Reducing the Global Burden of Cardiovascular Disease, Part 1

Joseph, Philip; Leong, Darryl P.; McKee, Martin; Anand, Sonia S.; Schwalm, Jon-David; Teo, Koon; Mente, Andrew; Yusuf, Salim

doi:10.1161/circresaha.117.308903

Cited by 780 publications

(535 citation statements)

References 113 publications

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“…Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, although approximately 25% reduction in mortality is expected by 2025, resulting in part from the development of novel diagnostic techniques and therapeutics . In response to stress stimuli, the hypertrophy, apoptosis, necrosis and autophagy of cardiomyocytes, as well as the proliferation and differentiation of cardiac fibroblasts, endothelial cells (ECs) and vascular smooth muscle cells (VSMCs), contribute to the initiation and progression of CVDs, such as cardiac hypertrophy and fibrosis, heart failure (HF), myocardial injury, atherosclerosis and arrhythmias.…”

Section: Introductionmentioning

confidence: 99%

The novel regulatory role of lncRNA‐miRNA‐mRNA axis in cardiovascular diseases

Huang

2018

J Cellular Molecular Medi

395

288

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Long noncoding RNAs (lncRNAs) are RNAs longer than 200 nt in length that are characterized by low levels of sequence conservation and expression; lncRNAs modulate various biological functions at epigenetic, transcriptional and post‐transcriptional levels, or directly regulate protein activity. As a family of small and evolutionarily conserved noncoding RNAs, microRNAs (miRNAs) are capable of regulating physiological and pathological processes via inhibiting target mRNA translation or promoting mRNA degradation. A number of studies have confirmed that both lncRNAs and miRNAs are closely associated with the development of cardiovascular diseases (CVDs), such as cardiac remodelling, heart failure, myocardial injury and arrhythmia, and that they act as biomarkers, potential therapeutic targets or strong indicators of prognosis; however, the underlying molecular mechanism has not been elucidated. Recently, emerging evidence showed that the novel regulatory mechanism underlying the crosstalk among lncRNAs, miRNAs and mRNAs plays a pivotal role in the pathophysiological processes of CVDs in response to stress stimuli. In this review, I comprehensively summarized the regulatory relationship of lncRNAs, miRNAs and mRNAs and highlighted the important role of the lncRNA‐miRNA‐mRNA axis in CVDs.

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Section: Introductionmentioning

confidence: 99%

The novel regulatory role of lncRNA‐miRNA‐mRNA axis in cardiovascular diseases

Huang

2018

J Cellular Molecular Medi

395

288

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“…It also highlights the fact that social and environmental factors play an important role in interindividual differences in health outcomes . For example, it is now commonly accepted that smoking cessation can drastically reduce risk of cardiovascular disease . More recent research has linked such properties as neighborhood ethnic density, childhood trauma, and poverty to health outcomes.…”

Section: Health Equity Challenges Of Genomics‐enabled Learning Healthmentioning

confidence: 99%

Leveraging the learning health care model to improve equity in the age of genomic medicine

Blizinsky

Bonham

2017

Learning Health Systems

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To fully achieve the goals of a genomics-enabled learning health care system, purposeful efforts to understand and reduce health disparities and improve equity of care are essential. This paper highlights 3 major challenges facing genomics-enabled learning health care systems, as they pertain to ancestrally diverse populations: inequality in the utility of genomic medicine; lack of access to pharmacogenomics in clinical care; and inadequate incorporation of social and environmental data into the electronic health care record. We advance a framework that cannot only be used to directly improve care for all within the learning health system but can also be used to focus on the needs to address racial and ethnic health disparities and improve health equity. KEYWORDShealth equity, health disparities, genomic medicine, precision medicine, learning health care system 1 | INTRODUCTION Electronic health records (EHRs) are revolutionizing the practice of medicine. Not only does the digitalization and standardization of medical records help to improve patient outcomes by facilitating integrated care within complex medical settings, 1 these properties also enable the improvement of the health care system itself 2-4 by simplifying the periodic assessment of system function and quality, 5,6 the socalled learning health system model. With the adoption of EHRs also comes changes to the content of the medical record. For example, the burgeoning ubiquity of genomic information in the clinical setting, from the increased use of genetic tests as an element of clinical care to direct to consumer testing results provided by patients, has galvanized efforts to integrate these data into the medical record. 7,8 This surge has also spurred the development of means of providing of relevant clinical knowledge and patient-specific information related to medical genomics, known as genomic clinical decision support (CDS). [9][10][11] However, the foundations for most integration and genomic CDS efforts, and indeed for the basis for many clinical genetic tests, rests in a body of work severely limited by the diversity of its participant populations. 12 In some cases, these shortcomings have been identified; yet without adequate data on diverse populations, we cannot truly know the extent of these limitations, potentially compounding already gaping disparities in health.Genomic integration into EHRs therefore comes with both significant caveats and substantial opportunity. On the one hand, care must be taken to assess the utility of genomic testing and the meaning of genomic results in specific contexts. 11,[13][14][15][16] On the other, the nature of EHRs and the facility with which they can be analyzed as a dataset allows health care systems to evaluate the effects of genetically enabled care on health outcomes, which have the potential to add not only to the improvement of patient care directly, through the refinement of clinical practices, but also indirectly through enhancement of the body of knowledge upon which biomedical discov...

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“…1 Reductions in CV disease burden have also been noted for organ transplant recipients, 2 although the incidence remains 3-5 times higher in kidney transplant recipients than in age-matched representatives of the general population. This has been achieved by both the successful modification of risk factors and the development and implementation of better therapeutic strategies in established CV disease.…”

Section: Introductionmentioning

confidence: 98%

Genetic markers associated with long-term cardiovascular outcome in kidney transplant recipients

Pihlstrøm

Mjøen

Mucha

et al. 2019

American Journal of Transplantation

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CorrespondenceHege Kampen Pihlstrøm Email: hegphi@ous-hf.no Funding information Novartis Pharma AGThere is a clear genetic contribution to the risk of cardiovascular diseases, and a composite genetic risk score (GRS) based on 27 single nucleotide polymorphisms (SNPs) was reported to predict risk of cardiovascular events in the general population. We aimed to evaluate this risk score in renal transplant recipients, a population with heightened cardiovascular risk, with a yet unknown genetic contribution. A total of 1640 participants from the ALERT trial (Assessment of Lescol in Renal Transplantation), a study comparing fluvastatin with placebo in stable renal transplant recipients, were genotyped for all SNPs making up the GRS. Risk alleles were weighted by the log of odds ratios reported in genome wide association studies and summed. Associations between GRS and time from study inclusion to first major cardiovascular event (MACE) were analyzed by Cox regression. In analyses adjusted for cardiovascular risk factors, GRS was significantly associated with MACE (hazard ratio [HR] 1.81, P = .006) when comparing genetic high-risk patients (quartile 4) with genetic low-risk participants (quartile 1). A 27-SNP GRS, which predicted cardiovascular events in the nontransplant population, appears to have predictive value also in kidney allograft recipients. Refining the score to better fit the transplant population seems feasible. K E Y W O R D Sbiomarker, clinical research/practice, coronary artery disease, epidemiology, genetics, kidney transplantation/nephrology, risk assessment/risk stratification, translational research/science

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Reducing the Global Burden of Cardiovascular Disease, Part 1

Cited by 780 publications

References 113 publications

The novel regulatory role of lncRNA‐miRNA‐mRNA axis in cardiovascular diseases

The novel regulatory role of lncRNA‐miRNA‐mRNA axis in cardiovascular diseases

Leveraging the learning health care model to improve equity in the age of genomic medicine

Genetic markers associated with long-term cardiovascular outcome in kidney transplant recipients

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