Opportunities for the Cardiovascular Community in the Precision Medicine Initiative

Heart Failure: Recent Advances and Breakthroughs

Heart failure (HF) is a common clinical condition encountered in various healthcare settings with a vast socioeconomic impact. Recent advancements in pharmacotherapy have led to the evolution of novel therapeutic agents with a decrease in hospitalization and mortality rates in HF with reduced left ventricular ejection fraction (HFrEF). Lately, the introduction of artificial intelligence (AI) to construct decision-making models for the early detection of HF has played a vital role in optimizing cardiovascular disease outcomes. In this review, we examine the newer therapies and evidence behind goal-directed medical therapy (GDMT) for managing HF. We also explore the application of AI and machine learning (ML) in HF, including early diagnosis and risk stratification for HFrEF.

C-Reactive Protein: The Quintessential Marker of Systemic Inflammation in Coronary Artery Disease-Advancing toward Precision Medicine

Atherosclerotic cardiovascular disease (CVD) remains the leading cause of mortality worldwide. While conventional risk factors have been studied and managed, CVD continues to pose a global threat. Risk scoring systems based on these factors have been developed to predict acute coronary syndromes and guide therapeutic interventions. However, traditional risk algorithms may not fully capture the complexities of individual patients. Recent research highlights the role of inflammation, particularly chronic low-grade inflammation, in the pathogenesis of coronary artery disease (CAD). C-reactive protein (CRP) is an inflammatory molecule that has demonstrated value as a predictive marker for cardiovascular risk assessment, both independently and in conjunction with other parameters. It has been incorporated into risk assessment algorithms, enhancing risk prediction and guiding therapeutic decisions. Pharmacological interventions with anti-inflammatory properties, such as statins, glucagon-like peptide-1 agonists, and interleukin-1 inhibitors, have shown promising effects in reducing both cardiovascular risks and CRP levels. This manuscript provides a comprehensive review of CRP as a marker of systemic inflammation in CAD. By exploring the current knowledge surrounding CRP and its implications for risk prediction and therapeutic interventions, this review contributes to the advancement of personalized cardiology and the optimization of patient care.

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Congenital long QT syndrome (LQTS) is characterized by a prolonged QT-interval on an electrocardiogram (ECG). An abnormal prolongation in the QT-interval increases the risk for fatal arrhythmias. Genetic variants in several different cardiac ion channel genes, including KCNH2, are known to cause LQTS. Here, we evaluated whether structure-based molecular dynamics (MD) simulations and machine learning (ML) could improve the identification of missense variants in LQTS-linked genes. To do this, we investigated KCNH2 missense variants in the Kv11.1 channel protein shown to have wild type (WT) like or class II (trafficking-deficient) phenotypes in vitro. We focused on KCNH2 missense variants that disrupt normal Kv11.1 channel protein trafficking, as it is the most common phenotype for LQTS-associated variants. Specifically, we used computational techniques to correlate structural and dynamic changes in the Kv11.1 channel protein PAS domain (PASD) with Kv11.1 channel protein trafficking phenotypes. These simulations unveiled several molecular features, including the numbers of hydrating waters and hydrogen bonding pairs, as well as folding free energy scores, that are predictive of trafficking. We then used statistical and machine learning (ML) (Decision tree (DT), Random forest (RF), and Support vector machine (SVM)) techniques to classify variants using these simulation-derived features. Together with bioinformatics data, such as sequence conservation and folding energies, we were able to predict with reasonable accuracy (≈75%) which KCNH2 variants do not traffic normally. We conclude that structure-based simulations of KCNH2 variants localized to the Kv11.1 channel PASD led to an improvement in classification accuracy. Therefore, this approach should be considered to complement the classification of variant of unknown significance (VUS) in the Kv11.1 channel PASD.

Financing and Reimbursement Models for Personalised Medicine: A Systematic Review to Identify Current Models and Future Options

BACKGROUND

The number of healthcare interventions described as 'personalised medicine' (PM) is increasing rapidly. As healthcare systems struggle to decide whether to fund PM innovations, it is unclear what models for financing and reimbursement are appropriate to apply in this context.

OBJECTIVE

To review financing and reimbursement models for PM, summarise their key characteristics, and describe whether they can influence the development and uptake of PM.

METHODS

A literature review was conducted in Medline, Embase, Web of Science, and Econlit to identify studies published in English between 2009 and 2021, and reviews published before 2009. Grey literature was identified through Google Scholar, Google and subject-specific webpages. Articles that described financing and reimbursement of PM, and financing of non-PM were included. Data were extracted and synthesised narratively to report on the models, as well as facilitators, incentives, barriers and disincentives that could influence PM development and uptake.

RESULTS

One hundred and fifty-three papers were included. Research and development of PM was financed through both public and private sources and reimbursed largely through traditional models such as single fees, Diagnosis-Related Groups, and bundled payments. Financial-based reimbursement, including rebates and price-volume agreements, was mainly applied to targeted therapies. Performance-based reimbursement was identified mainly for gene and targeted therapies, and some companion diagnostics. Gene therapy manufacturers offered outcome-based rebates for treatment failure for interventions including Luxturna®, Kymriah®, Yescarta®, Zynteglo®, Zolgensma® and Strimvelis®, and coverage with evidence development for Kymriah® and Yescarta®. Targeted testing with OncotypeDX® was granted value-based reimbursement through initial coverage with evidence development. The main barriers and disincentives to PM financing and reimbursement were the lack of strong links between stakeholders and the lack of demonstrable benefit and value of PM.

CONCLUSIONS

Public-private financing agreements and performance-based reimbursement models could help facilitate the development and uptake of PM interventions with proven clinical benefit.

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization

INTRODUCTION

The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders.

METHODS

We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017. An ensemble of ensemble (EoE) machine learning (ML) system composed of a supervised and an unsupervised ML layers was developed to generate a prediction model for CRT response.

RESULTS

We compared the performance of EoE against traditional ML methods and the state-of-the-art convolutional neural network (CNN) model trained on raw electrocardiographic (ECG) waveforms. We observed that the models exhibited improvement in performance as more features were incrementally used for training. Using the most comprehensive set of predictors, the performance of the EoE model in terms of the area under the receiver operating characteristic curve and F1-score were 0.76 and 0.73, respectively. Direct application of the CNN model on the raw ECG waveforms did not generate promising results.

CONCLUSION

The proposed CRT risk calculator effectively discriminates which heart failure (HF) patient is likely to respond to CRT significantly better than using clinical guidelines and traditional ML methods, thus suggesting that the tool can enhanced care management of HF patients by helping to identify high-risk patients.

Visualising the knowledge structure and evolution of wearable device research

In recent years, the literature associated with wearable devices has grown rapidly, but few studies have used bibliometrics and a visualisation approach to conduct deep mining and reveal a panorama of the wearable devices field. To explore the foundational knowledge and research hotspots of the wearable devices field, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the distribution of countries, a keyword co-occurrence analysis, theme evolution analysis and research hotspots and trends for the future. By conducting a literature content analysis and structure analysis, we found the following: (a) The subject evolution path includes sensor research, sensitivity research and multi-functional device research. (b) Wearable device research focuses on information collection, sensor materials, manufacturing technology and application, artificial intelligence technology application, energy supply and medical applications. The future development trend will be further studied in combination with big data analysis, telemedicine and personalised precision medical application.

A new approach to the clinical subclassification of heart failure with preserved ejection fraction

OBJECTIVE

Heart failure with preserved ejection (HFpEF) represents nearly half of all patients with heart failure (HF). The objective of this study was to determine whether patient characteristics identify discrete kinds of HFpEF.

METHODS

Data were collected on 196 patients with HFpEF in a non-hospitalized setting. Clinical and laboratory variables were collected, and 47 candidate variables were examined by the unsupervised clustering strategy partitioning around medoids. The Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score was calculated. Follow-up data on all-cause mortality, cardiovascular mortality, and HF exacerbation, were collected and were not part of the data used to identify subgroups.

RESULTS

Six significantly different groups or clusters were found. There were three groups of women (i) individuals with a low proportion of vascular risk factors (HFpEF₁) (ii) individuals with a high proportion of hypertension and diabetes, but lower proportion of kidney disease and diastolic dysfunction (HFpEF₃₎ (iii) older individuals with high rates of atrial fibrillation (AF), chronic kidney disease. They had the worst long-term outcomes (HFpEF₄). There were three groups of men (i) individuals with a high proportion of coronary artery disease (CAD), dyslipidemia, higher serum creatinine, and diastolic dysfunction (HFpEF₂)(ii) individuals with highest BMI, and high proportion of CAD, obstructive sleep apnea, and poorly controlled diabetes (HFpEF₅) (iii) individuals with high rates of AF, elevated BNP, biventricular remodeling (HFpEF₆). They had a high cardiovascular mortality.

CONCLUSIONS

HFpEF consists of a heterogenous group of individuals with six distinct clinical subsets that have different long-term outcomes.

Importance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach

BACKGROUND

Despite all having systolic heart failure and broad QRS intervals, patients screened for cardiac resynchronization therapy (CRT) are highly heterogeneous, and it remains extremely challenging to predict the impact of CRT devices on left ventricular function and outcomes. The aim of this study was to evaluate the relative impact of clinical, electrocardiographic, and echocardiographic data on the left ventricular remodeling and prognosis of CRT candidates by the application of machine learning approaches.

METHODS

One hundred ninety-three patients with systolic heart failure receiving CRT according to current recommendations were prospectively included in this multicenter study. A combination of the Boruta algorithm and random forest methods was used to identify features predicting both CRT volumetric response and prognosis. Model performance was tested using the area under the receiver operating characteristic curve. The k-medoid method was also applied to identify clusters of phenotypically similar patients.

RESULTS

From 28 clinical, electrocardiographic, and echocardiographic variables, 16 features were predictive of CRT response, and 11 features were predictive of prognosis. Among the predictors of CRT response, eight variables (50%) pertained to right ventricular size or function. Tricuspid annular plane systolic excursion was the main feature associated with prognosis. The selected features were associated with particularly good prediction of both CRT response (area under the curve, 0.81; 95% CI, 0.74-0.87) and outcomes (area under the curve, 0.84; 95% CI, 0.75-0.93). An unsupervised machine learning approach allowed the identification of two phenogroups of patients who differed significantly in clinical variables and parameters of biventricular size and right ventricular function. The two phenogroups had significantly different prognosis (hazard ratio, 4.70; 95% CI, 2.1-10.0; P < .0001; log-rank P < .0001).

CONCLUSIONS

Machine learning can reliably identify clinical and echocardiographic features associated with CRT response and prognosis. The evaluation of both right ventricular size and functional parameters has pivotal importance for the risk stratification of CRT candidates and should be systematically performed in patients undergoing CRT.

Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2 KCNH2 (hERG) Mutations and Identifying New Patients

Significant advances in our understanding of the molecular mechanisms that cause congenital long QT syndrome (LQTS) have been made. A wide variety of experimental approaches, including heterologous expression of mutant ion channel proteins and the use of inducible pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) from LQTS patients offer insights into etiology and new therapeutic strategies. This review briefly discusses the major molecular mechanisms underlying LQTS type 2 (LQT2), which is caused by loss-of-function (LOF) mutations in the KCNH2 gene (also known as the human ether-à-go-go-related gene or hERG). Almost half of suspected LQT2-causing mutations are missense mutations, and functional studies suggest that about 90% of these mutations disrupt the intracellular transport, or trafficking, of the KCNH2-encoded Kv11.1 channel protein to the cell surface membrane. In this review, we discuss emerging strategies that improve the trafficking and functional expression of trafficking-deficient LQT2 Kv11.1 channel proteins to the cell surface membrane and how new insights into the structure of the Kv11.1 channel protein will lead to computational approaches that identify which KCNH2 missense variants confer a high-risk for LQT2.

Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2 KCNH2 (hERG) Mutations and Identifying New Patients

Significant advances in our understanding of the molecular mechanisms that cause congenital long QT syndrome (LQTS) have been made. A wide variety of experimental approaches, including heterologous expression of mutant ion channel proteins and the use of inducible pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) from LQTS patients offer insights into etiology and new therapeutic strategies. This review briefly discusses the major molecular mechanisms underlying LQTS type 2 (LQT2), which is caused by loss-of-function (LOF) mutations in the KCNH2 gene (also known as the human ether-à-go-go-related gene or hERG). Almost half of suspected LQT2-causing mutations are missense mutations, and functional studies suggest that about 90% of these mutations disrupt the intracellular transport, or trafficking, of the KCNH2-encoded Kv11.1 channel protein to the cell surface membrane. In this review, we discuss emerging strategies that improve the trafficking and functional expression of trafficking-deficient LQT2 Kv11.1 channel proteins to the cell surface membrane and how new insights into the structure of the Kv11.1 channel protein will lead to computational approaches that identify which KCNH2 missense variants confer a high-risk for LQT2.

Fractal Complexity of Daily Physical Activity Patterns Differs With Age Over the Life Span and Is Associated With Mortality in Older Adults

BACKGROUND

Accelerometers are included in a wide range of devices that monitor and track physical activity for health-related applications. However, the clinical utility of the information embedded in their rich time-series data has been greatly understudied and has yet to be fully realized. Here, we examine the potential for fractal complexity of actigraphy data to serve as a clinical biomarker for mortality risk.

METHODS

We use detrended fluctuation analysis (DFA) to analyze actigraphy data from the National Health and Nutrition Examination Survey (NHANES; n = 11,694). The DFA method measures fractal complexity (signal self-affinity across time-scales) as correlations between the amplitude of signal fluctuations in time-series data across a range of time-scales. The slope, α, relating the fluctuation amplitudes to the time-scales over which they were measured describes the complexity of the signal.

RESULTS

Fractal complexity of physical activity (α) decreased significantly with age (p = 1.29E-6) and was lower in women compared with men (p = 1.79E-4). Higher levels of moderate-to-vigorous physical activity in older adults and in women were associated with greater fractal complexity. In adults aged 50-79 years, lower fractal complexity of activity (α) was associated with greater mortality (hazard ratio = 0.64; 95% confidence interval = 0.49-0.82) after adjusting for age, exercise engagement, chronic diseases, and other covariates associated with mortality.

CONCLUSIONS

Wearable accelerometers can provide a noninvasive biomarker of physiological aging and mortality risk after adjusting for other factors strongly associated with mortality. Thus, this fractal analysis of accelerometer signals provides a novel clinical application for wearable accelerometers, advancing efforts for remote monitoring of physiological health by clinicians.

A high-throughput and untargeted lipidomics approach reveals new mechanistic insight and the effects of salvianolic acid B on the metabolic profiles in coronary heart disease rats using ultra-performance liquid chromatography with mass spectrometry

High-throughput lipidomics provides the possibility for the development of new therapeutic drugs. Accordingly, herein, we reveal the protective role of salvianolic acid B (Sal B) in rats with coronary heart disease (CHD) and propose a new mechanism for its action through a high-throughput and non-targeted lipidomics strategy. A CHD animal model was induced by consecutive high-fat diet feeding with vitamin D3 injection. At the end of the 8th week, the serum sample was analyzed to explore the metabolic biomarker and pathway changes using untargeted lipidomics based on ultra-performance liquid chromatography with mass spectrometry (UPLC/MS). In addition, blood and heart tissue samples were collected and processed for the detection of biochemical indicators and liver histological observation. After salvianolic acid B treatment, the levels of LDH, CK, CK-MB, MYO, CTn1, TG, TC, LDL-c, and Apo(b) were significantly lower than that in the model group, while the levels of HDL-c and Apo(a1) were significantly higher than that in the model group. Furthermore, the histological features of fibrosis and steatosis were also evidently relieved in the model group. A total of twenty-six potential biomarkers were identified to express the lipid metabolic turbulence in the CHD animal models, of which twenty-two were regulated by salvianolic acid B trending to the normal state, including TG(20:0/20:4/o-18:0), PC(20:4/18:1(9Z)), PC(18:3/20:2), PA(18:0/18:2), LysoPE(18:2/0:0), SM(d18:0/22:1), PE(22:6/0:0), LysoPE (20:4/0:0), sphinganine, Cer(d18:0/18:0), PS(14:0/14:1), PC (18:0/16:0), LysoPC(17:0), PE(22:2/20:1), PC(20:3/20:4), PE(20:4/P-16:0), PS(20:3/18:0), cholesterol sulfate, TG(15:0/22:6/18:1), prostaglandin E2, arachidonic acid and sphingosine-1-phosphate. According to the metabolite enrichment and pathway analyses, the pharmacological activity of salvianolic acid B on CHD is mainly involved in three vital metabolic pathways including glycerophospholipid metabolism, sphingolipid metabolism and arachidonic acid metabolism. Thus, based on the lipidomics-guided biochemical analysis of the lipid biomarkers and pathways, Sal B protects against CHD with good therapeutic effect by regulating glycerophospholipid metabolism, sphingolipid metabolism and arachidonic acid metabolism, inhibiting oxidative stress damage and lipid peroxidation.

High-throughput lipidomics provides the possibility for the development of new therapeutic drugs.

Genetic considerations for adults with congenital heart disease

Congenital heart disease (CHD) remains the most common birth defect, with an estimated incidence of approximately 1% of all births. The population of adults with CHD is growing rapidly with advances in medical care. Overall survival to adulthood in the current era estimated to exceed 90%. Genetic causes of CHD can be classified into several broad categories: (a) chromosomal aneuploidy, (b) large chromosomal deletion or duplication, (c) single gene mutation, and (d) copy number variation. However, only 20-30% of CHD cases have an established etiology characterized by either genetic abnormalities or environmental factors. The role of genetics in the field of adult CHD is only increasing. More adult patients with CHD are seeking genetic counseling to understand the etiology of their underlying CHD and the risks to future offspring. A multidisciplinary approach is essential to provide appropriate counseling to patients regarding indications for genetic testing and interpretations of results. Novel advances with precision medicine may soon enable clinicians to individualize therapies for a comprehensive approach to the care of adult patients with CHD.

Immune cell phenotyping in low blood volumes for assessment of cardiovascular disease risk, development, and progression: a pilot study

Background

Cardiovascular disease (CVD) is the leading cause of death in the world. Given the role of immune cells in atherosclerosis development and progression, effective methods for characterizing immune cell populations are needed, particularly among populations disproportionately at risk for CVD.

Results

By using a variety of antibodies combined in one staining protocol, we were able to identify granulocyte, lymphocyte, and monocyte sub-populations by CD-antigen expression from 500 µl of whole blood, enabling a more extensive comparison than what is possible with a complete blood count and differential (CBC). The flow cytometry panel was established and tested in a total of 29 healthy men and women. As a proof of principle, these 29 samples were split by their race/ethnicity: African-Americans (AA) (N = 14) and Caucasians (N = 15). We found in accordance with the literature that AA had fewer granulocytes and more lymphocytes when compared to Caucasians, though the proportion of total monocytes was similar in both groups. Several new differences between AA and Caucasians were noted that had not been previously described. For example, AA had a greater proportion of platelet adhesion on non-classical monocytes when compared to Caucasians, a cell-to-cell interaction described as crucially important in CVD. We also examined our flow panel in a clinical population of AA women with known CVD risk factors (N = 20). Several of the flow cytometry parameters that cannot be measured with the CBC displayed correlations with clinical CVD risk markers. For instance, Framingham Risk Score (FRS) calculated for each participant correlated with immune cell platelet aggregates (PA) (e.g. T cell PA β = 0.59, p = 0.03 or non-classical monocyte PA β = 0.54, p = 0.02) after adjustment for body mass index (BMI).

Conclusion

A flow cytometry panel identified differences in granulocytes, monocytes, and lymphocytes between AA and Caucasians which may contribute to increased CVD risk in AA. Moreover, this flow panel identifies immune cell sub-populations and platelet aggregates associated with CVD risk. This flow cytometry panel may serve as an effective method for phenotyping immune cell populations involved in the development and progression of CVD.

Precision Medicine and Exercise Therapy in Duchenne Muscular Dystrophy

Precision medicine is being discussed and incorporated at all levels of health care and disease prevention, management, and treatment. Key components include new taxonomies of disease classification, the measurement and incorporation of genetics and "omics" data, biomarkers, and health care professionals who can optimize this information for a precision approach to treatment. The study and treatment of Duchenne Muscular Dystrophy is making rapid advances in these areas in addition to rapid advances in new gene and cell-based therapies. New therapies will increase the variability in disease severity, furthering a need for a precision-based approach. An area of therapy that is rarely considered in this approach is how the physiology of muscle contractions will interact with these therapies and a precision approach. As muscle pathology improves, physical activity levels will increase, which will likely be very beneficial to some patients but likely not to all. Physical activity is likely to synergistically improve these therapies and can be used to enhance muscle health and quality of life after these therapies are delivered using the tools of precision medicine.

RNA Therapeutics in Cardiovascular Precision Medicine

Since our knowledge on structure and function of messenger RNA (mRNA) has expanded from merely being an intermediate molecule between DNA and proteins to the notion that RNA is a dynamic gene regulator that can be modified and edited, RNA has become a focus of interest into developing novel therapeutic schemes. Therapeutic modulation of RNA molecules by DNA- and RNA-based therapies has broadened the scope of therapeutic targets in infectious diseases, cancer, neurodegenerative diseases and most recently in cardiovascular diseases as well. Currently, antisense oligonucleotides (ASO), small interfering RNAs (siRNAs), and microRNAs are the most widely applied therapeutic strategies to target RNA molecules and regulate gene expression and protein production. However, a number of barriers have to be overcome including instability, inadequate binding affinity and delivery to the tissues, immunogenicity, and off-target toxicity in order for these agents to evolve into efficient drugs. As cardiovascular diseases remain the leading cause of mortality worldwide, a large number of clinical trials are under development investigating the safety and efficacy of RNA therapeutics in clinical conditions such as familial hypercholesterolemia, diabetes mellitus, hypertriglyceridemia, cardiac amyloidosis, and atrial fibrillation. In this review, we summarize the clinical trials of RNA-targeting therapies in cardiovascular disease and critically discuss the advances, the outcomes, the limitations and the future directions of RNA therapeutics in precision transcriptomic medicine.

SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets

Owing to the rapid and robust clinical effects, electroconvulsive therapy (ECT) represents an optimal model to develop and test treatment predictors for major depressive disorders (MDDs), whereas imaging markers can be informative in identifying MDD patients who will respond to a specific antidepressant treatment or not. Here we aim to predict post-ECT depressive rating changes and remission status using pre-ECT gray matter (GM) in 38 MDD patients and validate in two independent data sets. Six GM regions including the right hippocampus/parahippocampus, right orbitofrontal gyrus, right inferior temporal gyrus (ITG), left postcentral gyrus/precuneus, left supplementary motor area, and left lingual gyrus were identified as predictors of ECT response, achieving accuracy of 89, 90 and 86% for remission prediction in three independent, age-matched data sets, respectively. For MDD patients, GM density increases only in the left supplementary motor cortex and left postcentral gyrus/precuneus after ECT. These results suggest that treatment-predictive and treatment-responsive regions may be anatomically different but functionally related in the context of ECT response. To the best of our knowledge, this is the first attempt to quantitatively identify and validate the ECT treatment biomarkers using multi-site GM data. We address a major clinical challenge and provide potential opportunities for more effective and timely interventions for electroconvulsive treatment.

Cardiovascular Genetic Risk Testing for Targeting Statin Therapy in the Primary Prevention of Atherosclerotic Cardiovascular Disease: A Cost-Effectiveness Analysis

BACKGROUND

It is unclear whether testing for novel risk factors, such as a cardiovascular genetic risk score (cGRS), improves clinical decision making or health outcomes when used for targeting statin initiation in the primary prevention of atherosclerotic cardiovascular disease (ASCVD). Our objective was to estimate the cost-effectiveness of cGRS testing to inform clinical decision making about statin initiation in individuals with low-to-intermediate (2.5%-7.5%) 10-year predicted risk of ASCVD.

METHODS AND RESULTS

We evaluated the cost-effectiveness of testing for a 27-single-nucleotide polymorphism cGRS comparing 4 test/treat strategies: treat all, treat none, test/treat if cGRS is high, and test/treat if cGRS is intermediate or high. We tested a set of clinical scenarios of men and women, aged 45 to 65 years, with 10-year ASCVD risks between 2.5% and 7.5%. Our primary outcome measure was cost per quality-adjusted life-year gained. Under base case assumptions for statin disutility and cost, the preferred strategy is to treat all patients with ASCVD risk >2.5% without cGRS testing. For certain clinical scenarios, such as a 57-year-old man with a 10-year ASCVD risk of 7.5%, cGRS testing can be cost-effective under a limited set of assumptions; for example, when statins cost $15 per month and statin disutility is 0.013 (ie, willing to trade 3 months of life in perfect health to avoid 20 years of statin therapy), the preferred strategy (using a willingness-to-pay threshold of $50 000 per quality-adjusted life-year gained) is to test and treat if cGRS is intermediate or high. Overall, the results were not sensitive to assumptions about statin efficacy and harms.

CONCLUSIONS

Testing for a 27-single-nucleotide polymorphism cGRS is generally not a cost-effective approach for targeting statin therapy in the primary prevention of ASCVD for low- to intermediate-risk patients.

Vocalization Subsystem Responses to a Temporarily Induced Unilateral Vocal Fold Paralysis

PURPOSE

The purpose of this study is to quantify the interactions of the 3 vocalization subsystems of respiration, phonation, and resonance before, during, and after a perturbation to the larynx (temporarily induced unilateral vocal fold paralysis) in 10 vocally healthy participants. Using dynamic systems theory as a guide, we hypothesized that data groupings would emerge revealing context-dependent patterns in the relationships of variables representing the 3 vocalization subsystems. We also hypothesized that group data would mask important individual variability important to understanding the relationships among the vocalization subsystems.

METHOD

A perturbation paradigm was used to obtain respiratory kinematic, aerodynamic, and acoustic formant measures from 10 healthy participants (8 women, 2 men) with normal voices. Group and individual data were analyzed to provide a multilevel analysis of the data. A 3-dimensional state space model was constructed to demonstrate the interactive relationships among the 3 subsystems before, during, and after perturbation.

RESULTS

During perturbation, group data revealed that lung volume initiations and terminations were lower, with longer respiratory excursions; airflow rates increased while subglottic pressures were maintained. Acoustic formant measures indicated that the spacing between the upper formants decreased (F3-F5), whereas the spacing between F1 and F2 increased. State space modeling revealed the changing directionality and interactions among the 3 subsystems.

CONCLUSIONS

Group data alone masked important variability necessary to understand the unique relationships among the 3 subsystems. Multilevel analysis permitted a richer understanding of the individual differences in phonatory regulation and permitted subgroup analysis. Dynamic systems theory may be a useful heuristic to model the interactive relationships among vocalization subsystems.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.5913532.

The impact of creating mathematical formula to predict cardiovascular events in patients with heart failure

Since our retrospective study has formed a mathematical formula, α = f(x₁, …, x₂₅₂), where α is the probability of cardiovascular events in patients with heart failure (HF) and x₁ is each clinical parameter, we prospectively tested the predictive capability and feasibility of the mathematical formula of cardiovascular events in HF patients. First of all, to create such a mathematical formula using limited number of the parameters to predict the cardiovascular events in HF patients, we retrospectively determined f(x) that formulates the relationship between the most influential 50 clinical parameters (x) among 252 parameters using 167 patients hospitalized due to acute HF; the nonlinear optimization could provide the formula of α = f(x₁, …, x₅₀) which fitted the probability of the actual cardiovascular events per day. Secondly, we prospectively examined the predictability of f(x) in other 213 patients using 50 clinical parameters in 3 hospitals, and we found that the Kaplan–Meier curves using actual and estimated occurrence probabilities of cardiovascular events were closely correlated. We conclude that we created a mathematical formula f(x) that precisely predicted the occurrence probability of future cardiovascular outcomes of HF patients per day. Mathematical modelling may predict the occurrence probability of cardiovascular events in HF patients.

Palliative treatment of coronary "atherosclerotic cancer" by drug-eluting or bare-metal stents: From oculo-stenotic reflex period to age of precision medicine

Medications and treatments are said to have a palliative effect if they relieve symptoms without having a curative effect on the underlying disease such as atherosclerosis or cancer. Some authors speculated that atherosclerotic coronary artery disease (CAD) could be considered a "cancer of the coronary arterial wall". Although the percutaneous coronary intervention (PCI) has proven to be effective in decreasing mortality rates among patients with acute coronary syndromes, the previous meta-analyses of PCI versus optimal medical therapy for stable CAD have not been able to demonstrate a reduction in major adverse cardiac outcomes. However, few cardiologists discussed the evidence-based benefits of angiogram and PCI for stable CAD, and some implicitly or explicitly overstated the benefits. Recently, the precision medicine is defined as an evidence-based approach that uses innovative tools and biological and data science to customize disease prevention, detection, and treatment, and improve the effectiveness and quality of patient care. Providing patients with accurate and complete information appears to be an effective way to combat the reliance on the oculostenotic reflex. The foundation of precision medicine is the ability to tailor therapy based upon the expected risks and benefits of treatment for each individual patient. As said by Doctor William Osler, "The good physician treats the disease; the great physician treats the patient who has the disease."

Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial

BACKGROUND

Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to predict outcomes after CRT.

METHODS AND RESULTS

Models were developed with machine learning algorithms to predict all-cause mortality or heart failure hospitalization at 12 months post-CRT in the COMPANION trial (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure). The best performing model was developed with the random forest algorithm. The ability of this model to predict all-cause mortality or heart failure hospitalization and all-cause mortality alone was compared with discrimination obtained using a combination of bundle branch block morphology and QRS duration. In the 595 patients with CRT-defibrillator in the COMPANION trial, 105 deaths occurred (median follow-up, 15.7 months). The survival difference across subgroups differentiated by bundle branch block morphology and QRS duration did not reach significance (P=0.08). The random forest model produced quartiles of patients with an 8-fold difference in survival between those with the highest and lowest predicted probability for events (hazard ratio, 7.96; P<0.0001). The model also discriminated the risk of the composite end point of all-cause mortality or heart failure hospitalization better than subgroups based on bundle branch block morphology and QRS duration.

CONCLUSIONS

In the COMPANION trial, a machine learning algorithm produced a model that predicted clinical outcomes after CRT. Applied before device implant, this model may better differentiate outcomes over current clinical discriminators and improve shared decision-making with patients.

Genetics: Implications for Prevention and Management of Coronary Artery Disease

An exciting new era has dawned for the prevention and management of coronary artery disease (CAD) utilizing genetic risk variants. The recent identification of over 60 susceptibility loci for CAD confirms not only the importance of established risk factors, but also the existence of many novel causal pathways that are expected to improve our understanding of the genetic basis of CAD and facilitate the development of new therapeutic agents over time. Concurrently, Mendelian randomization studies have provided intriguing insights on the causal relationship between CAD-related traits, and highlight the potential benefits of long-term modifications of risk factors. Last, genetic risk scores of CAD may serve not only as prognostic, but also as predictive markers, and carry the potential to considerably improve the delivery of established prevention strategies. This review will summarize the evolution and discovery of genetic risk variants for CAD and their current and future clinical applications.

Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.

Precision Medicine for Heart Failure with Preserved Ejection Fraction: An Overview

There are few proven therapies for heart failure with preserved ejection fraction (HFpEF). The lack of therapies, along with increased recognition of the disorder and its underlying pathophysiology, has led to the acknowledgement that HFpEF is heterogeneous and is not likely to respond to a one-size-fits-all approach. Thus, HFpEF is a prime candidate to benefit from a precision medicine approach. For this reason, we have assembled a compendium of papers on the topic of precision medicine in HFpEF in the Journal of Cardiovascular Translational Research. These papers cover a variety of topics relevant to precision medicine in HFpEF, including automated identification of HFpEF patients; machine learning, novel molecular approaches, genomics, and deep phenotyping of HFpEF; and clinical trial designs that can be used to advance precision medicine in HFpEF. In this introductory article, we provide an overview of precision medicine in HFpEF with the hope that the work described here and in the other papers in this special theme issue will stimulate investigators and clinicians to advance a more targeted approach to HFpEF classification and treatment.

Utilizing nutritional genomics to tailor diets for the prevention of cardiovascular disease: a guide for upcoming studies and implementations

INTRODUCTION

Personalized diets based on an individual's genome to optimize the success of dietary intervention and reduce genetic cardiovascular disease (CVD) risk, is one of the challenges most frequently discussed in the scientific community. Areas covered: The authors gathered literature-based evidence on nutritional genomics and CVD phenotypes, our own results and research experience to provide a critical overview of the current situation of using nutritional genomics to tailor diets for CVD prevention and to propose guidelines for future studies and implementations. Expert commentary: Hundreds of studies on gene-diet interactions determining CVD intermediate (plasma lipids, hypertension, etc.) and final phenotypes (stroke, etc.) have furnished top-level scientific evidence for claiming that the genetic effect in cardiovascular risk is not deterministic, but can be modified by diet. However, despite the many results obtained, there are still gaps in practically applying a personalized diet design to specific genotypes. Hence, a better systemization and methodological improvement of new studies is required to obtain top-level evidence that will allow their application in the future precision nutrition/medicine. The authors propose several recommendations for tackling new approaches and applications.

Perspective on precision medicine in paediatric heart failure

In 2015, President Obama launched the Precision Medicine Initiative (PMI), which introduced new funding to a method of research with the potential to study rare and complex diseases. Paediatric heart failure, a heterogeneous syndrome affecting approximately 1 in 100000 children, is one such condition in which precision medicine techniques may be applied with great benefit. Current heart failure therapies target downstream effects of heart failure rather than the underlying cause of heart failure. As such, they are often ineffective in paediatric heart failure, which is typically of primary (e.g. genetic) rather than secondary (e.g. acquired) aetiology. It is, therefore, important to develop therapies that can target the causes of heart failure in children with greater specificity thereby decreasing morbidity, mortality and burden of illness on both patients and their families. The benefits of co-ordinated research in genomics, proteomics, metabolomics, transcriptomics and phenomics along with dietary, lifestyle and social factors have led to novel therapeutic and prognostic applications in other fields such as oncology. Applying such co-ordinated research efforts to heart failure constitutes an important step in advancing care and improving the lives of those affected.

Mechanisms and Materials of Flexible and Stretchable Skin Sensors

Wearable technology has attracted significant public attention and has generated huge societal and economic impact, leading to changes of both personal lifestyles and formats of healthcare. An important type of devices in wearable technology is flexible and stretchable skin sensors used primarily for biophysiological signal sensing and biomolecule analysis on skin. These sensors offer mechanical compatibility to human skin and maximum compliance to skin morphology and motion, demonstrating great potential as promising alternatives to current wearable electronic devices based on rigid substrates and packages. The mechanisms behind the design and applications of these sensors are numerous, involving profound knowledge about the physical and chemical properties of the sensors and the skin. The corresponding materials are diverse, featuring thin elastic films and unique stretchable structures based on traditional hard or ductile materials. In addition, the fabrication techniques that range from complementary metal-oxide semiconductor (CMOS) fabrication to innovative additive manufacturing have led to various sensor formats. This paper reviews mechanisms, materials, fabrication techniques, and representative applications of flexible and stretchable skin sensors, and provides perspective of future trends of the sensors in improving biomedical sensing, human machine interfacing, and quality of life.

Using Zebrafish for High-Throughput Screening of Novel Cardiovascular Drugs

Cardiovascular diseases remain a major challenge for modern drug discovery. The diseases are chronic, complex, and the result of sophisticated interactions between genetics and environment involving multiple cell types and a host of systemic factors. The clinical events are often abrupt, and the diseases may be asymptomatic until a highly morbid event. Target selection is often based on limited information, and though highly specific agents are often identified in screening, their final efficacy is often compromised by unanticipated systemic responses, a narrow therapeutic index, or substantial toxicities. Our understanding of complexity of cardiovascular disease has grown dramatically over the past 2 decades, and the range of potential disease mechanisms now includes pathways previously thought only tangentially involved in cardiac or vascular disease. Despite these insights, the majority of active cardiovascular agents derive from a remarkably small number of classes of agents and target a very limited number of pathways. These agents have often been used initially for particular indications and then discovered serendipitously to have efficacy in other cardiac disorders or in a manner unrelated to their original mechanism of action. In this review, the rationale for in vivo screening is described, and the utility of the zebrafish for this approach and for complementary work in functional genomics is discussed. Current limitations of the model in this setting and the need for careful validation in new disease areas are also described. An overview is provided of the complex mechanisms underlying most clinical cardiovascular diseases, and insight is offered into the limits of single downstream pathways as drug targets. The zebrafish is introduced as a model organism, in particular for cardiovascular biology. Potential approaches to overcoming the hurdles to drug discovery in the face of complex biology are discussed, including in vivo screening of zebrafish genetic disease models.

Early somatic mosaicism is a rare cause of long-QT syndrome

Somatic mosaicism, the occurrence and propagation of genetic variation in cell lineages after fertilization, is increasingly recognized to play a causal role in a variety of human diseases. We investigated the case of life-threatening arrhythmia in a 10-day-old infant with long QT syndrome (LQTS). Rapid genome sequencing suggested a variant in the sodium channel Na_V1.5 encoded by SCN5A, NM_000335:c.5284G > T predicting p.(V1762L), but read depth was insufficient to be diagnostic. Exome sequencing of the trio confirmed read ratios inconsistent with Mendelian inheritance only in the proband. Genotyping of single circulating leukocytes demonstrated the mutation in the genomes of 8% of patient cells, and RNA sequencing of cardiac tissue from the infant confirmed the expression of the mutant allele at mosaic ratios. Heterologous expression of the mutant channel revealed significantly delayed sodium current with a dominant negative effect. To investigate the mechanism by which mosaicism might cause arrhythmia, we built a finite element simulation model incorporating Purkinje fiber activation. This model confirmed the pathogenic consequences of cardiac cellular mosaicism and, under the presenting conditions of this case, recapitulated 2:1 AV block and arrhythmia. To investigate the extent to which mosaicism might explain undiagnosed arrhythmia, we studied 7,500 affected probands undergoing commercial gene-panel testing. Four individuals with pathogenic variants arising from early somatic mutation events were found. Here we establish cardiac mosaicism as a causal mechanism for LQTS and present methods by which the general phenomenon, likely to be relevant for all genetic diseases, can be detected through single-cell analysis and next-generation sequencing.

Stem Cells for Modeling and Therapy of Parkinson's Disease

Parkinson's disease (PD) is the second most frequent neurodegenerative disease after Alzheimer's disease, which is characterized by a low level of dopamine being expressing in the striatum and a deterioration of dopaminergic neurons (DAn) in the substantia nigra pars compacta. Generation of PD-derived DAn, including differentiation of human embryonic stem cells, human neural stem cells, human-induced pluripotent stem cells, and direct reprogramming, provides an ideal tool to model PD, creating the possibility of mimicking key essential pathological processes and charactering single-cell changes in vitro. Furthermore, thanks to the understanding of molecular neuropathogenesis of PD and new advances in stem-cell technology, it is anticipated that optimal functionally transplanted DAn with targeted correction and transgene-free insertion will be generated for use in cell transplantation. This review elucidates stem-cell technology for modeling PD and offering desired safe cell resources for cell transplantation therapy.

The P4 Health Spectrum - A Predictive, Preventive, Personalized and Participatory Continuum for Promoting Healthspan

Chronic diseases (i.e., noncommunicable diseases), mainly cardiovascular disease, cancer, respiratory diseases and type-2-diabetes, are now the leading cause of death, disability and diminished quality of life on the planet. Moreover, these diseases are also a major financial burden worldwide, significantly impacting the economy of many countries. Healthcare systems and medicine have progressively improved upon the ability to address infectious diseases and react to adverse health events through both surgical interventions and pharmacology; we have become efficient in delivering reactive care (i.e., initiating interventions once an individual is on the verge of or has actually suffered a negative health event). However, with slowly progressing and often 'silent' chronic diseases now being the main cause of illness, healthcare and medicine must evolve into a proactive system, moving away from a merely reactive approach to care. Minimal interactions among the specialists and limited information to the general practitioner and to the individual receiving care lead to a fragmented health approach, non-concerted prescriptions, a scattered follow-up and a suboptimal cost-effectiveness ratio. A new approach in medicine that is predictive, preventive, personalized and participatory, which we label here as "P4" holds great promise to reduce the burden of chronic diseases by harnessing technology and an increasingly better understanding of environment-biology interactions, evidence-based interventions and the underlying mechanisms of chronic diseases. In this concept paper, we propose a 'P4 Health Continuum' model as a framework to promote and facilitate multi-stakeholder collaboration with an orchestrated common language and an integrated care model to increase the healthspan.

Precision medicine in cardiology

The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance our understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including 'omic' data, exposome and social determinants of health, the microbiome, behaviours and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer a passive entity on whom measurements are made, but instead are central stakeholders who contribute data and participate actively in shared decision-making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment.