Non-invasive biomarkers for brain aging: the role of autophagy-related microRNAs in plasma exosomes

Abstract

Aim: This study aimed to identify autophagy-related microRNAs (miRNAs) in plasma exosomes as non-invasive biomarkers for brain aging and explore their potential to improve early detection of age-associated neurodegeneration. With the increasing incidence of neurodegenerative disorders, such as Alzheimer's disease (AD), frontotemporal dementia (FTD), and Parkinson's disease (PD), non-invasive diagnostic tools are urgently needed.

Methods: Plasma samples were collected from 200 individuals, divided into three groups, including young (20-40 years), middle-aged (41-60 years), and elderly (> 60 years). Exosomes were isolated, followed by small RNA sequencing (sRNA-seq) to identify differentially expressed miRNAs, and differentially expressed miRNAs related to autophagy were validated using quantitative real-time PCR (qRT-PCR). Spearman correlation analysis was performed to assess the relationship between autophagy-related miRNAs and brain aging biomarkers. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance.

Results: Nine autophagy-related miRNAs were identified and validated as significantly upregulated in plasma exosomal from elderly, including hsa-miR-2110, hsa-miR-18a-3p, hsa-miR-766-3p, hsa-miR-4446-3p, hsa-miR-4667-5p, hsa-miR-4433b-3p, hsa-miR-146a-5p, hsa-miR-423-5p, and novel_260. These miRNAs were validated by qRT-PCR. Correlation analysis showed that several of these miRNAs, such as hsa-miR-2110 and hsa-miR-766-3p, were strongly correlated with NfL (r = 0.68, p = 0.002), Aβ42 (r = 0.62, p = 0.004), and p-Tau181 (r = 0.55, p = 0.008). ROC curve analysis showed that combining these miRNAs with NfL resulted in an area under the curve (AUC) of 0.92, outperforming NfL alone (AUC = 0.85) and miRNAs alone (AUC = 0.84). Further subgroup analysis revealed that multiple miRNAs, such as miR-2110, miR-4446-3p, and novel_260, achieved high AUCs (>0.83) in distinguishing middle-aged adults (41-60 years) from older adults (>60 years), supporting their potential utility for early detection of age-associated neurodegeneration.

Conclusion: This study identifies a set of autophagy-related miRNAs as promising biomarkers for brain aging. The combination of these miRNAs with traditional biomarkers offers a non-invasive and highly sensitive method for early detection of brain aging, providing significant potential to enhance diagnostic accuracy in neurodegenerative diseases.

Keywords: autophagy; biomarkers; brain aging; exosomes; miRNA.

Figure 1

Plasma exosomal miRNAs holding significant potential for diagnosing brain aging. Based on extracellular vesicle (EV) purification, sRNA sequencing, and qRT-PCR, a total of 9 exosomal miRNAs are identified as potential biomarkers for brain aging diagnosis.

Figure 2

KEGG enrichment analysis based on differentially expressed genes (DEGs) of elderly individuals vs. adults. (A) Volcano plot illustrating differentially expressed plasma exosomal miRNAs of elderly vs. adults based on sRNA sequencing. (B) Scatter diagram of KEGG enrichment analysis showing that the differentially expressed miRNAs are significantly enriched in pathways related to autophagy (highlighted in a red box). (C) Intersecting brain aging-related genes from the AgeAnno database with autophagy-related genes identified from sRNA sequencing indicating autophagy genes associated with brain aging.

Figure 3

Isolation and characterization of plasma-derived extracellular vesicles (EVs). (A) Western blot analysis of EV markers (ALIX and CD63) and the cellular contamination marker (GM130). ALIX and CD63 are detected in the EV fraction, while GM130 is absent, confirming effective removal of cellular debris. (B) Nanoparticle tracking analysis (NTA) revealing that the size distribution of isolated EVs predominantly falls within the 100–300 nm range. (C,D) Transmission electron microscopy (TEM) images showing the characteristic cup-shaped morphology of EVs, which are indicated by white arrows.

Figure 4

Spearman correlation analysis between plasma exosomal miRNA expression (measured in arbitrary units, AU) and plasma biomarkers related to brain aging, including (A) neurofilament light chain (NfL; pg./ml), (B) amyloid-beta 42 (Aβ42; pg./ml), (C) phosphorylated Tau181 (p-Tau181; pg./ml), (D) amyloid-beta 40 (Aβ40; pg./ml), and (E) the ratio of Aβ42 to Aβ40. Spearman correlation coefficient (r) and p-values are indicated for each panel.

Figure 5

ROC curves discriminating diffenrent age groups. (A) Young (20–40 years) vs. Middle-aged (41–60 years). (B) Middle-aged (41–60 years) vs. Elderly (>60 years).