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

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.

Multiple Biomarkers Are Equivalent 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 fragment of the prohormone brain natriuretic peptide (NT-proBNP), soluble suppressor of tumorigenicity, IL-6, endostatin, galectin 3, HDGF, 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 (ESC) and European Respiratory Society (ERS) risk models alone and combined clinical and biomarker models.

RESULTS

The algorithm generated 5 clusters with good risk discrimination using 6 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 ESC and ERS score and REVEAL score improved the ESC and ERS 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

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.

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.

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.

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.

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.

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.

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.

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.

Association of elevated hs-CRP and multiple infarctions with outcomes of minor stroke or TIA: subgroup analysis of CHANCE randomised clinical trial

Background and purpose

The relationship of high-sensitive C-reactive protein (hs-CRP) levels and infarction numbers with the prognosis of stroke is uncertain. This study evaluated the association of different hs-CRP levels and infarction numbers with the prognosis of acute minor ischaemic stroke or transient ischaemic attack (TIA).

Methods

A subset of 807 patients with both hs-CRP measurement and baseline MRI was included from the Clopidogrel in High-risk Patients with Acute Non-disabling Cerebrovascular Events trial. The primary efficacy outcome was the occurrence of an ischaemic stroke at the 1-year follow-up. Infarction numbers were classified as multiple acute infarctions (MAIs), single acute infarction and no acute infarction (NAI). The association between different hs-CRP levels with different infarction numbers and the risk of any outcome was analysed using multivariable Cox regression models.

Results

Among the 807 patients, 84 (10.4%) patients had a recurrent ischaemic stroke within 1 year. After adjustment for conventional confounding factors, patients with both elevated hs-CRP levels and MAIs were associated with approximately 4.7-fold of risk of ischaemic stroke within 1 year (16.7% vs 3.5%, HR 4.68, 95% CI 1.54 to 14.23, p=0.007), compared with those with non-elevated hs-CRP levels and NAI. Similar results were observed for the composite events.

Conclusions

Combined elevated hs-CRP levels and MAIs may increase 1-year stroke risk stratification efficiency in patients with minor ischaemic stroke or TIA compared with using those markers alone, which indicated that the combination of inflammatory and imaging markers might improve the effectiveness of risk stratification concerning minor ischaemic stroke or TIA.

ClinicalTrials.gov Registry (NCT00979589).

Valid cardiac biomarkers. Part I

Biomarkers are widely used for the diagnosing of diseases, evaluation of their severity, prediction of outcomes, and for monitoring the effectiveness and safety of targeted therapy. This review describes specific cardiac biomarkers approved by FDA (Food and Drug AdministrationбUSA). The list of described biomarkers is not exhaustive. In addition to the general concepts of biomarkers, definitions and classification, this Part I of the review contains data on diagnostic and prognostic biomarkers of cardiovascular diseases associated with atherosclerosis.

Association of Cardiovascular Risk Assessment with Early Colorectal Neoplasia Detection in Asymptomatic Population: A Systematic Review and Meta-Analysis

Previous studies have shown a strong coexistence of colorectal neoplasia (CRN) and cardiovascular diseases (CVD). This study was aimed to summarize the available evidence on association of CVD risk with early CRN detection in asymptomatic populations. PubMed, Web of Science, and Embase were systematically searched for eligible studies published until Dec 20, 2019. Studies exploring the associations of recommended CVD risk assessment methods (e.g., risk scores, carotid artery plaque, and coronary artery calcium score [CACS]) with risk of CRN were included. Meta-analyses were conducted to determine the overall association of CVD risk with the CRN. A total of 12 studies were finally included. The association of carotid artery plaque with the risk of colorectal adenoma (AD) was weakest (pooled odds ratio [OR)] 1.27, 95% confidence interval [CI), 1.12, 1.45]. Participants with CACS>100 had about 2-fold increased risk of AD than those with CACS=0. The pooled ORs were 3.36 (95% CI, 2.15, 5.27) and 2.30 (95% CI, 1.69, 3.13) for the risk of advanced colorectal neoplasia (AN) and AD, respectively, in participants with Framingham risk score (FRS)>20%, when compared to participants at low risk (FRS<10%). FRS might help identify subgroups at increased risk for AN, but further studies are needed.

Evidence on clinical relevance of cardiovascular risk evaluation in the general population using cardio-specific biomarkers

In recent years, the formulation of some immunoassays with high-sensitivity analytical performance allowed the accurate measurement of cardiac troponin I (cTnI) and T (cTnT) levels in reference subjects. Several studies have demonstrated the association between the risk of major cardiovascular events and cardiac troponin concentrations even for biomarker values within the reference intervals. High-sensitivity cTnI and cTnT methods (hs-cTn) enable to monitor myocardial renewal and remodelling, and to promptly identify patients at highest risk ofheart failure. An early and effective treatment of individuals at higher cardiovascular risk may revert the initial myocardial remodelling and slow down heart failure progression. Specific clinical trials should be carried out to demonstrate the efficacy and efficiency of the general population screening by means of cost-benefit analysis, in order to better identify individuals at higher risk for heart failure (HF) progression with hs-cTn methods.

Higher neutrophil-to-lymphocyte ratio (NLR) increases the risk of suboptimal platelet inhibition and major cardiovascular ischemic events among ACS patients receiving dual antiplatelet therapy with ticagrelor

BACKGROUND

Neutrophil to lymphocyte ratio (NLR) has emerged as a useful and easy-to-assess prognostic tool and biomarker of cardiovascular risk. However, few studies have evaluated its role on platelet inhibition among patients on dual antiplatelet therapy (DAPT), and especially in the settings of acute coronary syndromes (ACS). We aimed at assessing the impact of NLR on platelet reactivity and the risk of major ischemic events at long-term follow-up among ACS patients on DAPT with ticagrelor.

METHODS

Patients on dual antiplatelet therapy with ASA + ticagrelor (90 mg/twice a day) after percutaneous coronary revascularization for ACS were scheduled for platelet function assessment 30-90 days post-discharge. Aggregation tests were performed by Multiple Electrode Aggregometry (MEA). Suboptimal platelet inhibition (HRPR-high residual platelet reactivity was defined if above the lower limit of normality (417 AU*min). The primary study endpoint was defined as the occurrence of major cardiovascular events (a composite of cardiovascular death, recurrent acute coronary syndrome, target vessel revascularization) at longest available follow-up.

RESULTS

We included 397 patients, that were divided according to NLR tertiles. Patients with higher NLR were older (p < .001), less frequently smokers (p = .03), with higher rates of renal failure (p = .001), previous bypass surgery (p = .05) and use of statins (p = .03) and diuretics (p = .01). Higher white blood cells count and C-reactive protein (p < .001) and lower haemoglobin (p = .001) were associated with NLR. Mean platelet reactivity and the prevalence of high platelet reactivity (HRPR) on ticagrelor were significantly associated to higher NLR tertiles values (7% vs 12% vs 14.3%, p = .04), with a significant relationship between NLR and platelet reactivity being confirmed for all the different activating stimuli. At a mean follow-up of 939 ± 581.4 days, 21.2% of the patients experienced the primary composite endpoint, with a trend for a higher risk of events across NLR tertiles (15.4% vs 24.2% vs 24.4%, p = .09), that became statistically significant after correction for baseline confounders (adjusted HR[95%CI] = 1.13[1.008-1.26], p = .036). Moreover, NLR was significantly associated to overall mortality and recurrent ACS (adjusted p = .008, p = .06 and p = .02 respectively).

CONCLUSIONS

In the present study we found that among ACS patients treated with ASA and ticagrelor after PCI, suboptimal platelet inhibition despite DAPT was significantly increased for higher values of Neutrophil-to-Lymphocyte Ratio. Moreover, mortality and the risk of recurrent major ischemic events at long-term were associated to NLR.

Brain-Derived Neurotrophic Factor during Oral Glucose Tolerance Test Predicts Cardiovascular Outcomes

We investigated if brain-derived neurotrophic factor (BDNF) accumulation after glucose intake could predict cardiovascular outcomes. We enrolled patients admitted for angiography due to angina. After their conditions stabilized, serum BDNF levels were detected at 0, 30, and 120 min during oral glucose tolerance test (OGTT). Area under the curve (AUC) of BDNF was calculated. The first occurrence of nonfatal myocardial infarction, nonfatal stroke, and all-cause mortality served as the primary composite endpoint. Of 480 enrolled patients, 428 completed the follow-up, and 36 primary endpoint events occurred during a median follow-up of 4.4 years. The area under the receiver operating characteristic curve significantly increased from 0.61 (95% confidence interval (CI): 0.52–0.73) for the Framingham risk score (FRS) alone model to 0.72 (95%CI: 0.63–0.81) for the AUC of BDNF plus FRS model (p = 0.016) for predicting the primary endpoint, but not to 0.65 (95%CI: 0.55–0.75) for the fasting BDNF plus FRS model (p = 0.160). Grouped by median AUC of BDNF of 38.0 (ng/mL) × h, the low BDNF group had a significantly higher risk of the endpoint than the high BDNF group (hazard ratio = 3.410, 95%CI: 1.520–7.653, p = 0.003). In conclusion, AUC of BDNF during OGTT could be superior to fasting BDNF for predicting a low cardiovascular risk.

High-Resolution Regulatory Maps Connect Vascular Risk Variants to Disease-Related Pathways

Supplemental Digital Content is available in the text.

Genetic variant landscape of coronary artery disease is dominated by noncoding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct genes both to gain insights into perturbed functions and better assess the risk of disease.

Metabolomics and Proteomics in Type 2 Diabetes

The persistent increase in the worldwide burden of type 2 diabetes mellitus (T2D) and the accompanying rise of its complications, including cardiovascular disease, necessitates our understanding of the metabolic disturbances that cause diabetes mellitus. Metabolomics and proteomics, facilitated by recent advances in high-throughput technologies, have given us unprecedented insight into circulating biomarkers of T2D even over a decade before overt disease. These markers may be effective tools for diabetes mellitus screening, diagnosis, and prognosis. As participants of metabolic pathways, metabolite and protein markers may also highlight pathways involved in T2D development. The integration of metabolomics and proteomics with genomics in multiomics strategies provides an analytical method that can begin to decipher causal associations. These methods are not without their limitations; however, with careful study design and sample handling, these methods represent powerful scientific tools that can be leveraged for the study of T2D. In this article, we aim to give a timely overview of circulating metabolomics and proteomics findings with T2D observed in large human population studies to provide the reader with a snapshot into these emerging fields of research.

Research Priorities for Heart Failure With Preserved Ejection Fraction: National Heart, Lung, and Blood Institute Working Group Summary

Heart failure with preserved ejection fraction (HFpEF), a major public health problem that is rising in prevalence, is associated with high morbidity and mortality and is considered to be the greatest unmet need in cardiovascular medicine today because of a general lack of effective treatments. To address this challenging syndrome, the National Heart, Lung, and Blood Institute convened a working group made up of experts in HFpEF and novel research methodologies to discuss research gaps and to prioritize research directions over the next decade. Here, we summarize the discussion of the working group, followed by key recommendations for future research priorities. There was uniform recognition that HFpEF is a highly integrated, multiorgan, systemic disorder requiring a multipronged investigative approach in both humans and animal models to improve understanding of mechanisms and treatment of HFpEF. It was recognized that advances in the understanding of basic mechanisms and the roles of inflammation, macrovascular and microvascular dysfunction, fibrosis, and tissue remodeling are needed and ideally would be obtained from (1) improved animal models, including large animal models, which incorporate the effects of aging and associated comorbid conditions; (2) repositories of deeply phenotyped physiological data and human tissue, made accessible to researchers to enhance collaboration and research advances; and (3) novel research methods that take advantage of computational advances and multiscale modeling for the analysis of complex, high-density data across multiple domains. The working group emphasized the need for interactions among basic, translational, clinical, and epidemiological scientists and across organ systems and cell types, leveraging different areas or research focus, and between research centers. A network of collaborative centers to accelerate basic, translational, and clinical research of pathobiological mechanisms and treatment strategies in HFpEF was discussed as an example of a strategy to advance research progress. This resource would facilitate comprehensive, deep phenotyping of a multicenter HFpEF patient cohort with standardized protocols and a robust biorepository. The research priorities outlined in this document are meant to stimulate scientific advances in HFpEF by providing a road map for future collaborative investigations among a diverse group of scientists across multiple domains.