Precision Cardiovascular Medicine Research Articles

The Future of Artificial Intelligence and Genetic Insights in Precision Cardiovascular Medicine: A Comprehensive Review

Abstract A medical condition called cardiovascular disease (CVD) affects the heart or blood vessels, and about 40% of its causes can be attributed to genetic factors. The pathophysiology of CVD is still unknown despite numerous studies identifying important environmental and genetic factors. Genetic data research has significantly increased due to the application of genome-wide association studies. The utilization of artificial intelligence (AI) technology demonstrates clear advantages in managing intricate projects, outperforming traditional statistical methods in processing such data. The use of AI in the status of genetic research on CVD and medicine is briefly reviewed in the opening section of this article. Then, it gives a complete picture of how AI is used in genetic CVD research, including genetic data-driven diagnosis and prognosis, genetic variation analysis, gene expression profiles, gene interactions, and analysis of genes using knowledge bases. Even though much research has yielded significant findings, it is still early. The main disadvantages are database limitations, the underuse of AI in systematic biology analysis, and the lack of a theoretical framework for interpreting analysis results. The paper concludes with future directions and the significance of creating comprehensive, high-quality, large-sample-size data-sharing resources. Much research is going into how to use AI analysis techniques to help with development. Being creative with computers can help make new CVD intervention protocols and develop and test theoretical models.

Cardiovascular Imaging in the Era of Precision Medicine: Insights from Advanced Technologies - A Narrative Review.

Cardiovascular diseases are responsible for a high mortality rate globally. Precision medicine has emerged as an essential tool for improving cardiovascular disease outcomes. In this context, using advanced imaging exams is fundamental in cardiovascular precision medicine, enabling more accurate diagnoses and customized treatments. This review aims to provide a concise review on how advanced cardiovascular imaging supports precision medicine, highlighting its benefits, challenges, and future directions. A literature review was carried out using the Pubmed and Google Scholar databases, using search strategies that combined terms such as precision medicine, cardiovascular diseases, and imaging tests. More advanced analysis aimed at diagnosing and describing cardiovascular diseases in greater detail is made possible by tests such as cardiac computed tomography, cardiac magnetic resonance imaging, and cardiac positron emission tomography. In addition, the aggregation of imaging data with other omics data allows for more personalized treatment and a better description of patient profiles. The use of advanced imaging tests is essential in cardiovascular precision medicine. Although there are still technical and ethical obstacles, it is essential that there is collaboration between health professionals, as well as investments in technology and education to better disseminate cardiovascular precision medicine and consequently promote improved patient outcomes.

Cardiovascular Precision Medicine and Remote Intervention Trial Rationale and Design

Background: It has recently been shown that excessive fluctuation in blood pressure readings for an individual over time is closely associated with poor outcomes, including increased risk of cardiovascular mortality, coronary heart disease and stroke. Fluctuations may be associated with inconsistent adherence to medical recommendations. This new marker of risk has not yet been incorporated into a monitoring and intervention strategy that seeks to reduce cardiovascular risk by identifying patients through an algorithm tied to their electronic health record (EHR). Methods: We describe the methods used in an innovative “proof of concept” trial using CP&R (Cardiovascular Precision Medicine and Remote Intervention). A blood pressure variability index is calculated for clinic patients via an EHR review. Consenting patients with excessive variability are offered a remote intervention aimed at improving adherence to medical recommendations. The outcomes include the ability to identify and engage the identified patients and the effects of the intervention on blood pressure variability using a pre–post comparison design without parallel controls. Conclusions: Our innovative approach uses a recently identified marker based on reviewing and manipulating EHR data tied to a remote intervention. This design reduces patient burden and supports equitable and targeted resource allocation, utilizing an objective criterion for behavioral risk. This study is registered under ClinicalTrials.gov Identifier: NCT05814562.

Clinical trials in-a-dish for cardiovascular medicine.

Cardiovascular diseases persist as a global health challenge that requires methodological innovation for effective drug development. Conventional pipelines relying on animal models suffer from high failure rates due to significant interspecies variation between humans and animal models. In response, the recently enacted Food and Drug Administration Modernization Act 2.0 encourages alternative approaches including induced pluripotent stem cells (iPSCs). Human iPSCs provide a patient-specific, precise, and screenable platform for drug testing, paving the way for cardiovascular precision medicine. This review discusses milestones in iPSC differentiation and their applications from disease modelling to drug discovery in cardiovascular medicine. It then explores challenges and emerging opportunities for the implementation of 'clinical trials in-a-dish'. Concluding, this review proposes a framework for future clinical trial design with strategic incorporations of iPSC technology, microphysiological systems, clinical pan-omics, and artificial intelligence to improve success rates and advance cardiovascular healthcare.

Precision medicine enhances personalized medicine in cardiology

Personalized medicine and precision medicine, frequently used synonymously, shall be clearly differentiated. Accordingly, personalization in cardiac medicine is based on the clinical presentation of apatient, as well as his/her cardiovascular risk factors and comorbidities, electrocardiography, imaging, and biomarkers for myocardial load and ischemia. Personalization is based on large clinical trials with detailed subgroup analyses and is practiced on the basis of guidelines. Further in depth personalization is achieved by precision medicine, which is based on innovative imaging for myocardial structure, coronary morphology, and electrophysiology. From the clinical perspective, genome analyses are relevant for comparatively rare monogenetic cardiovascular diseases. While these as well as transcriptome and metabolome analyses play asignificant role in cardiovascular research with great translation potential, they have not yet been broadly introduced in the diagnosis, prevention, and treatment of complex cardiovascular diseases. Furthermore, digital technologies have considerable potential in cardiovascular precision medicine. On the one hand, this is based on the frequency of the diseases with the availability of Big Data and, on the other hand, on the availability of bio-signals and sensors of those signals in cardiovascular diseases.

Deciphering Congenital Heart Disease Using Human Induced Pluripotent Stem Cells.

Congenital heart disease (CHD) is a leading cause of birth defect-related death. Despite significant advances, the mechanisms underlying the development of CHD are complex and remain elusive due to a lack of efficient, reproducible, and translational model systems. Investigations relied on animal models have inherent limitations due to interspecies differences. Human induced pluripotent stem cells (iPSCs) have emerged as an effective platform for disease modeling. iPSCs allow for the production of a limitless supply of patient-specific somatic cells that enable advancement in cardiovascular precision medicine. Over the past decade, researchers have developed protocols to differentiate iPSCs to multiple cardiovascular lineages, as well as to enhance the maturity and functionality of these cells. With the development of physiologic three-dimensional cardiac organoids, iPSCs represent a powerful platform to mechanistically dissect CHD and serve as a foundation for future translational research.

Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine.

Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.

Overexpression of KCNJ2 enhances maturation of human-induced pluripotent stem cell-derived cardiomyocytes

BackgroundAlthough human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are a promising cell resource for cardiovascular research, these cells exhibit an immature phenotype that hampers their potential applications. The inwardly rectifying potassium channel Kir2.1, encoded by the KCNJ2 gene, has been thought as an important target for promoting electrical maturation of iPSC-CMs. However, a comprehensive characterization of morphological and functional changes in iPSC-CMs overexpressing KCNJ2 (KCNJ2 OE) is still lacking.MethodsiPSC-CMs were generated using a 2D in vitro monolayer differentiation protocol. Human KCNJ2 construct with green fluorescent protein (GFP) tag was created and overexpressed in iPSC-CMs via lentiviral transduction. The mixture of iPSC-CMs and mesenchymal cells was cocultured with decellularized natural heart matrix for generation of 3D human engineered heart tissues (EHTs).ResultsWe showed that mRNA expression level of KCNJ2 in iPSC-CMs was dramatically lower than that in human left ventricular tissues. KCNJ2 OE iPSC-CMs yielded significantly increased protein expression of Kir2.1 and current density of Kir2.1-encoded IK1. The larger IK1 linked to a quiescent phenotype that required pacing to elicit action potentials in KCNJ2 OE iPSC-CMs, which can be reversed by IK1 blocker BaCl2. KCNJ2 OE also led to significantly hyperpolarized maximal diastolic potential (MDP), shortened action potential duration (APD) and increased maximal upstroke velocity. The enhanced electrophysiological maturation in KCNJ2 OE iPSC-CMs was accompanied by improvements in Ca2+ signaling, mitochondrial energy metabolism and transcriptomic profile. Notably, KCNJ2 OE iPSC-CMs exhibited enlarged cell size and more elongated and stretched shape, indicating a morphological phenotype toward structural maturation. Drug testing using hERG blocker E-4031 revealed that a more stable MDP in KCNJ2 OE iPSC-CMs allowed for obtaining significant drug response of APD prolongation in a concentration-dependent manner. Moreover, KCNJ2 OE iPSC-CMs formed more mature human EHTs with better tissue structure and cell junction.ConclusionsOverexpression of KCNJ2 can robustly enhance maturation of iPSC-CMs in electrophysiology, Ca2+ signaling, metabolism, transcriptomic profile, cardiomyocyte structure and tissue engineering, thus providing more accurate cellular model for elucidating cellular and molecular mechanisms of cardiovascular diseases, screening drug-induced cardiotoxicity, and developing personalized and precision cardiovascular medicine.

Fast and Sensitive Detection of Protein Markers Using an All-Printing Photonic Crystal Microarray via Fingertip Blood.

With the demand for point-of-care testing (POCT) in cardiovascular diseases, the detection of biomarkers in trace blood samples is of great significance in emergency medicine settings. Here, we demonstrated an all-printed photonic crystal microarray for POCT of protein markers (named "P4 microarray"). The paired nanobodies were printed as probes to target the soluble suppression of tumorigenicity 2 (sST2) as a certified cardiovascular protein marker. Benefiting from photonic crystal-enhanced fluorescence and integrated microarrays, quantitative detection of sST2 is 2 orders of magnitude lower than that of a traditional fluorescent immunoassay. The limit of detection is down to 10 pg/mL with the coefficient of variation being less than 8%. Detection of sST2 via fingertip blood is achieved in 10 min. Moreover, the P4 microarray after 180 days of storage at room temperature showed excellent stability for detection. This P4 microarray, as a convenient and reliable immunoassay for rapid and quantitative detection of protein markers in trace blood samples, has high sensitivity and strong storage stability, which hold great potential to advance cardiovascular precision medicine.

Clonal hematopoiesis, multi-omics and coronary artery disease.

Studies of the genetic architecture of cardiovascular disease once focused on heritable germline factors. Newer work has shed light on the role of somatic mutations in blood cells. These mechanistic and multi-omics studies, along with phenotypic analyses, offer the prospect of new precision cardiovascular medicine paradigms.

Dissecting miRNA-Gene Networks to Map Clinical Utility Roads of Pharmacogenomics-Guided Therapeutic Decisions in Cardiovascular Precision Medicine.

MicroRNAs (miRNAs) create systems networks and gene-expression circuits through molecular signaling and cell interactions that contribute to health imbalance and the emergence of cardiovascular disorders (CVDs). Because the clinical phenotypes of CVD patients present a diversity in their pathophysiology and heterogeneity at the molecular level, it is essential to establish genomic signatures to delineate multifactorial correlations, and to unveil the variability seen in therapeutic intervention outcomes. The clinically validated miRNA biomarkers, along with the relevant SNPs identified, have to be suitably implemented in the clinical setting in order to enhance patient stratification capacity, to contribute to a better understanding of the underlying pathophysiological mechanisms, to guide the selection of innovative therapeutic schemes, and to identify innovative drugs and delivery systems. In this article, the miRNA–gene networks and the genomic signatures resulting from the SNPs will be analyzed as a method of highlighting specific gene-signaling circuits as sources of molecular knowledge which is relevant to CVDs. In concordance with this concept, and as a case study, the design of the clinical trial GESS (NCT03150680) is referenced. The latter is presented in a manner to provide a direction for the improvement of the implementation of pharmacogenomics and precision cardiovascular medicine trials.

Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine

Clinical databases, particularly those composed of big data, face growing security challenges. Blockchain, the open, decentralized, distributed public ledger technology powering cryptocurrency, records transactions securely without the need for third-party verification. In the health care setting, decentralized blockchain networks offer a secure interoperable gateway for clinical research and practice data. Here, we discuss recent advances and potential future directions for the application of blockchain and its integration with artificial intelligence (AI) in cardiovascular medicine. We first review the basic underlying concepts of this technology and contextualise it within the spectrum of current, well known applications. We then consider specific applications for cardiovascular medicine and research in areas such as high-throughput gene sequencing, wearable technologies, and clinical trials. We then evaluate current challenges to effective implementation and future directions. We also summarise the health care applications that can be realised by combining decentralized blockchain computing platforms (for data security) and AI computing (for data analytics). By leveraging high-performance computing and AI capable of securely managing large and rapidly expanding medical databases, blockchain incorporation can provide clinically meaningful predictions, help advance research methodology (eg, via robust AI-blockchain decentralized clinical trials), and provide virtual tools in clinical practice (eg, telehealth, sensory-based technologies, wearable medical devices). Integrating AI and blockchain approaches synergistically amplifies the strengths of both technologies to create novel solutions to serve the objective of providing precision cardiovascular medicine.

Non-invasive imaging as the cornerstone of cardiovascular precision medicine.

To provide an overview of the role of cardiovascular (CV) imaging in facilitating and advancing the field of precision medicine in CV disease. Non-invasive CV imaging is essential to accurately and efficiently phenotype patients with heart disease, including coronary artery disease (CAD) and heart failure (HF). Various modalities, such as echocardiography, nuclear cardiology, cardiac computed tomography (CT), cardiovascular magnetic resonance (CMR), and invasive coronary angiography, and in some cases a combination, can be required to provide sufficient information for diagnosis and management. Taking CAD as an example, imaging is essential for the detection and functional assessment of coronary stenoses, as well as for the quantification of cardiac function and ischaemic myocardial damage. Furthermore, imaging may detect and quantify coronary atherosclerosis, potentially identify plaques at increased risk of rupture, and guide coronary interventions. In patients with HF, imaging helps identify specific aetiologies, quantify damage, and assess its impact on cardiac function. Imaging plays a central role in individualizing diagnosis and management and to determine the optimal treatment for each patient to increase the likelihood of response and improve patient outcomes. Advances in all imaging techniques continue to improve accuracy, sensitivity, and standardization of functional and prognostic assessments, and identify established and novel therapeutic targets. Combining imaging with artificial intelligence, machine learning and computer algorithms, as well as with genomic, transcriptomic, proteomic, and metabolomic approaches, will become state of the art in the future to understand pathologies of CAD and HF, and in the development of new, targeted therapies.

Omics technologies in personalized combination therapy for cardiovascular diseases: challenges and opportunities.

The primary purpose of 'omics' technologies is to understand the intricacy of genomics, proteomics, metabolomics and other molecular mechanisms to reveal the complex traits of human diseases. The significant use of omics technologies and their applications in medicine gear up the study of the pathogenesis of several disorders. The detection of biomarkers in the early onset of diseases is challenging; still, omics can discover novel molecular mechanisms and biomarkers. In this review, the different types of omics and their technologies are explicated and aimed to provide their emerging applications in cardiovascular precision medicine. These technologies significantly impact optimizing medical treatment for individuals to reach a higher level in precision medicine.

What will we ask to artificial intelligence for cardiovascular medicine in the next decade?

Artificial intelligence (AI) comprises a wide range of technologies and methods with heterogeneous degrees of complexity, applications, and abilities. In the cardiovascular field, AI holds the potential to fulfil many unsolved challenges, eventually translating into improved patient care. In particular, AI appears as the most promising tool to overcome the gap between ever-increasing data-rich technologies and their practical implementation in cardiovascular research, in the cardiologist routine, in the patient daily life and at the healthcare-policy level. A multiplicity of AI technologies is progressively pervading several aspects of precision cardiovascular medicine including early diagnosis, automated imaging processing and interpretation, disease sub-phenotyping, risk prediction and remote monitoring systems. Several methodological, logistical, educational, and ethical challenges are emerging by integrating AI systems at any stage of cardiovascular medicine. This review will discuss the basics of AI methods, the growing body of evidence supporting the role of AI in the cardiovascular field and the challenges to overcome for an effective AI-integrated cardiovascular medicine.

Traditional Chinese Medicine Network Pharmacology in Cardiovascular Precision Medicine.

Cardiovascular disease is a major disease affecting human health, and its pathogenesis is caused by many factors. Through the use of "omics" technology, precision medicine is playing an increasingly important role in the prevention and treatment of cardiovascular diseases. Dialectical treatment with traditional Chinese medicine (TCM will result in personalized treatment, which is consistent with precision medicine to a certain extent. However, due to the multitarget, multipath, and multistep characteristics of TCM, its mechanism of action is not easy to elucidate. Network pharmacology can be used to predict the mechanism, toxicity and metabolic characteristics of TCM. This review summarizes commonly used bioinformatics resources for cardiovascular diseases and TCM, as well as the opportunities and challenges of TCM in cardiovascular precision medicine, with special emphasis on network pharmacology methods.

Disentangling the epigenetic landscape in cardiovascular patients: a path toward personalized medicine.

Despite significant advances in our understanding of cardiovascular disease (CVD) we are still far from having developed breakthrough strategies to combat coronary atherosclerosis and heart failure, which account for most of CV deaths worldwide. Available cardiovascular therapies have failed to show to be equally effective in all patients, suggesting that interindividual diversity is an important factor when it comes to conceive and deliver effective personalized treatments. Genome mapping has proved useful in identifying patients who could benefit more from specific drugs depending on genetic variances; however, our genetic make-up determines only a limited part of an individual's risk profile. Recent studies have demonstrated that epigenetic changes - defined as dynamic changes of DNA and histones which do not affect DNA sequence - are key players in the pathophysiology of cardiovascular disease and may participate to delineate cardiovascular risk trajectories over the lifetime. Epigenetic modifications include changes in DNA methylation, histone modifications and non-coding RNAs and these epigenetic signals have shown to cooperate in modulating chromatin accessibility to transcription factors and gene expression. Environmental factors such as air pollution, smoking, psychosocial context and unhealthy diet regimens have shown to significantly modify the epigenome thus leading to altered transcriptional programs and CVD phenotypes. Therefore, the integration of genetic and epigenetic information might be invaluable to build individual maps of cardiovascular risk and hence, could be employed for the design of customized diagnostic and therapeutic strategies. In the present review, we discuss the growing importance of epigenetic information and its putative implications in cardiovascular precision medicine.

Artificial intelligence: an attempt to automate remote device follow-up

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Artificial intelligence (AI) through machine learning (ML) refers to the simulation of human intelligence with the capacity for achieving goals within computers. In electrophysiology, ML has many applications in electrocardiography, intracardiac mapping and cardiac implantable electronic devices (CIEDs). Remote monitoring (RM) of patients equipped with CIEDs associates the analysis of event reports and calendar-based remote follow-ups (FU). ML applications have allowed for risk stratification, improved arrhythmia localisation and streamlined remote monitoring which may significantly reduce the workload faced by electrophysiologists. Aim To develop a system that automates cardiac implantable electronic devices remote follow-up. Methods and Results We created a Java software application, that uses the latest optical character recognition techniques combined with artificial intelligence and natural language processing to extract information from PDF reports of RM of CIEDs from different manufacturers. The current version is HIPAA (Health Insurance Portability and Accountability Act) complaint and runs on local computers only. Using the current system, we were able to run and extract data from 30 remote follow-up PDF reports of Cardiac Implantable Defibrillators (ICDs) and Cardiac Resynchronization Therapy with Defibrillator (CRT-Ds). Time taken from data extraction to conversion of all 30 device PDFs was under 5 minutes. Process and data extracted are presented in the figure below. (Figure 1) Conclusion This machine learning algorithm proved that it is possible to facilitate and automate remote follow-up of cardiac implantable electronic devices. In a near future this will allow to us to efficiently increase productivity, by speeding and facilitating interpretation of remote device follow-ups, leading to improvements in patientcare and precision cardiovascular medicine. Furthermore, in the current and future pandemics it may help prevent unnecessary in-person medical visits, avoiding additional, unnecessary strain on an already overburdened and overwhelmed healthcare system, and saving costs. Abstract Figure 1

From polygenic risk scores to integrative epigenomics: the dawn of a new era for cardiovascular precision medicine.

From polygenic risk scores to integrative epigenomics: the dawn of a new era for cardiovascular precision medicine.

The updated view on induced pluripotent stem cells for cardiovascular precision medicine.

Cardiovascular diseases have consistently been one of the leading causes of mortality, despite investigations by many scientists and clinicians. Animal models are versatile platforms to illustrate various mechanisms of different diseases, but are lacking in accurately portraying cardiovascular disease phenotypes. The advent of human pluripotent stem cells (PSCs) has led to much development in the construction of cardiovascular disease models. In this review, we provide a brief overview of the history and utilization of PSCs for cardiovascular precision medicine, including disease modeling, drug screening, and gene editing, and elaborate on the current updated research status of patient-specific induced pluripotent stem cell (iPSC)-based disease models for cardiac channelopathies, cardiomyopathies, and other cardiovascular diseases. Furthermore, we highlight the development of novel human iPSC-derived engineered heart tissues for cardiovascular disease modeling. Finally, we put forward our own views on the existing advantages and difficulties for moving forward in this field.