Exploration of Potential Genetic Biomarkers for Heart Failure: A Systematic Review

The role of biomarkers in the prognosis and risk stratification in heart failure: A systematic review

Acute heart failure (AHF) episodes are marked by high rates of morbidity and mortality during the episode and minimal advancements in its care. Multiple biomarker monitoring is now a crucial supplementary technique in the therapy of AHF. A scientific literature search was conducted by assessing and evaluating the most pertinent research that has been published, including original papers and review papers with the use of PubMed, Medline, and Cochrane databases. Established biomarkers like natriuretic peptides (BNP, NT-proBNP) and cardiac troponins play crucial roles in diagnostic and prognostic evaluation. Emerging biomarkers such as microRNAs, osteopontin, galectin-3, ST2, and GDF-15 show promise in enhancing risk stratification and predicting adverse outcomes in HF. However, while these biomarkers offer valuable insights, their clinical utility requires further validation and integration into practice. Continued research into novel biomarkers holds promise for early HF detection and risk assessment, potentially mitigating the global burden of HF. Understanding the nuances of biomarker utilization is crucial for their effective incorporation into clinical practice, ultimately improving HF management and patient care.

A Set of Possible Markers for Monitoring Heart Failure and Cognitive Impairment Associated: A Review of Literature from the Past 5 Years

BACKGROUND

Heart failure is an epidemiologically relevant disease because of the aging population and widespread lifestyles that promote it. In addition to the acute event, it is possible for the disease to become chronic with periodic flare-ups. It is essential to study pathology from a diagnostic and prognostic point of view and to identify parameters for effective monitoring. In addition, heart failure is associated with multiple comorbidities, including cognitive impairment, which is monitored clinically but not through specific biomarkers in these patients. The purpose of this review is to gather the most recent scientific evidence on a few possible biomarkers previously identified for monitoring heart failure and associated cognitive impairment.

METHODS

We surveyed studies inherent to a set of previously identified markers, evaluating English-language articles from the past five years conducted in adult heart failure patient populations. We used the databases PubMed, Web of Sciences, and Cochrane Library for search studies, and we considered articles published in journals with an impact factor greater than five in the publication year.

RESULTS

Among the biomarkers evaluated, a concordant indication for serial measurements for heart failure monitoring emerged only for interleukin-6. For the other markers, there is still little evidence available, which is interesting but sometimes conflicting. Interesting studies have also emerged for biomarkers of cognitive decline assessed in patients with heart failure, confirming the hypotheses of the increasingly studied heart-brain correlation.

CONCLUSION

Certainly, further studies in large populations are needed to identify effective biomarkers for monitoring heart failure and associated cognitive impairment.

Identification of hub genes in heart failure by integrated bioinformatics analysis and machine learning

Objective

To screen feature genes of heart failure patients through machine learning methods, in order to identify characteristic genes driving heart failure and investigate the progression of heart failure.

Methods

Heart failure patient samples were downloaded from the public database GEO (Gene Expression Omnibus), including the datasets GSE116250, GSE120895, and GSE59867. GSE116250 and GSE120895 were used as the testing set, while GSE59867 was used as the validation set. LASSO regression analysis and SVM-RFE were utilized to identify feature genes.

Results

Analysis showed that among the differentially expressed genes between normal and heart failure patients, 9 genes were upregulated and 10 genes were downregulated. ROC curve analysis in the training set showed that TAGLN and SGPP2 had AUC values greater than 0.7. Moreover, SDSL and SMTNL2 had even higher AUC values of greater than 0.9. However, further analysis in the validation set showed that only SDSL had an AUC value greater than 0.7. Western blot experiments, RT-PCR, and ISO-induced experiments confirmed that SDSL was highly expressed in heart failure patients and promoted heart failure progression. In addition, SDSL promoted PARP1 expression and knockdown of SDSL expression led to decreased Cleaved-PARP1 expression and reduced cardiomyocyte apoptosis. Conversely, overexpression of SDSL resulted in increased PARP1 expression and myocardial cell apoptosis. These results suggest that elevated expression of SDSL in cardiomyocytes from heart failure patients may be an important factor promoting the occurrence and development of heart failure.

Conclusions

Using machine learning methods and experimental validation, it has been demonstrated that SDSL is a driving gene in patients with heart failure, providing a new treatment direction for clinical treatment.

Do Heart Failure Biomarkers Influence Heart Failure Treatment Response?

PURPOSE OF REVIEW

Heart failure (HF) is one of the leading causes of cardiac morbidity and mortality around the world. Our evolving understanding of the cellular and molecular pathways of HF has led to the identification and evaluation of a growing number of HF biomarkers. Natriuretic peptides remain the best studied and understood HF biomarkers, with demonstrated clinical utility in the diagnosis and prognostication of HF. Less commonly understood is the utility of HF biomarkers for guiding and monitoring treatment response. In this review, we outline the current HF biomarker landscape and identify novel biomarkers that have potential to influence HF treatment response.

RECENT FINDINGS

An increasing number of biomarkers have been identified through the study of HF mechanisms. While these biomarkers hold promise, they have not yet been proven to be effective in guiding HF therapy. A more developed understanding of HF mechanisms has resulted in an increased number of available pharmacologic HF therapies. In the past, biomarkers have been useful for the diagnosis and prognostication of HF. Future evaluation on their use to guide pharmacologic therapy is ongoing, and there is promise that biomarker-guided therapy will allow clinicians to begin personalizing treatment for their HF patients.

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Heart failure (HF) is the main manifestation of cardiovascular disease. Recent studies have shown that various RNA molecules and their complex connections play an essential role in HF’s pathogenesis and pathological progression. This paper aims to mine key RNA molecules associated with HF. We proposed a Prior-knowledge Driven Joint Deep Semi-Negative Matrix Factorization (PD-JDSNMF) model that uses a hierarchical nonlinear feature extraction method that integrates three types of data: mRNA, lncRNA, and miRNA. The PPI information is added to the model as prior knowledge, and the Laplacian constraint is used to help the model resist the noise in the genetic data. We used the PD-JDSNMF algorithm to identify significant co-expression modules. The elements in the module are then subjected to bioinformatics analysis and algorithm performance analysis. The results show that the PD-JDSNMF algorithm can robustly select biomarkers associated with HF. Finally, we built a heart failure diagnostic model based on multiple classifiers and using the Top 13 genes in the significant module, the AUC of the internal test set was up to 0.8714, and the AUC of the external validation set was up to 0.8329, which further confirmed the effectiveness of the PD-JDSNMF algorithm.

Role of Trientine in Hypertrophic Cardiomyopathy: A Review of Mechanistic Aspects

Abnormality in myocardial copper homeostasis is believed to contribute to the development of cardiomyopathy. Trientine, a copper-chelating drug used in the management of patients with Wilson's disease, demonstrates beneficial effects in patients with hypertrophic cardiomyopathy. This review aims to present the updated development of the roles of trientine in hypertrophic cardiomyopathy. The drug has been demonstrated in animal studies to restore myocardial intracellular copper content. However, its mechanisms for improving the medical condition remain unclear. Thus, comprehending its mechanistic aspects in cardiomyopathy is crucial and could help to expedite future research.