Multiomics tools for improved atherosclerotic cardiovascular disease management

Scientometric analysis of evolution in sex-specific MSC therapy for cardiovascular diseases

BACKGROUND

Mesenchymal stem cell (MSC) therapy for cardiovascular diseases has shown promise; however, sex-specific differences remain understudied. This scientometric analysis provides the first comprehensive overview of sex-specific differences in mesenchymal stem cell (MSC) therapy for cardiovascular diseases, spanning from 1947 to 2024.

METHODS

We analyzed 61,029 publications using advanced bibliometric tools to identify research hotspots, publication trends, and collaborative networks.

RESULTS

A significant shift in research focus has been observed in the field of mesenchymal stem cell (MSC) therapy for cardiovascular diseases, transitioning from broad cardiovascular concepts in the 20th century to specialized sex-specific considerations in the 21st century. Furthermore, in the 21st-century research landscape, the formation of two distinct clusters for "male" and "female" in VOSviewer-generated network visualizations is highly important, emphasizing the growing recognition of sex-specific differences in MSC therapy responses and outcomes. This shift was accompanied by a marked increase in terminology related to sex-specific differences, with keywords like "genetic association" and "body mass index" forming distinct clusters in recent years.

CONCLUSIONS

This analysis underscores the critical need for sex-specific considerations in MSC therapy for cardiovascular disease. The emergence of distinct male and female clusters in research networks emphasizes the importance of tailoring approaches based on sex differences. Key areas identified for future investigation include the role of epigenetics in mediating sex-specific effects and the potential of sex-matched MSC-derived exosomes. These findings pave the way for more effective and personalized approaches in cardiovascular regenerative medicine, potentially leading to improved outcomes through sex-specific therapeutic strategies.

An integrated view of the pathophysiological crosstalk between adipose tissue, bone and cardiovascular system in men and women

BACKGROUND

Obesity, bone-related and cardiovascular diseases (CVD) are among the leading global health concerns. Growing evidence suggests that these conditions share common pathophysiological pathways and disease outcomes. PATHOGENETIC INTERACTIONS OF OBESITY, CVD AND BONE-RELATED DISEASES: Obesity is a well-established risk factor for atherosclerotic CVD (ASCVD), as dysfunctional ectopic adipose tissue may produce endocrine/paracrine hormones modulating metabolic processes and inflammation, predisposing to ASCVD. Although obesityhas been considered a protective factor for bone loss, it may lead to osteoporosis development and increased fracture risk at specific sites. Biological and epidemiological evidence has demonstrated the existence of a dynamic relationship between ASCVD and osteoporosis, since atherosclerotic calcification and bone mineralization share common pathophysiological mechanisms. Therefore, addressing ASCVD, obesity, and bone-related diseases requires multiple-level approach, which involve accurate screening, lifestyle modifications and pharmacological interventions.The current evidence about the pathophysiological relationships between obesity, bone-related diseases and ASCVD is discussed herein, highlighting common risk factors, proposed biomolecular mechanisms, clinical outcomes, lifestyle changes and pharmacological treatments.

CONCLUSIONS

As populations become increasingly older and obese, understanding the correlation within this triad highlights an unmet clinical need. Applying this knowledge would help to reduce both societal and individual costs, while supporting the development of novel preventive, diagnostic and therapeutic strategies to reduce morbidity and disability associated with cardio-metabolic and bone-related diseases.

An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018

Current research on the association between demographic variables and dietary patterns with atherosclerotic cardiovascular disease (ASCVD) is limited in breadth and depth. This study aimed to construct a machine learning (ML) algorithm that can accurately and transparently establish correlations between demographic variables, dietary habits, and ASCVD. The dataset used in this research originates from the United States National Health and Nutrition Examination Survey (U.S. NHANES) spanning 1999-2018. Five ML models were developed to predict ASCVD, and the best-performing model was selected for further analysis. The study included 40,298 participants. Using 20 population characteristics, the eXtreme Gradient Boosting (XGBoost) model demonstrated high performance, achieving an area under the curve value of 0.8143 and an accuracy of 88.4%. The model showed a positive correlation between male sex and ASCVD risk, while age and smoking also exhibited positive associations with ASCVD risk. Dairy product intake exhibited a negative correlation, while a lower intake of refined grains did not reduce the risk of ASCVD. Additionally, the poverty income ratio and calorie intake exhibited non-linear associations with the disease. The XGBoost model demonstrated significant efficacy, and precision in determining the relationship between the demographic characteristics and dietary intake of participants in the U.S. NHANES 1999-2018 dataset and ASCVD.

EDITORIAL for BJP themed issue "noncoding RNA therapeutics"

LINKED ARTICLES

This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc.

Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns

Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δβ >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.

Cardiovascular Imaging in the Era of Precision Medicine: Insights from Advanced Technologies - A Narrative Review

Background and Aims

Cardiovascular diseases are responsible for a high mortality rate globally. Precision medicine has emerged as an essential tool for improving cardiovascular disease outcomes. In this context, using advanced imaging exams is fundamental in cardiovascular precision medicine, enabling more accurate diagnoses and customized treatments. This review aims to provide a concise review on how advanced cardiovascular imaging supports precision medicine, highlighting its benefits, challenges, and future directions.

Methods

A literature review was carried out using the Pubmed and Google Scholar databases, using search strategies that combined terms such as precision medicine, cardiovascular diseases, and imaging tests.

Results

More advanced analysis aimed at diagnosing and describing cardiovascular diseases in greater detail is made possible by tests such as cardiac computed tomography, cardiac magnetic resonance imaging, and cardiac positron emission tomography. In addition, the aggregation of imaging data with other omics data allows for more personalized treatment and a better description of patient profiles.

Conclusion

The use of advanced imaging tests is essential in cardiovascular precision medicine. Although there are still technical and ethical obstacles, it is essential that there is collaboration between health professionals, as well as investments in technology and education to better disseminate cardiovascular precision medicine and consequently promote improved patient outcomes.

Multiomic biomarkers after cardiac arrest

Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient's care.

Decoding viral and host microRNA signatures in airway-derived biosamples: Insights for biomarker discovery in viral respiratory infections

The global public health crisis caused by the COVID-19 pandemic has intensified the global concern regarding viral respiratory tract infections. Despite their considerable impact on health, society and the economy, effective management of these conditions remains a significant challenge. Integrating high-throughput analyses is pivotal for early detection, prognostication of adverse outcomes, elucidating pathogenetic pathways and developing therapeutic approaches. In recent years, microRNAs (miRNAs), a subset of small noncoding RNAs (ncRNAs), have emerged as promising tools for molecular phenotyping. Current evidence suggests that miRNAs could serve as innovative biological markers, aiding in informed medical decision-making. The cost-effective quantification of miRNAs in standardized samples using techniques routinely employed in clinical laboratories has become feasible. In this context, samples obtained from the airways represent a valuable source of information due to their direct exposure to the infectious agent and host response within the respiratory tract. This review explores viral and host miRNA profiling in airway-derived biosamples as a source of molecular information to guide patient management, with a specific emphasis on SARS-CoV-2 infection.