Putting it into perspective: multimarker panels for cardiovascular disease risk assessment

Application of RNA-seq for single nucleotide variation identification in a cohort of patients with hypertrophic cardiomyopathy

A variety of techniques for DNA sequencing, such as specific gene sequencing, whole genome sequencing, or exome sequencing, are currently used to detect single nucleotide variations (SNVs). Although RNA-seq can be used to identify SNVs, studies that employ this approach are uncommon, and those that do often rely on outdated mapping methods or methods that are more suitable for genomic and exomic alignment. In this work, our aim is to apply modern RNA-seq specific alignment method in order to identify SNV in a cohort of HCMP patients, and characterize those SNV to gain insight into possible mechanisms of HCMP pathogenesis. The algorithm of identification of SNV based on transcriptomic sequencing data has been developed and evaluated. The algorithm was evaluated and the optimal quality threshold was determined based on allelic discrimination for the rs397516037 mutation (MYBPC3 c.3697 C > T) among patients. A total of 42,809 SNVs with a quality of 75 or higher were identified in 48 transcriptomes of hypertrophic cardiomyopathy (HCMP) myocardial tissue. Verification of missense and nonsense variants in key HCMP genes using Sanger sequencing confirmed the accuracy of the pipeline results. To identify variants potentially associated with HCMP pathogenesis, a filtration process was conducted based on minor allele frequency, substitution prediction score and ClinVar outcome. 214 missense mutations and 6 nonsense mutations were selected. Together with nonsense mutations, 19 mutations meeting the strictest SIFT and PolypPhen criteria were identified as potential factors influencing HCMP pathogenesis. We have developed and validated a method for identifying SNVs based on transcriptomic data, which can be used to identify putative pathogenic variants. We identified mutations in key HCMP genes MYBPC3 and MYH7 in a cohort of patients. We also found potentially pathologic mutations in genes ANXA6 and FEM1 A and obtained data supporting the role of NEBL in myocardial diseases. This method would be useful in analyzing transcriptomic data available in the Gene Expression Omnibus, but should be used with caution as we have tested it on a specific disease.

Cholesterol, high-density lipoprotein, and glucose index versus triglyceride-glucose index in predicting cardiovascular disease risk: a cohort study

BACKGROUND

Cardiovascular disease (CVD) represents a significant global health challenge, characterized by high incidence rates and substantial morbidity and mortality. A newer index, the Cholesterol, High-Density Lipoprotein, and Glucose (CHG) index, has been proposed as a potential diagnostic tool for metabolic disorders but has not been investigated for its ability to predict CVD risk. This study aims to evaluate the predictive efficacy of the CHG index in comparison to the well-established Triglyceride-Glucose (TyG) index.

METHODS

In this cohort study, 6249 adults aged 45 and older were recruited from the CHARLS database, with data collected from 2011 to 2020. CVD events were tracked over a nine-year follow-up. The TyG and CHG indices were calculated, and their relationships with CVD risk were assessed using univariate and multivariate Cox regression models. Additionally, restricted cubic spline (RCS) analysis was performed to further explore these associations. Receiver operating characteristic (ROC) analysis was conducted to compare the predictive performance of both indices, and subgroup analysis evaluated their applicability in different populations.

RESULTS

Among the 6249 participants, 1667 (26.68%) developed CVD during the nine-year follow-up. In unadjusted Cox regression models, the TyG index had a hazard ratio (HR) of 1.18 (95% confidence interval CI 1.10-1.27, p < 0.001), while the CHG index showed a higher HR of 1.35 (95% CI 1.21-1.51, p < 0.001). In the adjusted models, the relationship still persisted. The RCS models showed that the TyG index exhibited a non-linear relationship with the risk of CVD, while the CHG index demonstrated a positive linear correlation. ROC curve analysis revealed comparable predictive performance for both indices. The subgroup analysis indicated that there was no interaction between the subgroups and the both indices (p for interaction > 0.05).

CONCLUSIONS

An elevated CHG index is significantly correlated with an increased risk of CVD, demonstrating a linear relationship. Furthermore, it exhibits predictive capabilities comparable to those of the TyG index in assessing CVD risk.

TRIAL REGISTRATION

Not applicable.

Circulating Factors as Potential Biomarkers of Cardiovascular Damage Progression Associated with Type 2 Diabetes

Background: Diabetes, particularly type 2 diabetes (T2D), is linked with an increased risk of developing coronary heart disease (CHD). The present study aimed to evaluate potential circulating biomarkers of CHD by adopting a targeted proteomic approach based on proximity extension assays (PEA). Methods: The study was based on 30 patients with both T2D and CHD (group DC), 30 patients with T2D without CHD (group DN) and 29 patients without diabetes but with a diagnosis of CHD (group NC). Plasma samples were analyzed using PEA, with an Olink Target 96 cardiometabolic panel expressed as normalized protein expression (NPX) units. Results: Lysosomal Pro-X carboxypeptidase (PRCP), Liver carboxylesterase 1 (CES1), Complement C2 (C2), and Intercellular adhesion molecule 3 (ICAM3) were lower in the DC and NC groups compared with the DN groups. Lithostathine-1-alpha (REG1A) and Immunoglobulin lambda constant 2 (IGLC2) were found higher in the DC group compared to DN and NC groups. ROC analysis suggested a significant ability of the six proteins to distinguish among the three groups (whole model test p < 0.0001, AUC 0.83-0.88), with a satisfactory discriminating performance in terms of sensitivity (77-90%) and specificity (70-90%). A possible role of IGLC2, PRCP, and REG1A in indicating kidney impairment was found, with a sensitivity of 92% and specificity of 83%. Conclusions: The identified panel of six plasma proteins, using a targeted proteomic approach, provided evidence that these parameters could be considered in the chronic evolution of T2D and its complications.

Multiplexed MRM-based proteomics for identification of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) increases the risk of coronary heart disease (CHD) by 2-4 fold, and is associated with endothelial dysfunction, dyslipidaemia, insulin resistance, and chronic hyperglycaemia. The aim of this investigation was to assess, by a multimarker mass spectrometry approach, the predictive role of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus.

METHODS

The study considered 34 patients with both T2DM and CHD, 31 patients with T2DM and without CHD, and 30 patients without diabetes with a diagnosis of CHD. Plasma samples of subjects were analysed through a multiplexed targeted liquid chromatography mass spectrometry (LC-MS)-based assay, namely Multiple Reaction Monitoring (MRM), allowing the simultaneous detection of peptides derived from a protein of interest. Gene Ontology (GO) Analysis was employed to identify enriched GO terms in the biological process, molecular function, or cellular component categories. Non-parametric multivariate methods were used to classify samples from patients and evaluate the relevance of the analysed proteins' panel.

RESULTS

A total of 81 proteins were successfully quantified in the human plasma samples. Gene Ontology analysis assessed terms related to blood microparticles, extracellular exosomes and collagen-containing extracellular matrix. Preliminary evaluation using analysis of variance (ANOVA) of the differences in the proteomic profile among patient groups identified 13 out of the 81 proteins as significantly different. Multivariate analysis, including cluster analysis and principal component analysis, identified relevant grouping of the 13 proteins. The first main cluster comprises apolipoprotein C-III, apolipoprotein C-II, apolipoprotein A-IV, retinol-binding protein 4, lysozyme C and cystatin-C; the second one includes, albeit with sub-grouping, alpha 2 macroglobulin, afamin, kininogen 1, vitronectin, vitamin K-dependent protein S, complement factor B and mannan-binding lectin serine protease 2. Receiver operating characteristic (ROC) curves obtained with the 13 selected proteins using a nominal logistic regression indicated a significant overall distinction (p < 0.001) among the three groups of subjects, with area under the ROC curve (AUC) ranging 0.91-0.97, and sensitivity and specificity ranging from 85 to 100%.

CONCLUSIONS

Targeted mass spectrometry approach indicated 13 multiple circulating proteins as possible biomarkers of cardiovascular damage progression associated with T2DM, with excellent classification results in terms of sensitivity and specificity.

Nanotechnology: A Promising Approach for Cancer Diagnosis, Therapeutics and Theragnosis

Cancer remains the most devastating disease and the major cause of mortality worldwide. Although early diagnosis and treatment are the key approach in fighting against cancer, the available conventional diagnostic and therapeutic methods are not efficient. Besides, ineffective cancer cell selectivity and toxicity of traditional chemotherapy remain the most significant challenge. These limitations entail the need for the development of both safe and effective cancer diagnosis and treatment options. Due to its robust application, nanotechnology could be a promising method for in-vivo imaging and detection of cancer cells and cancer biomarkers. Nanotechnology could provide a quick, safe, cost-effective, and efficient method for cancer management. It also provides simultaneous diagnosis and treatment of cancer using nano-theragnostic particles that facilitate early detection and selective destruction of cancer cells. Updated and recent discussions are important for selecting the best cancer diagnosis, treatment, and management options, and new insights on designing effective protocols are utmost important. This review discusses the application of nanotechnology in cancer diagnosis, therapeutics, and theragnosis and provides future perspectives in the field.

Multiplexed measurement of candidate blood protein biomarkers of heart failure

AIMS

There is a critical need for better biomarkers so that heart failure can be diagnosed at an earlier stage and with greater accuracy. The purpose of this study was to design a robust mass spectrometry (MS)-based assay for the simultaneous measurement of a panel of 35 candidate protein biomarkers of heart failure, in blood. The overall aim was to evaluate the potential clinical utility of this biomarker panel for prediction of heart failure in a cohort of 500 patients.

METHODS AND RESULTS

Multiple reaction monitoring (MRM) MS assays were designed with Skyline and Spectrum Mill PeptideSelector software and developed using nanoflow reverse phase C18 chromatographic Chip Cube-based separation, coupled to a 6460 triple quadrupole mass spectrometer. Optimized MRM assays were applied, in a sample-blinded manner, to serum samples from a cohort of 500 patients with heart failure and non-heart failure (non-HF) controls who had cardiovascular risk factors. Both heart failure with reduced ejection fraction (HFrEF) patients and heart failure with preserved ejection fraction (HFpEF) patients were included in the study. Peptides for the Apolipoprotein AI (APOA1) protein were the most significantly differentially expressed between non-HF and heart failure patients (P = 0.013 and P = 0.046). Four proteins were significantly differentially expressed between non-HF and the specific subtypes of HF (HFrEF and HFpEF); Leucine-rich-alpha-2-glycoprotein (LRG1, P < 0.001), zinc-alpha-2-glycoprotein (P = 0.005), serum paraoxanse/arylesterase (P = 0.013), and APOA1 (P = 0.038). A statistical model found that combined measurements of the candidate biomarkers in addition to BNP were capable of correctly predicting heart failure with 83.17% accuracy and an area under the curve (AUC) of 0.90. This was a notable improvement on predictive capacity of BNP measurements alone, which achieved 77.1% accuracy and an AUC of 0.86 (P = 0.005). The protein peptides for LRG1, which contributed most significantly to model performance, were significantly associated with future new onset HF in the non-HF cohort [Peptide 1: odds ratio (OR) 2.345 95% confidence interval (CI) (1.456-3.775) P = 0.000; peptide 2: OR 2.264 95% CI (1.422-3.605), P = 0.001].

CONCLUSIONS

This study has highlighted a number of promising candidate biomarkers for (i) diagnosis of heart failure and subtypes of heart failure and (ii) prediction of future new onset heart failure in patients with cardiovascular risk factors. Furthermore, this study demonstrates that multiplexed measurement of a combined biomarker signature that includes BNP is a more accurate predictor of heart failure than BNP alone.

Multiplex biomarker approach to cardiovascular diseases

Personalized medicine is partly based on biomarker-guided diagnostics, therapy and prognosis, which is becoming an unavoidable concept in modern cardiology. However, the clinical significance of single biomarker studies is rather limited. A promising novel approach involves combining multiple markers into a multiplex panel, which could refine the management of a particular patient with cardiovascular pathology. Two principally different assay formats have been developed to facilitate simultaneous quantification of multiple antigens: planar array assays and microbead assays. These approaches may help to better evaluate the complexity and dynamic nature of pathologic processes and offer substantial cost and sample savings compared with traditional enzyme-linked immunosorbent assay (ELISA) measurements. However, a multiplex multimarker approach cannot become a generally disseminated method until analytical problems are solved and further studies confirming improved clinical outcomes are accomplished. These drawbacks underlie the fact that a limited number of systematic studies are available regarding the use of a multiplex biomarker approach in cardiovascular medicine to date. Our perspective underscores the significant potential of the use of the multiplex approach in a wider conceptual framework under the close cooperation of clinical and experimental cardiologists, pathophysiologists and biochemists so that the personalized approach based on standardized multimarker testing may improve the management of various cardiovascular pathologies and become a ubiquitous partner of population-derived evidence-based medicine.

Pentraxin 3, ficolin-2 and lectin pathway associated serine protease MASP-3 as early predictors of myocardial infarction - the HUNT2 study

The lectin complement pathway is suggested to play a role in atherogenesis. Pentraxin-3 (PTX3), ficolin-1, ficolin-2, ficolin-3, MBL/ficolin/collectin-associated serine protease-3 (MASP-3) and MBL/ficolin/collectin-associated protein-1 (MAP-1) are molecules related to activation of the lectin complement pathway. We hypothesized that serum levels of these molecules may be associated with the incidence of myocardial infarction (MI). In a Norwegian population-based cohort (HUNT2) where young to middle-aged relatively healthy Caucasians were followed up for a first-time MI from 1995-1997 through 2008, the 370 youngest MI patients were matched by age (range 29-62 years) and gender to 370 controls. After adjustments for traditional risk factors, the two highest tertiles of PTX3 and the highest tertiles of ficolin-2 and MASP-3 were associated with MI, with odds ratios (95% confidence interval) of 1.65 (1.10-2.47) and 2.79 (1.83-4.24) for PTX3, 1.55 (1.04-2.30) for ficolin-2, and 0.63 (0.043-0.94) for MASP-3. Ficolin-1, ficolin-3 and MAP-1 were not associated with MI. In a multimarker analysis of all associated biomarkers, only PTX3 and MASP-3 remained significant. PTX-3 and MASP-3 enhanced prediction of MI compared to the traditional Framingham risk score alone (AUC increased from 0.64 to 0.68, p = 0.006). These results support the role of complement-dependent inflammation in the pathophysiology of cardiovascular disease.

Evaluation of Cardiac Toxicity Biomarkers in Rats from Different Laboratories

There is a great need for improved diagnostic and prognostic accuracy of potential cardiac toxicity in drug development. This study reports the evaluation of several commercially available biomarker kits by 3 institutions (SRI, Eli Lilly, and Pfizer) for the discrimination between myocardial degeneration/necrosis and cardiac hypertrophy as well as the assessment of the interlaboratory and interplatform variation in results. Serum concentrations of natriuretic peptides (N-terminal pro-atrial natriuretic peptide [NT-proANP] and N-terminal pro-brain natriuretic peptide [NT-proBNP]), cardiac and skeletal troponins (cTnI, cTnT, and sTnI), myosin light chain 3 (Myl3), and fatty acid binding protein 3 (FABP3) were assessed in rats treated with minoxidil (MNX) and isoproterenol (ISO). MNX caused increased heart-to-body weight ratios and prominent elevations in NT-proANP and NT-proBNP concentrations detected at 24-hr postdose without elevation in troponins, Myl3, or FABP3 and with no abnormal histopathological findings. ISO caused ventricular leukocyte infiltration, myocyte fibrosis, and necrosis with increased concentrations of the natriuretic peptides, cardiac troponins, and Myl3. These results reinforce the advantages of a multimarker strategy in elucidating the underlying cause of cardiac insult and detecting myocardial tissue damage at 24-hr posttreatment. The interlaboratory and interplatform comparison analyses also showed that the data obtained from different laboratories and platforms are highly correlated and reproducible, making these biomarkers widely applicable in preclinical studies.