The characteristic large-scale annual analysis by gender and age in heart failure patients: cohort for 10 years in Korea

Editors' highlight picks from 2023 in ESC heart failure

Heart failure is a devastating syndrome affecting an increasingly high number of patients worldwide. Its aetiology and pathogenesis are complex with the involvement of factors ranging from the genetic material through valvular dysfunctions to numerous organs beyond the entire cardiovascular system. Based on continuous efforts of the heart failure scientific community we have witnessed major advances in many related disciplines during the last year. For example, epidemiological aspects-paving the road for improved risk prevention-have been thoroughly analysed for various geographical regions. Additionally, evidence-based approaches now allow the introduction of novel guideline recommended medical therapies (i.e. sodium-glucose transporter 2 inhibitors, and iron supplementation) while basic and translational research aim to explore additional molecular targets for future heart failure diagnostics and medications. All above aspects are addressed in this article, where a selection of articles published in the ESC Heart Failure journal in 2023 are highlighted. The editors are confident that the scientific contributions of ESC Heart Failure effectively served a highly relevant area of cardiovascular research last year.

Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms

Background

In the face of continued growth in the elderly population, the need to understand and combat age-related cardiac decline becomes even more urgent, requiring us to uncover new pathological and cardioprotective pathways.

Methods

We obtained the aging-related genes of heart failure through WGCNA and CellAge database. We elucidated the biological functions and signaling pathways involved in heart failure and aging through GO and KEGG enrichment analysis. We used three machine learning algorithms: LASSO, RF and SVM-RFE to further screen the aging-related genes of heart failure, and fitted and verified them through a variety of machine learning algorithms. We searched for drugs to treat age-related heart failure through the DSigDB database. Finally, We use CIBERSORT to complete immune infiltration analysis of aging samples.

Results

We obtained 57 up-regulated and 195 down-regulated aging-related genes in heart failure through WGCNA and CellAge databases. GO and KEGG enrichment analysis showed that aging-related genes are mainly involved in mechanisms such as Cellular senescence and Cell cycle. We further screened aging-related genes through machine learning and obtained 14 key genes. We verified the results on the test set and 2 external validation sets using 15 machine learning algorithm models and 207 combinations, and the highest accuracy was 0.911. Through screening of the DSigDB database, we believe that rimonabant and lovastatin have the potential to delay aging and protect the heart. The results of immune infiltration analysis showed that there were significant differences between Macrophages M2 and T cells CD8 in aging myocardium.

Conclusion

We identified aging signature genes and potential therapeutic drugs for heart failure through bioinformatics and multiple machine learning algorithms, providing new ideas for studying the mechanism and treatment of age-related cardiac decline.