Cardiovascular diseases in the elderly: possibilities for modulating autophagy using non-coding RNAs

Abstract

Autophagy is a crucial mechanism implicated in both aging and cardiovascular disease, which are two closely interconnected conditions. Modulation of autophagy is expected to have profound impacts on cellular aging and maintenance of cardiovascular functions under physiological or pathological conditions. Consequently, modulation of autophagy could be an effective strategy for counteracting age-induced vascular and cardiac remodelling as well as alleviating cardiovascular disease. The present review comprehensively elucidates the multifaceted impacts of autophagy on aging of the cardiovascular system. We comprehensively analyse both vascular and cardiac tissues, including vascular and cardiac malignancies, in distinct contexts. We also emphasize the significance of non-coding RNAs (ncRNAs) in the epigenetic regulation of gene expression and their roles as biomarkers of cardiovascular pathologies while maintaining clear distinctions between the vascular and cardiac tissues. Preclinical and clinical models are described herein to highlight the importance of ncRNAs in disease treatment by considering their involvement in the modulation of autophagy within the cardiocirculatory system. Finally, we conducted a comprehensive meta-analysis of transcriptomic data to underscore the paramount importance of autophagy while demonstrating it as a process that is frequently dysregulated in both cardiac and vascular cells under pathological conditions. The findings presented herein emphasize the importance of investigating novel strategies for modulating autophagy as a potential therapeutic approach to the management of age-related cardiovascular disorders.

Keywords: aging; autophagy; cardiovascular disease; meta-analysis; non-coding RNAs.

FIGURE 1

Age-standardized mortality rate per 100,000 persons for cardiovascular diseases (CVDs). The data were retrieved from Timmis et al. (2024). The orange bars represent men, and blue bars indicate women. UK, United Kingdom; BiH, Bosnia and Herzegovina.

FIGURE 2

Distribution showing the global causes of death. The data were retrieved from the IHME global burden and disease and global terrorism databases. The data are representative for 2019, when the total number of deaths was 55 million.

FIGURE 3

Non-coding RNAs (ncRNAs) affecting autophagy in CVDs. Overview of the autophagy pathway (blue) highlighting the interactions with microRNAs (red) and long non-coding RNAs (lncRNAs; green) in regulating the autophagy process in CVDs. The dotted lines indicate indirect interactions. (A) Non-coding RNAs involved with vascular tissues. MicroRNAs: miR-874-5p (Zhang et al., 2020b), miR-204-5p (Tian et al., 2024), miR-183-5p (Lin et al., 2024), miR-30a (Shi et al., 2022), miR-15b-5p (Zhang et al., 2023a), miR-424-5p (Zhang et al., 2023b), miR-188-3p (Song et al., 2023), miR-92a (Cao et al., 2024), miR-130a (Zheng et al., 2021a), miR-125b-1-3p (Chen et al., 2024a), miR-145-5p (Zhang et al., 2022a), miR-21-5p (Ke et al., 2022), and miR-216a-5p (Wang et al., 2019b). Long non-coding and circular RNAs: SNHG12 (Li et al., 2019b), MALAT1 (Wang et al., 2019c; Zhu et al., 2019), PVT1 (Zhang et al., 2023c), RASSF8-AS1 (Song et al., 2023), circ0001402 (Lin et al., 2024), and circ00300442 (Yu et al., 2021). (B) Non-coding RNAs involved with cardiac tissues. MicroRNAs: miR-30a (Wang et al., 2019a), miR-873-5p (Zhu et al., 2024), miR-129-5p (Mi et al., 2023), miR-34c-5p (Zhang et al., 2022b), miR-374a-5p (Chen et al., 2021c), miR-520d-3p (Wu et al., 2021c), miR-383-5p (Liu et al., 2024a), miR-485-5p (Zhou et al., 2020), miR-103-3p (Xue et al., 2023), miR-384-5p (Zhang et al., 2019), miR-490-3p (Wu et al., 2021b), miR-494 (Ning et al., 2020), miR-143 (Lv et al., 2021), miR-20b (Wang et al., 2019c; Qiu et al., 2021), miR-378a-3p (Zhao et al., 2020), miR-302a-3p (Zeng et al., 2024), and miR-186 (Ouyang et al., 2020). Long non-coding and circular RNAs: AK088388 (Wang et al., 2019a), TUG1 (You et al., 2020; Tan et al., 2023), H19 (Lv et al., 2021), MALAT1 (Wang et al., 2019c), NEAT1 (Zhao et al., 2020), PART1 (Zeng et al., 2024), MEG3 (Mi et al., 2023), TTTY15 (Chen et al., 2021c), ZFAS1 (Liu et al., 2024b), circHIPK2 (Zhou et al., 2020), and circ-HIPK3 (Qiu et al., 2021).Created with Biorender.com.

FIGURE 4

Representation of differentially expressed genes (DEGs). (A) Venn diagram of the DEGs reported in different studies. (B) Pathway enrichment analysis of the DEGs common to at least two studies. RA, Reactome database; KEGG, Kyoto encyclopedia of genes and genomes pathway database.

FIGURE 5

Network of DEGs shared by at least two studies and related to autophagy. The DEGs shared by at least two studies code for proteins related with autophagy as well as proteins that interact with each other to form a network described by two big clusters—one associated with ubiquitination (blue nodes) and another associated with autophagy and chaperone-mediated protein folding (red and green nodes, respectively)—according to the Reactome database. The light-green edges represent interactions retrieved by text mining, violet ones represent experimentally identified interactions, and light-blue ones represent interactions sourced from curated databases.