucleotide variations were investigated in 109 patients with coronary atherosclerosis and 105 control subjects with no family history of cardiovascular disease. The molecular analysis of related mitochondrial genes was performed by polymerase chain reaction sequencing. Our results showed 25 nucleotide variations (10 missense mutations, 9 synonymous polymorphisms, and 6 variants in tRNA genes) that for the first time were presented in coronary artery disease. Our results suggest that novel heteroplasmic m.8231 C>A mutation is involved in CAD (p = 0.007). These nucleotide variations suggest the role of mitochondrial mutations as a predisposing factor which in combination with environmental risk factors may affect the pathogenesis of coronary atherosclerosis. So, further investigation is needed for a better understanding of the pathogenesis and predisposing effects of these variations on the disease.

ALOX5, LPA, MMP9 and TPO gene polymorphisms increase atherothrombosis susceptibility in middle-aged Mexicans

Atherothrombosis is the cornerstone of cardiovascular diseases and the primary cause of death worldwide. Genetic contribution to disturbances in lipid metabolism, coagulation, inflammation and oxidative stress increase the susceptibility to its development and progression. Given its multifactorial nature, the multiloci studies have been proposed as potential predictors of susceptibility. A cross-sectional study was conducted to explore the contribution of nine genes involved in oxidative stress, inflammatory and thrombotic processes in 204 subjects with atherothrombosis matched by age and gender with a healthy group (n = 204). To evaluate the possibility of spurious associations owing to the Mexican population genetic heterogeneity as well as its ancestral origins, 300 unrelated mestizo individuals and 329 Native Americans were also included. ALOX5, LPA, MMP9 and TPO gene polymorphisms, as well as their multiallelic combinations, were twice to four times more frequent in those individuals with clinical manifestations of atherothrombosis than in the healthy group. Once adjusting for population stratification was done, these differences remained. Our results add further evidence on the contribution of ALOX5, LPA, MMP9 and TPO polymorphisms to atherothrombosis development in the middle-aged group, emphasizing the multiethnic studies in search of gene risk polymorphisms.

Decoding communication patterns of the innate immune system by quantitative proteomics

The innate immune system is a collective network of cell types involved in cell recruitment and activation using a robust and refined communication system. Engagement of receptor-mediated intracellular signaling initiates communication cascades by conveying information about the host cell status to surrounding cells for surveillance and protection. Comprehensive profiling of innate immune cells is challenging due to low cell numbers, high dynamic range of the cellular proteome, low abundance of secreted proteins, and the release of degradative enzymes (e.g., proteases). However, recent advances in mass spectrometry-based proteomics provides the capability to overcome these limitations through profiling the dynamics of cellular processes, signaling cascades, post-translational modifications, and interaction networks. Moreover, integration of technologies and molecular datasets provide a holistic view of a complex and intricate network of communications underscoring host defense and tissue homeostasis mechanisms. In this Review, we explore the diverse applications of mass spectrometry-based proteomics in innate immunity to define communication patterns of the innate immune cells during health and disease. We also provide a technical overview of mass spectrometry-based proteomic workflows, with a focus on bottom-up approaches, and we present the emerging role of proteomics in immune-based drug discovery while providing a perspective on new applications in the future.

Human iPSCs-Derived Endothelial Cells with Mutation in HNF1A as a Model of Maturity-Onset Diabetes of the Young

Patients with HNF1A-maturity-onset diabetes of the young (MODY) often develop endothelial dysfunction and related microvascular complications, like retinopathy. As the clinical phenotype of HNF1A-MODY diabetes varies considerably, we used human induced pluripotent stem cells (hiPSCs) from two healthy individuals (control) to generate isogenic lines with mutation in HNF1A gene. Subsequently, control hiPSCs and their respective HNF1A clones were differentiated toward endothelial cells (hiPSC-ECs) and different markers/functions were compared. Human iPSC-ECs from all cell lines showed similar expression of CD31 and Tie-2. VE-cadherin expression was lower in HNF1A-mutated isogenic lines, but only in clones derived from one control hiPSCs. In the other isogenic set and cells derived from HNF1A-MODY patients, no difference in VE-cadherin expression was observed, suggesting the impact of the genetic background on this endothelial marker. All tested hiPSC-ECs showed an expected angiogenic response regardless of the mutation introduced. Isogenic hiPSC-ECs responded similarly to stimulation with pro-inflammatory cytokine TNF-α with the increase in ICAM-1 and permeability, however, HNF1A mutated hiPSC-ECs showed higher permeability in comparison to the control cells. Summarizing, both mono- and biallelic mutations of HNF1A in hiPSC-ECs lead to increased permeability in response to TNF-α in normal glycemic conditions, which may have relevance to HNF1A-MODY microvascular complications.

Selected psychological comorbidities in coronary heart disease: Challenges and grand opportunities

Evidence of popular interest in the interrelationships between mind, body, and heart disease dates to Ancient Grecian times and paved the way for modern-day scientific inquiry into the relationships between psychological comorbidities in coronary heart disease. Although the systematic evidence has suggested an association of poor medical prognosis and lower quality of life among patients with coronary heart disease with comorbid psychological conditions, the mechanisms are less well understood. In this selective review article, the epidemiology, mechanisms, screening, and treatment recommendations for 4 common psychological conditions (depression, anxiety, stress, and insomnia) comorbid with coronary heart disease are presented. We focus on the grand challenges and unprecedented opportunities for research in this area considering the methodological and technological innovations of the 21st century. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Cardiovascular Disease, Aging, and Clonal Hematopoiesis

Traditional risk factors are incompletely predictive of cardiovascular disease development, a leading cause of death in the elderly. Recent epidemiological studies have shown that human aging is associated with an increased frequency of somatic mutations in the hematopoietic system, which provide a competitive advantage to a mutant cell, thus allowing for its clonal expansion, a phenomenon known as clonal hematopoiesis. Unexpectedly, these mutations have been associated with a higher incidence of cardiovascular disease, suggesting a previously unrecognized connection between somatic mutations in hematopoietic cells and cardiovascular disease. Here, we provide an up-to-date review of clonal hematopoiesis and its association with aging and cardiovascular disease. We also give a detailed report of the experimental studies that have been instrumental in understanding the relationship between clonal hematopoiesis and cardiovascular disease and have shed light on the mechanisms by which hematopoietic somatic mutations contribute to disease pathology.

Quantitative Phenotyping of Xenopus Embryonic Heart Pathophysiology Using Hemoglobin Contrast Subtraction Angiography to Screen Human Cardiomyopathies

Congenital heart disease (CHD) is a significant cause of mortality in infants and adults. Currently human genomic analysis has identified a number of candidate genes in these patients. These genes span diverse categories of gene function suggesting that despite the similarity in cardiac lesion, the underlying pathophysiology may be different. In fact, patients with similar CHDs can have drastically different outcomes, including a dramatic decrease in myocardial function. To test these human candidate genes for their impact on myocardial function, we need efficient animals models of disease. For this purpose, we paired Xenopus tropicalis with our microangiography technique, hemoglobin contrast subtraction angiography (HCSA). To demonstrate the gene-teratogen-physiology relationship, we modeled human cardiomyopathy in tadpoles. First we depleted the sarcomeric protein myosin heavy chain 6 (myh6) expression using morpholino oligos. Next, we exposed developing embryos to the teratogen ethanol and in both conditions showed varying degrees of cardiac dysfunction. Our results demonstrate that HCSA can distinguish biomechanical phenotypes in the context of gene dysfunction or teratogen. This approach can be used to screen numerous candidate CHD genes or suspected teratogens for their effect on cardiac function.

Receptor for advanced glycation end products gene polymorphisms in cardiac syndrome X

Endothelial and microvascular dysfunction serve important roles in the formation and pathogenesis of cardiac syndrome X (CSX). Expression of receptor for advanced glycation end products (RAGE) is suggested to be increased in several conditions, including diabetes, inflammation and vascular diseases. In the present study, RAGE gene polymorphisms in patients with CSX and healthy controls were investigated. A total of 114 patients, diagnosed with CSX using coronary angiography results following complaints of angina and objective ischemia, and 103 healthy controls participated in the study. Whether there was a difference in genotype distributions of RAGE gene -374T/A, -429T/C and Glys82Ser polymorphisms between patients with CSX and healthy controls was investigated. Following DNA isolation from blood samples of the participants, the polymorphic regions were examined by quantitative polymerase chain reaction, and the genotyping results were statistically analyzed. When the genotypic distributions of -374T/A, -429T/C and Gly82Ser polymorphisms were investigated in patients with CSX and healthy controls, no statistically significant differences were identified between the two groups (P>0.05). Likewise, no statistically significant differences were observed in the allelic distributions of all 3 polymorphic regions (P>0.05). To the best of our knowledge, the present study also investigated the association between CSX and RAGE gene polymorphisms for the first time. No statistically significant differences in RAGE gene polymorphisms between the CSX and control groups were observed. We hypothesized that significant results may be obtained by increasing the numbers of patients and healthy controls in future studies.