Precision Medicine and Personalized Medicine in Cardiovascular Disease

Precision Medicine Approaches in Cardiology and Personalized Therapies for Improved Patient Outcomes: A systematic review

Personalized medicine is a novel and rapidly evolving approach to clinical practice that involves making decisions about disease prediction, prevention, diagnosis, and treatment by utilizing modern technologies. The concepts of precision medicine have grown as a result of ongoing developments in genomic analysis, molecular diagnostics, and technology. These advancements have enabled a deeper understanding and interpretation of the human genome, allowing for a personalized approach to clinical care. The primary objective of this research is to assess personalized medicine in terms of its indications, advantages, practical clinical uses, potential future directions, problems, and effects on healthcare. An extensive analysis of the scientific literature regarding this topic demonstrated the new medical approach's relevance and usefulness, as well as the fact that personalized medicine is becoming increasingly prevalent in various sectors. The online, internationally indexed databases PubMed and Cochrane Reviews were used to conduct searches for and critically evaluate the most relevant published research including original papers and reviews in the scientific literature. The findings suggest that precision medicine has a lot of potential and its implementation lowers the incidence of stroke as well as coronary heart disease and improves patient health in cardiology.

Sex- and Gender-Based Reporting in Antihypertensive Medication Literature Informing Hypertension Guidelines

BACKGROUND

Hypertension is the leading modifiable cardiovascular risk factor with recognized sex- and gender-based differences. We assessed the incorporation of sex and gender reporting in the antihypertensive medication literature informing hypertension guidelines.

METHODS AND RESULTS

Literature cited in the International Society of Hypertension (2020), European Society of Cardiology/European Society of Hypertension (2018), American College of Cardiology/American Heart Association (2017), Latin American Society of Hypertension (2017), Pan-African Society of Cardiology (2020), and Hypertension Canada (2020) guidelines was systematically reviewed. Observational studies, randomized controlled trials, and systematic reviews involving antihypertensive medications were included. Studies with participants of a single sex, guidelines, and commentaries were excluded. Data on study participation-to-prevalence ratio by sex, analysis of baseline demographics and study outcomes by sex, and stratification of adverse events by sex were extracted. Of 1659 unique citations, 331 studies met inclusion criteria. Of those, 81% reported the sex of participants, and 22% reported a male-to-female participation-to-prevalence ratio of 0.8 to 1.2. Three percent of studies stratified baseline characteristics by sex, and 20% considered sex during analysis through statistical adjustment or stratification. Although 32% of studies reported adverse events, only 0.6% stratified adverse events by sex. Most (58%) studies reporting sex/gender used sex and gender terms interchangeably.

CONCLUSIONS

Incorporation of sex- and gender-based considerations in study population, analysis, or reporting of results and adverse events is not common in the antihypertensive medication literature informing international hypertension guidelines. Greater attention to sex- and gender-based factors in research is required to optimally inform management of hypertension.

Women and Cardiovascular Health: Unraveling Gender-Specific Factors, Risks, and Therapeutic Approaches in Contemporary Medicine

Background and objective Cardiovascular diseases (CVDs) constitute a significant global health challenge, causing millions of deaths annually and straining healthcare systems worldwide. This study aimed to investigate and elucidate gender-specific factors, risks, and therapeutic approaches related to cardiovascular health in women within the context of contemporary medicine. Methodology We conducted a prospective observational study spanning one year (November 2022 to October 2023) at the Peshawar Medical Complex Hospital, to meticulously explore the field of women's cardiovascular health. With a diverse cohort of 435 women (age range: 18-55 years), representing various socioeconomic backgrounds and geographic locations, our study aimed to elicit comprehensive insights. Through structured interviews covering reproductive history, lifestyle, and psychosocial aspects, coupled with clinical assessments, we gathered multifaceted data. Statistical analysis was done using SPSS Statistics version 23.0 (IBM Corp., Armonk, NY). By employing descriptive and t-tests for quantitative analysis and by thematically analyzing qualitative insights, our approach ultimately sought to provide a nuanced understanding of gender-specific factors impacting women's cardiovascular health. Results The study, involving 435 women, revealed various prevalent cardiovascular risk factors. Notable findings include a high incidence of a family history of CVD (n=213, 48.96%, p=0.013), hypertension (n=207, 47.58%), hypercholesterolemia (n=114, 26.21%), elevated triglycerides (n=162, 37.24%), and diabetes (n=64, 14.71%). Physical inactivity was also significantly more common (53.56%, p=0.004) compared to those engaging in regular activity. Women-specific risk factors comprised miscarriage (n=191, 43.91%). Therapeutic preferences varied, with a majority opting for lifestyle modifications (n=263, 60.39%) and pharmacological interventions (n=331, 76.33%). Conclusions This study provides a comprehensive understanding of prevalent cardiovascular risk factors, distinctive women-specific contributors, and diverse therapeutic preferences, highlighting the importance of personalized and targeted interventions to optimize women's cardiovascular health outcomes in contemporary medicine.

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

RNA in cardiovascular disease: A new frontier of personalized medicine

Personalized medicine has witnessed remarkable progress with the emergence of RNA therapy, offering new possibilities for the treatment of various diseases, and in particular in the context of cardiovascular disease (CVD). The ability to target the human genome through RNA manipulation offers great potential not only in the treatment of cardiac pathologies but also in their diagnosis and prevention, notably in cases of hyperlipidemia and myocardial infarctions. While only a few RNA-based treatments have entered clinical trials or obtained approval from the US Food and Drug Administration, the growing body of research on this subject is promising. However, the development of RNA therapies faces several challenges that must be overcome. These include the efficient delivery of drugs into cells, the potential for immunogenic responses, and safety. Resolving these obstacles is crucial to advance the development of RNA therapies. This review explores the newest developments in medical studies, treatment plans, and results related to RNA therapies for heart disease. Furthermore, it discusses the exciting possibilities and difficulties in this innovative area of research.

Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling

Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.

Association between polycyclic aromatic hydrocarbon exposure and blood lipid levels: the indirect effects of inflammation and oxidative stress

Although previous studies have indicated polycyclic aromatic hydrocarbons (PAHs) as cardiovascular health risk factors, evidence linking exposure to PAHs and blood lipids is still lacking, and the mechanism remains largely unknown. In this study, we evaluated the association between human internal exposure to PAHs and blood lipid levels in adults, as well as the indirect effects of inflammation and oxidative stress. The internal exposure of PAHs was assessed by determining serum PAHs and their hydroxylated metabolites (OH-PAHs) in the paired urine samples. Multivariable linear regression results demonstrated significant positive associations of individual PAHs and OH-PAHs with blood lipid biomarkers. The Bayesian kernel machine regression model revealed positive joint effects of PAH internal exposure on the fasting blood glucose, low-density lipoprotein cholesterol, total cholesterol, and total triglyceride, as well as an increased ratio of apolipoprotein B to apolipoprotein A1. In evaluating individual effects, serum phenanthrene played the most significant role in the association of increased PAH exposure with elevated fasting blood glucose. Quantile g-computation demonstrated the significant change in the levels of apolipoprotein B, ratio of apolipoprotein B to apolipoprotein A1, low-density lipoprotein cholesterol, and total cholesterol per quartile increase in PAH internal exposure. The restricted cubic spline analysis demonstrated the non-linear relationship between individual PAHs and OH-PAHs on blood lipid biomarkers. The mediation analysis indicated that PAH exposure may affect blood lipids not directly, but rather indirectly through intermediate inflammation and oxidative stress. The results demonstrated a significant association between increased PAH exposure levels and elevated blood lipids, highlighting the indirect effects of inflammation and oxidative stress.

Functional mutation, splice, distribution, and divergence analysis of impactful genes associated with heart failure and other cardiovascular diseases

Cardiovascular disease (CVD) is caused by a multitude of complex and largely heritable conditions. Identifying key genes and understanding their susceptibility to CVD in the human genome can assist in early diagnosis and personalized treatment of the relevant patients. Heart failure (HF) is among those CVD phenotypes that has a high rate of mortality. In this study, we investigated genes primarily associated with HF and other CVDs. Achieving the goals of this study, we built a cohort of thirty-five consented patients, and sequenced their serum-based samples. We have generated and processed whole genome sequence (WGS) data, and performed functional mutation, splice, variant distribution, and divergence analysis to understand the relationships between each mutation type and its impact. Our variant and prevalence analysis found FLNA, CST3, LGALS3, and HBA1 linked to many enrichment pathways. Functional mutation analysis uncovered ACE, MME, LGALS3, NR3C2, PIK3C2A, CALD1, TEK, and TRPV1 to be notable and potentially significant genes. We discovered intron, 5' Flank, 3' UTR, and 3' Flank mutations to be the most common among HF and other CVD genes. Missense mutations were less common among HF and other CVD genes but had more of a functional impact. We reported HBA1, FADD, NPPC, ADRB2, ADBR1, MYH6, and PLN to be consequential based on our divergence analysis.

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.

Analysis of the disease burden of cardiomyopathy in children aged 0-14 years in China from 1990 to 2019

Objectives

To assess the disease burden and changing trend of cardiomyopathy in children aged 0-14 years in China from 1990 to 2019.

Methods

This study was based on the Global Burden of Disease Study 2019; the age-specific prevalence rate, mortality rate and disability-adjusted life year (DALY) rate were used for analysis. Estimated annual percentage change (EAPC) in burden rate and its 95% confidence interval were calculated. The data of China were compared with the global average level.

Results

In 2019, the numbers of prevalence, deaths, and DALYs of cardiomyopathy in children aged 0-14 years in China were 4,493 [95% uncertainty interval (UI): 2687 ~ 6,838], 434 (95%UI: 337 ~ 565) and 37,522 (95%UI: 29,321 ~ 48,891), with declining amplitudes of 16.32, 70.56, and 70.74%, compared with 1990, respectively. In 2019, the prevalence rate of cardiomyopathy in Chinese children aged 0-14 years was 2.00/100,000 (95%UI: 1.2/100,000 ~ 3.04/100,000), higher than 1990 [1.66/100,000 (95%UI:1.00/100,000 ~ 2.53/100,000)]; mortality rate was 0.19/100,000 (95%UI: 0.15/100,000 ~ 0.25/100,000), significantly lower than 1990 [0.46/100,000 (95%UI: 0.25/100,000 ~ 0.95/100,000)]; DALY rate was 16.69/100,000 (95%UI: 13.04/100,000 ~ 21.75/100,000), also significantly lower than 1990 [39.71/100,000 (95%UI: 22.06/100,000 ~ 82.8/100,000)]. All burden rates of cardiomyopathy in Chinese children aged 0-14 years old were all lower than the global averages of 2019; the burden rates of male children were higher than female children. In all calendar years from 1990 to 2019, the mortality and DALY rates of children younger than 1-year-old were significantly higher than in the other age groups of 0-14 years old. From 1990 to 2019, the prevalence rate of cardiomyopathy aged 0-14 years old gradually increased, with EAPC of 0.82 (95%CI: 0.71-0.93); mortality rate and DALY rate decreased [EAPC = -2.32 (95%CI: -2.59 to -2.05)].

Conclusion

From 1990 to 2019, the disease burden of cardiomyopathy in children of China aged 0-14 years was heterogeneous; the burden of male children was higher than females; and the burden of cardiomyopathy in children younger than 1 year old needs more attention.

Monitoring policy in the context of preventive treatment of cardiovascular disease

Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.

Precision Medicine and the future of Cardiovascular Diseases: A Clinically Oriented Comprehensive Review

Cardiac diseases form the lion's share of the global disease burden, owing to the paradigm shift to non-infectious diseases from infectious ones. The prevalence of CVDs has nearly doubled, increasing from 271 million in 1990 to 523 million in 2019. Additionally, the global trend for the years lived with disability has doubled, increasing from 17.7 million to 34.4 million over the same period. The advent of precision medicine in cardiology has ignited new possibilities for individually personalized, integrative, and patient-centric approaches to disease prevention and treatment, incorporating the standard clinical data with advanced "omics". These data help with the phenotypically adjudicated individualization of treatment. The major objective of this review was to compile the evolving clinically relevant tools of precision medicine that can help with the evidence-based precise individualized management of cardiac diseases with the highest DALY. The field of cardiology is evolving to provide targeted therapy, which is crafted as per the "omics", involving genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics, for deep phenotyping. Research for individualizing therapy in heart diseases with the highest DALY has helped identify novel genes, biomarkers, proteins, and technologies to aid early diagnosis and treatment. Precision medicine has helped in targeted management, allowing early diagnosis, timely precise intervention, and exposure to minimal side effects. Despite these great impacts, overcoming the barriers to implementing precision medicine requires addressing the economic, cultural, technical, and socio-political issues. Precision medicine is proposed to be the future of cardiovascular medicine and holds the potential for a more efficient and personalized approach to the management of cardiovascular diseases, contrary to the standardized blanket approach.

Sex and Gender and Allostatic Mechanisms of Cardiovascular Risk and Disease

Cardiovascular diseases are leading causes of mortality and morbidity in adults worldwide. Multiple studies suggest that there are clinically relevant sex differences in cardiovascular disease. Women and men differ substantially in terms of prevalence, presentation, management, and outcomes of cardiovascular disease. To date, however, little is known about why cardiovascular disease affects women and men differently. Because many studies do not differentiate the concept of sex and gender, it is sometimes difficult to discriminate sociocultural vs biological contributors that drive observed clinical differences. Female sex has some biological advantages in relation to cardiovascular disease, but many of these advantages seem to disappear as soon as women develop cardiovascular risk factors (eg, type 2 diabetes, hypertension, dyslipidemia). Furthermore, stress and allostatic load could play an important role in the relationship between sex/gender and cardiovascular diseases. In this narrative review, we argue that chronic stress and psychosocial factors might better encompass the patterns of allostatic load increases seen in women, while biological risk factors and unhealthy behaviours might be more important mechanisms that drive increased allostatic load in men. Indeed, men show allostatic load patterns that are more associated with impaired anthropometric, metabolic, and cardiovascular functioning and women have greater dysregulation in neuroendocrine and immune functioning. Thus gender-related factors might contribute to the pathogenesis of cardiovascular disease especially through stress mechanisms. It is important to continue to study the mechanisms by which gender influences chronic stress, because chronic stress could influence modifiable gendered factors to promote cardiovascular disease prevention.

Sex Differences in Adiposity and Cardiovascular Diseases

Body fat distribution is a well-established predictor of adverse medical outcomes, independent of overall adiposity. Studying body fat distribution sheds insights into the causes of obesity and provides valuable information about the development of various comorbidities. Compared to total adiposity, body fat distribution is more closely associated with risks of cardiovascular diseases. The present review specifically focuses on the sexual dimorphism in body fat distribution, the biological clues, as well as the genetic traits that are distinct from overall obesity. Understanding the sex determinations on body fat distribution and adiposity will aid in the improvement of the prevention and treatment of cardiovascular diseases (CVD).

Exosomal Composition, Biogenesis and Profiling Using Point-of-Care Diagnostics—Implications for Cardiovascular Disease

Arteriosclerosis is an important age-dependent disease that encompasses atherosclerosis, in-stent restenosis (ISR), pulmonary hypertension, autologous bypass grafting and transplant arteriosclerosis. Endothelial dysfunction and the proliferation of vascular smooth muscle cell (vSMC)-like cells is a critical event in the pathology of arteriosclerotic disease leading to intimal-medial thickening (IMT), lipid retention and vessel remodelling. An important aspect in guiding clinical decision-making is the detection of biomarkers of subclinical arteriosclerosis and early cardiovascular risk. Crucially, relevant biomarkers need to be good indicators of injury which change in their circulating concentrations or structure, signalling functional disturbances. Extracellular vesicles (EVs) are nanosized membraneous vesicles secreted by cells that contain numerous bioactive molecules and act as a means of intercellular communication between different cell populations to maintain tissue homeostasis, gene regulation in recipient cells and the adaptive response to stress. This review will focus on the emerging field of EV research in cardiovascular disease (CVD) and discuss how key EV signatures in liquid biopsies may act as early pathological indicators of adaptive lesion formation and arteriosclerotic disease progression. EV profiling has the potential to provide important clinical information to complement current cardiovascular diagnostic platforms that indicate or predict myocardial injury. Finally, the development of fitting devices to enable rapid and/or high-throughput exosomal analysis that require adapted processing procedures will be evaluated.

Can CHA2DS2-VASc and HAS-BLED Foresee the Presence of Cerebral Microbleeds, Lacunar and Non-Lacunar Infarcts in Elderly Patients With Atrial Fibrillation? Data From Strat-AF Study

Anticoagulants reduce embolic risk in atrial fibrillation (AF), despite increasing hemorrhagic risk. In this context, validity of congestive heart failure, hypertension, age ≥ 75 years, diabetes, stroke, vascular disease, age 65-74 years and sex category (CHA2DS2-VASc) and hypertension, abnormal renal/liver function, stroke, bleeding history or predisposition, labile international normalized ratio, elderly, drugs/alcohol concomitantly (HAS-BLED) scales, used to respectively evaluate thrombotic and hemorrhagic risks, is incomplete. In patients with AF, brain MRI has led to the increased detection of "asymptomatic" brain changes, particularly those related to small vessel disease, which also represent the pathologic substrate of intracranial hemorrhage, and silent brain infarcts, which are considered risk factors for ischemic stroke. Routine brain MRI in asymptomatic patients with AF is not yet recommended. Our aim was to test predictive ability of risk stratification scales on the presence of cerebral microbleeds, lacunar, and non-lacunar infarcts in 170 elderly patients with AF on oral anticoagulants. Ad hoc developed R algorithms were used to evaluate CHA2DS2-VASc and HAS-BLED sensitivity and specificity on the prediction of cerebrovascular lesions: (1) Maintaining original items' weights; (2) augmenting weights' range; (3) adding cognitive, motor, and depressive scores. Accuracy was poor for each outcome considering both scales either in phase 1 or phase 2. Accuracy was never improved by the addition of cognitive scores. The addition of motor and depressive scores to CHA2DS2-VASc improved accuracy for non-lacunar infarcts (sensitivity = 0.70, specificity = 0.85), and sensitivity for lacunar-infarcts (sensitivity = 0.74, specificity = 0.61). Our results are a very first step toward the attempt to identify those elderly patients with AF who would benefit most from brain MRI in risk stratification.

Locational effects on oral microbiota among long-term care patients

Background

Dysbiosis of oral microbiota is the cause of many diseases related to oral and general health. However, few Asia-based studies have evaluated the role of oral microbiota in patients receiving long-term care. Thus, new indications are needed for early prevention and risk management based on information derived from the oral microbiota.

Methods

We used next-generation sequencing (NGS) to identify the oral bacterial composition and abundance in patients receiving long-term care: 20 from the outpatient department (OPD) and 20 home-care patients. Their microbial compositions, taxonomy, and alpha/beta diversity were characterized.

Results

Microbiota from the two groups showed different diversity and homogeneity, as well as distinct bacterial species. A more diverse and stable microbial population was observed among OPD patients. Our findings indicated that home-care patients had a higher risk of oral diseases due to the existence of dominant species and a less stable microbial community.

Conclusion

This work was the first in Taiwan to use NGS to investigate the oral microbiota of long-term care patients. Our study demonstrated the potential use of dominant bacterial species as biomarkers for the risk management of posttreatment complications.

Unraveling and Targeting Myocardial Regeneration Deficit in Diabetes

Cardiomyopathy is a common complication in diabetic patients. Ventricular dysfunction without coronary atherosclerosis and hypertension is driven by hyperglycemia, hyperinsulinemia and impaired insulin signaling. Cardiomyocyte death, hypertrophy, fibrosis, and cell signaling defects underlie cardiomyopathy. Notably, detrimental effects of the diabetic milieu are not limited to cardiomyocytes and vascular cells. The diabetic heart acquires a senescent phenotype and also suffers from altered cellular homeostasis and the insufficient replacement of dying cells. Chronic inflammation, oxidative stress, and metabolic dysregulation damage the population of endogenous cardiac stem cells, which contribute to myocardial cell turnover and repair after injury. Therefore, deficient myocardial repair and the progressive senescence and dysfunction of stem cells in the diabetic heart can represent potential therapeutic targets. While our knowledge of the effects of diabetes on stem cells is growing, several strategies to preserve, activate or restore cardiac stem cell compartments await to be tested in diabetic cardiomyopathy.

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.

The Role of Computer Skills in Personal Health Record Adoption Among Patients With Heart Disease: Multidimensional Evaluation of Users Versus Nonusers

Background

In the era of precision medicine, it is critical for health communication efforts to prioritize personal health record (PHR) adoption.

Objective

The objective of this study was to describe the characteristics of patients with heart disease that choose to adopt a PHR.

Methods

A total of 79 patients with chronic cardiovascular disease participated in this study: 48 PHR users and 31 nonusers. They completed 5 surveys related to their choice to use or not use the PHR: demographics, patient activation, medication adherence, health literacy, and computer self-efficacy (CSE).

Results

There was a significant difference between users and nonusers in the sociodemographic measure education (P=.04). There was no significant difference between users and nonusers in other sociodemographic measures: age (P=.20), sex (P=.35), ethnicity (P=.43), race (P=.42), and employment (P=.63). There was a significant difference between PHR users and PHR nonusers in CSE (P=.006).

Conclusions

In this study, we demonstrate that sociodemographic characteristics were not an important factor in patients’ use of their PHR, except for education. This study had a small sample size and may not have been large enough to detect differences between groups. Our results did demonstrate that there is a difference between PHR users and nonusers related to their CSE. This work suggests that incorporating CSE into the design of PHRs is critical. The design of patient-facing tools must take into account patients’ preferences and abilities when developing effective user-friendly health information technologies.

The Benefits of Telemedicine in Personalized Prevention of Cardiovascular Diseases (CVD): A Systematic Review

INTRODUCTION

Adverse effects on personalized care and outcomes of cardiovascular diseases (CVD) could occur if health systems do not work in an efficient manner. The pandemic caused by COVID-19 has opened new perspectives for the execution and advancement of cardiovascular tests through telemedicine platforms.

OBJECTIVE

This study aimed to analyze the usefulness of telemedical systems for providing personal care in the prevention of CVD.

METHODS

A systematic review analysis was conducted on the literature available from libraries such as PubMed (Medline), Scopus (Embase), and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Data available in the last 10 years (2011-2020) were also examined by PRISMA guidelines. The selected studies were divided into two categories: (1) benefits of telemedicine in CVD prevention, and (2) recent progress in telemedical services for personalized care of CVD.

RESULTS

The literature search produced 587 documents, and 19 articles were considered in this review. Results highlighted that the timely delivery of preventive care for CVD which can be implemented virtually can benefit and modify morbidity and mortality. This could also reduce the pressure on hospitals by decreasing acute CVD occurrence among the general population. The use of these technologies can also help to reduce access to hospitals and other medical devices when not necessary.

CONCLUSIONS

Telemedicine platforms can be used for regular checkups for CVD and contribute to preventing the occurrence of acute events and more in general the progression of CVD.

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.

Sex Differences of the Diabetic Heart

Type 2 diabetes is a chronic disease associated with micro- and macro-vascular complications, including myocardial ischemia, and also with a specific and intrinsic cardiac dysfunction called diabetic cardiomyopathy (DCM). Both clinical and animal studies demonstrate significant sex differences in prevalence, pathophysiology, and outcomes of cardiovascular diseases (CVDs), including those associated with diabetes. The increased risk of CVDs with diabetes is higher in women compared to men with 50% higher risk of coronary artery diseases and increased mortality when exposed to acute myocardial infarction. Clinical studies also reveal a sexual dimorphism in the incidence and outcomes of DCM. Based on these clinical findings, growing experimental research was initiated to understand the impact of sex on CVDs associated with diabetes and to identify the molecular mechanisms involved. Endothelial dysfunction, atherosclerosis, coagulation, and fibrosis are mechanisms found to be sex-differentially modulated in the diabetic cardiovascular system. Recently, impairment of energy metabolism also emerged as a determinant of multiple CVDs associated with diabetes. Therefore, future studies should thoroughly analyze the sex-specific metabolic determinants to propose new therapeutic targets. With current medicine tending toward more personalized care of patients, we finally propose to discuss the importance of sex as determinant in the treatment of diabetes-associated cardiac diseases to promote a more systemic inclusion of both males and females in clinical and preclinical studies.

Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel

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Impact of sex and pathophysiology on optimal drug choice in hypertensive rats: quantitative insights for precision medicine

Less than half of all hypertensive patients receiving treatment are successful in normalizing their blood pressure. Despite the complexity and heterogeneity of hypertension, the current antihypertensive guidelines are not tailored to the individual patient. As a step toward individualized treatment, we develop a quantitative systems pharmacology model of blood pressure regulation in the spontaneously hypertensive rat (SHR) and generate sex-specific virtual populations of SHRs to account for the heterogeneity between the sexes and within the pathophysiology of hypertension. We then used the mechanistic model integrated with machine learning tools to study how variability in these mechanisms leads to differential responses in rodents to the four primary classes of antihypertensive drugs. We found that both the sex and the pathophysiological profile of the individual play a major role in the response to hypertensive treatments. These results provide insight into potential areas to apply precision medicine in human primary hypertension.

Association is not prediction: A landscape of confused reporting in diabetes - A systematic review

AIMS

Appropriate analysis of big data is fundamental to precision medicine. While statistical analyses often uncover numerous associations, associations themselves do not convey predictive value. Confusion between association and prediction harms clinicians, scientists, and ultimately, the patients. We analyzed published papers in the field of diabetes that refer to "prediction" in their titles. We assessed whether these articles report metrics relevant to prediction.

METHODS

A systematic search was undertaken using NCBI PubMed. Articles with the terms "diabetes" and "prediction" were selected. All abstracts of original research articles, within the field of diabetes epidemiology, were searched for metrics pertaining to predictive statistics. Simulated data was generated to visually convey the differences between association and prediction.

RESULTS

The search-term yielded 2,182 results. After discarding non-relevant articles, 1,910 abstracts were evaluated. Of these, 39% (n = 745) reported metrics of predictive statistics, while 61% (n = 1,165) did not. The top reported metrics of prediction were ROC AUC, sensitivity and specificity. Using the simulated data, we demonstrated that biomarkers with large effect sizes and low P values can still offer poor discriminative utility.

CONCLUSIONS

We demonstrate a landscape of confused reporting within the field of diabetes epidemiology where the term "prediction" is often incorrectly used to refer to association statistics. We propose guidelines for future reporting, and two major routes forward in terms of main analytic procedures and research goals: the explanatory route, which contributes to precision medicine, and the prediction route which contributes to personalized medicine.

Quantitative proteomics reveal lineage-specific protein profiles in iPSC-derived Marfan syndrome smooth muscle cells

Marfan syndrome (MFS) is a connective tissue disorder caused by mutations in the FBN1 gene that produces wide disease phenotypic variability. The lack of ample genotype-phenotype correlation hinders translational study development aimed at improving disease prognosis. In response to this need, an induced pluripotent stem cell (iPSC) disease model has been used to test patient-specific cells by a proteomic approach. This model has the potential to risk stratify patients to make clinical decisions, including timing for surgical treatment. The regional propensity for aneurysm formation in MFS may be related to distinct smooth muscle cell (SMC) embryologic lineages. Thus, peripheral blood mononuclear cell (PBMC)-derived induced pluripotent stem cells (iPSC) were differentiated into lateral mesoderm (LM, aortic root) and neural crest (NC, ascending aorta/transverse arch) SMC lineages to model MFS aortic pathology. Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) proteomic analysis by tandem mass spectrometry was applied to profile LM and NC iPSC SMCs from four MFS patients and two healthy controls. Analysis revealed 45 proteins with lineage-dependent expression in MFS patients, many of which were specific to diseased samples. Single protein-level data from both iPSC SMCs and primary MFS aortic root aneurysm tissue confirmed elevated integrin αV and reduced MRC2 in clinical disease specimens, validating the iPSC iTRAQ findings. Functionally, iPSC SMCs exhibited defective adhesion to a variety of extracellular matrix proteins, especially laminin-1 and fibronectin, suggesting altered cytoskeleton dynamics. This study defines the aortic embryologic origin-specific proteome in a validated iPSC SMC model to identify novel protein markers associated with MFS aneurysm phenotype. Translating iPSC findings into clinical aortic aneurysm tissue samples highlights the potential for iPSC-based methods to model MFS disease for mechanistic studies and therapeutic discovery in vitro.

Genetics of Familial Hypercholesterolemia: New Insights

Familial hypercholesterolemia (FH) is one of the most common monogenic diseases, leading to an increased risk of premature atherosclerosis and its cardiovascular complications due to its effect on plasma cholesterol levels. Variants of three genes (LDL-R, APOB and PCSK9) are the major causes of FH, but in some probands, the FH phenotype is associated with variants of other genes. Alternatively, the typical clinical picture of FH can result from the accumulation of common cholesterol-increasing alleles (polygenic FH). Although the Czech Republic is one of the most successful countries with respect to FH detection, approximately 80% of FH patients remain undiagnosed. The opportunities for international collaboration and experience sharing within international programs (e.g., EAS FHSC, ScreenPro FH, etc.) will improve the detection of FH patients in the future and enable even more accessible and accurate genetic diagnostics.

Precision and Personalized Medicine: How Genomic Approach Improves the Management of Cardiovascular and Neurodegenerative Disease

Life expectancy has gradually grown over the last century. This has deeply affected healthcare costs, since the growth of an aging population is correlated to the increasing burden of chronic diseases. This represents the interesting challenge of how to manage patients with chronic diseases in order to improve health care budgets. Effective primary prevention could represent a promising route. To this end, precision, together with personalized medicine, are useful instruments in order to investigate pathological processes before the appearance of clinical symptoms and to guide physicians to choose a targeted therapy to manage the patient. Cardiovascular and neurodegenerative diseases represent suitable models for taking full advantage of precision medicine technologies applied to all stages of disease development. The availability of high technology incorporating artificial intelligence and advancement progress made in the field of biomedical research have been substantial to understand how genes, epigenetic modifications, aging, nutrition, drugs, microbiome and other environmental factors can impact health and chronic disorders. The aim of the present review is to address how precision and personalized medicine can bring greater clarity to the clinical and biological complexity of these types of disorders associated with high mortality, involving tremendous health care costs, by describing in detail the methods that can be applied. This might offer precious tools for preventive strategies and possible clues on the evolution of the disease and could help in predicting morbidity, mortality and detecting chronic disease indicators much earlier in the disease course. This, of course, will have a major effect on both improving the quality of care and quality of life of the patients and reducing time efforts and healthcare costs.

Challenge-comet assay, a functional and genomic biomarker for precision risk assessment and disease prevention among exposed workers

Advancements in genomic technologies have ushered application of innovative changes into biomedical sciences and clinical medicine. Consequently, these changes have created enormous opportunities to implement precision population/occupational disease prevention and target-specific disease intervention (or personalized medicine). To capture the opportunities, however, it is necessary is to develop novel, especially genomic-based, biomarkers which can provide precise and individualized health risk assessment. In this review, development of the Challenge-comet assay is used as an example to demonstrate how assays need to be validated for its sensitivity, specificity, and functional and quantitative features, and how assays can be used to provide individualized health risk assessment for precision prevention and intervention. Other examples of genomic-based novel biomarkers will also be discussed. Furthermore, no biomarkers can be used alone therefore their integrated usage with other biomarkers and with personal data bases will be discussed.

Ratiology and a Complementary Class of Metrics for Cardiovascular Investigations

Cardiovascular investigations often involve ratio-based metrics or differences: ejection fraction, arterial pressure augmentation index, coronary fractional flow reserve, pulse pressure. Focusing on a single number (ratio or difference) implies that information is lost. The lost companions constitute a well-defined but thus far unrecognized class, having additive value, a physical dimension, and often a physiological meaning. Physiologists should play a prominent role in exploring these complementary avenues and also define alternatives.

Genetic Basis of Obesity and Type 2 Diabetes in Africans: Impact on Precision Medicine

PURPOSE OF REVIEW

Recent advances in genomics provide opportunities for novel understanding of the biology of human traits with the goal of improving human health. Here, we review recent obesity and type 2 diabetes (T2D)-related genomic studies in African populations and discuss the implications of limited genomics studies on health disparity and precision medicine.

RECENT FINDINGS

Genome-wide association studies in Africans have yielded genetic discovery that would otherwise not be possible; these include identification of novel loci associated with obesity (SEMA-4D, PRKCA, WARS2), metabolic syndrome (CA-10, CTNNA3), and T2D (AGMO, ZRANB3). ZRANB3 was recently demonstrated to influence beta cell mass and insulin response. Despite these promising results, genomic studies in African populations are still limited and thus genomics tools and approaches such as polygenic risk scores and precision medicine are likely to have limited utility in Africans with the unacceptable possibility of exacerbating prevailing health disparities. African populations provide unique opportunities for increasing our understanding of the genetic basis of cardiometabolic disorders. We highlight the need for more coordinated and sustained efforts to increase the representation of Africans in genomic studies both as participants and scientists.

Vascular biomedicine in an era of chronic disease and multimorbidity

It is increasingly common that patients present with more than one disease and that diseases are chronic in nature. Cardiovascular conditions such as hypertension, heart failure and stroke, renal diseases and cardiometabolic conditions such as diabetes are prime examples of chronic diseases which pose major challenges in contemporary healthcare provision. The complex features of multimorbidity call for precision medicine approaches that take comorbidity and chronicity into account. The research basis of chronic disease and multimorbidity, however, is currently in its infancy. This applies to all domains including basic, translational and clinical science. In this article we call for development of new models, smarter use of existing models and better characterisation of vascular and cardiovascular phenotypes in studies not directly related to cardiovascular diseases. This has the potential to further improve the quality of translational research, papers in journals such as Clinical Science and ultimately translate into better patient care.