Personalized Cardiovascular Disease Prediction and Treatment-A Review of Existing Strategies and Novel Systems Medicine Tools

Cardiovascular health research priorities in the United Arab Emirates

Background

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality in the United Arab Emirates (UAE) and have been prioritized for intervention by healthcare authorities and clinicians.

Aim

To identify clinically relevant research priorities for the treatment and prevention of CVDs in the UAE.

Methods

This study used the nominal group technique to identify CVD-related research priorities. Participants were 37 experts from UAE hospitals, academic and research institutions, CVD associations, and paramedical organizations.

Results

Initially, 138 research topics were suggested by participating experts. These topics were then refined to identify the most important research priorities related to CVD prevention and treatment. The top research priority areas were: development of evidence-based, customized algorithms for CVD prevention and in-hospital emergency interventions; the availability, accessibility, and affordability of CVD treatment and rehabilitation; identification of relationships between CVDs, lifestyle factors, and mental health; efficacy and constraints in the management of cardiac emergencies; and epidemiological studies that trace CVD in the UAE.

Conclusion

The identified research priorities will guide a more informed research program for CVD treatment and prevention in the UAE. Funding opportunities and support for researchers should be prioritized for these identified research areas.

Improvement in the diagnosis and practices of emergency healthcare providers for heat emergencies after HEAT (heat emergency awareness & treatment) an educational intervention: a multicenter quasi-experimental study

BACKGROUND

The incidence of heat emergencies, including heat stroke and heat exhaustion, have increased recently due to climate change. This has affected global health and has become an issue of consideration for human health and well-being. Due to overlapping clinical manifestations with other diseases, and most of these emergencies occurring in an elderly patient, patients with a comorbid condition, or patients on poly medicine, diagnosing and managing them in the emergency department can be challenging. This study assessed whether an educational training on heat emergencies, defined as heat intervention in our study, could improve the diagnosis and management practices of ED healthcare providers in the ED setting.

METHODS

A quasi-experimental study was conducted in the EDs of four hospitals in Karachi, Pakistan. Eight thousand two hundred three (8203) patients were enrolled at the ED triage based on symptoms of heat emergencies. The pre-intervention data were collected from May to July 2017, while the post-intervention data were collected from May to July 2018. The HEAT intervention, consisting of educational activities targeted toward ED healthcare providers, was implemented in April 2018. The outcomes assessed were improved recognition-measured by increased frequency of diagnosing heat emergencies and improved management-measured by increased temperature monitoring, external cooling measures, and intravenous fluids in the post-intervention period compared to pre-intervention.

RESULTS

Four thousand one hundred eighty-two patients were enrolled in the pre-intervention period and 4022 in the post-intervention period, with at least one symptom falling under the criteria for diagnosis of a heat emergency. The diagnosis rate improved from 3% (n = 125/4181) to 7.5% (n = 7.5/4022) (p-value < 0.001), temperature monitoring improved from 0.9% (n = 41/4181) to 13% (n = 496/4022) (p-value < 0.001) and external cooling measure (water sponging) improved from 1.3% (n = 89/4181) to 3.4% (n = 210/4022) (p-value < 0.001) after the administration of the HEAT intervention.

CONCLUSION

The HEAT intervention in our study improved ED healthcare providers' approach towards diagnosis and management practices of patients presenting with health emergencies (heat stroke or heat exhaustion) in the ED setting. The findings support the case of training ED healthcare providers to address emerging health issues due to rising temperatures/ climate change using standardized treatment algorithms.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine

Mesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.

Glial cells influence cardiac permittivity as evidenced through in vitro and in silico models

Excitation-contraction (EC) coupling in the heart has, until recently, been solely accredited to cardiomyocytes. The inherent complexities of the heart make it difficult to examine non-muscle contributions to contraction in vivo, and conventional in vitro models fail to capture multiple features and cellular heterogeneity of the myocardium. Here, we report on the development of a 3D cardiac μTissue to investigate changes in the cellular composition of native myocardium in vitro. Cells are encapsulated within micropatterned gelatin-based hydrogels formed via visible light photocrosslinking. This system enables spatial control of the microarchitecture, perturbation of the cellular composition, and functional measures of EC coupling via video microscopy and a custom algorithm to quantify beat frequency and degree of coordination. To demonstrate the robustness of these tools and evaluate the impact of altered cell population densities on cardiac μTissues, contractility and cell morphology were assessed with the inclusion of exogenous non-myelinating Schwann cells (SCs). Results demonstrate that the addition of exogenous SCs alter cardiomyocyte EC, profoundly inhibiting the response to electrical pacing. Computational modeling of connexin-mediated coupling suggests that SCs impact cardiomyocyte resting potential and rectification following depolarization. Cardiac μTissues hold potential for examining the role of cellular heterogeneity in heart health, pathologies, and cellular therapies.

TCSBN: a database of tissue and cancer specific biological networks

Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.

Novel Biomarkers of Early Atherosclerotic Changes for Personalised Prevention of Cardiovascular Disease in Cervical Cancer and Human Papillomavirus Infection

Cervical cancer is associated with a causative role of human papillomavirus (HPV), which is a highly prevalent infection. Recently, women with a genital HPV infection were found to have increased incidence of cardiovascular diseases (CVD), including severe cardiovascular events such as myocardial infarction and stroke. The pathomechanisms of this relation are not yet fully understood, and may significantly affect the health of a large part of the population. Accelerated atherosclerosis is assumed to play a key role in the pathophysiology of this relationship. To identify high-risk groups of the population, it is necessary to stratify the CVD risk. Current algorithms, as widely used for the estimation of CVD risk, seem to be limited by the individual misclassification of high-risk subjects. However, personalised prediction of cardiovascular events is missing. Regarding HPV-related CVD, identification of novel sensitive biomarkers reflecting early atherosclerotic changes could be of major importance for such personalised cardiovascular risk prediction. Therefore, this review focuses on the pathomechanisms leading to HPV-related cardiovascular diseases with respect to atherosclerosis, and the description of potential novel biomarkers to detect the earliest atherosclerotic changes important for the prevention of CVD in HPV infection and cervical cancer.

Synthetic consortia of nanobody-coupled and formatted bacteria for prophylaxis and therapy interventions targeting microbiome dysbiosis-associated diseases and co-morbidities

Designed nanobody-linked synthetic consortia for microbiota dysbiosis therapies. A. Nanobodies (Nb) are selected for specific antigens on target bacteria destined for a synthetic therapy consortium that may consist of two (B) or multiple (C) members. For the treatment of dysbiosis co-morbidities requiring two functionally distinct consortia, these may be linked through a common member to generate a single bi-functional microbiota therapy (D).

Systems biology in hepatology: approaches and applications

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

Deprescribing preventive cardiovascular medication in patients with predicted low cardiovascular disease risk in general practice - the ECSTATIC study: a cluster randomised non-inferiority trial

BACKGROUND

The use of cardiovascular medication for the primary prevention of cardiovascular disease (CVD) is potentially inappropriate when potential risks outweigh the potential benefits. It is unknown whether deprescribing preventive cardiovascular medication in patients without a strict indication for such medication is safe and cost-effective in general practice.

METHODS

In this pragmatic cluster randomised controlled non-inferiority trial, we recruited 46 general practices in the Netherlands. Patients aged 40-70 years who were using antihypertensive and/or lipid-lowering drugs without CVD and with low risk of future CVD were followed for 2 years. The intervention was an attempt to deprescribe preventive cardiovascular medication. The primary outcome was the difference in the increase in predicted (10-year) CVD risk in the per-protocol (PP) population with a non-inferiority margin of 2.5 percentage points. An economic evaluation was performed in the intention-to-treat (ITT) population. We used multilevel (generalised) linear regression with multiple imputation of missing data.

RESULTS

Of 1067 participants recruited between 7 November 2012 and 18 February 2014, 72% were female. Overall, their mean age was 55 years and their mean predicted CVD risk at baseline was 5%. Of 492 participants in the ITT intervention group, 319 (65%) quit the medication (PP intervention group); 135 (27%) of those participants were still not taking medication after 2 years. The predicted CVD risk increased by 2.0 percentage points in the PP intervention group compared to 1.9 percentage points in the usual care group. The difference of 0.1 (95% CI -0.3 to 0.6) fell within the non-inferiority margin. After 2 years, compared to the usual care group, for the PP intervention group, systolic blood pressure was 6 mmHg higher, diastolic blood pressure was 4 mmHg higher and total cholesterol and low-density lipoprotein-cholesterol levels were both 7 mg/dl higher (all P < 0.05). Cost and quality-adjusted life years did not differ between the groups.

CONCLUSIONS

The results of the ECSTATIC study show that an attempt to deprescribe preventive cardiovascular medication in low-CVD-risk patients is safe in the short term when blood pressure and cholesterol levels are monitored after stopping. An attempt to deprescribe medication can be considered, taking patient preferences into consideration.

TRIAL REGISTRATION

This study was registered with Dutch trial register on 20 June 2012 ( NTR3493 ).

Identification of the difference in the pathogenesis in heart failure arising from different etiologies using a microarray dataset

OBJECTIVES

Clinically, patients with chronic heart failure arising from different etiologies receive the same treatment. However, the prognoses of these patients differ. The purpose of this study was to elucidate whether the pathogenesis of heart failure arising from different etiologies differs.

METHODS

Heart failure-related dataset GSE1145 was obtained from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. A protein-protein interaction network of the differentially expressed genes was constructed using Search Tool for the Retrieval of Interacting Genes. The modules in each network were analyzed by Molecular Complex Detection of Cytoscape. The Database for Annotation, Visualization and Integrated Discovery was used to obtain the functions of the modules.

RESULTS

Samples contained in GSE1145 were myocardial tissues from patients with dilated cardiomyopathy, familial cardiomyopathy, hypertrophic cardiomyopathy, ischemic cardiomyopathy, and post-partum cardiomyopathy. The differentially expressed genes, modules, and functions of the modules associated with different etiologies varied. Abnormal formation of extracellular matrix was overlapping among five etiologies. The change in cytoskeleton organization was specifically detected in dilated cardiomyopathy. The activation of the Wnt receptor signaling pathway was limited to hypertrophic cardiomyopathy. The change in nucleosome and chromatin assembly was associated with only familial cardiomyopathy. Germ cell migration and disrupted cellular calcium ion homeostasis were solely detected in ischemic cardiomyopathy. The change in the metabolic process of glucose and triglyceride was detected in only post-partum cardiomyopathy.

CONCLUSION

These results indicate that the pathogenesis of heart failure arising from different etiologies varies, which may provide molecular evidence supporting etiology-based treatment for heart failure patients.

Better Indigenous Risk stratification for Cardiac Health study (BIRCH) protocol: rationale and design of a cross-sectional and prospective cohort study to identify novel cardiovascular risk indicators in Aboriginal Australian and Torres Strait Islander adults

BACKGROUND

Of the estimated 10-11 year life expectancy gap between Indigenous (Aboriginal and Torres Strait Islander people) and non-Indigenous Australians, approximately one quarter is attributable to cardiovascular disease (CVD). Risk prediction of CVD is imperfect, but particularly limited for Indigenous Australians. The BIRCH (Better Indigenous Risk stratification for Cardiac Health) project aims to identify and assess existing and novel markers of early disease and risk in Indigenous Australians to optimise health outcomes in this disadvantaged population. It further aims to determine whether these markers are relevant in non-Indigenous Australians.

METHODS/DESIGN

BIRCH is a cross-sectional and prospective cohort study of Indigenous and non-Indigenous Australian adults (≥ 18 years) living in remote, regional and urban locations. Participants will be assessed for CVD risk factors, left ventricular mass and strain via echocardiography, sleep disordered breathing and quality via home-based polysomnography or actigraphy respectively, and plasma lipidomic profiles via mass spectrometry. Outcome data will comprise CVD events and death over a period of five years.

DISCUSSION

Results of BIRCH may increase understanding regarding the factors underlying the increased burden of CVD in Indigenous Australians in this setting. Further, it may identify novel markers of early disease and risk to inform the development of more accurate prediction equations. Better identification of at-risk individuals will promote more effective primary and secondary preventive initiatives to reduce Indigenous Australian health disadvantage.

New Challenges to Study Heterogeneity in Cancer Redox Metabolism

Reactive oxygen species (ROS) are important pathophysiological molecules involved in vital cellular processes. They are extremely harmful at high concentrations because they promote the generation of radicals and the oxidation of lipids, proteins, and nucleic acids, which can result in apoptosis. An imbalance of ROS and a disturbance of redox homeostasis are now recognized as a hallmark of complex diseases. Considering that ROS levels are significantly increased in cancer cells due to mitochondrial dysfunction, ROS metabolism has been targeted for the development of efficient treatment strategies, and antioxidants are used as potential chemotherapeutic drugs. However, initial ROS-focused clinical trials in which antioxidants were supplemented to patients provided inconsistent results, i.e., improved treatment or increased malignancy. These different outcomes may result from the highly heterogeneous redox responses of tumors in different patients. Hence, population-based treatment strategies are unsuitable and patient-tailored therapeutic approaches are required for the effective treatment of patients. Moreover, due to the crosstalk between ROS, reducing equivalents [e.g., NAD(P)H] and central metabolism, which is heterogeneous in cancer, finding the best therapeutic target requires the consideration of system-wide approaches that are capable of capturing the complex alterations observed in all of the associated pathways. Systems biology and engineering approaches may be employed to overcome these challenges, together with tools developed in personalized medicine. However, ROS- and redox-based therapies have yet to be addressed by these methodologies in the context of disease treatment. Here, we review the role of ROS and their coupled redox partners in tumorigenesis. Specifically, we highlight some of the challenges in understanding the role of hydrogen peroxide (H₂O₂), one of the most important ROS in pathophysiology in the progression of cancer. We also discuss its interplay with antioxidant defenses, such as the coupled peroxiredoxin/thioredoxin and glutathione/glutathione peroxidase systems, and its reducing equivalent metabolism. Finally, we highlight the need for system-level and patient-tailored approaches to clarify the roles of these systems and identify therapeutic targets through the use of the tools developed in personalized medicine.

Systems Medicine-Complexity Within, Simplicity Without

This paper presents a brief history of Systems Theory, progresses to Systems Biology, and its relation to the more traditional investigative method of reductionism. The emergence of Systems Medicine represents the application of Systems Biology to disease and clinical issues. The challenges faced by this transition from Systems Biology to Systems Medicine are explained; the requirements of physicians at the bedside, caring for patients, as well as the place of human-human interaction and the needs of the patients are addressed. An organ-focused transition to Systems Medicine, rather than a genomic-, molecular-, or cell-based effort is emphasized. Organ focus represents a middle-out approach to ease this transition and to maximize the benefits of scientific discovery and clinical application. This method manages the perceptions of time and space, the massive amounts of human- and patient-related data, and the ensuing complexity of information.

Personalized Management of Cardiovascular Disorders

Personalized management of cardiovascular disorders (CVD), also referred to as personalized or precision cardiology in accordance with general principles of personalized medicine, is selection of the best treatment for an individual patient. It involves the integration of various "omics" technologies such as genomics and proteomics as well as other new technologies such as nanobiotechnology. Molecular diagnostics and biomarkers are important for linking diagnosis with therapy and monitoring therapy. Because CVD involve perturbations of large complex biological networks, a systems biology approach to CVD risk stratification may be used for improving risk-estimating algorithms, and modeling of personalized benefit of treatment may be helpful for guiding the choice of intervention. Bioinformatics tools are helpful in analyzing and integrating large amounts of data from various sources. Personalized therapy is considered during drug development, including methods of targeted drug delivery and clinical trials. Individualized recommendations consider multiple factors - genetic as well as epigenetic - for patients' risk of heart disease. Examples of personalized treatment are those of chronic myocardial ischemia, heart failure, and hypertension. Similar approaches can be used for the management of atrial fibrillation and hypercholesterolemia, as well as the use of anticoagulants. Personalized management includes pharmacotherapy, surgery, lifestyle modifications, and combinations thereof. Further progress in understanding the pathomechanism of complex cardiovascular diseases and identification of causative factors at the individual patient level will provide opportunities for the development of personalized cardiology. Application of principles of personalized medicine will improve the care of the patients with CVD.

Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention

Intervention with Mediterranean diet (MedDiet) has provided a high level of evidence in primary prevention of cardiovascular events. Besides enhancing protection from classical risk factors, an improvement has also been described in a number of non-classical ones. Benefits have been reported on biomarkers of oxidation, inflammation, cellular adhesion, adipokine production, and pro-thrombotic state. Although the benefits of the MedDiet have been attributed to its richness in antioxidants, the mechanisms by which it exercises its beneficial effects are not well known. It is thought that the integration of omics including genomics, transcriptomics, epigenomics, and metabolomics, into studies analyzing nutrition and cardiovascular diseases will provide new clues regarding these mechanisms. However, omics integration is still in its infancy. Currently, some single-omics analyses have provided valuable data, mostly in the field of genomics. Thus, several gene-diet interactions in determining both intermediate (plasma lipids, etc.) and final cardiovascular phenotypes (stroke, myocardial infarction, etc.) have been reported. However, few studies have analyzed changes in gene expression and, moreover very few have focused on epigenomic or metabolomic biomarkers related to the MedDiet. Nevertheless, these preliminary results can help to better understand the inter-individual differences in cardiovascular risk and dietary response for further applications in personalized nutrition.

Transcriptomics resources of human tissues and organs

Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large‐scale transcriptomics studies have analyzed the expression of protein‐coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue‐restricted manner. Here, we review publicly available human transcriptome resources and discuss body‐wide data from independent genome‐wide transcriptome analyses of different tissues. Gene expression measurements from these independent datasets, generated using samples from fresh frozen surgical specimens and postmortem tissues, are consistent. Overall, the different genome‐wide analyses support a distribution in which many proteins are found in all tissues and relatively few in a tissue‐restricted manner. Moreover, we discuss the applications of publicly available omics data for building genome‐scale metabolic models, used for analyzing cell and tissue functions both in physiological and in disease contexts.