Novel biomarkers for cardiovascular risk assessment: current status and future directions

Molecular Insights into the Relationship Between Platelet Activation and Endothelial Dysfunction: Molecular Approaches and Clinical Practice

Platelets are one of the coagulation cells. When platelet activation occurs, many mediators are released and affect endothelial cells (ECs) and lead to endothelial dysfunction (ED). ED plays an important role in the pathogenesis of many diseases, including cardiovascular disease (CVD). Platelet are of important factors in ED. The release of mediators by platelets causes the stimulation of inflammatory pathways, oxidative stress, and apoptosis, which ultimately result in ED.On the other hand, platelet activation in CVD patients can be associated with a bad prognosis. Platelet activation can increase the level of markers such as p-selectin in the serum. Also, in this study, we have discussed the role of platelet as a diagnostic factor, as well as its use as a treatment option. In addition, we discussed some of the molecular pathways that are used to target platelet activation.

Blood C-peptide concentration as a proxy marker of cardiovascular disease: An observational cross-sectional study

Background and Aims

Cardiovascular diseases (CVDs) are among the leading causes of disability and early death in sub-Saharan Africa. Most of the current blood tests for CVD diagnosis involve performing about three test profiles; often at additional cost to patients. C-peptide, a cleavage product of proinsulin, is a promising marker that has the potential to serve as a proxy marker for diagnosing CVDs in resource-poor settings.

Methodology

The study was an observational cross-sectional one and involved 127 consenting persons diagnosed with CVD and 127 individuals without CVD. The socio-demographic and clinical characteristics of participants were obtained. Blood levels of C-peptide, fasting plasma glucose (FPG), total creatinine kinase (CK), creatine kinase myocardial bound (CKMB), lactate dehydrogenase (LDH), propeptide of brain natriuretic peptide (PBNP), Troponin T, lipids, and biomarkers of kidney and liver function were analyzed using ELISA and an automated analyzer. Insulin resistance was computed using the modified homeostatic model assessment (HOMA-IR).

Results

The CVD Group had significantly higher levels of C-peptide, CK, CKMB, troponin T, PBNP, FPG, HOMA-IR, and several selected kidney, liver, and lipid parameters compared to the non-CVD Group (p < 0.05 for all). Troponin T recorded a positive correlation (r = 0.34, p < 0.001) with C-peptide among the CVD Group. The sensitivity and specificity of C-peptide in identifying CVD were 96.1% and 91.3% respectively (area under the curve = 0.938, p < 0.001).

Conclusion

C-peptide levels were higher in the CVD Group and appeared to be a valuable (high sensitivity and specificity) biomarker in detecting CVD.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Sphingolipid Profiling: A Promising Tool for Stratifying the Metabolic Syndrome-Associated Risk

Metabolic syndrome (MetS) is a multicomponent risk condition that reflects the clustering of individual cardiometabolic risk factors related to abdominal obesity and insulin resistance. MetS increases the risk for cardiovascular diseases (CVD) and type 2 diabetes mellitus (T2DM). However, there still is not total clinical consensus about the definition of MetS, and its pathophysiology seems to be heterogeneous. Moreover, it remains unclear whether MetS is a single syndrome or a set of diverse clinical conditions conferring different metabolic and cardiovascular risks. Indeed, traditional biomarkers alone do not explain well such heterogeneity or the risk of associated diseases. There is thus a need to identify additional biomarkers that may contribute to a better understanding of MetS, along with more accurate prognosis of its various chronic disease risks. To fulfill this need, omics technologies may offer new insights into associations between sphingolipids and cardiometabolic diseases. Particularly, ceramides –the most widely studied sphingolipid class– have been shown to play a causative role in both T2DM and CVD. However, the involvement of simple glycosphingolipids remains controversial. This review focuses on the current understanding of MetS heterogeneity and discuss recent findings to address how sphingolipid profiling can be applied to better characterize MetS-associated risks.

Prognosis and Reclassification by YKL-40 in Stable Coronary Artery Disease

Background

The inflammatory biomarker YKL‐40 has previously been studied as a potential risk marker in cardiovascular disease. We aimed to assess the prognostic reclassification potential of serum YKL‐40 in patients with stable coronary artery disease.

Methods and Results

The main study population was the placebo group of the CLARICOR (Effect of Clarithromycin on Mortality and Morbidity in Patients With Ischemic Heart Disease) trial. The primary outcome was a composite of acute myocardial infarction, unstable angina pectoris, cerebrovascular disease, and all‐cause mortality. We used Cox proportional hazards regression models adjusted for C‐reactive protein level and baseline cardiovascular risk factors. Improvement in prediction by adding serum YKL‐40 to the risk factors was calculated using the Cox‐Breslow method and c‐statistic. A total of 2200 patients were randomized to placebo, with a follow‐up duration of 10 years. YKL‐40 was associated with an increased risk of the composite outcome (hazard ratio per unit increase in (YKL‐40) 1.13, 95% CI 1.03–1.24, P=0.013) and all‐cause mortality (hazard ratio 1.32, 95% CI 1.17–1.49, P<0.0001). Considering whether a composite‐outcome event was more likely to have, or not have, occurred to date, we found 68.4% of such predictions to be correct when based on the standard predictors, and 68.5% when serum YKL‐40 was added as a predictor. Equivalent results were obtained with c‐statistics.

Conclusions

Higher serum YKL‐40 was independently associated with an increased risk of adverse cardiovascular outcomes and mortality. Addition of YKL‐40 did not improve risk prediction in patients with stable coronary artery disease.

Clinical Trial Registration

URL: https://www.clinicaltrials.gov/. Unique identifier: NCT00121550.

Pro-inflammatory biomarkers in women with non-obstructive angina pectoris and coronary microvascular dysfunction

Background

Studies that evaluate larger numbers of protein biomarkers in patients with coronary microvascular dysfunction (CMD) have not previously been performed, and very little is known concerning the pathogenetic mechanisms leading to CMD.

Our objective was to analyze associations between a broad cardiovascular disease (CVD) protein biomarker assay and CMD, and further explore internal biomarker relations in order to identify possible targets for future treatment interventions.

Methods

In 174 women with angina pectoris and no significant obstructive coronary artery disease (<50% stenosis on invasive coronary angiography), CMD was assessed by transthoracic Doppler echocardiography measuring coronary flow velocity reserve (CFVR). Blood samples were analyzed with a CVD proteomic panel encompassing 92 biomarkers. The relation between biomarkers and CFVR was evaluated by regression analysis, and possible interrelations between significant biomarkers were investigated by principal component analysis (PCA).

Results

Median age (SD) was 64 years (9.8), median CFVR (IQR) was 2.3 (1.9–2.7), and 28% of patients had CFVR < 2.0. Eighteen biomarkers were significantly correlated with CFVR. In PCA, 8 of the biomarkers significantly related to CFVR showed high loadings on principal component 1 (PC1). The component scores of PC1 were significantly related to CFVR (p = 0.002). The majority of the 8 interrelated PC1 biomarkers were related to the pro-inflammatory TNF-α – IL-6 – CRP pathway.

Conclusion

Eighteen protein biomarkers were significantly associated with CMD. Eight biomarkers were interrelated in PCA, and share connection with pro-inflammatory pathways, highlighting a possible important role of inflammation in CMD.

The Association between Mineral and Trace Element Concentrations in Hair and the 10-Year Risk of Atherosclerotic Cardiovascular Disease in Healthy Community-Dwelling Elderly Individuals

This cross-sectional analysis included 137 Korean subjects aged 60⁻79 years. All subjects underwent anthropometric measurements and laboratory tests. Scalp hair samples were obtained from each individual, the concentrations of 36 minerals and trace elements were analyzed, and 16 ratios of elements were calculated. ASCVD risk was estimated using pooled cohort ASCVD risk assessment equations for 10-year risk profiles. The 137 subjects were divided into three risk groups: low (<5%, n = 28), intermediate (5% to <7.5%, n = 21), and high (≥7.5%, n = 88) risk groups. After adjusting for obesity (BMI ≥ 25 kg/m²) and sex, Na concentration (mg%) in hair was significantly lower in the low-risk (13.91 ± 7.02) than in the intermediate-risk (47.18 ± 8.08) and high-risk (36.76 ± 3.95) groups (p for trend = 0.024). The concentration of K (mg%) in hair was also positively associated with the severity of ASCVD risk (10.50 ± 8.37, 23.62 ± 9.63, 33.31 ± 4.71, respectively; p for trend = 0.017), but their differences were not statistically significant (p = 0.059). By contrast, the levels of Co, U, and Hg, and the Ca/P and Ca/Mg ratios, were negatively correlated with the severity of ASCVD risk (p for trend < 0.05). Mean Na concentration in hair was significantly lower in the low-risk than in the other risk groups. By contrast, Co, U, and Hg concentrations showed significant negative associations with risk severity. Further studies are needed to assess whether dietary modification for trace elements could lower the risk of ASCVD.

Novel biomarkers for cardiovascular risk

Cardiovascular disease refers to different diseases involving the heart and/or the arteries and/or the veins. Cardiovascular disease, overall considered, is a notable source of morbidity and mortality worldwide. Therefore, several research studies are dedicated to explore, by means of biomarkers, the possiblity to calculate the cardiovascular risk both for the onset and for the complications of the related clinical manifestations such as coronary artery disease, carotid artery stenosis, peripheral artery disease, arterial aneurysm, chronic venous disease and venous thromboembolism. This review discusses the most updated information in the area of the novel biomarkers related to omics, imaging techniques and clinical data, that may help physicians in order to improve the knowledge and the management of the cardiovascular risk.

Fast and general tests of genetic interaction for genome-wide association studies

A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two ‘tag’ variants in the LPA locus (p = 2.42 ⋅ 10⁻⁰⁹) as well as replicate the interaction (p = 6.97 ⋅ 10⁻⁰⁷). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.

Interaction between organic molecules forms the basis of all biological systems. The availability of high-throughput genotyping and sequencing platforms enables us to cost-effectively genotype a large number of individuals. For sufficiently large datasets it is possible to reconstruct the genetic dependencies that underlie complex traits and diseases. However, there is a need for efficient statistical methodologies that can tackle the large sample size and computational resources required to study interaction. In this work we provide theory that reduces the required computational resources, and enable multiple research groups to effectively combine their results.