Emerging Role of Precision Medicine in Cardiovascular Disease

Estimating and presenting hazard ratios and absolute risks from a Cox model with complex nonlinear interactions

Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification.

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction

OBJECTIVES

Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications.

MATERIALS AND METHODS

A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions.

RESULTS

Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032).

DISCUSSION

These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions.

CONCLUSIONS

Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

Deep Diving Into the Cardiovascular Health Paradox: A Journey Towards Personalized Prevention

Objectives

The Life's Simple 7 score (LS7) promotes cardiovascular health (CVH). Despite this, some with optimal LS7 develop cardiovascular disease (CVD), while others with poor CVH do not, termed the "CVH paradox." This paper explores pathways explaining this paradox.

Methods

We examined methodological aspects: 1) misclassification bias in self-reported lifestyle factors (smoking, physical activity, diet); 2) cumulative exposure to risk factors over a lifetime, impacting the CVH paradox. Punctual risk factor assessments are suboptimal for predicting outcomes. We proposed personalized prevention using "novel" elements to refine CVH assessment: 1) subclinical vascular disease markers, 2) metabolic biomarkers in blood and urine, 3) emerging risk factors, 4) polygenic risk scores (PRS), 5) epigenetics, and 6) the exposome.

Results

Addressing the CVH paradox requires a multifaceted approach, reducing misclassification bias, considering cumulative risk exposure, and incorporating novel personalized prevention elements.

Conclusion

A holistic, individualized approach to CVH assessment and CVD prevention can better reduce cardiovascular outcomes and improve population health. Collaboration among researchers, healthcare providers, policymakers, and communities is essential for effective implementation and realization of these strategies.

Stratified medicine for acute and chronic coronary syndromes: A patient-tailored approach

The traditional approach to management of cardiovascular disease relies on grouping clinical presentations with common signs and symptoms into pre-specified disease pathways, all uniformly treated according to evidence-based guidelines ("one-size-fits-all"). The goal of precision medicine is to provide the right treatment to the right patients at the right time, combining data from time honoured sources (e.g., history, physical examination, imaging, laboratory) and those provided by multi-omics technologies. In patients with ischemic heart disease, biomarkers and intravascular assessment can be used to identify endotypes with different pathophysiology who may benefit from distinct treatments. This review discusses strategies for the application of stratified management to patients with acute and chronic coronary syndromes.

Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome- based identification to be successful and explored various features of these data.

Effect of timed dosing of usual antihypertensives according to patient chronotype on cardiovascular outcomes: the Chronotype sub-study cohort of the Treatment in Morning versus Evening (TIME) study

Background

Timing drug administration to endogenous circadian rhythms may enhance treatment efficacy. In the Chronotype sub-study of the Treatment in Morning versus Evening (TIME) clinical trial we examined whether timing of usual antihypertensive medications according to patient chronotype (a behavioural marker of personal circadian rhythm) may influence clinical cardiovascular outcomes.

Methods

This was a cohort sub-study of TIME, a prospective, randomised, open-label, blinded-endpoint, UK clinical trial of morning versus evening dosing of usual antihypertensive medications and cardiovascular outcomes. On August 3rd, 2020, all active TIME participants were invited to complete a validated chronotype questionnaire. Chronotype was quantitatively assessed as the mid sleep time on free days corrected for sleep debt on workdays (MSFsc). We analysed associations between chronotype and antihypertensive dosing time and explored their combined effect on cardiovascular outcomes (a composite endpoint of hospitalisation for non-fatal myocardial infarction (MI) or non-fatal stroke, and single components) using proportional hazard time-to-event models adjusted for baseline covariates. These were used to specifically test for interactions between dosing time and chronotype.

Findings

Between August 3, 2020, and March 31, 2021, 5358 TIME participants completed the online questionnaire. 2778 were previously randomised to morning dosing and 2580 to evening dosing of their usual antihypertensives. Chronotype was symmetrically distributed around a median MSFsc of 3:07 am. The composite endpoint increased for later MSFsc (later chronotype) dosed in the morning but not in those dosed in the evening (hazard ratios 1.46 [95% CI 1.14-1.86] and 0.96 [95% CI 0.70-1.30] per hour of MSFsc, respectively; interaction p = 0.036). Later chronotype was associated with increased risk of hospitalisation for non-fatal MI in the morning dosing group, and reduced risk in the evening dosing group (hazard ratios 1.62 [95% CI 1.18-2.22] and 0.66 [95% CI 0.44-1.00] per hour of MSFsc, respectively; interaction p < 0.001). No interaction between chronotype and antihypertensive dosing time was observed for stroke events.

Interpretation

Alignment of dosing time of usual antihypertensives with personal chronotype could lower the incidence of non-fatal MI compared to a 'misaligned' dosing time regimen. Future studies are warranted to establish whether synchronizing administration time of antihypertensive therapy with individual chronotype reduces risk of MI.

Funding

The TIME study was funded by the British Heart Foundation (CS/14/1/30659) with support from the British and Irish Hypertension Society.

Evaluating thrombosis risk and patient-specific treatment strategy using an atherothrombosis-on-chip model

Platelets play an essential role in thrombotic processes. Recent studies suggest a direct link between increased plasma glucose, lipids, and inflammatory cytokines with platelet activation and aggregation, resulting in an increased risk of atherothrombotic events in cardiovascular patients. Antiplatelet therapies are commonly used for the primary prevention of atherosclerosis. Transitioning from a population-based strategy to patient-specific care requires a better understanding of the risks and advantages of antiplatelet therapy for individuals. This proof-of-concept study evaluates the potential to assess an individual's risk of forming atherothrombosis using a dual-channel microfluidic model emulating multiple atherogenic factors in vitro, including high glucose, high cholesterol, and inflammatory cytokines along with stenosis vessel geometry. The model shows precise sensitivity toward increased plasma glucose, cholesterol, and tumour necrosis factor-alpha (TNF-α)-treated groups in thrombus formation. An in vivo-like dose-dependent increment in platelet aggregation is observed in different treated groups, benefiting the evaluation of thrombosis risk in the individual condition. Moreover, the model could help decide the effective dosing of aspirin in multi-factorial complexities. In the high glucose-treated group, a 50 μM dose of aspirin could significantly reduce platelet aggregation, while a 100 μM dose of aspirin was required to reduce platelet aggregation in the glucose-TNF-α-treated group, which proves the model's potentiality as a tailored tool for customised therapy.

Bile Acids and Bilirubin Role in Oxidative Stress and Inflammation in Cardiovascular Diseases

Bile acids (BAs) and bilirubin, primarily known for their role in lipid metabolism and as heme catabolite, respectively, have been found to have diverse effects on various physiological processes, including oxidative stress and inflammation. Indeed, accumulating evidence showed that the interplay between BAs and bilirubin in these processes involves intricate regulatory mechanisms mediated by specific receptors and signaling pathways under certain conditions and in specific contexts. Oxidative stress plays a significant role in the development and progression of cardiovascular diseases (CVDs) due to its role in inflammation, endothelial dysfunction, hypertension, and other risk factors. In the cardiovascular (CV) system, recent studies have suggested that BAs and bilirubin have some opposite effects related to oxidative and inflammatory mechanisms, but this area of research is still under investigation. This review aims to introduce BAs and bilirubin from a biochemical and physiological point of view, emphasizing their potential protective or detrimental effects on CVDs. Moreover, clinical studies that have assessed the association between BAs/bilirubin and CVD were examined in depth to better interpret the possible link between them.

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.

Genetics of Cardiac Tumours: A Narrative Review

Cardiac tumours can occur in association with genetic syndromes. Rhabdomyomas have been reported in association with tuberous sclerosis, myxomas with Carney's complex, cardiac fibromas with Gorlin syndrome, and paragangliomas with multiple endocrine neoplasm syndrome. The presentation and prognosis of cardiac tumours associated with genetic syndromes differ compared with sporadic cases. Knowledge about the associated syndromes' genetic features and extracardiac manifestations is essential for the diagnosis, prognosis, and management of cardiac neoplasms. Moreover, identifying genetic mutations in benign and malignant cardiac tumours is needed to personalise management and improve treatment outcomes. Thus, this review discusses the genetic abnormalities associated with cardiac tumours, the current genetic screening recommendations, and the effect of those genetic mutations on the outcomes.

Precision Cardio-oncology: Update on Omics-Based Diagnostic Methods

OPINION STATEMENT

Cardio-oncology is an emerging interdisciplinary field dedicated to the early detection and treatment of adverse cardiovascular events associated with anticancer treatment, and current clinical management of anticancer-treatment-related cardiovascular toxicity (CTR-CVT) remains limited by a lack of detailed phenotypic data. However, the promise of diagnosing CTR-CVT using deep phenotyping has emerged with the development of precision medicine, particularly the use of omics-based methodologies to discover sensitive biomarkers of the disease. In the future, combining information produced by a variety of omics methodologies could expand the clinical practice of cardio-oncology. In this review, we demonstrate how omics approaches can improve our comprehension of CTR-CVT deep phenotyping, discuss the positive and negative aspects of available omics approaches for CTR-CVT diagnosis, and outline how to integrate multiple sets of omics data into individualized monitoring and treatment. This will offer a reliable technical route for lowering cardiovascular morbidity and mortality in cancer patients and survivors.

Absorbed Bioactive Compounds Replicate Guanxin II-Induced Endothelium-Associated in/ex vivo Vasodilation

OBJECTIVE

To develop an interference-free and rapid method to elucidate Guanxin II (GX II)'s representative vasodilator absorbed bioactive compounds (ABCs) among enormous phytochemicals.

METHODS

The contents of ferulic acid, tanshinol, and hydroxysafflor yellow A (FTA) in GX II/rat serum after the oral administration of GX II (30 g/kg) were detected using ultra-performance liquid chromatography-mass spectrometry. Totally 18 rats were randomly assigned to the control group (0.9% normal saline), GX II (30 g/kg) and FTA (5, 28 and 77 mg/kg) by random number table method. Diastolic coronary flow velocity-time integral (VTI), i.e., coronary flow or coronary flow-mediated dilation (CFMD), and endothelium-intact vascular tension of isolated aortic rings were measured. After 12 h of exposure to blank medium or 0.5 mmol/L H2O2, endothelial cells (ECs) were treated with post-dose GX II of supernatant from deproteinized serum (PGSDS, 300 µL PGSDS per 1 mL of culture medium) or FTA (237, 1539, and 1510 mg/mL) for 10 min as control, H2O2, PGSDS and FTA groups. Nitric oxide (NO), vascular endothelial growth factor (VEGF), endothelin-1 (ET-1), superoxide dismutase (SOD), malondialdehyde (MDA) and phosphorylated phosphoinositide 3 kinase (p-PI3K), phosphorylated protein kinase B (p-AKT), phosphorylated endothelial nitric oxide synthase (p-eNOS) were analyzed. PGSDS was developed as a GX II proxy of ex vivo herbal crude extracts.

RESULTS

PGSDS effectively eliminates false responses caused by crude GX II preparations. When doses equaled the contents in GX II/its post-dose serum, FTA accounted for 98.17% of GX II -added CFMD and 92.99% of PGSDS-reduced vascular tension. In ECs, FTA/PGSDS was found to have significant antioxidant (lower MDA and higher SOD, P<0.01) and endothelial function-protective (lower VEGF, ET-1, P<0.01) effects. The increases in aortic relaxation, endothelial NO levels and phosphorylated PI3K/Akt/eNOS protein induced by FTA/PGSDS were markedly abolished by NG-nitro-L-arginine methyl ester (L-NA, eNOS inhibitor) and wortmannin (PI3K/AKT inhibitor), respectively, indicating an endothelium-dependent vasodilation via the PI3K/AKT-eNOS pathway (P<0.01).

CONCLUSION

This study provides a strategy for rapidly and precisely elucidating GX II's representative in/ex vivo cardioprotective absorbed bioactive compounds (ABCs)-FTA, suggesting its potential in advancing precision ethnomedicine.

Multimodal Analytical Tools to Enhance Mechanistic Understanding of Aortic Valve Calcification

This review focuses on technologies at the core of calcific aortic valve disease (CAVD) and drug target research advancement, including transcriptomics, proteomics, and molecular imaging. We examine how bulk RNA sequencing and single-cell RNA sequencing have engendered organismal genomes and transcriptomes, promoting the analysis of tissue gene expression profiles and cell subpopulations, respectively. We bring into focus how the field is also largely influenced by increasingly accessible proteome profiling techniques. In unison, global transcriptional and protein expression analyses allow for increased understanding of cellular behavior and pathogenic pathways under pathologic stimuli including stress, inflammation, low-density lipoprotein accumulation, increased calcium and phosphate levels, and vascular injury. We also look at how direct investigation of protein signatures paves the way for identification of targetable pathways for pharmacologic intervention. Here, we note that imaging techniques, once a clinical diagnostic tool for late-stage CAVD, have since been refined to address a clinical need to identify microcalcifications using positron emission tomography/computed tomography and even detect in vivo cellular events indicative of early stage CAVD and map the expression of identified proteins in animal models. Together, these techniques generate a holistic approach to CAVD investigation, with the potential to identify additional novel regulatory pathways.

Nuclear Cardiology in the Era of Precision Medicine: Tailoring Treatment to the Individual Patient

Nuclear cardiology, employing advanced imaging technologies like positron emission tomography (PET) and single photon emission computed tomography (SPECT), is instrumental in diagnosing, risk stratifying, and managing heart diseases. Concurrently, precision medicine advocates for treatments tailored to each patient's genetic, environmental, and lifestyle specificities, promising a revolution in personalized cardiovascular care. This review explores the synergy between nuclear cardiology and precision medicine, highlighting advancements, potential enhancements in patient outcomes, and the challenges and opportunities of this integration. We examined the evolution of nuclear cardiology technologies, including PET and SPECT, and their role in cardiovascular diagnostics. We also delved into the principles of precision medicine, focusing on genetic and molecular profiling, data analytics, and individualized treatment strategies. The integration of these domains aims to optimize diagnostic accuracy, therapeutic interventions, and prognostic evaluations in cardiovascular care. Advancements in molecular imaging and the application of artificial intelligence in nuclear cardiology have significantly improved the precision of diagnostics and treatment plans. The adoption of precision medicine principles in nuclear cardiology enables the customization of patient care, leveraging genetic information and biomarkers for enhanced therapeutic outcomes. However, challenges such as data integration, accessibility, cost, and the need for specialized expertise persist. The confluence of nuclear cardiology and precision medicine offers a promising pathway toward revolutionizing cardiovascular healthcare, providing more accurate, effective, and personalized patient care. Addressing existing challenges and fostering interdisciplinary collaboration is crucial for realizing the full potential of this integration in improving patient outcomes.

Tailoring Treatment in Cardiovascular Diseases: The Role of Targeted Therapies

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality around the globe. To address this public health burden, innovative therapeutic agents are being developed to specifically target molecular and genetic markers. Various therapeutic modalities have been implemented, including vaccines, monoclonal or bispecific antibodies, and gene-based therapies. Such drugs precisely target the underlying disease pathophysiology, aiming at notable molecules such as lipid metabolism regulators, proinflammatory cytokines, and growth factors. This review focuses on the latest advancements in different targeted therapies. It provides an insightful overview of the current landscape of targeted cardiovascular therapies, highlighting promising strategies with potential to transform the treatment of CVDs into an era of precision medicine.

Arrhythmogenic Left Ventricular Cardiomyopathy: From Diagnosis to Risk Management

PURPOSE OF REVIEW

Left ventricular arrhythmogenic cardiomyopathy (ALVC) is a rare and poorly characterized cardiomyopathy that has recently been reclassified in the group of non-dilated left ventricular cardiomyopathies. This review aims to summarize the background, diagnosis, and sudden cardiac death risk in patients presenting this cardiomyopathy.

RECENT FINDINGS

Although there is currently a lack of data on this condition, arrhythmogenic left ventricular dysplasia can be considered a specific disease of the left ventricle (LV). We have collected the latest evidence about the management and the risks associated with this cardiomyopathy.

SUMMARY

Left ventricular arrhythmogenic cardiomyopathy is still poorly characterized. ALVC is characterized by fibrofatty replacement in the left ventricular myocardium, with variable phenotypic expression. Diagnosis is based on a multiparametric approach, including cardiac magnetic resonance (CMR) and genetic testing, and is important for sudden cardiac death (SCD) risk stratification and management. Recent guidelines have improved the management of left ventricular arrhythmogenic cardiomyopathy. Further studies are necessary to improve knowledge of this cardiomyopathy.

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.

Precision medicine in Myocardial Infarction With Non-obstructive Coronary Disease (MINOCA): A comprehensive review

Cardiovascular diseases, particularly myocardial infarction (MI), are a significant cause of mortality globally. Traditional MIs are commonly linked to substantial coronary artery blockage. However, a distinct subset of patients experience MI with non-obstructive coronary arteries, known as MINOCA. Imaging techniques, such as invasive coronary angiograms, are employed to diagnose MI or assess predisposition to one. Coronary angiograms help visualize vessel blockages; however, these blockages are absent in MINOCA cases, posing a diagnostic challenge. Precision medicine aims to introduce new diagnostic tools to assist in early diagnosis and further management of MINOCA. As percutaneous coronary intervention (PCI) does not benefit MINOCA patients, medical management tailored to the specific pathophysiological mechanism of MINOCA is employed. For example, if MINOCA is attributed to plaque disruption with or without plaque thrombus formation, the fundamental treatments may include statins, agents that modulate the renin-angiotensin system (RAS), and antiplatelet therapies. On the other hand, if coronary artery spasm is identified as the primary cause, essential intervention involves the use of calcium channel blockers. This approach has been previously utilized in patients with vasospastic angina and could be utilized in MINOCA, although research specific to MINOCA is ongoing. Therefore, the handling of MINOCA underscores the necessity for a tailored therapeutic strategy that corresponds to the underlying physiological mechanism responsible for the patient's clinical symptoms. Ongoing research initiatives are directed at expanding the availability of these treatments, uncovering new biomarkers, creating advanced diagnostic instruments, and establishing a more individualized approach for managing MINOCA patients.

Precision medicine: Hype or hope?

In recent years, precision medicine has steadily risen to the forefront of many aspects of medicine, including cardiology [1]. While this field has exponentially expanded and advanced in the last few years, a lot of questions remain regarding exact definition, usage, and clinical applications [2,3]. This review will provide a brief synopsis of the current state of precision medicine, its limitations, future directions, as well as analyze emerging clinical applications in cardiology.

AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score

Background

Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE).

Objectives

Create novel, hand-crafted EAT features, "fat-omics", to capture the pathophysiology of EAT and improve MACE prediction.

Methods

We segmented EAT using a previously-validated deep learning method with optional manual correction. We extracted 148 radiomic features (morphological, spatial, and intensity) and used Cox elastic-net for feature reduction and prediction of MACE.

Results

Traditional fat features gave marginal prediction (EAT-volume/EAT-mean-HU/BMI gave C-index 0.53/0.55/0.57, respectively). Significant improvement was obtained with 15 fat-omics features (C-index=0.69, test set). High-risk features included volume-of-voxels-having-elevated-HU-[-50, -30-HU] and HU-negative-skewness, both of which assess high HU, which as been implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, while high-risk group having HR 2.4 times that of the low-risk group (P<0.001).

Conclusion

Preliminary findings indicate an opportunity to use more finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction.

Role of Polyunsaturated Fat in Modifying Cardiovascular Risk Associated With Family History of Cardiovascular Disease: Pooled De Novo Results From 15 Observational Studies

BACKGROUND

It is unknown whether dietary intake of polyunsaturated fatty acids (PUFA) modifies the cardiovascular disease (CVD) risk associated with a family history of CVD. We assessed interactions between biomarkers of low PUFA intake and a family history in relation to long-term CVD risk in a large consortium.

METHODS

Blood and tissue PUFA data from 40 885 CVD-free adults were assessed. PUFA levels ≤25th percentile were considered to reflect low intake of linoleic, alpha-linolenic, and eicosapentaenoic/docosahexaenoic acids (EPA/DHA). Family history was defined as having ≥1 first-degree relative who experienced a CVD event. Relative risks with 95% CI of CVD were estimated using Cox regression and meta-analyzed. Interactions were assessed by analyzing product terms and calculating relative excess risk due to interaction.

RESULTS

After multivariable adjustments, a significant interaction between low EPA/DHA and family history was observed (product term pooled RR, 1.09 [95% CI, 1.02-1.16]; P=0.01). The pooled relative risk of CVD associated with the combined exposure to low EPA/DHA, and family history was 1.41 (95% CI, 1.30-1.54), whereas it was 1.25 (95% CI, 1.16-1.33) for family history alone and 1.06 (95% CI, 0.98-1.14) for EPA/DHA alone, compared with those with neither exposure. The relative excess risk due to interaction results indicated no interactions.

CONCLUSIONS

A significant interaction between biomarkers of low EPA/DHA intake, but not the other PUFA, and a family history was observed. This novel finding might suggest a need to emphasize the benefit of consuming oily fish for individuals with a family history of CVD.

Artificial Intelligence in Heart Failure: Friend or Foe?

In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.

Neuromuscular disorders in the omics era

Neuromuscular disorders encompass a spectrum of conditions characterized by primary lesions within the peripheral nervous system, which include the anterior horn cell, peripheral nerve, neuromuscular junction, and muscle. In pediatrics, most of these disorders are linked to genetic causes. Despite the considerable progress, the diagnosis of these disorders remains a challenging due to wide clinical presentation, disease heterogeneity and rarity. It is noteworthy that certain neuromuscular disorders, once deemed untreatable, can now be effectively managed through novel therapies. Biomarkers emerge as indispensable tools, serving as objective measures that not only refine diagnostic accuracy but also provide guidance for therapeutic decision-making and the ongoing monitoring of long-term outcomes. Herein a comprehensive review of biomarkers in neuromuscular disorders is provided. We highlight the role of omics-based technologies that further characterize neuromuscular pathophysiology as well as identify potential therapeutic targets to guide treatment strategies.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.

Genomic insights into heart health: Exploring the genetic basis of cardiovascular disease

Cardiovascular diseases (CVDs) are considered as the leading cause of death worldwide. CVD continues to be a major cause of death and morbidity despite significant improvements in its detection and treatment. Therefore, it is strategically important to be able to precisely characterize an individual's sensitivity to certain illnesses. The discovery of genes linked to cardiovascular illnesses has benefited from linkage analysis and genome-wide association research. The last 20 years have seen significant advancements in the field of molecular genetics, particularly with the development of new tools like genome-wide association studies. In this article we explore the profound impact of genetic variations on disease development, prognosis, and therapeutic responses. And the significance of genetics in cardiovascular risk assessment and the ever-evolving realm of genetic testing, offering insights into the potential for personalized medicine in this domain. Embracing the future of cardiovascular care, the article explores the implications of pharmacogenomics for tailored treatments, the promise of emerging technologies in cardiovascular genetics and therapies, including the transformative influence of nanotechnology. Furthermore, it delves into the exciting frontiers of gene editing, such as CRISPR/Cas9, as a novel approach to combat cardiovascular diseases. And also explore the potential of stem cell therapy and regenerative medicine, providing a holistic view of the dynamic landscape of cardiovascular genomics and its transformative potential for the field of cardiovascular medicine.

The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine

Cardiovascular diseases (CVD) are the leading cause of death worldwide and have an increasing impact on society. Precision medicine, in which optimal care is identified for an individual or a group of individuals rather than for the average population, might provide significant health benefits for this patient group and decrease CVD morbidity and mortality. Molecular imaging provides the opportunity to assess biological processes in individuals in addition to anatomical context provided by other imaging modalities and could prove to be essential in the implementation of precision medicine in CVD. New developments in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) systems, combined with rapid innovations in promising and specific radiopharmaceuticals, provide an impressive improvement of diagnostic accuracy and therapy evaluation. This may result in improved health outcomes in CVD patients, thereby reducing societal impact. Furthermore, recent technical advances have led to new possibilities for accurate image quantification, dynamic imaging, and quantification of radiotracer kinetics. This potentially allows for better evaluation of disease activity over time and treatment response monitoring. However, the clinical implementation of these new methods has been slow. This review describes the recent advances in molecular imaging and the clinical value of quantitative PET and SPECT in various fields in cardiovascular molecular imaging, such as atherosclerosis, myocardial perfusion and ischemia, infiltrative cardiomyopathies, systemic vascular diseases, and infectious cardiovascular diseases. Moreover, the challenges that need to be overcome to achieve clinical translation are addressed, and future directions are provided.

A signature of platelet reactivity in CBC scattergrams reveals genetic predictors of thrombotic disease risk

Genetic studies of platelet reactivity (PR) phenotypes may identify novel antiplatelet drug targets. However, such studies have been limited by small sample sizes (n < 5000) because of the complexity of measuring PR. We trained a model to predict PR from complete blood count (CBC) scattergrams. A genome-wide association study of this phenotype in 29 806 blood donors identified 21 distinct associations implicating 20 genes, of which 6 have been identified previously. The effect size estimates were significantly correlated with estimates from a study of flow cytometry-measured PR and a study of a phenotype of in vitro thrombus formation. A genetic score of PR built from the 21 variants was associated with the incidence rates of myocardial infarction and pulmonary embolism. Mendelian randomization analyses showed that PR was causally associated with the risks of coronary artery disease, stroke, and venous thromboembolism. Our approach provides a blueprint for using phenotype imputation to study the determinants of hard-to-measure but biologically important hematological traits.

Elevated Cardiac Biomarkers in Relatively Healthy U.S. Adults

Background

Despite the importance of early cardiovascular disease (CVD) intervention, little data exists for evaluating cardiovascular risk in adults without traditional CVD risk factors (e.g., diabetes, hypertension).

Methods

We included 4,544 adults from the 1999-2004 National Health and Nutrition Examination Survey without prevalent diabetes, hypertension, chronic kidney disease, or CVD. We used multi-variable adjusted Cox proportional hazards regression modeling to assess the relationship between logarithmically transformed cardiac biomarkers (high sensitivity cardiac troponin T (hs-cTnT), hs-cTnI (Abbott, Ortho, and Siemens assays), and NT-proBNP) and CVD mortality among a nationally representative cohort of relatively healthy adults.

Results

The mean age was 38.2 years (SD 12.8) and 53.9% were women. 8.7% had elevated levels of hs-cTnT or NT-proBNP above previously established thresholds for subclinical CVD. In multivariable adjusted models, each doubling of hs-cTnT was associated with a 49% increased risk of CVD mortality (Hazard Ratio (HR) 1.49, 95%CI 1.02-2.17, p=0.04). Only two of the hs-cTnI assays (Abbott and Ortho) were significantly associated with CVD mortality (Abbott HR 1.48, 95%CI 1.06-2.07, p=0.02; Ortho HR 1.47, 95%CI 1.23-1.77, p=0.0001). Each doubling of NT-proBNP was associated with a 41% increased risk of CVD mortality (HR 1.38, 95%CI 1.09-1.74, p=0.008).

Conclusion

Younger patients who maintain relatively good health may still carry occult CVD risk. Efforts to reduce population-wide CVD should consider novel methods for risk stratification, as standard CVD risk factors may overlook subpopulations at risk.

Multiomics Analysis Provides Novel Pathways Related to Progression of Heart Failure

BACKGROUND

Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies.

OBJECTIVES

We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death.

METHODS

We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients.

RESULTS

The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro-B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients.

CONCLUSIONS

A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin.

RNA modification in cardiovascular disease: implications for therapeutic interventions

Cardiovascular disease (CVD) is the leading cause of death in the world, with a high incidence and a youth-oriented tendency. RNA modification is ubiquitous and indispensable in cell, maintaining cell homeostasis and function by dynamically regulating gene expression. Accumulating evidence has revealed the role of aberrant gene expression in CVD caused by dysregulated RNA modification. In this review, we focus on nine common RNA modifications: N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), N7-methylguanosine (m7G), N4-acetylcytosine (ac4C), pseudouridine (Ψ), uridylation, adenosine-to-inosine (A-to-I) RNA editing, and modifications of U34 on tRNA wobble. We summarize the key regulators of RNA modification and their effects on gene expression, such as RNA splicing, maturation, transport, stability, and translation. Then, based on the classification of CVD, the mechanisms by which the disease occurs and progresses through RNA modifications are discussed. Potential therapeutic strategies, such as gene therapy, are reviewed based on these mechanisms. Herein, some of the CVD (such as stroke and peripheral vascular disease) are not included due to the limited availability of literature. Finally, the prospective applications and challenges of RNA modification in CVD are discussed for the purpose of facilitating clinical translation. Moreover, we look forward to more studies exploring the mechanisms and roles of RNA modification in CVD in the future, as there are substantial uncultivated areas to be explored.

Panoramic on Epigenetics in Coronary Artery Disease and the Approach of Personalized Medicine

Epigenetic modifications play a fundamental role in the progression of coronary artery disease (CAD). This panoramic review aims to provide an overview of the current understanding of the epigenetic mechanisms involved in CAD pathogenesis and highlights the potential implications for personalized medicine approaches. Epigenetics is the study of heritable changes that do not influence alterations in the DNA sequence of the genome. It has been shown that epigenetic processes, including DNA/histone methylation, acetylation, and phosphorylation, play an important role. Additionally, miRNAs, lncRNAs, and circRNAs are also involved in epigenetics, regulating gene expression patterns in response to various environmental factors and lifestyle choices. In the context of CAD, epigenetic alterations contribute to the dysregulation of genes involved in inflammation, oxidative stress, lipid metabolism, and vascular function. These epigenetic changes can occur during early developmental stages and persist throughout life, predisposing individuals to an increased risk of CAD. Furthermore, in recent years, the concept of personalized medicine has gained significant attention. Personalized medicine aims to tailor medical interventions based on an individual's unique genetic, epigenetic, environmental, and lifestyle factors. In the context of CAD, understanding the interplay between genetic variants and epigenetic modifications holds promise for the development of more precise diagnostic tools, risk stratification models, and targeted therapies. This review summarizes the current knowledge of epigenetic mechanisms in CAD and discusses the fundamental principles of personalized medicine.

Model Systems to Study the Mechanism of Vascular Aging

Cardiovascular diseases are the leading cause of death globally. Within cardiovascular aging, arterial aging holds significant importance, as it involves structural and functional alterations in arteries that contribute substantially to the overall decline in cardiovascular health during the aging process. As arteries age, their ability to respond to stress and injury diminishes, while their luminal diameter increases. Moreover, they experience intimal and medial thickening, endothelial dysfunction, loss of vascular smooth muscle cells, cellular senescence, extracellular matrix remodeling, and deposition of collagen and calcium. This aging process also leads to overall arterial stiffening and cellular remodeling. The process of genomic instability plays a vital role in accelerating vascular aging. Progeria syndromes, rare genetic disorders causing premature aging, exemplify the impact of genomic instability. Throughout life, our DNA faces constant challenges from environmental radiation, chemicals, and endogenous metabolic products, leading to DNA damage and genome instability as we age. The accumulation of unrepaired damages over time manifests as an aging phenotype. To study vascular aging, various models are available, ranging from in vivo mouse studies to cell culture options, and there are also microfluidic in vitro model systems known as vessels-on-a-chip. Together, these models offer valuable insights into the aging process of blood vessels.

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.

Machine Learning Applied to Cholesterol-Lowering Pharmacotherapy: Proof-of-Concept in High-Risk Patients Treated in Primary Care

Objectives: Machine learning has potential to improve the management of lipid disorders. We explored the utility of machine learning in high-risk patients in primary care receiving cholesterol-lowering medications. Methods: Machine learning algorithms were created based on lipid management guidelines for England [National Institute for Health and Care Excellence (NICE) CG181] to reproduce the guidance with >95% accuracy. Natural language processing and therapy identification algorithms were applied to anonymized electronic records from six South London primary care general practices to extract medication information from free text fields. Results: Among a total of 48,226 adult patients, a subset of 5630 (mean ± standard deviation, age = 67 ± 13 years; male:female = 55:45) with a history of lipid-lowering therapy were identified. Additional major cardiometabolic comorbidities included type 2 diabetes in 13% (n = 724) and hypertension in 32% (n = 1791); all three risk factors were present in a further 28% (n = 1552). Of the 5630 patients, 4290 (76%) and 1349 (24%) were in primary and secondary cardiovascular disease prevention cohorts, respectively. Statin monotherapy was the most common current medication (82%, n = 4632). For patients receiving statin monotherapy, 71% (n = 3269) were on high-intensity therapy aligned with NICE guidance with rates being similar for the primary and secondary prevention cohorts. In the combined cohort, only 46% of patients who had been prescribed lipid-lowering therapy in the previous 12 months achieved the NICE treatment goal of >40% reduction in non-high-density lipoprotein cholesterol from baseline pretreatment levels. Based on the most recent data entry for patients not at goal the neural network recommended either increasing the dose of statin, adding complementary cholesterol-lowering medication, or obtaining an expert lipid opinion. Conclusions: Machine learning can be of value in (a) quantifying suboptimal lipid-lowering prescribing patterns, (b) identifying high-risk patients who could benefit from more intensive therapy, and (c) suggesting evidence-based therapeutic options.

Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks

Genome-wide association studies (GWAS) involving increasing sample sizes have identified hundreds of genetic variants associated with complex diseases, such as type 2 diabetes (T2D); however, it is unclear how GWAS hits form unique topological structures in protein-protein interaction (PPI) networks. Using persistent homology, this study explores the evolution and persistence of the topological features of T2D GWAS hits in the PPI network with increasing p-value thresholds. We define an n-dimensional persistent disease module as a higher-order generalization of the largest connected component (LCC). The 0-dimensional persistent T2D disease module is the LCC of the T2D GWAS hits, which is significantly detected in the PPI network (196 nodes and 235 edges, P< 0.05). In the 1-dimensional homology group analysis, all 18 1-dimensional holes (loops) of the T2D GWAS hits persist over all p-value thresholds. The 1-dimensional persistent T2D disease module comprising these 18 persistent 1-dimensional holes is significantly larger than that expected by chance (59 nodes and 83 edges, P< 0.001), indicating a significant topological structure in the PPI network. Our computational topology framework potentially possesses broad applicability to other complex phenotypes in identifying topological features that play an important role in disease pathobiology.

Advancing diagnosis and management of liver disease in adults through exome sequencing

BACKGROUND

Whole-exome sequencing (WES) is an effective tool for diagnosis in patients who remain undiagnosed despite a comprehensive clinical work-up. While WES is being used increasingly in pediatrics and oncology, it remains underutilized in non-oncological adult medicine, including in patients with liver disease, in part based on the faulty premise that adults are unlikely to harbor rare genetic variants with large effect size. Here, we aim to assess the burden of rare genetic variants underlying liver disease in adults at two major tertiary referral academic medical centers.

METHODS

WES analysis paired with comprehensive clinical evaluation was performed in fifty-two adult patients with liver disease of unknown etiology evaluated at two US tertiary academic health care centers.

FINDINGS

Exome analysis uncovered a definitive or presumed diagnosis in 33% of patients (17/52) providing insight into their disease pathogenesis, with most of these patients (12/17) not having a known family history of liver disease. Our data shows that over two-thirds of undiagnosed liver disease patients attaining a genetic diagnosis were being evaluated for cholestasis or hepatic steatosis of unknown etiology.

INTERPRETATION

This study reveals an underappreciated incidence and spectrum of genetic diseases presenting in adulthood and underscores the clinical value of incorporating exome sequencing in the evaluation and management of adults with liver disease of unknown etiology.

FUNDING

S.V. is supported by the NIH/NIDDK (K08 DK113109 and R01 DK131033-01A1) and the Doris Duke Charitable Foundation Grant #2019081. This work was supported in part by NIH-funded Yale Liver Center, P30 DK34989.

Hepatocytic Ballooning in Non-alcoholic Steatohepatitis: Bridging the Knowledge Gap and Charting Future Avenues

Non-alcoholic steatohepatitis (NASH) is emerging as a significant global health concern, characterized by hepatic lipid accumulation, inflammation, and hepatocellular injury. Hepatocytic ballooning, a histological feature of NASH, has gained prominence for its role in disease progression and potential as a therapeutic target. This review provides an overview of the current knowledge regarding hepatocytic ballooning in NASH, highlighting the key molecular and cellular mechanisms implicated in its development. We delve into the intricate interplay of metabolic dysregulation, oxidative stress, and lipid toxicity as drivers of hepatocytic ballooning, shedding light on the pathways responsible for its initiation and perpetuation. Furthermore, we explore the diagnostic challenges associated with hepatocytic ballooning and its significance as a prognostic indicator in NASH patients. While hepatocytic ballooning holds promise as a therapeutic target, this abstract discusses the various experimental and clinical approaches to ameliorate this histological hallmark. Potential interventions, including lifestyle modifications, pharmacological agents, and emerging therapies, are evaluated in terms of their efficacy and safety profiles. In conclusion, this review underscores the need to bridge the knowledge gap surrounding hepatocytic ballooning in NASH and emphasizes its importance in understanding disease pathogenesis and progression. By charting future research avenues and clinical strategies, we aspire to advance our comprehension of NASH and ultimately improve patient outcomes in this rapidly evolving field of hepatology.

Sex-Specific Differences in Lipoprotein Production and Clearance

Therapeutic approaches to reduce atherogenic lipid and lipoprotein levels remain the most effective and assessable strategies to prevent and treat cardiovascular disease. The discovery of novel research targets linked to pathways associated with cardiovascular disease development has enhanced our ability to decrease disease burden; however, residual cardiovascular disease risks remain. Advancements in genetics and personalized medicine are essential to understand some of the factors driving residual risk. Biological sex is among the most relevant factors affecting plasma lipid and lipoprotein profiles, playing a pivotal role in the development of cardiovascular disease. This minireview summarizes the most recent preclinical and clinical studies covering the effect of sex on plasma lipid and lipoprotein levels. We highlight the recent advances in the mechanisms regulating hepatic lipoprotein production and clearance as potential drivers of disease presentation. We focus on using sex as a biological variable in studying circulating lipid and lipoprotein levels.

Isolation of Vascular Wall Mesenchymal Stem Cells from the Thoracic Aorta of Adult Göttingen Minipigs: A New Protocol for the Simultaneous Endothelial Cell Collection

Two main classes of perivascular multipotent populations have been described: the microvascular pericytes and the vascular wall mesenchymal stem cells (VW-MSCs). VW-MSCs are isolated from large vessels in many species and they participate in vascular remodeling together with other cellular components such as endothelial cells. Considering that the Göttingen Minipigs are widely used in Europe as a translational model in the field of cardiovascular diseases, the aim of the present research was to isolate VW-MSCs from the adult aorta of Göttingen Minipigs while preserving and also collecting endothelial cells. The results obtained in the present research demonstrated that this new protocol allows us to obtain a pure population of VW-MSCs and endothelial cells. VW-MSCs from Göttingen Minipigs responded fully to the MSC minima international criteria, being positive to CD105, CD90, and CD44 and negative to CD45 and CD34. Moreover, VW-MSCs presented a differentiative potential towards osteogenic, chondrogenic, and adipogenic lineages. Overall, the present protocol, preserving the viability and phenotypic features of the two isolated populations, opens future possibilities of using minipig VW-MSCs and endothelial cells in in vitro vascular remodeling studies.

The Impact of Menopause on Cardiovascular Aging: A Comprehensive Review of Androgen Influences

Menopause represents a critical life stage in women, characterized by hormonal changes that significantly impact cardiovascular health. While the decline in estrogen levels has long been recognized as a major contributor to cardiovascular aging in menopausal women, the role of androgens, particularly testosterone, has gained increasing attention in recent years. This comprehensive review aims to provide a thorough understanding of the impact of menopause on cardiovascular aging, with a specific focus on the influences of androgens. A literature search was conducted to gather relevant studies and clinical evidence exploring the relationship between menopause, androgens, and cardiovascular health. The review integrates findings from various studies to present a holistic view of the topic. The review outlines the changes in hormone levels during menopause and discusses the cardiovascular risk factors associated with this transition. Furthermore, it explores the impact of menopause on cardiovascular structure and function, elucidating the underlying mechanisms that contribute to cardiovascular aging. Androgens' significance in maintaining cardiovascular homeostasis is discussed, followed by exploring the effects of androgen decline during menopause on lipid profiles, insulin sensitivity, vascular function, and other cardiovascular parameters. The review delves into the mechanisms of androgen action on the cardiovascular system, emphasizing the role of androgen receptors and the intricate interplay between androgens, estrogens, and other hormones. Clinical evidence supporting the effects of androgens on cardiovascular aging is presented, including studies investigating the association between androgen levels and cardiovascular outcomes. Additionally, the impact of androgen replacement therapy (ART) on cardiovascular risk markers and events in menopausal women is examined, along with controversies and conflicting findings surrounding the use of androgen therapy in cardiovascular aging. This structured review provides a comprehensive understanding of the impact of menopause on cardiovascular aging, with a specific focus on the role of androgens. By highlighting the significance of androgens in cardiovascular health during menopause, this review aims to create an initial impression and interest among readers, inviting potential citations in the future. The findings underscore the need for further research and offer insights into managing cardiovascular aging in menopausal women, including lifestyle interventions, pharmacological approaches, and the potential role of personalized medicine and precision therapies.

Precision Medicine: Disease Subtyping and Tailored Treatment

The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

INTRODUCTION

Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice.

AREAS COVERED

In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP.

EXPERT OPINION

The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping

The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.

Patients' perspectives related to ethical issues and risks in precision medicine: a systematic review

Background

Precision medicine is growing due to technological advancements including next generation sequencing techniques and artificial intelligence. However, with the application of precision medicine many ethical and potential risks may emerge. Although, its benefits and potential harms are relevantly known to professional societies and practitioners, patients' attitudes toward these potential ethical risks are not well-known. The aim of this systematic review was to focus on patients' perspective on ethics and risks that may rise with the application of precision medicine.

Methods

A systematic search was conducted on 4/1/2023 in the database of PubMed, for the period 1/1/2012 to 4/1/2023 identifying 914 articles. After initial screening, only 50 articles were found to be relevant. From these 50 articles, 24 articles were included in this systematic review, 2 articles were excluded as not in English language, 1 was a review, and 23 articles did not include enough relevant qualitative data regarding our research question to be included. All full texts were evaluated following PRISMA guidelines for reporting systematic reviews following the Joanna Briggs Institute criteria.

Results

There were eight main themes emerging from the point of view of the patients regarding ethical concerns and risks of precision medicine: privacy and security of patient data, economic impact on the patients, possible harms of precision medicine including psychosocial harms, risk for discrimination of certain groups, risks in the process of acquiring informed consent, mistrust in the provider and in medical research, issues with the diagnostic accuracy of precision medicine and changes in the doctor-patient relationship.

Conclusion

Ethical issues and potential risks are important for patients in relation to the applications of precision medicine and need to be addressed with patient education, dedicated research and official policies. Further research is needed for validation of the results and awareness of these findings can guide clinicians to understand and address patients concerns in clinical praxis.

Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study

OBJECTIVE

This study aimed to propose a data-driven framework for classification of at-risk people for cardiovascular outcomes regarding obesity and metabolic syndrome.

DESIGN

A population-based prospective cohort study with a long-term follow-up.

SETTING

Data from the Tehran Lipid and Glucose Study (TLGS) were interrogated.

PARTICIPANTS

12 808 participants of the TLGS cohort, aged ≥20 years who have followed for over 15 years were assessed.

MAIN OUTCOME MEASURES

Data for 12 808 participants, aged ≥20 years who have followed for over 15 years, collected through TLGS as a prospective, population-based cohort study, were analysed. Feature engineering followed by hierarchical clustering was used to determine meaningful clusters and novel endophenotypes. Cox regression was used to demonstrate the clinical validity of phenomapping. The performance of endophenotype compared with traditional classifications was evaluated by the value of Akaike information criterion/Bayesian information criterion. R software V.4.2 was employed.

RESULTS

The mean age was 42.1±14.9 years, 56.2% were female, 13.1%, 2.8% and 6.2% had experienced cardiovascular disease (CVD), CVD mortality and hard CVD, respectively. Low-risk cluster compared with the high risk had significant difference in age, body mass index, waist-to-hip ratio, 2 hours post load plasma glucose, triglyceride, triglycerides to high density lipoprotein ratio, education, marital status, smoking and the presence of metabolic syndrome. Eight distinct endophenotypes were detected with significantly different clinical characteristics and outcomes.

CONCLUSION

Phenomapping resulted in a novel classification of population with cardiovascular outcomes, which can, better, stratify individuals into homogeneous subclasses for prevention and intervention as an alternative of traditional methods solely based on either obesity or metabolic status. These findings have important clinical implications for a particular part of the Middle Eastern population for which it is a common practice to use tools/evidence derived from western populations with substantially different backgrounds and risk profiles.

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.