Emerging Role of Precision Medicine in Cardiovascular Disease

Personalizing treatments for patients based on cardiovascular phenotyping

Introduction

Cardiovascular disease persists as the leading cause of death worldwide despite continued advances in diagnostics and therapeutics. Our current approach to patients with cardiovascular disease is rooted in reductionism, which presupposes that all patients share a similar phenotype and will respond the same to therapy; however, this is unlikely as cardiovascular diseases exhibit complex heterogeneous phenotypes.

Areas covered

With the advent of high-throughput platforms for omics testing, phenotyping cardiovascular diseases has advanced to incorporate large-scale molecular data with classical history, physical examination, and laboratory results. Findings from genomics, proteomics, and metabolomics profiling have been used to define more precise cardiovascular phenotypes and predict adverse outcomes in population-based and disease-specific patient cohorts. These molecular data have also been utilized to inform drug efficacy based on a patient's unique phenotype.

Expert opinion

Multiscale phenotyping of cardiovascular disease has revealed diversity among patients that can be used to personalize pharmacotherapies and predict outcomes. Nonetheless, precision phenotyping for cardiovascular disease remains a nascent field that has not yet translated into widespread clinical practice despite its many potential advantages for patient care. Future endeavors that demonstrate improved pharmacotherapeutic responses and associated reduction in adverse events will facilitate mainstream adoption of precision cardiovascular phenotyping.

Strategies to minimize heterogeneity and optimize clinical trials in Acute Respiratory Distress Syndrome (ARDS): Insights from mathematical modelling

Background

Mathematical modelling may aid in understanding the complex interactions between injury and immune response in critical illness.

Methods

We utilize a system biology model of COVID-19 to analyze the effect of altering baseline patient characteristics on the outcome of immunomodulatory therapies. We create example parameter sets meant to mimic diverse patient types. For each patient type, we define the optimal treatment, identify biologic programs responsible for clinical responses, and predict biomarkers of those programs.

Findings

Model states representing older and hyperinflamed patients respond better to immunomodulation than those representing obese and diabetic patients. The disparate clinical responses are driven by distinct biologic programs. Optimal treatment initiation time is determined by neutrophil recruitment, systemic cytokine expression, systemic microthrombosis and the renin-angiotensin system (RAS) in older patients, and by RAS, systemic microthrombosis and trans IL6 signalling for hyperinflamed patients. For older and hyperinflamed patients, IL6 modulating therapy is predicted to be optimal when initiated very early (<4^th day of infection) and broad immunosuppression therapy (corticosteroids) is predicted to be optimally initiated later in the disease (7^th – 9^th day of infection). We show that markers of biologic programs identified by the model correspond to clinically identified markers of disease severity.

Interpretation

We demonstrate that modelling of COVID-19 pathobiology can suggest biomarkers that predict optimal response to a given immunomodulatory treatment. Mathematical modelling thus constitutes a novel adjunct to predictive enrichment and may aid in the reduction of heterogeneity in critical care trials.

Funding

C.V. received a Marie Skłodowska Curie Actions Individual Fellowship (MSCA-IF-GF-2020-101028945). R.K.J.'s research is supported by R01-CA208205, and U01-CA 224348, R35-CA197743 and grants from the National Foundation for Cancer Research, Jane's Trust Foundation, Advanced Medical Research Foundation and Harvard Ludwig Cancer Center. No funder had a role in production or approval of this manuscript.

Pharmacogenomics and circadian rhythms as mediators of cardiovascular drug-drug interactions

This article summarizes the current literature and documents new evidence concerning drug-drug interactions (DDI) stemming from pharmacogenomic and circadian rhythm determinants of therapies used to treat common cardiovascular diseases (CVD), such as atherosclerosis and hypertension. Patients with CVD often have more than one pathophysiologic condition, namely metabolic syndromes, hypertension, hyperlipidemia, and hyperglycemia, among others, which necessitate polytherapeutic or polypharmaceutic management. Interactions between drugs, drugs and food/food supplements, or drugs and genetic/epigenetic factors may have adverse impacts on the cardiovascular and other systems of the body. The mechanisms underlying cardiovascular DDI may involve the formation of a complex pharmacointeractome, including the absorption, distribution, metabolism, and elimination of drugs, which affect their respective bioavailability, efficacy, and/or harmful metabolites. The pharmacointeractome of cardiovascular drugs is likely operated with endogenous rhythms controlled by circadian clock genes. Basic and clinical investigations have improved the knowledge and understanding of cardiovascular pharmacogenomics and pharmacointeractomes, and additionally they have presented new evidence that the staging of deterministic circadian rhythms, according to the dosing time of drugs, e.g., upon awakening vs. at bedtime, cannot only differentially impact their pharmacokinetics and pharmacodynamics but also mediate agonistic/synergetic or antagonistic DDI. To properly manage CVD patients and avoid DDI, it is important that clinicians have sufficient knowledge of their multiple risk factors, i.e., age, gender, and life style elements (like diet, smoking, psychological stress, and alcohol consumption), and comorbidities, such as diabetes, hypertension, dyslipidemia, and depression, and the potential interactions between genetic or epigenetic background of their prescribed therapeutics.

Personalized medicine in cardiovascular disease: review of literature

Purpose

Personalized medicine (PM) is the concept of managing patients based on their characteristics, including genotypes. In the field of cardiology, advantages of PM could be found in the diagnosis and treatment of several conditions such as arrhythmias and cardiomyopathies; moreover, it may be beneficial to prevent adverse drug reactions (ADR) and select the best medication. Genetic background can help us in selecting effective treatments, appropriate dose requirements, and preventive strategies in individuals with particular genotypes.

Method

In this review, we provide examples of personalized medicine based on human genetics for the most used pharmaceutics in cardiology, including warfarin, clopidogrel, and statins. We also review cardiovascular diseases, including coronary artery disease, arrhythmia, and cardiomyopathies.

Conclusion

Genetic factors are as important as environmental factors and they should be tested and evaluated more in the future by improving in genetic testing tools.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-021-00840-0.

Towards an improved definition of periprocedural myocardial infarction: The role of high-sensitivity cardiac troponins

In the past few years, many have disputed the optimal biomarker for confirming or ruling out a diagnosis of periprocedural myocardial infarction (PMI) and the optimal cut‐off concentrations to apply. In this issue of the Journal of Cardiac Surgery, Niclauss et al. performed a retrospective analysis of CK‐MB and high‐sensitivity cardiac troponin T (hs‐cTnT) dynamics and peak concentrations following different cardiac surgical interventions in 400 patients during a 2‐year period in a single center. The authors found that CK‐MB and hs‐cTnT predict PMI with a comparable diagnostic accuracy and discriminatory power >95%. They also attempted to propose an improved, more sensitive threshold of hs‐cTnT for PMI. Their findings could have implications for clinical practice, but more research is warranted to identify more appropriate cut‐offs. This could include hs‐cTnT release pattern, slope steepness, and changes. Ultimately, this could results in patient‐specific model, able to predict expected and abnormal ranges of hs‐cTnT release, enabling an improved and timely diagnosis of PMI.

Multiparameter phenotyping of platelet reactivity for stratification of human cohorts

Accurate and comprehensive assessment of platelet function across cohorts of donors may be key to understanding the risk of thrombotic events associated with cardiovascular disease, and, hence, to help personalize the application of antiplatelet drugs. However, platelet function tests can be difficult to perform and analyze; they also can be unreliable or uninformative and poorly standardized across studies. The Platelet Phenomic Analysis (PPAnalysis) assay and associated open-source software platform were developed in response to these challenges. PPAnalysis utilizes preprepared freeze-dried microtiter plates to provide a detailed characterization of platelet function. The automated analysis of the high-dimensional data enables the identification of subpopulations of donors with distinct platelet function phenotypes. Using this approach, we identified that the sensitivity of a donor's platelets to an agonist and their capacity to generate a functional response are distinct independent metrics of platelet reactivity. Hierarchical clustering of these metrics identified 6 subgroups with distinct platelet phenotypes within healthy cohorts, indicating that platelet reactivity does not fit into the traditional simple categories of "high" and "low" responders. These platelet phenotypes were found to exist in 2 independent cohorts of healthy donors and were stable on recall. PPAnalysis is a powerful tool for stratification of cohorts on the basis of platelet reactivity that will enable investigation of the causes and consequences of differences in platelet function and drive progress toward precision medicine.

Neural network fast-classifies biological images through features selecting to power automated microscopy

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy.

Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations

Cardiovascular diseases (CVDs) are a group of disorders of the blood vessels and heart, which are considered as the leading causes of death worldwide. The pathology of CVDs could be related to the functional abnormalities of multiple cell types in the heart. Single-cell RNA sequencing (scRNA-seq) technology is a powerful method for characterizing individual cells and elucidating the molecular mechanisms by providing a high resolution of transcriptomic changes at the single-cell level. Specifically, scRNA-seq has provided novel insights into CVDs by identifying rare cardiac cell types, inferring the trajectory tree, estimating RNA velocity, elucidating the cell-cell communication, and comparing healthy and pathological heart samples. In this review, we summarize the different scRNA-seq platforms and published single-cell datasets in the cardiovascular field, and describe the utilities and limitations of this technology. Lastly, we discuss the future perspective of the application of scRNA-seq technology into cardiovascular research.

Phenotypes of Cardiovascular Diseases: Current Status and Future Perspectives

Cardiovascular diseases (CVDs) are a large group of diseases and have become the leading cause of morbidity and mortality worldwide. Although considerable progresses have been made in the diagnosis, treatment and prognosis of CVD, communication barriers between clinicians and researchers still exist because the phenotypes of CVD are complex and diverse in clinical practice and lack of unity. Therefore, it is particularly important to establish a standardized and unified terminology to describe CVD. In recent years, there have been several studies, such as the Human Phenotype Ontology, attempting to provide a standardized description of the disease phenotypes. In the present article, we outline recent advances in the classification of the major types of CVD to retrospectively review the current progresses of phenotypic studies in the cardiovascular field and provide a reference for future cardiovascular research.

Longevity leap: mind the healthspan gap

Life expectancy has increased by three decades since the mid-twentieth century. Parallel healthspan expansion has however not followed, largely impeded by the pandemic of chronic diseases afflicting a growing older population. The lag in quality of life is a recognized challenge that calls for prioritization of disease-free longevity. Contemporary communal, clinical and research trends aspiring to extend the health horizon are here outlined in the context of an evolving epidemiology. A shared action integrating public and societal endeavors with emerging interventions that target age-related multimorbidity and frailty is needed. A multidimensional buildout of a curative perspective, boosted by modern anti-senescent and regenerative technology with augmented decision making, would require dedicated resources and cost-effective validation to responsibly bridge the healthspan-lifespan gap for a future of equitable global wellbeing.

DNA Methylation Profile of the SREBF2 Gene in Human Fetal Aortas

Increasing evidence suggests that maternal cholesterol represents an important risk factor for atherosclerotic disease in offspring already during pregnancy, although the underlying mechanisms have not yet been elucidated. Eighteen human fetal aorta samples were collected from the spontaneously aborted fetuses of normal cholesterolemic and hypercholesterolemic mothers. Maternal total cholesterol levels were assessed during hospitalization. DNA methylation profiling of the whole SREBF2 gene CpG island was performed (p value <0.05). The Mann-Whitney U test was used for comparison between the 2 groups. For the first time, our study revealed that in fetal aortas obtained from hypercholesterolemic mothers, the SREBF2 gene shows 4 significant differentially hypermethylated sites in the 5'UTR-CpG island. This finding indicates that more effective long-term primary cardiovascular prevention programs need to be designed for the offspring of mothers with hypercholesterolemia. Further studies should be conducted to clarify the epigenetic mechanisms underlying the association between early atherogenesis and maternal hypercholesterolemia during pregnancy.

Multi-omics approaches for revealing the complexity of cardiovascular disease

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

The Korean Hypertension Cohort study: design and baseline characteristics

BACKGROUND/AIMS

Hypertension (HT) has a significant impact on public health and medical expenses. However, HT is a chronic disease that requires the long-term follow-up of a large number of patients.

METHODS

The Korean Hypertension Cohort (KHC) study aimed to develop a model for calculating cardiovascular risk in HT patients by linking and utilizing the detailed clinical and longitudinal data from hospitals and the national health insurance claim database, respectively. This cohort had a planned sample size of over 11,000 HT patients and 100,000 non-HT controls. Eligible patients were hypertensive patients, who were presenting for the first time and were diagnosed with HT as a main disease from 2006 to 2011. Long-term survival data over a period of approximately 9 years were obtained from the national health insurance claim and national health examination data.

RESULTS

This cohort enrolled 11,083 patients with HT. The mean age was 58.87 ± 11.5 years, 50.5% were male, and 31.4% were never-treated HT. Of the enrolled patients, 32.9% and 37.7% belonged to the high and moderate cardiovascular risk groups, respectively. Initial blood pressures were 149.4 ± 18.5/88.5 ± 12.5 mmHg. During the 2 years hospital data follow-up period, blood pressures lowered to 130.8 ± 14.1/78.0 ± 9.7 mmHg with 1.9 ± 1.0 tablet doses of antihypertensive medication. Cardiovascular events occurred in 7.5% of the overall patients; 8.5%, 8.8%, and 4.7% in the high, moderate, and low risk patients, respectively.

CONCLUSION

The KHC study has provided important information on the long-term outcomes of HT patients according to the blood pressure, comorbid diseases, medication, and adherence, as well as health behaviors and health resource use.

Interactions Networks for Primary Heart Sarcomas

Personalized medicine incorporates genetic information into medical practice so as to optimize the management of chronic diseases. In rare diseases, such as heart cancer (incidence 0.0017-0.33%), this may be elusive. Ninety-five percent of the cases are due to secondary involvementwith the neoplasm originating in the lungs, breasts, kidney, blood, or skin. The clinical manifestations of heart tumors (benign or malignant) include heart failure, hypertension, and cardiac arrhythmias of varying severity, frequently resulting in blood vessel emboli, including strokes. This study aims to explain the pathophysiology and contribute to a P4 medicine model for use by cardiologists, pathologists, and oncologists. We created six gene/protein heart-related and tumor-related targets high-confidence interactomes, which unfold the main pathways that may lead to cardiac diseases (heart failure, hypertension, coronary artery disease, arrhythmias), i.e., the sympathetic nervous system, the renin-angiotensin-aldosterone axis and the endothelin pathway, and excludes others, such as the K oxidase or cytochrome P450 pathways. We concluded that heart cancer patients could be affected by beta-adrenergic blockers, ACE inhibitors, QT-prolonging antiarrhythmic drugs, antibiotics, and antipsychotics. Interactomes may elucidate unknown pathways, adding to patient/survivor wellness during/after chemo- and/or radio-therapy.

Cardiac risk stratification in cancer patients: A longitudinal patient-patient network analysis

BACKGROUND

Cardiovascular disease is a leading cause of death in general population and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the United States. The growing awareness of cancer therapy-related cardiac dysfunction (CTRCD) has led to an emerging field of cardio-oncology; yet, there is limited knowledge on how to predict which patients will experience adverse cardiac outcomes. We aimed to perform unbiased cardiac risk stratification for cancer patients using our large-scale, institutional electronic medical records.

METHODS AND FINDINGS

We built a large longitudinal (up to 22 years' follow-up from March 1997 to January 2019) cardio-oncology cohort having 4,632 cancer patients in Cleveland Clinic with 5 diagnosed cardiac outcomes: atrial fibrillation, coronary artery disease, heart failure, myocardial infarction, and stroke. The entire population includes 84% white Americans and 11% black Americans, and 59% females versus 41% males, with median age of 63 (interquartile range [IQR]: 54 to 71) years old. We utilized a topology-based K-means clustering approach for unbiased patient-patient network analyses of data from general demographics, echocardiogram (over 25,000), lab testing, and cardiac factors (cardiac). We performed hazard ratio (HR) and Kaplan-Meier analyses to identify clinically actionable variables. All confounding factors were adjusted by Cox regression models. We performed random-split and time-split training-test validation for our model. We identified 4 clinically relevant subgroups that are significantly correlated with incidence of cardiac outcomes and mortality. Among the 4 subgroups, subgroup I (n = 625) has the highest risk of de novo CTRCD (28%) with an HR of 3.05 (95% confidence interval (CI) 2.51 to 3.72). Patients in subgroup IV (n = 1,250) had the worst survival probability (HR 4.32, 95% CI 3.82 to 4.88). From longitudinal patient-patient network analyses, the patients in subgroup I had a higher percentage of de novo CTRCD and a worse mortality within 5 years after the initiation of cancer therapies compared to long-time exposure (6 to 20 years). Using clinical variable network analyses, we identified that serum levels of NT-proB-type Natriuretic Peptide (NT-proBNP) and Troponin T are significantly correlated with patient's mortality (NT-proBNP > 900 pg/mL versus NT-proBNP = 0 to 125 pg/mL, HR = 2.95, 95% CI 2.28 to 3.82, p < 0.001; Troponin T > 0.05 μg/L versus Troponin T ≤ 0.01 μg/L, HR = 2.08, 95% CI 1.83 to 2.34, p < 0.001). Study limitations include lack of independent cardio-oncology cohorts from different healthcare systems to evaluate the generalizability of the models. Meanwhile, the confounding factors, such as multiple medication usages, may influence the findings.

CONCLUSIONS

In this study, we demonstrated that the patient-patient network clustering methodology is clinically intuitive, and it allows more rapid identification of cancer survivors that are at greater risk of cardiac dysfunction. We believed that this study holds great promise for identifying novel cardiac risk subgroups and clinically actionable variables for the development of precision cardio-oncology.

A precision medicine approach to sex-based differences in ideal cardiovascular health

Cardiovascular disease risk factor profiles and health behaviors are known to differ between women and men. Sex-based differences in ideal cardiovascular health were examined in the My Research Legacy study, which collected cardiovascular health and lifestyle data via Life’s Simple 7 survey and digital health devices. As the study overenrolled women (n = 1251) compared to men (n = 310), we hypothesized that heterogeneity among women would affect comparisons of ideal cardiovascular health. We identified 2 phenogroups of women in our study cohort by cluster analysis. The phenogroups differed significantly across all 7 cardiovascular health and behavior domains (all p < 0.01) with women in phenogroup 1 having a lower Life’s Simple 7 Health Score than those in phenogroup 2 (5.9 ± 1.3 vs. 7.6 ± 1.3, p < 0.01). Compared to men, women in phenogroup 1 had a higher burden of cardiovascular disease risk factors, exercised less, and had lower ideal cardiovascular health scores (p < 0.01). In contrast, women in phenogroup 2 had fewer cardiovascular risk factors but similar exercise habits and higher ideal cardiovascular health scores than men (p < 0.01). These findings suggest that heterogeneity among study participants should be examined when evaluating sex-based differences in ideal cardiovascular health.

Modulated molecular markers of restenosis and thrombosis byin-vitrovascular cells exposed to bioresorbable scaffolds

Drug-eluting bioresorbable vascular scaffolds (BVSs) have emerged as a potential breakthrough for the treatment of coronary artery stenosis, providing mechanical support and drug delivery followed by complete resorption. Restenosis and thrombosis remain the primary limitations in clinical use. The study aimed to identify potential markers of restenosis and thrombosis analyzing the vascular wall cell transcriptomic profile modulation triggered by BVS at different values of shear stress (SS). Human coronary artery endothelial cells and smooth muscle cells were cultured under SS (1 and 20 dyne cm^-2) for 6 h without and with application of BVS and everolimus 600 nM. Cell RNA-Seq and bioinformatics analysis identified modulated genes by direct comparison of SS conditions and Gene Ontology (GO). The results of different experimental conditions and GO analysis highlighted the modulation of specific genes as semaphorin 3E, mesenchyme homeobox 2, bone morphogenetic protein 4, (heme oxygenase 1) and selectin E, with different roles in pathological evolution of disease. Transcriptomic analysis of dynamic vascular cell cultures identifies candidate genes related to pro-restenotic and pro-thrombotic mechanisms in anin-vitrosetting of BVS, which are not adequately contrasted by everolimus addition.

Genetic information improves the prediction of major adverse cardiovascular events in the GENEMACOR population

The inclusion of a genetic risk score (GRS) can modify the risk prediction of coronary artery disease (CAD), providing an advantage over the use of traditional models. The predictive value of the genetic information on the recurrence of major adverse cardiovascular events (MACE) remains controversial. A total of 33 genetic variants previously associated with CAD were genotyped in 1587 CAD patients from the GENEMACOR study. Of these, 18 variants presented an hazard ratio >1, so they were selected to construct a weighted GRS (wGRS). MACE discrimination and reclassification were evaluated by C-Statistic, Net Reclassification Index and Integrated Discrimination Improvement methodologies. After the addition of wGRS to traditional predictors, the C-index increased from 0.566 to 0.572 (p=0.0003). Subsequently, adding wGRS to traditional plus clinical risk factors, this model slightly improved from 0.620 to 0.622 but with statistical significance (p=0.004). NRI showed that 17.9% of the cohort was better reclassified when the primary model was associated with wGRS. The Kaplan-Meier estimator showed that, at 15-year follow-up, the group with a higher number of risk alleles had a significantly higher MACE occurrence (p=0.011). In CAD patients, wGRS improved MACE risk prediction, discrimination and reclassification over the conventional factors, providing better cost-effective therapeutic strategies.

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.

Targeting cardiovascular inflammation: next steps in clinical translation

Systemic vascular inflammation plays multiple maladaptive roles which contribute to the progression and destabilization of atherosclerotic cardiovascular disease (ASCVD). These roles include: (i) driving atheroprogression in the clinically stable phase of disease; (ii) inciting atheroma destabilization and precipitating acute coronary syndromes (ACS); and (iii) responding to cardiomyocyte necrosis in myocardial infarction (MI). Despite an evolving understanding of these biologic processes, successful clinical translation into effective therapies has proven challenging. Realizing the promise of targeting inflammation in the prevention and treatment of ASCVD will likely require more individualized approaches, as the degree of inflammation differs among cardiovascular patients. A large body of evidence has accumulated supporting the use of high-sensitivity C-reactive protein (hsCRP) as a clinical measure of inflammation. Appreciating the mechanistic diversity of ACS triggers and the kinetics of hsCRP in MI may resolve purported inconsistencies from prior observational studies. Future clinical trial designs incorporating hsCRP may hold promise to enable individualized approaches. The aim of this Clinical Review is to summarize the current understanding of how inflammation contributes to ASCVD progression, destabilization, and adverse clinical outcomes. We offer forward-looking perspective on what next steps may enable successful clinical translation into effective therapeutic approaches-enabling targeting the right patients with the right therapy at the right time-on the road to more individualized ASCVD care.

GENDER DIFFERENCES IN COMPLICATIONS FOLLOWING CATHETER ABLATION OF ATRIAL FIBRILLATION

AIM

Population studies that provide unbiased estimates of gender differences in risk of complications following catheter ablation of atrial fibrillation (AF) are sparse. We sought to evaluate the association of female gender and risk of complications following AF ablation in a nation-wide cohort.

METHODS AND RESULTS

We identified 35,211 patients (29.5% females) undergoing AF ablations from 2008-17 using national hospitalization data from Australia and New Zealand. The primary outcome was any procedural complication occurring up to 30-days after discharge. Logistic regression was used to adjust for differences in baseline characteristics between sexes. Compared with males, females were older (mean age 64.9 vs. 61.2 years), had higher rates of hypertension (14.0% vs. 11.6%) and hematological disorders (5.3% vs. 3.8%) and experienced a higher rate of procedural complications (6.96% vs. 5.41%) (all p<0.001). This gender disparity remained significant after adjustment (OR 1.25 [95%CI 1.14-1.38], p<0.001) and was driven by an increased risk of vascular injury (OR 1.86 [1.23-2.82], p=0.003), pericarditis (OR 1.86 [1.16-2.67], p=0.008), pericardial effusion (OR 1.71 [1.35-2.17], p< 0.001), and bleeding (OR 1.30 [1.15-1.46], p<0.001). Notably, the gender difference persisted over time (OR for the most recent period 1.19 [1.003-1.422], p=0.046) despite a declining complication rate in both men and women.

CONCLUSION

Females undergoing AF ablations experienced a 25% higher risk of procedural complications compared with males, a disparity that has persisted over time despite a falling complication rate. Efforts to reduce this gender disparity should focus on reducing the incidence of pericardial effusion, pericarditis, vascular injury, and bleeding.

Senescence mechanisms and targets in the heart

Cellular senescence is a state of irreversible cell cycle arrest associated with ageing. Senescence of different cardiac cell types can direct the pathophysiology of cardiovascular diseases such as atherosclerosis, myocardial infarction, and cardiac fibrosis. While age-related telomere shortening represents a major cause of replicative senescence, the senescent state can also be induced by oxidative stress, metabolic dysfunction, and epigenetic regulation, among other stressors. It is critical that we understand the molecular pathways that lead to cellular senescence and the consequences of cellular senescence in order to develop new therapeutic approaches to treat cardiovascular disease. In this review, we discuss molecular mechanisms of cellular senescence, explore how cellular senescence of different cardiac cell types (including cardiomyocytes, cardiac endothelial cells, cardiac fibroblasts, vascular smooth muscle cells, valve interstitial cells) can lead to cardiovascular disease, and highlight potential therapeutic approaches that target molecular mechanisms of cellular senescence to prevent or treat cardiovascular disease.

Systems biology in cardiovascular disease: a multiomics approach

Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.

Perspectives on Precision Medicine Approaches to NAFLD Diagnosis and Management

Precision medicine defines the attempt to identify the most effective approaches for specific subsets of patients based on their genetic background, clinical features, and environmental factors. Nonalcoholic fatty liver disease (NAFLD) encompasses the alcohol-like spectrum of liver disorders (steatosis, steatohepatitis with/without fibrosis, and cirrhosis and hepatocellular carcinoma) in the nonalcoholic patient. Recently, disease renaming to MAFLD [metabolic (dysfunction)-associated fatty liver disease] and positive criteria for diagnosis have been proposed. This review article is specifically devoted to envisaging some clues that may be useful to implementing a precision medicine-oriented approach in research and clinical practice. To this end, we focus on how sex and reproductive status, genetics, intestinal microbiota diversity, endocrine and metabolic status, as well as physical activity may interact in determining NAFLD/MAFLD heterogeneity. All these factors should be considered in the individual patient with the aim of implementing an individualized therapeutic plan. The impact of considering NAFLD heterogeneity on the development of targeted therapies for NAFLD subgroups is also extensively discussed.

Discovery of an orally active VHL-recruiting PROTAC that achieves robust HMGCR degradation and potent hypolipidemic activity in vivo

HMG-CoA reductase (HMGCR) protein is usually upregulated after statin (HMGCR inhibitor) treatment, which inevitably diminishes its therapeutic efficacy, provoking the need for higher doses associated with adverse effects. The proteolysis targeting chimera (PROTAC) technology has recently emerged as a powerful approach for inducing protein degradation. Nonetheless, due to their bifunctional nature, developing orally bioavailable PROTACs remains a great challenge. Herein, we identified a powerful HMGCR-targeted PROTAC (21c) comprising a VHL ligand conjugated to lovastatin acid that potently degrades HMGCR in Insig-silenced HepG2 cells (DC₅₀ = 120 nmol/L) and forms a stable ternary complex, as predicated by a holistic modeling protocol. Most importantly, oral administration of the corresponding lactone 21b reveled favorable plasma exposures referring to both the parent 21b and the conversed acid 21c. Further in vivo studies of 21b demonstrated robust HMGCR degradation and potent cholesterol reduction in mice with diet-induced hypercholesterolemia, highlighting a promising strategy for treating hyperlipidemia and associated diseases.

Role of Pharmacogenetics in Adverse Drug Reactions: An Update towards Personalized Medicine

Adverse drug reactions (ADRs) are an important and frequent cause of morbidity and mortality. ADR can be related to a variety of drugs, including anticonvulsants, anaesthetics, antibiotics, antiretroviral, anticancer, and antiarrhythmics, and can involve every organ or apparatus. The causes of ADRs are still poorly understood due to their clinical heterogeneity and complexity. In this scenario, genetic predisposition toward ADRs is an emerging issue, not only in anticancer chemotherapy, but also in many other fields of medicine, including hemolytic anemia due to glucose-6-phosphate dehydrogenase (G6PD) deficiency, aplastic anemia, porphyria, malignant hyperthermia, epidermal tissue necrosis (Lyell's Syndrome and Stevens-Johnson Syndrome), epilepsy, thyroid diseases, diabetes, Long QT and Brugada Syndromes. The role of genetic mutations in the ADRs pathogenesis has been shown either for dose-dependent or for dose-independent reactions. In this review, we present an update of the genetic background of ADRs, with phenotypic manifestations involving blood, muscles, heart, thyroid, liver, and skin disorders. This review aims to illustrate the growing usefulness of genetics both to prevent ADRs and to optimize the safe therapeutic use of many common drugs. In this prospective, ADRs could become an untoward "stress test," leading to new diagnosis of genetic-determined diseases. Thus, the wider use of pharmacogenetic testing in the work-up of ADRs will lead to new clinical diagnosis of previously unsuspected diseases and to improved safety and efficacy of therapies. Improving the genotype-phenotype correlation through new lab techniques and implementation of artificial intelligence in the future may lead to personalized medicine, able to predict ADR and consequently to choose the appropriate compound and dosage for each patient.

Risk Stratification Towards Precision Medicine in Heart Failure - Current Progress and Future Perspectives

Clinical risk stratification is a key strategy used to identify low- and high-risk subjects to optimize the management, ranging from pharmacological treatment to palliative care, of patients with heart failure (HF). Using statistical modeling techniques, many HF risk prediction models that combine predictors to assess the risk of specific endpoints, including death or worsening HF, have been developed. However, most risk prediction models have not been well-integrated into the clinical setting because of their inadequacy and diverse predictive performance. To improve the performance of such models, several factors, including optimal sampling and biomarkers, need to be considered when deriving the models; however, given the large heterogeneity of HF, the currently advocated one-size-fits-all approach is not appropriate for every patient. Recent advances in techniques to analyze biological "omics" information could allow for the development of a personalized medicine platform, and there is growing awareness that an integrated approach based on the concept of system biology may be an excessively naïve view of the multiple contributors and complexity of an individual's HF phenotype. This review article describes the progress in risk stratification strategies and perspectives of emerging precision medicine in the field of HF management.

Metabolic Signatures in Coronary Artery Disease: Results from the BioHEART-CT Study

Despite effective prevention programs targeting cardiovascular risk factors, coronary artery disease (CAD) remains the leading cause of death. Novel biomarkers are needed for improved risk stratification and primary prevention. To assess for independent associations between plasma metabolites and specific CAD plaque phenotypes we performed liquid chromatography mass-spectrometry on plasma from 1002 patients in the BioHEART-CT study. Four metabolites were examined as candidate biomarkers. Dimethylguanidino valerate (DMGV) was associated with presence and amount of CAD (OR) 1.41 (95% Confidence Interval [CI] 1.12-1.79, p = 0.004), calcified plaque, and obstructive CAD (p < 0.05 for both). The association with amount of plaque remained after adjustment for traditional risk factors, ß-coefficient 0.17 (95% CI 0.02-0.32, p = 0.026). Glutamate was associated with the presence of non-calcified plaque, OR 1.48 (95% CI 1.09-2.01, p = 0.011). Phenylalanine was associated with amount of CAD, ß-coefficient 0.33 (95% CI 0.04-0.62, p = 0.025), amount of calcified plaque, (ß-coefficient 0.88, 95% CI 0.23-1.53, p = 0.008), and obstructive CAD, OR 1.84 (95% CI 1.01-3.31, p = 0.046). Trimethylamine N-oxide was negatively associated non-calcified plaque OR 0.72 (95% CI 0.53-0.97, p = 0.029) and the association remained when adjusted for traditional risk factors. In targeted metabolomic analyses including 53 known metabolites and controlling for a 5% false discovery rate, DMGV was strongly associated with the presence of calcified plaque, OR 1.59 (95% CI 1.26-2.01, p = 0.006), obstructive CAD, OR 2.33 (95% CI 1.59-3.43, p = 0.0009), and amount of CAD, ß-coefficient 0.3 (95% CI 0.14-0.45, p = 0.014). In multivariate analyses the lipid and nucleotide metabolic pathways were both associated with the presence of CAD, after adjustment for traditional risk factors. We report novel associations between CAD plaque phenotypes and four metabolites previously associated with CAD. We also identified two metabolic pathways strongly associated with CAD, independent of traditional risk factors. These pathways warrant further investigation at both a biomarker and mechanistic level.

Precision Phenomapping of Acute Coronary Syndromes to Improve Patient Outcomes

Acute coronary syndromes (ACS) are a global leading cause of death. These syndromes show heterogeneity in presentation, mechanisms, outcomes and responses to treatment. Precision medicine aims to identify and synthesize unique features in individuals, translating the acquired data into improved personalised interventions. Current precision treatments of ACS include immediate coronary revascularisation driven by ECG ST-segment elevation, early coronary angiography based on elevated blood cardiac troponins in patients without ST-segment elevation, and duration of intensified antithrombotic therapy according to bleeding risk scores. Phenotypically stratified analyses of multi-omic datasets are urgently needed to further refine and couple the diagnosis and treatment of these potentially life-threatening conditions. We provide definitions, examples and possible ways to advance precision treatments of ACS.

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in "Personalized Medicine" and "Innovative Biomedical Technologies") could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

A Heart-Breast Cancer-on-a-Chip Platform for Disease Modeling and Monitoring of Cardiotoxicity Induced by Cancer Chemotherapy

Cardiotoxicity is one of the most serious side effects of cancer chemotherapy. Current approaches to monitoring of chemotherapy-induced cardiotoxicity (CIC) as well as model systems that develop in vivo or in vitro CIC platforms fail to notice early signs of CIC. Moreover, breast cancer (BC) patients with preexisting cardiac dysfunctions may lead to different incident levels of CIC. Here, a model is presented for investigating CIC where not only induced pluripotent stem cell (iPSC)-derived cardiac tissues are interacted with BC tissues on a dual-organ platform, but electrochemical immuno-aptasensors can also monitor cell-secreted multiple biomarkers. Fibrotic stages of iPSC-derived cardiac tissues are promoted with a supplement of transforming growth factor-β 1 to assess the differential functionality in healthy and fibrotic cardiac tissues after treatment with doxorubicin (DOX). The production trend of biomarkers evaluated by using the immuno-aptasensors well-matches the outcomes from conventional enzyme-linked immunosorbent assay, demonstrating the accuracy of the authors' sensing platform with much higher sensitivity and lower detection limits for early monitoring of CIC and BC progression. Furthermore, the versatility of this platform is demonstrated by applying a nanoparticle-based DOX-delivery system. The proposed platform would potentially help allow early detection and prediction of CIC in individual patients in the future.

Molecular and Genetic Profiling for Precision Medicines in Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) is a rare and chronic lung disease characterized by progressive occlusion of the small pulmonary arteries, which is associated with structural and functional alteration of the smooth muscle cells and endothelial cells within the pulmonary vasculature. Excessive vascular remodeling is, in part, responsible for high pulmonary vascular resistance and the mean pulmonary arterial pressure, increasing the transpulmonary gradient and the right ventricular “pressure overload”, which may result in right ventricular (RV) dysfunction and failure. Current technological advances in multi-omics approaches, high-throughput sequencing, and computational methods have provided valuable tools in molecular profiling and led to the identification of numerous genetic variants in PAH patients. In this review, we summarized the pathogenesis, classification, and current treatments of the PAH disease. Additionally, we outlined the latest next-generation sequencing technologies and the consequences of common genetic variants underlying PAH susceptibility and disease progression. Finally, we discuss the importance of molecular genetic testing for precision medicine in PAH and the future of genomic medicines, including gene-editing technologies and gene therapies, as emerging alternative approaches to overcome genetic disorders in PAH.

Mechanisms of troponin release into serum in cardiac injury associated with COVID-19 patients

Serum levels of thin filament proteins, cardiac troponin T (cTnT) and cardiac troponin I (cTnI) employing high sensitivity antibodies provide a state-of-the art determination of cardiac myocyte injury in COVID-19 patients. Although there is now sufficient evidence of the value of these determinations in patients infected with SARS-CoV-2, mechanisms of their release have not been considered in depth. We summarize the importance of these mechanisms with emphasis on their relation to prognosis, stratification, and treatment of COVID-19 patients. Apart from frank necrotic cell death, there are other mechanisms of myocyte injury leading to membrane fragility that provoke release of cTnT and cTnI. We discuss a rationale for understanding these mechanisms in COVID-19 patients with co-morbidities associated with myocyte injury such as heart failure, hypertension, arrythmias, diabetes, and inflammation. We describe how understanding these significant aspects of these mechanisms in the promotion of angiotensin signaling by SARS-CoV-2 can affect treatment options in the context of individualized therapies. Moreover, with likely omic data related to serum troponins and with the identification of elevations of serum troponins now more broadly detected employing high sensitivity antibodies, we think it is important to consider molecular mechanisms of elevations in serum troponin as an element in clinical decisions and as a critical aspect of development of new therapies.

Application of physics-based flow models in cardiovascular medicine: Current practices and challenges

Personalized physics-based flow models are becoming increasingly important in cardiovascular medicine. They are a powerful complement to traditional methods of clinical decision-making and offer a wealth of physiological information beyond conventional anatomic viewing using medical imaging data. These models have been used to identify key hemodynamic biomarkers, such as pressure gradient and wall shear stress, which are associated with determining the functional severity of cardiovascular diseases. Importantly, simulation-driven diagnostics can help researchers understand the complex interplay between geometric and fluid dynamic parameters, which can ultimately improve patient outcomes and treatment planning. The possibility to compute and predict diagnostic variables and hemodynamics biomarkers can therefore play a pivotal role in reducing adverse treatment outcomes and accelerate development of novel strategies for cardiovascular disease management.

Case report: 68 yo Chinese-American woman with high HDL-C and ischemic stroke attributed to intracranial atherosclerotic stenosis

Atherosclerotic cardiovascular disease (ASCVD) events are the most common cause of death in the United States and for most individuals who experience these events, may be predicted by risk identification tools. ASCVD risk calculators enable a clinician-patient discussion and the presence of risk-enhancing factors may further inform decision-making with respect to preventive pharmacotherapy, especially statin prescription. In cases where the decision of whether to treat with medicine is unclear, coronary artery calcium scoring by computed tomography offers enhanced risk stratification and may allow both clinicians and patients to feel more at ease with the decision to withhold statin therapy. Despite this thoughtful approach, individual risk may still be underestimated. We present a case of a woman whose family history suggested increased short- and long-term ASCVD risk due to intracranial atherosclerosis, but whose tests suggested a more equivocal indication for treatment. Neither she nor her clinician appreciated the presence of significant enough risk to persevere through minor statin side effects for primary prevention, but she was lucky to have survived without appreciable harm from an acute cerebrovascular event and is now able to pursue an appropriate secondary preventive strategy. We discuss how exceptional characteristics may mislead clinicians, including misperception about lower risk due to gender, East Asian predisposition to intracranial more than coronary atherosclerosis, high levels of high density lipoprotein cholesterol (HDL-C), and CACS = 0.

A Pharmacovigilance Study of Adverse Drug Reactions Reported for Cardiovascular Disease Medications Approved Between 2012 and 2017 in the United States Food and Drug Administration Adverse Event Reporting System (FAERS) Database

PURPOSE

Between 2012 and 2017, the FDA approved 29 therapies for a cardiovascular disease (CVD) indication. Due to the limited literature on patient safety outcomes for recently approved CVD medications, this study investigated adverse drug reports (ADRs) reported in the FDA Adverse Event Reporting System (FAERS).

METHODS

A disproportionality analysis of spontaneously reported ADR was conducted. Reports in FAERS from Quarter 1, 2012, through Quarter 1, 2019, were compiled, allowing a 2-year buffer following drug approval in 2017. Top 10 reported ADRs and reporting odds ratios (ROR; confidence interval (CI)), a measure of disproportionality, were analyzed and compared to drugs available prior to 2012 as appropriate.

RESULTS

Of 7,952,147 ADR reports, 95,016 (1.19%) consisted of reports for newly approved CVD medications. For oral anticoagulants, apixaban had significantly lower reports for anemia and renal failure compared to dabigatran and rivaroxaban but greater reports for neurological signs/symptoms, and arrhythmias. Evaluating heart failure drugs, sacubitril/valsartan had greater reports for acute kidney injury, coughing, potassium imbalances, and renal impairment but notably, lower for angioedema compared to lisinopril. Assessing familial hypercholesterolemia drugs, alirocumab had greater reports for joint-related-signs/symptoms compared to other agents in this category. A newer pulmonary arterial hypertension treatment, selexipag, had greater reports of reporting for bone/joint-related-signs/symptoms but riociguat had greater reports for hemorrhages and vascular hypotension.

CONCLUSION

Pharmacovigilance studies allow an essential opportunity to evaluate the safety profile of CVD medications in clinical practice. Additional research is needed to evaluate these reported safety concerns for recently approved CVD medications.

Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes

Progress in precision medicine is limited by insufficient knowledge of transcriptomic or proteomic features in involved tissues that define pathobiological differences between patients. Here, myectomy tissue from patients with obstructive hypertrophic cardiomyopathy and heart failure is analyzed using RNA-Seq, and the results are used to develop individualized protein-protein interaction networks. From this approach, hypertrophic cardiomyopathy is distinguished from dilated cardiomyopathy based on the protein-protein interaction network pattern. Within the hypertrophic cardiomyopathy cohort, the patient-specific networks are variable in complexity, and enriched for 30 endophenotypes. The cardiac Janus kinase 2-Signal Transducer and Activator of Transcription 3-collagen 4A2 (JAK2-STAT3-COL4A2) expression profile informed by the networks was able to discriminate two hypertrophic cardiomyopathy patients with extreme fibrosis phenotypes. Patient-specific network features also associate with other important hypertrophic cardiomyopathy clinical phenotypes. These proof-of-concept findings introduce personalized protein-protein interaction networks (reticulotypes) for characterizing patient-specific pathobiology, thereby offering a direct strategy for advancing precision medicine.

Elucidation of Epigenetic Landscape in Coronary Artery Disease: A Review on Basic Concept to Personalized Medicine

Despite extensive clinical research and management protocols applied in the field of coronary artery diseases (CAD), it still holds the number 1 position in mortality worldwide. This indicates that we need to work on precision medicine to discover the diagnostic, therapeutic, and prognostic targets to improve the outcome of CAD. In precision medicine, epigenetic changes play a vital role in disease onset and progression. Epigenetics is the study of heritable changes that do not affect the alterations of DNA sequence in the genome. It comprises various covalent modifications that occur in DNA or histone proteins affecting the spatial arrangement of the DNA and histones. These multiple modifications include DNA/histone methylation, acetylation, phosphorylation, and SUMOylation. Besides these covalent modifications, non-coding RNAs-viz. miRNA, lncRNA, and circRNA are also involved in epigenetics. Smoking, alcohol, diet, environmental pollutants, obesity, and lifestyle are some of the prime factors affecting epigenetic alterations. Novel molecular techniques such as next-generation sequencing, chromatin immunoprecipitation, and mass spectrometry have been developed to identify important cross points in the epigenetic web in relation to various diseases. The studies regarding exploration of epigenetics, have led researchers to identify multiple diagnostic markers and therapeutic targets that are being used in different disease diagnosis and management. Here in this review, we will discuss various ground-breaking contributions of past and recent studies in the epigenetic field in concert with coronary artery diseases. Future prospects of epigenetics and its implication in CAD personalized medicine will also be discussed in brief.

The Molecular Basis of Predicting Atherosclerotic Cardiovascular Disease Risk

Atherosclerotic cardiovascular disease (ASCVD) proceeds through a series of stages: initiation, progression (or regression), and complications. By integrating known biology regarding molecular signatures of each stage with recent advances in high-dimensional molecular data acquisition platforms (to assay the genome, epigenome, transcriptome, proteome, metabolome, and gut microbiome), snapshots of each phase of atherosclerotic cardiovascular disease development can be captured. In this review, we will summarize emerging approaches for assessment of atherosclerotic cardiovascular disease risk in humans using peripheral blood molecular signatures and molecular imaging approaches. We will then discuss the potential (and challenges) for these snapshots to be integrated into a personalized movie providing dynamic readouts of an individual's atherosclerotic cardiovascular disease risk status throughout the life course.

Gut Microbiota Functional Dysbiosis Relates to Individual Diet in Subclinical Carotid Atherosclerosis

Gut Microbiota (GM) dysbiosis associates with Atherosclerotic Cardiovascular Diseases (ACVD), but whether this also holds true in subjects without clinically manifest ACVD represents a challenge of personalized prevention. We connected exposure to diet (self-reported by food diaries) and markers of Subclinical Carotid Atherosclerosis (SCA) with individual taxonomic and functional GM profiles (from fecal metagenomic DNA) of 345 subjects without previous clinically manifest ACVD. Subjects without SCA reported consuming higher amounts of cereals, starchy vegetables, milky products, yoghurts and bakery products versus those with SCA (who reported to consume more mechanically separated meats). The variety of dietary sources significantly overlapped with the separations in GM composition between subjects without SCA and those with SCA (RV coefficient between nutrients quantities and microbial relative abundances at genus level = 0.65, p-value = 0.047). Additionally, specific bacterial species (Faecalibacterium prausnitzii in the absence of SCA and Escherichia coli in the presence of SCA) are directly related to over-representation of metagenomic pathways linked to different dietary sources (sulfur oxidation and starch degradation in absence of SCA, and metabolism of amino acids, syntheses of palmitate, choline, carnitines and Trimethylamine n-oxide in presence of SCA). These findings might contribute to hypothesize future strategies of personalized dietary intervention for primary CVD prevention setting.

The Need of Personalized Medicine in Coping with Stress during Infertility Treatment

The term personalized medicine was created for oncological patients, but due to its positive clinical results it is now used in many other fields of medicine, including reproductive medicine. The aim of the study was to determine the level of stress and strategies of coping with stress in patients treated for infertility. The study—using a questionnaire developed by the authors, the Perceived Stress Scale-10 (PSS-10), and the Coping Orientation to Problems Experienced Inventory (Mini-COPE)—was conducted among 456 people from infertile couples. Conclusions: More than half of the studied patients demonstrated a high level of stress. The choice of coping strategies was related to the respondents’ gender and level of stress as well as their experience with assisted reproductive technology.

Integrative Omics to Characterize and Classify Pulmonary Vascular Disease

Advances in high-throughput biotechnologies have facilitated omics profiling, a key component of precision phenotyping, in patients with pulmonary vascular disease. Omics provides comprehensive information pertaining to genes, transcripts, proteins, and metabolites. The resulting omics big datasets may be integrated for more robust results and are amenable to analysis using machine learning or newer analytical methodologies, such as network analysis. Results from fully integrated multi-omics datasets combined with clinical data are poised to provide novel insight into pulmonary vascular disease as well as diagnose the presence of disease and prognosticate outcomes.

Cardioinformatics: the nexus of bioinformatics and precision cardiology

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

Differential epigenetic factors in the prediction of cardiovascular risk in diabetic patients

Hyperglycaemia can strongly alter the epigenetic signatures in many types of human vascular cells providing persistent perturbations of protein–protein interactions both in micro- and macro-domains. The establishment of these epigenetic changes may precede cardiovascular (CV) complications and help us to predict vascular lesions in diabetic patients. Importantly, these epigenetic marks may be transmitted across several generations (transgenerational effect) and increase the individual risk of disease. Aberrant DNA methylation and imbalance of histone modifications, mainly acetylation and methylation of H3, represent key determinants of vascular lesions and, thus, putative useful biomarkers for prevention and diagnosis of CV risk in diabetics. Moreover, a differential expression of some micro-RNAs (miRNAs), mainly miR-126, may be a useful prognostic biomarker for atherosclerosis development in asymptomatic subjects. Recently, also environmental-induced chemical perturbations in mRNA (epitranscriptome), mainly the N⁶-methyladenosine, have been associated with obesity and diabetes. Importantly, reversal of epigenetic changes by modulation of lifestyle and use of metformin, statins, fenofibrate, and apabetalone may offer useful therapeutic options to prevent or delay CV events in diabetics increasing the opportunity for personalized therapy. Network medicine is a promising molecular-bioinformatic approach to identify the signalling pathways underlying the pathogenesis of CV lesions in diabetic patients. Moreover, machine learning tools combined with tomography are advancing the individualized assessment of CV risk in these patients. We remark the need for combining epigenetics and advanced bioinformatic platforms to improve the prediction of vascular lesions in diabetics increasing the opportunity for CV precision medicine.