Assessing the role of circulating, genetic, and imaging biomarkers in cardiovascular risk prediction

Integration of Left Atrial Function Assessment, Genetic Risk, and Clinical Risk Factors Improves Prediction of Incident Atrial Fibrillation

BACKGROUND

Integration of imaging, genetic, and clinical markers might improve risk assessment of atrial fibrillation (AF). We explored whether the addition of left atrial (LA) function and polygenic risk score (PRS) to a clinical risk score improves risk prediction of AF.

METHODS

A total of 36 919 individuals without AF who were assessed for LA emptying fraction (evaluated by cardiac magnetic resonance imaging) and PRS were analyzed from the UK Biobank imaging enhancement.

RESULTS

Over a median of 2.9 (2.0-4.2) years, 535 individuals developed incident AF. Per-SD decrease in LA emptying fraction and increase in PRS was associated with a hazard ratio of 2.13 (95% CI, 1.99-2.27) and 1.65 (95% CI, 1.52-1.79) for incident AF, respectively. C-index increase when LA emptying fraction was added to CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation) was 0.036 (95% CI, 0.013-0.057) and when joint LA emptying fraction and PRS was added to CHARGE-AF was 0.057 (95% CI, 0.034-0.081). At a 3-year risk threshold of 3%, the predicted net benefit was 2.48 per 1000 people for a model including LA emptying fraction, PRS, and CHARGE-AF compared with 0.30 per 1000 people for a model including CHARGE-AF only. In addition, a 6.5-fold risk gradient was observed for AF-mediated stroke or other systemic embolism after accounting for LA emptying fraction and PRS.

CONCLUSIONS

Integration of LA emptying fraction and PRS significantly improved risk prediction of incident AF when added to CHARGE-AF.

Coronary artery disease is associated with particular change of serum metabolome: a case-control study

INTRODUCTION

Cardiovascular disease (CVD) is a significant cause of mortality worldwide. Preventive programs are trying to reduce the burden of the disease. Recent advances in metabolomics profiling open a new avenue for developing complementary CVD evaluation strategies.

OBJECTIVES

The aim of the study was to investigate whether a metabolomic profile can provide an additional characterisation of individuals with coronary artery disease (CAD).

METHODS

The study included 167 participants with CAD aged 41-79 years. A control group was formed of 166 individuals without CAD, gender- and age-matched to the study group. A total of 188 metabolites were profiled in serum by liquid chromatography-tandem mass spectrometry. After clearing the data, associations between 132 metabolites and CAD presence were analysed using multiple linear regression models.

RESULTS

We observed significant differences in serum metabolic profiles between analysed groups on various levels. However, a deeper analysis revealed sphingomyelin 41:1 (SM 41:1) as the main metabolite independently associated with CAD after correction for classical CV risk factors. Its concentration was lower in the CAD group (median 9.79 µmol/L, interquartile range (IQR) 7.92-12.23) compared to control one (median 13.60 µmol/L, IQR 11.30-16.15) (p < 0.001). Further analysis showed that SM 41:1 concentration was inversely correlated with CAD, current smoking, and hypertension; and positively associated with female gender and non-HDL level.

CONCLUSIONS

CAD patients present lower plasma concentrations of SM 41:1 than healthy subjects. A better understanding of the biological function of sphingomyelin in CAD patients may help develop therapeutic approaches and risk stratification in this group.

Heart failure and microvascular dysfunction: an in-depth review of mechanisms, diagnostic strategies, and innovative therapies

Microvascular dysfunction (MVD) is increasingly recognized as a critical contributor to the pathogenesis of heart failure (HF), particularly in heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF). Coronary microvascular dysfunction (CMD) significantly impacts HFpEF by reducing coronary flow reserve and myocardial perfusion reserve, leading to adverse outcomes such as myocardial ischemia, diastolic dysfunction, and increased risk of major cardiovascular events, including atrial fibrillation. In HFrEF, microvascular impairment is linked to heightened oxidative stress, reduced nitric oxide production, and activation of the renin-angiotensin-aldosterone system, further driving disease progression and contributing to poor prognosis. Advancements in diagnostic techniques, such as positron emission tomography, cardiac magnetic resonance imaging, and biomarker analysis, improve our ability to assess CMD in heart failure patients, enabling earlier diagnosis and risk stratification. Emerging therapies, including sodium-glucose cotransporter-2 inhibitors, angiotensin receptor-neprilysin inhibitors, and endothelial-targeted interventions, enhance microvascular function and improve patient outcomes. The role of personalized medicine is becoming increasingly important, as individualized therapeutic approaches tailored to patient-specific microvascular abnormalities are essential for optimizing treatment effectiveness. This review underscores the pivotal role of MVD in HF. It highlights the urgent need for innovative therapeutic strategies and diagnostic tools to address this complex condition and improve clinical outcomes for HF patients.

Artificial Intelligence in Ischemic Heart Disease Prevention

PURPOSE OF REVIEW

This review discusses the transformative potential of artificial intelligence (AI) in ischemic heart disease (IHD) prevention. It explores advancements of AI in predictive modeling, biomarker discovery, and cardiovascular imaging. Finally, considerations for clinical integration of AI into preventive cardiology workflows are reviewed.

RECENT FINDINGS

AI-driven tools, including machine learning (ML) models, have greatly enhanced IHD risk prediction by integrating multimodal data from clinical sources, patient-generated inputs, biomarkers, and imaging. Applications in these various data sources have demonstrated superior diagnostic accuracy compared to traditional methods. However, ensuring algorithm fairness, mitigating biases, enhancing explainability, and addressing ethical concerns remain critical for successful deployment. Emerging technologies like federated learning and explainable AI are fostering more robust, scalable, and equitable adoption. AI holds promise in reshaping preventive cardiology workflows, offering more precise risk assessment and personalized care. Addressing barriers related to equity, transparency, and stakeholder engagement is key for seamless clinical integration and sustainable, lasting improvements in cardiovascular care.

Bovine Serum Albumin-Capped Fluorescent Copper Nanocluster Incorporated with 2D-Molybdenum Disulfide Nanosheets as a FRET-Based Immune Probe for the "Turn-On" Detection of cTnT

Cardiovascular disease is the primary cause of mortality worldwide, as stated by the World Health Organization. We utilized the red fluorescence emitted by copper nanoclusters (CuNCs) to detect cardiac Troponin T (cTnT). We designed a fluorescent probe to detect cTnT using an on-off-on technique. The cTnT antibody was coupled on the surface of a bovine serum albumin-capped CuNC (Ab-BSA©CuNC) MoS2 nanosheet, efficiently quenching the luminescence of Ab-BSA©CuNCs due to their high affinity and binding interaction by establishing an Ab-BSA©CuNC/MoS2 platform. The luminescence of Ab-BSA©CuNCs was restored due to the antigen-antibody interaction. With a detection limit of 9.4 pg/mL, a linear relationship between the luminescence intensity concentrations of cTnT was observed in the 0.161-1.57 ng/mL ranges. Additionally, the efficacy of the developed technique to measure cTnT in blood serum samples was evaluated, and it exhibited a good recovery percentage.

Developing technologies to assess vascular ageing: a roadmap from VascAgeNet

Vascular ageing (vascular ageing) is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.

Application of laboratory models for sarcoidosis research

This manuscript will review the implications and applications of sarcoidosis models towards advancing our understanding of sarcoidosis disease mechanisms, identification of biomarkers, and preclinical testing of novel therapies. Emerging disease models and innovative research tools will also be considered.

Equivalency of Multiple Biomarkers to Clinical Pulmonary Arterial Hypertension Survival Risk Models

BACKGROUND

Risk assessment in pulmonary arterial hypertension (PAH) is fundamental to guiding treatment and improved outcomes. Clinical models are excellent at identifying high-risk patients, but leave uncertainty amongst moderate-risk patients.

RESEARCH QUESTION

Can a multiple blood biomarker model of PAH, using previously described biomarkers, improve risk discrimination over current models?

STUDY DESIGN AND METHODS

Using a multiplex enzyme-linked immunosorbent assay, we measured N-terminal pro-B-type natriuretic peptide (NT-proBNP), soluble suppressor of tumorigenicity, IL-6, endostatin, galectin 3, hepatoma derived growth factor, and insulin-like growth factor binding proteins (IGFBP1-7) in training (n = 1,623), test (n = 696), and validation (n = 237) cohorts. Clinical variables and biomarkers were evaluated by principal component analysis. NT-proBNP was not included to develop a model independent of NT-proBNP. Unsupervised k-means clustering classified participants into clusters. Transplant-free survival by cluster was examined using Kaplan-Meier and Cox proportional hazard regressions. Hazard by cluster was compared with NT-proBNP, Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL), and European Society of Cardiology and European Respiratory Society risk models alone and combined clinical and biomarker models.

RESULTS

The algorithm generated five clusters with good risk discrimination using six biomarkers, weight, height, and age at PAH diagnosis. In the test and validation cohorts, the biomarker model alone performed equivalent to REVEAL (area under the receiver operating characteristic curve, 0.74). Adding the biomarker model to the European Society of Cardiology and European Respiratory Society score and REVEAL score improved the European Society of Cardiology and European Respiratory Society score and REVEAL score. The best overall model was the biomarker model adjusted for NT-proBNP with the best C statistic, Akaike information criterion, and calibration for the adjusted model compared with either the biomarker or NT-proBNP model alone.

INTERPRETATION

In this study, a multibiomarker model alone was equivalent to current PAH clinical mortality risk prediction models and improved performance when combined and added to NT-proBNP. Clinical risk scores offer excellent predictive models, but require multiple tests; adding blood biomarkers to models can improve prediction or can enable more frequent, noninvasive monitoring of risk in PAH to support therapeutic decision-making.

Plasma Protein Biomarkers and Long-Term Cardiovascular Mortality Risk in Patients With Chronic Coronary Heart Disease

BACKGROUND

Protein biomarkers that reflect different pathophysiological pathways have been associated with the risk of adverse cardiovascular events. However, it is uncertain whether these associations are sustained with increasing years after the biomarkers are measured.

METHODS AND RESULTS

In this cohort study, 7745 patients with coronary heart disease who participated in the LIPID (Long-Term Intervention With Pravastatin in Ischemic Disease) trial, BNP (B-type natriuretic peptide), troponin I, cystatin-C, C-reactive protein, d-dimer and midregional proadrenomedullin were measured at baseline and after 1 year. Discrimination of plasma biomarker concentrations for cardiovascular death were evaluated in landmark analyses from 1 year for the next 5 years of the randomized trial, and for 10 additional years after trial completion. All 6 biomarkers were associated with risk of cardiovascular death (n=1903) both during and after the clinical trial (each P<0.001). C-statistics for BNP were 0.706 and 0.704; cystatin-C, 0.686 and 0.693; troponin I, 0.686 and 0.689; C-reactive protein, 0.655 and 0.684; d-dimer, 0.670 and 0.679, and midregional adrenomedullin, 0.686 and 0.688, respectively. In multivariable models, adding all 6 biomarkers to models with clinical risk factors increased the C-statistic for cardiovascular death from 0.709 to 0.775 during the clinical trial, and from 0.713 to 0.751 during 10-year follow-up after the randomized trial (P<0.001 for both).

CONCLUSIONS

In patients with chronic coronary heart disease, biomarkers that reflect different pathophysiological pathways are associated with the risk of cardiovascular death for at least the next 15 years.

Relationship of proteins and subclinical cardiovascular traits in the population-based LIFE-Adult study

BACKGROUND AND AIMS

Understanding molecular processes of the early phase of atherosclerotic cardiovascular disease conditions is of utmost importance for early prediction and intervention measures.

METHODS

We measured 92 cardiovascular-disease-related proteins (Olink, Cardiovascular III) in 2024 elderly participants of the population-based LIFE-Adult study. We analysed the impact of 27 covariables on these proteins including blood counts, cardiovascular risk factors and life-style-related parameters. We also analysed protein associations with 13 subclinical cardiovascular traits comprising carotid intima media thickness, plaque burden, three modes of Vicorder-based pulse-wave velocities, ankle-brachial index and ECLIA-based N-terminal prohormone of brain natriuretic peptide (NT-proBNP).

RESULTS

Estimated glomerular filtration rate, triglycerides and sex where the most relevant covariables explaining more than 1 % variance of 49, 22 and 20 proteins, respectively. A total of 43 proteins were significantly associated with at least one of the analysed subclinical cardiovascular traits. NT-pro-BNP, brachial-ankle pulse-wave velocity (baPWV) and parameters of carotid plaque burden accounted for the largest number of associations. Association overlaps were relatively sparse. Only growth/differentiation factor 15, low density lipoprotein receptor and interleukin-1 receptor type 2 are associated with these three different cardiovascular traits. We confirmed several literature findings and found yet unreported associations for carotid plaque presence (von-Willebrand factor, galectin 4), carotid intima-media thickness (carboxypeptidase A1 andB1), baPWV (cathepsin D) and NT-proBNP (cathepsin Z, low density lipoprotein receptor, neurogenic locus homolog protein 3, trem-like transcript 2). Sex-interaction effects were observed, e.g. for spondin-1 and growth/differentiation factor 15 likely regulated by androgen response elements.

CONCLUSIONS

We extend the catalogue of proteome biomarkers possibly involved in early stages of cardiovascular disease pathologies providing targets for early risk prediction or intervention strategies.

Optical coherence tomography angiography in cardiovascular disease

Endothelial dysfunction and microvascular remodeling underly the development and progression of a host of cardiovascular disease (CVD). However, current methods to assess coronary epicardial microvascular function are invasive, time-intensive, and costly. Optical coherence tomography angiography (OCTA) is an established technology within ophthalmology that provides a quick, noninvasive assessment of vascular structures within the retina. As a growing body of evidence reveals strong associations between the retinal changes on OCTA and the development and progression of CVD, OCTA may indeed be a surrogate test for end-organ dysfunction. OCTA has potential to enhance diagnostic performance, refine cardiovascular risk assessment, strengthen prognostication, and ultimately, improve patient care. We explore the current literature on OCTA in cardiovascular diseases to summarize the clinical utility of retinal OCTA imaging and discuss next-generation cardiovascular applications.

Unveiling TIMPs: A Systematic Review of Their Role as Biomarkers in Atherosclerosis and Coronary Artery Disease

Coronary artery disease (CAD) is the leading cause of death globally and is a heart condition involving insufficient blood supply to the heart muscle due to atherosclerotic plaque formation. Atherosclerosis is a chronic disease in which plaques, made up of fat, cholesterol, calcium, and other substances, build up on the inner walls of arteries. Recently, there has been growing interest in finding reliable biomarkers to understand the pathogenesis and progression of atherosclerosis. Tissue Inhibitors of Metalloproteinases (TIMPs) have emerged as potential candidates for monitoring atherosclerotic development. TIMPs are a family of endogenous proteins that regulate matrix metalloproteinases (MMPs), enzymes involved in remodeling the extracellular matrix. A systematic search using Prisma guidelines was conducted and eleven studies were selected from four different databases: Web of Science (WOS), Scopus, Ovid, and PubMed. The Newcastle-Ottawa Scale (NOS) score was used to assess the risk of bias for each study. A meta-analysis was performed, and the hazard ratio (HR) and its 95% confidence interval (CI) were determined. Among the eleven studies, six reported a positive association between higher levels of TIMPs and an increased risk of atherosclerosis. Conversely, four studies support low TIMPs with high CAD risk and one study showed no significant association between TIMP-2 G-418C polymorphism and CAD. This divergence in findings underscores the complexity of the relationship between TIMPs, atherosclerosis, and CAD. In addition, a meta-analysis from two studies yielded a HR (95% CI) of 1.42 (1.16-1.74; p < 0.001; I2 = 0%) for TIMP-2 in predicting major adverse cardiovascular events (MACEs). In conclusion, the existing evidence supports the notion that TIMPs can serve as biomarkers for predicting the severity of atherosclerosis, myocardial damage, and future MACEs among CAD patients. However, further exploration is warranted through larger-scale human studies, coupled with in vitro and in vivo investigations.

Risk estimation for the primary prevention of cardiovascular disease: considerations for appropriate risk prediction model selection

Cardiovascular diseases remain the number one cause of death globally. Cardiovascular disease risk scores are an integral tool in primary prevention, being used to identify individuals at the highest risk and guide the assignment of preventive interventions. Available risk scores differ substantially in terms of the population sample data sources used for their derivation and, consequently, in the absolute risks they assign to individuals. Differences in cardiovascular disease epidemiology between the populations contributing to the development of risk scores, and the target populations in which they are applied, can result in overestimation or underestimation of cardiovascular disease risks for individuals, and poorly informed clinical decisions. Given the wide plethora of cardiovascular disease risk scores available, identification of an appropriate risk score for a target population can be challenging. This Review provides an up-to-date overview of guideline-recommended cardiovascular disease risk scores from global, regional, and national contexts, evaluates their comparative characteristics and qualities, and provides guidance on selection of an appropriate risk score.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

Does Systemic Anti-Psoriatic Treatment Impact the Risk of Cardiovascular Disease? A Review Over Cardiovascular Imaging Studies

Psoriasis is an immune-mediated inflammatory disease associated with an increased risk of cardiovascular disease (CVD). The risk of CVD increases with the severity of psoriasis, and exposure to systemic inflammation may partly explain the increased risk of CVD in these patients. This raises the question of whether anti-psoriatic treatment, in addition to treating the skin lesions, also lowers the risk of developing CVD. Different types of studies have examined the impact of systemic anti-psoriatic treatments on the risk of CVD in patients with psoriasis and epidemiological observational studies with, e.g., myocardial infarction and stroke as outcomes, and clinical studies investigating circulating inflammatory biomarkers in the blood indicate that anti-psoriatic therapy has a protective effect; however, no randomized controlled trial (RCT) has examined the impact of systemic anti-psoriatic treatment on future hard cardiovascular endpoints. This narrative review provides an overview of the clinical cardiovascular imaging studies examining the effect of systemic anti-psoriatic treatment on the risk of subclinical CVD in patients with psoriasis. We found a total of 24 clinical imaging studies, where 16 of these were observational cohort studies and eight were RCTs. The observational studies suggest an improvement in the risk of subclinical CVD based on different cardiovascular imaging biomarkers; however, the RCTs showed inconsistent results and mainly included vascular inflammation as the outcome. Future RCTs including other imaging biomarkers as surrogates for subclinical CVD, with longer follow-up and with hard cardiovascular endpoints are warranted to address whether systemic anti-psoriatic treatments reduce the risk of CVD.

Biomarkers for Risk Stratification in Patients With Type A Acute Aortic Dissection

Type A acute aortic dissection (AAD) is a fatal disease and thus, accurate and objective risk stratification is essential. In this study, we evaluated the prognostic value of readily available and assessable biomarkers in patients with type A AAD. This was a retrospective, multicenter, observational study. A total of 703 patients with type A AAD diagnosed using contrast-enhanced computed tomography were included. Therapeutic strategies were left to the physician's discretion in a real-world clinical setting. The prognostic value for in-hospital mortality was examined in 15 circulating biomarkers on admission, which are routinely available in clinical practice. Of the 703 patients, 126 (17.9%) died during the hospitalization. Of the 15 biomarkers, the multivariable analysis identified positive cardiac troponin, a low total bilirubin (T-Bil) level, and increased levels of brain natriuretic peptide (BNP) and lactate dehydrogenase (LDH) as significant predictors of in-hospital death. The receiver operating characteristics curve analysis showed that these 4 biomarkers had an independent additive prognostic value. With the cut-off values of T-Bil, BNP, and LDH, in combination with positive troponin, the increase in the number of positive biomarkers was progressively associated with higher in-hospital mortality from 1.3% to 9.8%, 20.5%, 36.4%, and 75.0% (p <0.001). In conclusion, in patients with type A AAD, positive cardiac troponin, a low T-Bil level, and increased levels of BNP and LDH on admission were related to higher in-hospital mortality, with an incremental prognostic value, suggesting that the readily available and assessable biomarkers can aid in decision-making in therapeutic strategies.

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

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

Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease

Purpose of Review

Coronary artery disease (CAD) is a common and etiologically complex disease worldwide. Current guidelines for primary prevention, or the prevention of a first acute event, include relatively simple risk assessment and leave substantial room for improvement both for risk ascertainment and selection of prevention strategies. Here, we review how advances in big data and predictive modeling foreshadow a promising future of improved risk assessment and precision medicine for CAD.

Recent Findings

Artificial intelligence (AI) has improved the utility of high dimensional data, providing an opportunity to better understand the interplay between numerous CAD risk factors. Beyond applications of AI in cardiac imaging, the vanguard application of AI in healthcare, recent translational research is also revealing a promising path for AI in multi-modal risk prediction using standard biomarkers, genetic and other omics technologies, a variety of biosensors, and unstructured data from electronic health records (EHRs). However, gaps remain in clinical validation of AI models, most notably in the actionability of complex risk prediction for more precise therapeutic interventions.

Summary

The recent availability of nation-scale biobank datasets has provided a tremendous opportunity to richly characterize longitudinal health trajectories using health data collected at home, at laboratories, and through clinic visits. The ever-growing availability of deep genotype-phenotype data is poised to drive a transition from simple risk prediction algorithms to complex, “data-hungry,” AI models in clinical decision-making. While AI models provide the means to incorporate essentially all risk factors into comprehensive risk prediction frameworks, there remains a need to wrap these predictions in interpretable frameworks that map to our understanding of underlying biological mechanisms and associated personalized intervention. This review explores recent advances in the role of machine learning and AI in CAD primary prevention and highlights current strengths as well as limitations mediating potential future applications.

Changes in Cardiovascular and Renal Biomarkers Associated with SGLT2 Inhibitors Treatment in Patients with Type 2 Diabetes Mellitus

Type 2 diabetes mellitus is a major health problem worldwide with a steadily increasing prevalence reaching epidemic proportions. The major concern is the increased morbidity and mortality due to diabetic complications. Traditional but also nontraditional risk factors have been proposed to explain the pathogenesis of type 2 diabetes mellitus and its complications. Hyperglycemia has been considered an important risk factor, and the strict glycemic control can have a positive impact on microangiopathy but not macroangiopathy and its related morbidity and mortality. Thus, the therapeutic algorithm has shifted focus from a glucose-centered approach to a strategy that now emphasizes target-organ protection. Sodium-glucose transporter 2 inhibitors is an extremely important class of antidiabetic medications that, in addition to their glucose lowering effect, also exhibit cardio- and renoprotective effects. Various established and novel biomarkers have been described, reflecting kidney and cardiovascular function. In this review, we investigated the changes in established but also novel biomarkers of kidney, heart and vascular function associated with sodium-glucose transporter 2 inhibitors treatment in patients with type 2 diabetes mellitus.

Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: advantages of training with high-resolution imaging and histology

Introduction

A reliable and automated method to segment and classify carotid artery atherosclerotic plaque components is needed to efficiently analyze multi-weighted magnetic resonance (MR) images to allow their integration into patient risk assessment for ischemic stroke. Certain plaque components such as lipid-rich necrotic core (LRNC) with hemorrhage suggest a greater likelihood of plaque rupture and stroke event. Assessment for presence and extent of LRNC could assist in directing treatment with impact upon patient outcomes.

Methods

To address the need to accurately determine the presence and extent of plaque components on carotid plaque MRI, we proposed a two-staged deep-learning-based approach that consists of a convolutional neural network (CNN), followed by a Bayesian neural network (BNN). The rationale for the two-stage network approach is to account for the class imbalance of vessel wall and background by providing an attention mask to the BNN. A unique feature of the network training was to use ground truth defined by both high-resolution ex vivo MRI data and histopathology. More specifically, standard resolution 1.5 T in vivo MR image sets with corresponding high resolution 3.0 T ex vivo MR image sets and histopathology image sets were used to define ground-truth segmentations. Of these, data from 7 patients was used for training and from the remaining two was used for testing the proposed method. Next, to evaluate the generalizability of the method, we tested the method with an additional standard resolution 3.0 T in vivo data set of 23 patients obtained from a different scanner.

Results

Our results show that the proposed method yielded accurate segmentation of carotid atherosclerotic plaque and outperforms not only manual segmentation by trained readers, who did not have access to the ex vivo or histopathology data, but also three state-of-the-art deep-learning-based segmentation methods. Further, the proposed approach outperformed a strategy where the ground truth was generated without access to the high resolution ex vivo MRI and histopathology. The accurate performance of this method was also observed in the additional 23-patient dataset from a different scanner.

Conclusion

In conclusion, the proposed method provides a mechanism to perform accurate segmentation of the carotid atherosclerotic plaque in multi-weighted MRI. Further, our study shows the advantages of using high-resolution imaging and histology to define ground truth for training deep-learning-based segmentation methods.

Lipoprotein associated- phospholipase A2 in STEMI vs. NSTE-ACS patients: a marker of cardiovascular atherosclerotic risk rather than thrombosis

The precise role of Lipoprotein associated phospholipase A2 (Lp-PlA2) in the pathogenesis of acute coronary syndromes (ACS) and in the prediction of future cardiovascular events is still debated. So far, few data exist on the variations of Lp-PlA2 activity in ACS and especially in NSTE-ACS vs. STEMI patients, where thrombotic and atherosclerotic mechanisms could play a differential role. The aim of the present study was, then, to compare Lp-PlA2 activity according to the type of ACS presentation.

METHODS

A consecutive cohort of patients undergoing coronary angiography for acute coronary syndrome (ACS) were included and divided according to presentation for non ST-segment elevation-ACS or ST-segment elevation Myocardial Infarction (STEMI). Lp-PLA2 activity was assessed in blood samples drawn at admission using the Diazyme Lp-PlA2 Activity Assay.

RESULTS

We included in our study 117 patients, of whom 31 (26.5%) presented with STEMI. STEMI patients were significantly younger (p = 0.05), displayed a lower rate of hypertension (p = 0.002), previous MI (p = 0.001) and PCI (p = 0.01) and used less frequently statins (p = 0.01) and clopidogrel (p = 0.02). White blood cells and admission glycemia were increased in STEMI (p = 0.001, respectively). The prevalence and severity of CAD was not different according to ACS types, but for a higher prevalence of thrombus (p < 0.001) and lower TIMI flow (p = 0.002) in STEMI. The levels of Lp-PlA2 were significantly lower in STEMI as compared to NSTE-ACS patients, (132 ± 41.1 vs. 154.6 ± 40.9 nmol/min/mL, p = 0.01). In fact, the rate of patients with Lp-PlA2 above the median (148 nmol/min/mL) was significantly lower in STEMI patients as compared to NSTE-ACS (32.3% vs. 57%, p = 0.02, adjusted OR[95% CI] = 0.20[0.06-0.68], p = 0.010). Moreover, a direct linear relationship was observed between Lp-PlA2 and LDL-C (r = 0.47, p < 0.001), but not with inflammatory biomarkers.

CONCLUSION

The present study shows that among ACS patients, the levels of Lp-PlA2 are inversely associated with STEMI presentation and thrombotic coronary occlusion, being instead increased in NSTE-ACS patients, therefore potentially representing a marker of more aggressive chronic cardiovascular disease with an increased risk of recurrent cardiovascular events.

Negative findings but positive contributions in cardiovascular research

Researchers have always concluded that results that do not support the hypothesis as unimportant, unworthy, or simply not good enough for publication. However, negative findings are essential for the progress of science and its self-correcting nature. We also believe in the importance and indispensability of negative results. Therefore, in this review, we discussed the factors contributing to the publication bias of negative results and the problems to assess the factuality and validity of negative results. Moreover, we emphasized the importance of reporting negative results in cardiovascular research, including treatments, and suggest that the negative results could clarify previously controversial topics in the treatment of cardiovascular diseases and prompt the translation of research on precision cardiovascular disease prevention and treatment.

Association of red cell distribution width (RDW) and the RDW to platelet count ratio with cardiovascular disease among US adults: a cross-sectional study based on the National Health and Nutrition Examination Survey 1999-2020

OBJECTIVE

To investigate the association between red cell distribution width (RDW) and the RDW to platelet count ratio (RPR) and cardiovascular diseases (CVDs) and to further investigate whether the association involves population differences and dose-response relationships.

DESIGN

Cross-sectional population-based study.

SETTING

The National Health and Nutrition Examination Survey (1999-2020).

PARTICIPANTS

A total of 48 283 participants aged 20 years or older (CVD, n=4593; non-CVD, n=43 690) were included in this study.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was the presence of CVD, while the secondary outcome was the presence of specific CVDs. Multivariable logistic regression analysis was performed to determine the relationship between RDW or the RPR and CVD. Subgroup analyses were performed to test the interactions between demographics variables and their associations with disease prevalence.

RESULTS

A logistic regression model was fully adjusted for potential confounders; the ORs with 95% CIs for CVD across the second to fourth quartiles were 1.03 (0.91 to 1.18), 1.19 (1.04 to 1.37) and 1.49 (1.29 to 1.72) for RDW (p for trend <0.0001) compared with the lowest quartile. The ORs with 95% CIs for CVD across the second to fourth quartiles were 1.04 (0.92 to 1.17), 1.22 (1.05 to 1.42) and 1.64 (1.43 to 1.87) for the RPR compared with the lowest quartile (p for trend <0.0001). The association of RDW with CVD prevalence was more pronounced in females and smokers (all p for interaction <0.05). The association of the RPR with CVD prevalence was more pronounced in the group younger than 60 years (p for interaction=0.022). The restricted cubic spline also suggested a linear association between RDW and CVD and a non-linear association between the RPR and CVD (p for non-linear <0.05).

CONCLUSION

There are statistical heterogeneities in the association between RWD, RPR distributions and the CVD prevalence, across sex, smoking status and age groups.

Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery

Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.

A Comprehensive Review: Epidemiological strategies, Catheterization and Biomarkers used as a Bioweapon in Diagnosis and Management of Cardio Vascular Diseases

Coronary artery disease (CAD) is a serious health problem that causes a considerable number of mortality in a number of affluent nations throughout the world. The estimated death encountered in many developed countries includes including Pakistan, reached 111,367 and accounted for 9.87% of all deaths, despite the mortality rate being around 7.2 million deaths per year, or 12% of all estimated deaths accounted annually around the globe, with improved health systems. Atherosclerosis progressing causes the coronary arteries to become partially or completely blocked, which results in CAD. Additionally, smoking, diabetes mellitus, homocystinuria, hypertension, obesity, hyperlipidemia, and psychological stress are risk factors for CAD. The symptoms of CAD include angina which is described as a burning, pain or discomfort in the chest, nausea, weakness, shortness of breath, lightheadedness, and pain or discomfort in the arms or shoulders. Atherosclerosis and thrombosis are the two pathophysiological pathways most frequently involved in acute coronary syndrome (ACS). Asymptomatic plaque disruption, plaque bleeding, symptomatic coronary blockage, and myocardial infarction are the prognoses for CAD. In this review, we will focus on medicated therapy which is being employed for the relief of angina linked with CAD including antiplatelet medicines, nitrates, calcium antagonists, blockers, catheterization, and the frequency of recanalized infarct-related arteries in patients with acute anterior wall myocardial infarction (AWMI). Furthermore, we have also enlightened the importance of biomarkers that are helpful in the diagnosis and management of CAD.

Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction

Cardiovascular disease (CVD), the leading cause of death globally, is associated with complicated underlying risk factors. We develop an artificial intelligence model to identify CVD using multimodal data, including clinical risk factors and fundus photographs from the Samsung Medical Center (SMC) for development and internal validation and from the UK Biobank for external validation. The multimodal model achieves an area under the receiver operating characteristic curve (AUROC) of 0.781 (95% confidence interval [CI] 0.766-0.798) in the SMC and 0.872 (95% CI 0.857-0.886) in the UK Biobank. We further observe a significant association between the incidence of CVD and the predicted risk from at-risk patients in the UK Biobank (hazard ratio [HR] 6.28, 95% CI 4.72-8.34). We visualize the importance of individual features in photography and traditional risk factors. The results highlight that non-invasive fundus photography can be a possible predictive marker for CVD.

Pediatric Population with Down Syndrome: Obesity and the Risk of Cardiovascular Disease and Their Assessment Using Omics Techniques-Review

People with Down syndrome (PWDS) are more at risk for developing obesity, oxidative stress disorders, metabolic disorders, and lipid and carbohydrate profile disorders than the general population. The presence of an additional copy of genes on chromosome 21 (i.e., the superoxide dismutase 1 gene (SOD1) and gene coding for the cystathionine β-synthase (CBS) enzyme) raises the risk for cardiovascular disease (CVD). As a result of disorders in metabolic processes and biochemical pathways, theoretically protective factors (low homocysteine level, high SOD1 level) do not fulfil their original functions. Overexpression of the CBS gene leads to the accumulation of homocysteine-a CVD risk factor. An excessive amount of protective SOD1, in the case of a lack of compensatory increase in the activity of catalase and peroxidase, leads to intensifying free radical processes. The occurrence of metabolic disorders and the amplified effect of oxidative stress carries higher risk of exposure of people with DS to CVD. At present, classic predispositions are known, but it is necessary to identify early risk factors in order to be able to employ CVD and obesity prophylaxis. Detailed determination of the metabolic and lipid profile may provide insight into the molecular mechanisms underlying CVD.

Evaluation of the cardiovascular risk in patients undergoing major non-cardiac surgery: role of cardiac-specific biomarkers

Major adverse cardiovascular events are frequently observed in patients undergoing major non-cardiac surgery during the peri-operative period. At this time, the possibility to predict cardiovascular events remains limited, despite the introduction of several algorithms to calculate the risk of adverse events, mainly death and major adverse cardiovascular events (MACE) based on the clinical history, risk factors (sex, age, lipid profile, serum creatinine) and non-invasive cardiac exams (electrocardiogram, echocardiogram, stress tests). The cardiac-specific biomarkers natriuretic peptides (NPs) and cardiac troponins (cTn) have been proposed as additional tools for risk prediction in the peri-operative period, particularly for the identification of myocardial injury in patients undergoing major non-cardiac surgery. The prognostic information from the measurement of BNP/NT-proBNP and hs-cTn is independent and complementary to other important indicators of risk, also including ECG and imaging techniques. Elevated levels of cardiac-specific biomarkers before surgery are associated with a markedly higher risk of MACE during the peri-operative period. BNP/NT-proBNP and hs-cTn should be measured in all patients during the clinical evaluation before surgery, particularly during intermediate- or high-risk surgery, in patients aged >65 years and/or with comorbidities. Several questions remain to be assessed in dedicated clinical studies, such as how to optimize the management of patients with raised cardiac specific biomarkers before surgery, and whether a strategy based on biomarker measurement improves patient outcomes and is cost-effective.

Osteocalcin-expressing endothelial progenitor cells and serum osteocalcin forms are independent biomarkers of coronary atherosclerotic disease severity in male and female patients

PURPOSE

Osteocalcin (OC), an osteoblast-derived regulator of metabolic processes, and circulating early endothelial progenitor cells (EPC, CD34 - /CD133 + /KDR +) expressing OC (OC +) are potential candidates linking bone metabolism and the vasculature and might be involved in vascular atherosclerotic calcification. This study aimed at assessing the association of circulating levels of different OC forms and of EPCs count with disease severity in patients with documented coronary atherosclerosis (CAD).

METHODS

Patients (n = 59) undergoing coronary angiography were divided, according to stenosis severity, into (1) early coronary atherosclerosis (ECA) (n = 22), and (2) late coronary atherosclerosis (LCA) (n = 37). Total OC (TOC), carboxylated OC (cOC), undercarboxylated OC (unOC) were quantified by ELISA. EPC OC + count was assessed by flow cytometry.

RESULTS

EPC OC + counts showed significant differences between ECA and LCA groups. unOC and unOC/TOC ratio were inversely correlated with EPC OC + count. A significant decrease in TOC and unOC plasma levels was associated with higher cardiovascular risk factors (CVRFs) number. EPC OC + count was correlated with LDL-C, total cholesterol, and triglycerides, with a greater significance in the LCA group. No association between the different forms of circulating OC (TOC, ucOC, cOC) and severity of CAD was found.

CONCLUSION

This study showed a significant association between EPCs (CD34 - /CD133 + /KDR + /OC +), CAD severity and CVRFs, suggesting an active role for EPC OC + in the development of CAD. An inverse correlation between TOC, ucOC, and number of CVRFs was observed, suggesting that OC, regardless of its carboxylation status, may be developed as a further cardiovascular risk biomarker.

Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field

The atherosclerotic vascular disease is a cardiovascular continuum in which the main role is attributed to atherosclerosis, from its appearance to its associated complications. The increasing prevalence of cardiovascular risk factors, population ageing, and burden on both the economy and the healthcare system have led to the development of new diagnostic and therapeutic strategies in the field. The better understanding or discovery of new pathophysiological mechanisms and molecules modulating various signaling pathways involved in atherosclerosis have led to the development of potential new biomarkers, with key role in early, subclinical diagnosis. The evolution of technological processes in medicine has shifted the attention of researchers from the profiling of classical risk factors to the identification of new biomarkers such as midregional pro-adrenomedullin, midkine, stromelysin-2, pentraxin 3, inflammasomes, or endothelial cell-derived extracellular vesicles. These molecules are seen as future therapeutic targets associated with decreased morbidity and mortality through early diagnosis of atherosclerotic lesions and future research directions.

Natriuretic Peptides and Troponins to Predict Cardiovascular Events in Patients Undergoing Major Non-Cardiac Surgery

Patients undergoing major surgery have a substantial risk of cardiovascular events during the perioperative period. Despite the introduction of several risk scores based on medical history, classical risk factors and non-invasive cardiac tests, the possibility of predicting cardiovascular events in patients undergoing non-cardiac surgery remains limited. The cardiac-specific biomarkers, natriuretic peptides (NPs) and cardiac troponins (cTn) have been proposed as additional tools for risk prediction in the perioperative period. This review paper aims to discuss the value of preoperative levels and perioperative changes in cardiac-specific biomarkers to predict adverse outcomes in patients undergoing major non-cardiac surgery. Based on several prospective observational studies and six meta-analyses, some guidelines recommended the measurement of NPs to refine perioperative cardiac risk estimation in patients undergoing non-cardiac surgery. More recently, several studies reported a higher mortality in surgical patients presenting an elevation in high-sensitivity cardiac troponin T and I, especially in elderly patients or those with comorbidities. This evidence should be considered in future international guidelines on the evaluation of perioperative risk in patients undergoing major non-cardiac surgery.

Utility of extracellular vesicles as a potential biological indicator of physiological resilience during military operational stress

Extracellular vesicles (EVs) transport biological content between cells to mediate physiological processes. The association between EVs and resilience, the ability to cope with stress, is unknown. Using unbiased machine learning approaches, we aimed to identify a biological profile of resilience. Twenty servicemen (27.8 ± 5.9 years) completed the Connor Davidson Resilience (CD-RISC) questionnaire and were exposed to daily physical and cognitive exertion with 48-hr sleep and caloric restriction. Blood samples from baseline and the second day of stress were analyzed for neuroendocrine biomarkers impacted by military stress. EVs were isolated from plasma and stained with antibodies associated with exosomes (CD63), microvesicles (VAMP3), and apoptotic bodies (THSD1). Individuals were separated into high (n = 10, CD-RISC > 90) and low (n = 10, CD-RISC < 79) resilience. EV features were stratified by size, then down-selected using regression trees and compared between groups. Diagnostic accuracy was assessed using receiver operating characteristic curves. Compared to low resilience, high resilience demonstrated a greater increase in variability of THSD1 local bright spot intensities among large-sized EVs in response to stress (p = 0.002, Hedges' g = 1.59). Among medium-sized EVs, high resilience exhibited a greater decrease in side scatter intensity (p = 0.014, Hedges' g = 1.17). Both features demonstrated high to moderate diagnostic accuracy for high resilience (AUC = 0.90 and 0.79). In contrast, neuroendocrine biomarker concentrations were similar between groups. The increase in variability among THSD1 + EVs in high, but not low, resilient individuals following stress may suggest high resilience is accompanied by stress-triggered apoptotic adaptations to the environment that are not detected in neuroendocrine biomarkers.

A Novel Integrated Biomarker for Evaluation of Risk and Severity of Coronary Atherosclerosis, and Its Validation

Objective: To assess the feasibility of a combination of biochemical and imaging parameters for estimation of risk and severity of coronary atherosclerosis (CA), and to verify the created integrated biomarker (i-BIO) on independent cohort. Methods: Two cohorts of patients admitted to the hospital for coronary angiography and ultrasound carotid dopplerography were enrolled into the study (n = 205 and n = 216, respectively). The extent of CA was assessed by Gensini Score (GS). Results: According to GS, participants were distributed as follows: atherosclerosis-free (GS = 0), CA of any stage (GS > 0), subclinical CA (GS < 35), severe CA (GS ≥ 35). Based on the analysis of mathematical models, including biochemical and imaging parameters, we selected and combined the most significant variables as i-BIO. The ability of i-BIO to detect the presence and severity of CA was estimated using ROC-analysis with cut-off points determination. Risk of any CA (GS > 0) at i-BIO > 4 was 7.3 times higher than in those with i-BIO ≤ 4; risk of severe CA (GS ≥ 35) at i-BIO ≥ 9 was 3.1 times higher than at i-BIO < 9. Results on the tested cohort confirmed these findings. Conclusions: The i-BIO > 4 detected CA (GS > 0) with sensitivity of 87.9%, i-BIO ≥ 9 excluded patients without severe CA (GS < 35), specificity 79.8%. Validation of i-BIO confirmed the feasibility of i-BIO > 4 to separate patients with any CA with sensitivity 76.2%, and of i-BIO ≥ 9 to exclude atherosclerosis-free subjects with specificity of 84.0%.

Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage

Objective: The accurate evaluation of outcomes at a personalized level in patients with intracerebral hemorrhage (ICH) is critical clinical implications. This study aims to evaluate how machine learning integrates with routine laboratory tests and electronic health records (EHRs) data to predict inpatient mortality after ICH.

Methods: In this machine learning-based prognostic study, we included 1,835 consecutive patients with acute ICH between October 2010 and December 2018. The model building process incorporated five pre-implant ICH score variables (clinical features) and 13 out of 59 available routine laboratory parameters. We assessed model performance according to a range of learning metrics, such as the mean area under the receiver operating characteristic curve [AUROC]. We also used the Shapley additive explanation algorithm to explain the prediction model.

Results: Machine learning models using laboratory data achieved AUROCs of 0.71–0.82 in a split-by-year development/testing scheme. The non-linear eXtreme Gradient Boosting model yielded the highest prediction accuracy. In the held-out validation set of development cohort, the predictive model using comprehensive clinical and laboratory parameters outperformed those using clinical alone in predicting in-hospital mortality (AUROC [95% bootstrap confidence interval], 0.899 [0.897–0.901] vs. 0.875 [0.872–0.877]; P <0.001), with over 81% accuracy, sensitivity, and specificity. We observed similar performance in the testing set.

Conclusions: Machine learning integrated with routine laboratory tests and EHRs could significantly promote the accuracy of inpatient ICH mortality prediction. This multidimensional composite prediction strategy might become an intelligent assistive prediction for ICH risk reclassification and offer an example for precision medicine.

Innovative Approaches to Assess Intermediate Cardiovascular Risk Subjects: A Review From Clinical to Metabolomics Strategies

Current risk stratification strategies for coronary artery disease (CAD) have low predictive value in asymptomatic subjects classified as intermediate cardiovascular risk. This is relevant because not all coronary events occur in individuals with traditional multiple risk factors. Most importantly, the first manifestation of the disease may be either sudden cardiac death or acute coronary syndrome, after rupture and thrombosis of an unstable non-obstructive atherosclerotic plaque, which was previously silent. The inaccurate stratification using the current models may ultimately subject the individual to excessive or insufficient preventive therapies. A breakthrough in the comprehension of the molecular mechanisms governing the atherosclerosis pathology has driven many researches toward the necessity for a better risk stratification. In this Review, we discuss how metabolomics screening integrated with traditional risk assessments becomes a powerful approach to improve non-invasive CAD subclinical diagnostics. In addition, this Review highlights the findings of metabolomics studies performed by two relevant analytical platforms in current use-mass spectrometry (MS) hyphenated to separation techniques and nuclear magnetic resonance spectroscopy (NMR) -and evaluates critically the challenges for further clinical implementation of metabolomics data. We also discuss the modern understanding of the pathophysiology of atherosclerosis and the limitations of traditional analytical methods. Our aim is to show how discriminant metabolites originated from metabolomics approaches may become promising candidate molecules to aid intermediate risk patient stratification for cardiovascular events and how these tools could successfully meet the demands to translate cardiovascular metabolic biomarkers into clinical settings.

Deep neural survival networks for cardiovascular risk prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)

BACKGROUND

There is growing interest in utilizing machine learning techniques for routine atherosclerotic cardiovascular disease (ASCVD) risk prediction. We investigated whether novel deep learning survival models can augment ASCVD risk prediction over existing statistical and machine learning approaches.

METHODS

6814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed over 16 years to assess incidence of all-cause mortality (mortality) or a composite of major adverse events (MAE). Features were evaluated within the categories of traditional risk factors, inflammatory biomarkers, and imaging markers. Data was split into an internal training/testing (four centers) and external validation (two centers). Both machine learning (COXPH, RSF, and lSVM) and deep learning (nMTLR and DeepSurv) models were evaluated.

RESULTS

In comparison to the COXPH model, DeepSurv significantly improved ASCVD risk prediction for MAE (AUC: 0.82 vs. 0.80, P ≤ 0.001) and mortality (AUC: 0.87 vs. 0.84, P ≤ 0.001) with traditional risk factors alone. Implementing non-categorical NRI, we noted a >40% increase in correct reclassification compared to the COXPH model for both MAE and mortality (P ≤ 0.05). Assessing the relative risk of participants, DeepSurv was the only learning algorithm to develop a significantly improved risk score criteria, which outcompeted COXPH for both MAE (4.22 vs. 3.61, P = 0.043) and mortality (6.81 vs. 5.52, P = 0.044). The addition of inflammatory or imaging biomarkers to traditional risk factors showed minimal/no significant improvement in model prediction.

CONCLUSION

DeepSurv can leverage simple office-based clinical features alone to accurately predict ASCVD risk and cardiovascular outcomes, without the need for additional features, such as inflammatory and imaging biomarkers.

Targeted metabolomic profiling and prediction of cardiovascular events: a prospective study of patients with psoriatic arthritis and psoriasis

OBJECTIVE

In patients with psoriatic disease (PsD), we sought serum metabolites associated with cardiovascular (CV) events and investigated whether they could improve CV risk prediction beyond traditional risk factors and the Framingham Risk Score (FRS).

METHODS

Nuclear magnetic resonance metabolomics identified biomarkers for incident CV events in patients with PsD. The association of each metabolite with incident CV events was analysed using Cox proportional hazards regression models first adjusted for age and sex, and subsequently for traditional CV risk factors. Variable selection was performed using penalisation with boosting after adjusting for age and sex, and the FRS.

RESULTS

Among 977 patients with PsD, 70 patients had incident CV events. In Cox regression models adjusted for CV risk factors, alanine, tyrosine, degree of unsaturation of fatty acids and high-density lipoprotein particles were associated with decreased CV risk. Glycoprotein acetyls, apolipoprotein B and cholesterol remnants were associated with increased CV risk. The age-adjusted and sex-adjusted expanded model with 13 metabolites significantly improved prediction of CV events beyond the model with age and sex alone, with an area under the receiver operator characteristic curve (AUC) of 79.9 versus 72.6, respectively (p=0.02). Compared with the FRS alone (AUC=73.9), the FRS-adjusted expanded model with 11 metabolites (AUC=75.0, p=0.72) did not improve CV risk discrimination.

CONCLUSIONS

We identify novel metabolites associated with the development of CV events in patients with PsD. Further study of their underlying causal role may clarify important pathways leading to CV events in this population.

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies

The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.

Prognostic value of high-sensitivity cardiac troponin I in heart failure patients with mid-range and reduced ejection fraction

Background

The identification of high-risk heart failure (HF) patients makes it possible to intensify their treatment. Our aim was to determine the prognostic value of a newly developed, high-sensitivity troponin I assay (Atellica^®, Siemens Healthcare Diagnostics) for patients with HF with reduced ejection fraction (HFrEF; LVEF < 40%) and HF with mid-range EF (HFmrEF) (LVEF 40%–49%).

Methods and results

A total of 520 patients with HFrEF and HFmrEF were enrolled in this study. Two-year all-cause mortality, heart transplantation, and/or left ventricular assist device implantation were defined as the primary endpoints (EP). A logistic regression analysis was used for the identification of predictors and development of multivariable models. The EP occurred in 14% of the patients, and these patients had higher NT-proBNP (1,950 vs. 518 ng/l; p < 0.001) and hs-cTnI (34 vs. 17 ng/l, p < 0.001) levels. C-statistics demonstrated that the optimal cut-off value for the hs-cTnI level was 17 ng/l (AUC 0.658, p < 0.001). Described by the AUC, the discriminatory power of the multivariable model (NYHA > II, NT-proBNP, hs-cTnI and urea) was 0.823 (p < 0.001). Including heart failure hospitalization as the component of the combined secondary endpoint leads to a diminished predictive power of increased hs-cTnI.

Conclusion

hs-cTnI levels ≥ 17 ng/l represent an independent increased risk of an adverse prognosis for patients with HFrEF and HFmrEF. Determining a patient’s hs-cTnI level adds prognostic value to NT-proBNP and clinical parameters.

Role of Matrix Gla Protein in the Complex Network of Coronary Artery Disease: A Comprehensive Review

Coronary artery disease (CAD) is widely recognized as one of the most important clinical entities. In recent years, a large body of accumulated data suggest that coronary artery calcification, a process highly prevalent in patients with CAD, occurs via well-organized biologic processes, rather than passively, as previously regarded. Matrix Gla protein (MGP), a vitamin K-dependent protein, emerged as an important inhibitor of both intimal and medial vascular calcification. The functionality of MGP hinges on two post-translational modifications: phosphorylation and carboxylation. Depending on the above-noted modifications, various species of MGP may exist in circulation, each with their respective level of functionality. Emerging data suggest that dysfunctional species of MGP, markedly, dephosphorylated-uncarboxylated MGP, might find its application as biomarkers of microvascular health, and assist in clinical decision making with regard to initiation of vitamin K supplementation. Hence, in this review we summarized the current knowledge with respect to the role of MGP in the complex network of vascular calcification with concurrent inferences to CAD. In addition, we discussed the effects of warfarin use on MGP functionality, with concomitant implications to coronary plaque stability.

Evaluation of pathophysiological relationships between renin-angiotensin and ACE-ACE2 systems in cardiovascular disorders: from theory to routine clinical practice in patients with heart failure

Despite the progressive improvements in diagnosis and therapy during the first 20 years of this century, the morbidity and mortality of patients with heart failure (HF) remain high, resulting in an enormous health and economic burden. Only a further improvement in understanding the pathophysiological mechanisms related to the development of cardiac injury and dysfunction can allow more innovative and personalized approaches to HF management. The renin-angiotensin system (RAS) has a critical role in cardiovascular physiology by regulating blood pressure and electrolyte balance. The RAS is mainly regulated by both angiotensin converting enzyme (ACE) and type 2 angiotensin converting enzyme (ACE2). However, the balance between the various peptides and peptidases constituting the RAS/ACE pathway remains in great part unraveled in patients with HF. This review summarizes the role of the RAS/ACE axis in cardiac physiology and HF pathophysiology as well as some analytical issues relevant to the clinical and laboratory assessment of inter-relationships between these two systems. There is evidence that RAS peptides represent a dynamic network of peptides, which are altered in different HF states and influenced by medical therapy. However, the mechanisms of signal transduction have not been fully elucidated under physiological and pathophysiological conditions. Further investigations are necessary to explore novel molecular mechanisms related to the RAS, which will provide alternative therapeutic agents. Moreover, monitoring the circulating levels of active RAS peptides in HF patients may enable a personalized approach by facilitating assessment of the pathophysiological status of several cardiovascular diseases and thus better selection of therapies for HF patients.

Prediction of Hemorrhagic Transformation After Ischemic Stroke: Development and Validation Study of a Novel Multi-biomarker Model

Objectives: We aimed to develop and validate a novel multi-biomarker model for predicting hemorrhagic transformation (HT) risk after acute ischemic stroke (AIS).

Methods: We prospectively included patients with AIS admitted within 24 h of stroke from January 1st 2016 to January 31st 2019. A panel of 17 circulating biomarkers was measured and analyzed in this cohort. We assessed the ability of individual circulating biomarkers and the combination of multiple biomarkers to predict any HT, symptomatic HT (sHT) and parenchymal hematoma (PH) after AIS. The strategy of multiple biomarkers in combination was then externally validated in an independent cohort of 288 Chinese patients.

Results: A total of 1207 patients with AIS (727 males; mean age, 67.2 ± 13.9 years) were included as a derivation cohort, of whom 179 patients (14.8%) developed HT. The final multi-biomarker model included three biomarkers [platelets, neutrophil-to-lymphocyte ratios (NLR), and high-density lipoprotein (HDL)] from different pathways, showing a good performance for predicting HT in both the derivation cohort (c statistic = 0·64, 95% CI 0·60–0·69), and validation cohort (c statistic = 0·70, 95% CI 0·58–0·82). Adding these three biomarkers simultaneously to the basic model with conventional risk factors improved the ability of HT reclassification [net reclassification improvement (NRI) 65.6%, P < 0.001], PH (NRI 64.7%, P < 0.001), and sHT (NRI 71.3%, P < 0.001).

Conclusion: This easily applied multi-biomarker model had a good performance for predicting HT in both the derivation and external validation cohorts. Incorporation of biomarkers into clinical decision making may help to identify patients at high risk of HT after AIS and warrants further consideration.

Development of a novel CT-derived measure of cardiovascular health: the CT aortic stiffness index (CTASI)

AIMS

Aortic stiffness, measured as aortic pulse wave velocity (PWV), is a powerful predictor of cardiovascular health but is difficult to accurately obtain non-invasively. This study sought to develop a novel CT aortic stiffness index (CTASI) which incorporates both anatomical (calcification) and physiological (distensibility) aspects of aortic health.

METHODS

Invasive PWV and CT scans were obtained for 80 patients undergoing TAVI (cohort 1). CT data alone were obtained from an additional 238 patients (cohort 2). Aortic calcification was quantified using a modified Agatston's methodology. Distensibility-PWV was calculated from minimum and maximum ascending aorta areas. Linear regression of these values was used to construct CTASI from cohort 1. CTASI was then calculated for cohort 2 who were prospectively followed-up.

RESULTS

CTASI correlated with invasive PWV (rho = 0.47, p < 0.01) with a higher correlation coefficient than distensibility-PWV (rho = 0.35, p < 0.01) and aortic calcification (rho = 0.36, p < 0.01). Compared to invasive PWV, CTASI had a good accuracy as a diagnostic test (AOC 0.72 [95% CI 0.61-0.84]), superior to aortic calcification and distensibility-PWV alone (χ² = 0.82, p = 0.02). There were 61 deaths during a median follow-up of 771 days (95% CI 751.4-790.5). CTASI was able to predict 1-year mortality (OR 2.58, 95% CI 1.18-5.61, p = 0.02) and Kaplan-Meier survival (log-rank p = 0.03).

CONCLUSION

CTASI is a stronger measure of aortic stiffness than aortic calcification or distensibility alone. Given the prolific use of CT scanning for assessing coronary and vascular disease, the additional calculation of CTASI during these scans could provide an important direct measurement of vascular health and guide pharmacological therapy.

The Time Has Come to Explore Plasma Biomarkers in Genetic Cardiomyopathies

For patients with hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) or arrhythmogenic cardiomyopathy (ACM), screening for pathogenic variants has become standard clinical practice. Genetic cascade screening also allows the identification of relatives that carry the same mutation as the proband, but disease onset and severity in mutation carriers often remains uncertain. Early detection of disease onset may allow timely treatment before irreversible changes are present. Although plasma biomarkers may aid in the prediction of disease onset, monitoring relies predominantly on identifying early clinical symptoms, on imaging techniques like echocardiography (Echo) and cardiac magnetic resonance imaging (CMR), and on (ambulatory) electrocardiography (electrocardiograms (ECGs)). In contrast to most other cardiac diseases, which are explained by a combination of risk factors and comorbidities, genetic cardiomyopathies have a clear primary genetically defined cardiac background. Cardiomyopathy cohorts could therefore have excellent value in biomarker studies and in distinguishing biomarkers related to the primary cardiac disease from those related to extracardiac, secondary organ dysfunction. Despite this advantage, biomarker investigations in cardiomyopathies are still limited, most likely due to the limited number of carriers in the past. Here, we discuss not only the potential use of established plasma biomarkers, including natriuretic peptides and troponins, but also the use of novel biomarkers, such as cardiac autoantibodies in genetic cardiomyopathy, and discuss how we can gauge biomarker studies in cardiomyopathy cohorts for heart failure at large.

Circulating Levels of Dephosphorylated-Uncarboxylated Matrix Gla Protein in Patients with Acute Coronary Syndrome

Vascular calcification contributes to the pathogenesis of coronary artery disease while matrix Gla protein (MGP) was recently identified as a potent inhibitor of vascular calcification. MGP fractions, such as dephosphorylated-uncarboxylated MGP (dp-ucMGP), lack post-translational modifications and are less efficient in vascular calcification inhibition. We sought to compare dp-ucMGP levels between patients with acute coronary syndrome (ACS), stratified by ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) status. Physical examination and clinical data, along with plasma dp-ucMGP levels, were obtained from 90 consecutive ACS patients. We observed that levels of dp-ucMGP were significantly higher in patients with NSTEMI compared to STEMI patients (1063.4 ± 518.6 vs. 742.7 ± 166.6 pmol/L, p < 0.001). NSTEMI status and positive family history of cardiovascular diseases were only independent predictors of the highest tertile of dp-ucMGP levels. Among those with NSTEMI, patients at a high risk of in-hospital mortality (adjudicated by GRACE score) had significantly higher levels of dp-ucMGP compared to non-high-risk patients (1417.8 ± 956.8 vs. 984.6 ± 335.0 pmol/L, p = 0.030). Altogether, our findings suggest that higher dp-ucMGP levels likely reflect higher calcification burden in ACS patients and might aid in the identification of NSTEMI patients at increased risk of in-hospital mortality. Furthermore, observed dp-ucMGP levels might reflect differences in atherosclerotic plaque pathobiology between patients with STEMI and NSTEMI.

Risk-Based Approach for the Prediction and Prevention of Heart Failure

Targeted prevention of heart failure (HF) remains a critical need given the high prevalence of HF morbidity and mortality. Similar to risk-based prevention of atherosclerotic cardiovascular disease, optimal HF prevention strategies should include quantification of risk in the individual patient. In this review, we discuss incorporation of a quantitative risk-based approach into the existing HF staging landscape and the clinical opportunity that exists to translate available data on risk estimation to help guide personalized decision making. We first summarize the recent development of key HF risk prediction tools that can be applied broadly at a population level to estimate risk of incident HF. Next, we provide an in-depth description of the clinical utility of biomarkers to personalize risk estimation in select patients at the highest risk of developing HF. We also discuss integration of genomics-enhanced approaches (eg, Titin [TTN]) and other risk-enhancing features to reclassify risk with a precision medicine approach to HF prevention. Although sequential testing is very likely to identify low and high-risk individuals with excellent accuracy, whether or not interventions based on these risk models prevent HF in clinical practice requires prompt attention including randomized placebo-controlled trials of candidate therapies in risk-enriched populations. We conclude with a summary of unanswered questions and gaps in evidence that must be addressed to move the field of HF risk assessment forward.

Advances in Multimodality Carotid Plaque Imaging: AJR Expert Panel Narrative Review

Contemporary imaging methods provide detailed visualization of carotid athero-sclerotic plaque, enabling a major evolution of in vivo carotid plaque imaging evaluation. The degree of luminal stenosis in the carotid artery bifurcation, as assessed by ultrasound, has historically served as the primary imaging feature for determining ischemic stroke risk and the potential need for surgery. However, stroke risk may be more strongly driven by the presence of specific characteristics of vulnerable plaque, as visualized on CT and MRI, than by traditional ultrasound-based assessment of luminal narrowing. This review highlights six promising imaging-based plaque characteristics that harbor unique information regarding plaque vulnerability: maximum plaque thickness and volume, calcification, ulceration, intraplaque hemorrhage, lipid-rich necrotic core, and thin or ruptured fibrous cap. Increasing evidence supports the association of these plaque characteristics with risk of ischemic stroke, although these characteristics have varying suitability for clinical implementation. Key aspects of CT and MRI protocols for carotid plaque imaging are also considered. Practical next steps and hurdles are explored for implementing routine imaging assessment of these plaque characteristics in addition to, or even as replacement for, traditional assessment of the degree of vascular stenosis on ultrasound, in the identification of individuals at high risk of ischemic stroke.

Omics and Cardiometabolic Disease Risk Prediction

Risk assessments are integral for the prevention and management of cardiometabolic disease (CMD). However, individuals may develop CMD without traditional risk factors, necessitating the development of novel biomarkers to aid risk prediction. The emergence of omic technologies, including genomics, proteomics, and metabolomics, has allowed for assessment of orthogonal measures of cardiometabolic risk, potentially improving the ability for novel biomarkers to refine disease risk assessments. While omics has shed light on novel mechanisms for the development of CMD, its adoption in clinical practice faces significant challenges. We review select omic technologies and cardiometabolic investigations for risk prediction, while highlighting challenges and opportunities for translating findings to clinical practice.