Recognized and Potentially New Biomarkers-Their Role in Diagnosis and Prognosis of Cardiovascular Disease

AI-Enhanced Predictive Modeling for Identifying Depression and Delirium in Cardiovascular Patients Scheduled for Cardiac Surgery

Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional factors. Addressing this issue, our study pioneers a straightforward, explainable, and data-driven pipeline for predicting depression in CVD patients.

METHODS

Our study was conducted at a cardiac surgical intensive care unit. A total of 224 participants who were scheduled for elective coronary artery bypass graft surgery (CABG) were enrolled in the study. Prior to surgery, each patient underwent psychiatric evaluation to identify major depressive disorder (MDD) based on the DSM-5 criteria. An advanced data curation workflow was applied to eliminate outliers and inconsistencies and improve data quality. An explainable AI-empowered pipeline was developed, where sophisticated machine learning techniques, including the AdaBoost, random forest, and XGBoost algorithms, were trained and tested on the curated data based on a stratified cross-validation approach.

RESULTS

Our findings identified a significant correlation between the biomarker "sRAGE" and depression (r = 0.32, p = 0.038). Among the applied models, the random forest classifier demonstrated superior accuracy in predicting depression, with notable scores in accuracy (0.62), sensitivity (0.71), specificity (0.53), and area under the curve (0.67).

CONCLUSIONS

This study provides compelling evidence that depression in CVD patients, particularly those with elevated "sRAGE" levels, can be predicted with a 62% accuracy rate. Our AI-driven approach offers a promising way for early identification and intervention, potentially revolutionizing care strategies in this vulnerable population.

Adverse effects of tyrosine kinase inhibitors in cancer therapy: pathophysiology, mechanisms and clinical management

Since their invention in the early 2000s, tyrosine kinase inhibitors (TKIs) have gained prominence as the most effective pathway-directed anti-cancer agents. TKIs have shown significant utility in the treatment of multiple hematological malignancies and solid tumors, including chronic myelogenous leukemia, non-small cell lung cancers, gastrointestinal stromal tumors, and HER2-positive breast cancers. Given their widespread applications, an increasing frequency of TKI-induced adverse effects has been reported. Although TKIs are known to affect multiple organs in the body including the lungs, liver, gastrointestinal tract, kidneys, thyroid, blood, and skin, cardiac involvement accounts for some of the most serious complications. The most frequently reported cardiovascular side effects range from hypertension, atrial fibrillation, reduced cardiac function, and heart failure to sudden death. The potential mechanisms of these side effects are unclear, leading to critical knowledge gaps in the development of effective therapy and treatment guidelines. There are limited data to infer the best clinical approaches for the early detection and therapeutic modulation of TKI-induced side effects, and universal consensus regarding various management guidelines is yet to be reached. In this state-of-the-art review, we examine multiple pre-clinical and clinical studies and curate evidence on the pathophysiology, mechanisms, and clinical management of these adverse reactions. We expect that this review will provide researchers and allied healthcare providers with the most up-to-date information on the pathophysiology, natural history, risk stratification, and management of emerging TKI-induced side effects in cancer patients.

The Role of Gut Microbiota in the Clinical Outcome of Septic Patients: State of the Art and Future Perspectives

Sepsis is a life-threatening multiple-organ dysfunction caused by a dysregulated host response to infection, with high mortality worldwide; 11 million deaths per year are attributable to sepsis in high-income countries. Several research groups have reported that septic patients display a dysbiotic gut microbiota, often related to high mortality. Based on current knowledge, in this narrative review, we revised original articles, clinical trials, and pilot studies to evaluate the beneficial effect of gut microbiota manipulation in clinical practice, starting from an early diagnosis of sepsis and an in-depth analysis of gut microbiota.

Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

Is It Feasible to Predict Cardiovascular Risk among Healthy Vegans, Lacto-/Ovo-Vegetarians, Pescatarians, and Omnivores under Forty?

Guidelines for cardiovascular (CV) risk assessment among young adults are uncertain. Researchers are still looking for new tools for earlier diagnosis of cardiovascular diseases (CVD), the leading cause of mortality in the modern world. This study aimed to assess whether CV risk estimation is possible in groups of healthy individuals under the age of 40 on different dietary patterns (vegans-VEGAN (n = 48), lacto-/ovo-vegetarians-VEGE (n = 49), pescatarians-PESCA (n = 23), and omnivores-OMN (n = 35)) during the pandemic period. Four metrics containing selected risk classifiers were created, and participants were assessed using them. Groups including meat consumption showed increased CV risk predictions in the metrics assessment. The next analyzes showed statistically significant relationships between the results from the created metrics and selected non-basic biomarkers for ApoA1 (OMN group, p = 0.028), IL-6 (PESCA group, p = 0.048), HCY (VEGAN group, p = 0.05), and hsCRP (OMN + PESCA groups, p = 0.025). We found that predicting CV risk among healthy people under 40 adhering to different dietary patterns, taking into account basic and non-basic laboratory assessments and created metrics, is challenging but feasible. Furthermore, the OMN group appeared to be at the highest risk of increased CV risk in the future, while risk tended to be the lowest in the VEGAN group.

Quantitative Data-Independent Acquisition Mass Spectrometry Proteomics and Weighted Correlation Network Analysis of Plasma Samples for the Discovery of Chronic Kidney Disease-Specific Atherosclerosis Risk Factors

Chronic kidney disease (CKD) accelerates atherosclerosis. The mechanism of CKD-related atherosclerosis is complex, and CKD-specific risk factors may contribute to this process in addition to traditional risk factors such as hypertension, diabetes, and hypercholesterolemia. In the present study, to discover CKD-specific atherosclerosis risk factors, a total of 62 patients with different stages of kidney function were enrolled. All patients underwent coronary angiographies and the severity of coronary atherosclerosis was defined by the SYNTAX score. Patients were divided into different groups according to their kidney function levels and coronary atherosclerosis severity. Data-independent acquisition mass spectrometry was used to identify differentially expressed proteins (DEPs) in the plasma samples, and weighted correlation network analysis (WGCNA) was employed to identify significant protein modules and hub proteins related to CKD-specific atherosclerosis. The results showed that 10 DEPs associated with atherosclerosis were found in the comparative groups with modest and severe CKD. Through WGCNA, 1768 proteins were identified and 8 protein modules were established. Enrichment analyses of protein modules revealed functional clusters mainly associated with inflammation and the complement and coagulation cascade as atherosclerosis developed under CKD conditions. The results may help to better understand the mechanisms of CKD-related atherosclerosis and guide future research on developing treatments for CKD-related atherosclerosis.

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Objective

Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.

Materials and methods

The microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).

Results

64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.

Conclusion

SERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.

Prediction and validation of potential molecular targets for the combination of Astragalus membranaceus and Angelica sinensis in the treatment of atherosclerosis based on network pharmacology

Since the 20th century, mortality rate due to cardiovascular diseases has increased, posing a substantial economic burden on society. Atherosclerosis is a common cardiovascular disease that requires urgent and careful attention. This study was conducted to predict and validate the potential molecular targets and pathways of Astragalus membranaceus and Angelica sinensis (A&A) in the treatment of atherosclerosis using network pharmacology. The active ingredients of A&A were obtained using the TCMSP database, while the target genes of atherosclerosis were acquired using 2 databases, namely GeneCards and DrugBank. The disease-target-component model map and the core network were obtained using Cytoscape 3.8.2 and MCODE plug-in, respectively. The core network was then imported into the STRING database to obtain the protein-protein interaction (PPI) network diagram. Moreover, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed using the HIPLOT online website. Finally, the small molecules related to key signaling pathways were molecularly docked and visualized. Under the screening conditions of oral bioavailability ≥ 30% and drug-likeness ≥ 0.18, 22 active ingredients were identified from A&A, and 174 relevant targets were obtained. Additionally, 54 active ingredients were found in the extracted core network. Interleukin (IL)-17 signaling pathway, tumor necrosis factor (TNF) signaling pathway, and Toll-like receptor (TLR) signaling pathway were selected as the main subjects through KEGG enrichment analysis. Core targets (RELA, IKBKB, CHUK, and MMP3) and active ingredients (kaempferol, quercetin, and isorhamnetin) were selected and validated using molecular docking. This study identified multiple molecular targets and pathways for A&A in the treatment of atherosclerosis. A&A has the potential to treat atherosclerosis through an antiinflammatory approach.

Increased Serum Mir-150-3p Expression Is Associated with Radiological Lung Injury Improvement in Patients with COVID-19

Coronavirus disease 2019 (COVID-19) is caused by the SARS-CoV-2 virus, responsible for an atypical pneumonia that can progress to acute lung injury. MicroRNAs are small non-coding RNAs that control specific genes and pathways. This study evaluated the association between circulating miRNAs and lung injury associated with COVID-19. Methods: We evaluated lung injury by computed tomography at hospital admission and discharge and the serum expression of 754 miRNAs using the TaqMan OpenArray after hospital discharge in 27 patients with COVID-19. In addition, miR-150-3p was validated by qRT-PCR on serum samples collected at admission and after hospital discharge. Results: OpenArray analysis revealed that seven miRNAs were differentially expressed between groups of patients without radiological lung improvement compared to those with lung improvement at hospital discharge, with three miRNAs being upregulated (miR-548c-3p, miR-212-3p, and miR-548a-3p) and four downregulated (miR-191-5p, miR-151a-3p, miR-92a-3p, and miR-150-3p). Bioinformatics analysis revealed that five of these miRNAs had binding sites in the SARS-CoV-2 genome. Validation of miR-150-3p by qRT-PCR confirmed the OpenArray results. Conclusions: The present study shows the potential association between the serum expression of seven miRNAs and lung injury in patients with COVID-19. Furthermore, increased expression of miR-150 was associated with pulmonary improvement at hospital discharge.

Cardiovascular Biomarkers: Lessons of the Past and Prospects for the Future

Cardiovascular diseases (CVDs) are a major healthcare burden on the population worldwide. Early detection of this disease is important in prevention and treatment to minimise morbidity and mortality. Biomarkers are a critical tool to either diagnose, screen, or provide prognostic information for pathological conditions. This review discusses the historical cardiac biomarkers used to detect these conditions, discussing their application and their limitations. Identification of new biomarkers have since replaced these and are now in use in routine clinical practice, but still do not detect all disease. Future cardiac biomarkers are showing promise in early studies, but further studies are required to show their value in improving detection of CVD above the current biomarkers. Additionally, the analytical platforms that would allow them to be adopted in healthcare are yet to be established. There is also the need to identify whether these biomarkers can be used for diagnostic, prognostic, or screening purposes, which will impact their implementation in routine clinical practice.

Angiogenin Levels and Their Association with Cardiometabolic Indices Following Vitamin D Status Correction in Saudi Adults

Simple Summary

Angiogenin (ANG) is a small 123 amino acid protein which in normal growth is associated with formation of new blood vessels in a process called angiogenesis; however, the abnormal levels of this protein in blood has been associated with diseases such as cancer, neurological disorders, and cardiovascular diseases. Vitamin D deficiency and elevated levels of blood lipids have also been associated with many diseases including cardiovascular diseases. In this study, the investigators tried to evaluate the relationship between the circulating levels of ANG, vitamin D, and lipids. The model used was vitamin D supplementation of deficient Saudi adults in order to find the effect of vitamin D correction on circulating levels of ANG and blood lipids. With vitamin D supplementation, modest but non-significant elevation in ANG was observed, as well as significant increase in apolipoproteins CIII and E and significant decrease in apo B. In addition, the correlation between circulating levels of ANG and apolipoproteins especially apo E observed in this study are interesting and should be investigated more as both are linked with neurologic disorders like Alzheimer’s and Parkinson’s diseases.

Abstract

Angiogenin (ANG), a multifunctional protein known to induce blood vessel formation, is a potential biomarker for cardiovascular diseases; however, whether it is affected by vitamin D supplementation is not known. This interventional study in vitamin D-deficient Saudi adults was designed to investigate it. A total of 100 vitamin D-deficient Saudi adults aged 30–50 years were randomly selected to undergo 6-month vitamin D supplementation. Circulating levels of fasting glucose, lipids, vitamin D, apolipoproteins (AI, AII, B, CI, CII, CIII, E, and H), and ANG were measured using commercially available assays at baseline and after six months. Overall, vitamin D levels increased significantly post intervention. With this, levels of apo-CIII and apo-E significantly increased (p-values of 0.001 and 0.009, respectively) with a significant parallel decrease in apo-B (p = 0.003). ANG levels were significantly positively associated with most apolipoproteins and inversely correlated with HDL-cholesterol. Post intervention, the changes in ANG levels were positively correlated with apo-E (r = 0.32; p < 0.01 in all subjects and r = 0.40; p < 0.05 in males). Vitamin D supplementation may modestly affect ANG levels. The association observed between ANG and apo-E is worthy of further investigation since both biomarkers have been linked to neurodegenerative disorders.

Beneficial Impact of Pork Dry-Cured Ham Consumption on Blood Pressure and Cardiometabolic Markers in Individuals with Cardiovascular Risk

Background: Evidence suggests that bioactive peptides reduce hypertension and affect certain metabolic pathways. Methods: Fifty-four volunteers with stage 1 prehypertension and/or hypercholesterolemia and/or basal glucose >100 mg/dL were recruited and randomized to pork dry-cured ham (n = 35) or cooked ham (placebo group; n = 19) for 28 days. After a wash-out period, meat products were changed for 28 additional days. Bioactive peptides composition and enzyme inhibitory activities of both products were characterized. Treatment comparisons for the main effects were made using a two (treatment) × two (times) repeated measures minus the effect of cooked ham (placebo). Results: 24 h mean systolic and diastolic pressures decreased up to 2.4 mmHg in the dry-cured ham period (treatment effect, p = 0.0382 y p = 0.0233, respectively) as well as the number of systolic pressure measures > 135 mmHg (treatment effect, p = 0.0070). Total cholesterol levels also decreased significantly after dry-cured ham intake (p = 0.049). No significant differences were observed between the two treatments for basal glucose, HOMA-IR index and insulin levels (p > 0.05). However, a significant rise of ghrelin levels was observed (treatment effect, p = 0.0350), while leptin plasma values slightly decreased (treatment effect, p = 0.0628). Conclusions: This study suggested the beneficial effects of regular dry-cured ham consumption on the improvement of systolic/diastolic blood pressures and facilitated the maintenance of metabolic pathways, which may be beneficial in the primary prevention of cardiovascular disease.