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. 2025 Dec;57(1):2493767.
doi: 10.1080/07853890.2025.2493767. Epub 2025 Apr 18.

Correlation of extracellular vesicle Alu RNA with brain aging and neuronal injury: a potential biomarker for brain aging

Shuyi Yu  1   2 , Jing Fan  1   3 , Shuai Zong  2 , Qian Yu  2 , Qian Cheng  2 , Yunshan Wang  2 , Ming Li  2 , Zhiming Lu  1   2
Affiliations

Correlation of extracellular vesicle Alu RNA with brain aging and neuronal injury: a potential biomarker for brain aging

Shuyi Yu et al. Ann Med. 2025 Dec.

Abstract

Background: Extracellular vesicles (EVs) are promising biomarkers for neurodegeneration. Alu elements are retrotransposons increasingly expressed with age and may be involved in aging-related diseases.

Objective: To determine the potential of Alu RNA in plasma-derived EVs as a biomarker for brain aging and neuronal injury.

Methods: EVs were isolated from plasma samples across different age groups. EV Alu RNA levels were measured and their associations with biomarkers of brain aging, including plasma neurofilament light chain (NfL), plasma amyloid-beta (Aβ42 and Aβ40), and plasma phosphorylated tau (p-Tau181), were analyzed.

Results: EV Alu RNA levels were increased significantly with age and were strongly correlated with plasma NfL, suggesting a strong association between EV Alu RNA and neuronal injury. Significant correlations were also found between EV Alu RNA and plasma amyloid-beta levels, while no significant association was observed with tau pathology.

Conclusions: EV Alu RNA levels are elevated with age and associated with neuronal injury, highlighting their potential as a novel, non-invasive biomarker for brain aging and neurodegeneration.

Keywords: Extracellular vesicle; alu RNA; biomarker; brain aging; neurodegeneration.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1.
Figure 1.
Isolation and characterization of plasma-derived extracellular vesicles (EVs). (A) NTA showing the particle size distribution of isolated EVs largely in the range of 100–300 nm. (B) Western blot analysis for EV markers (ALIX and CD63) and a cellular contamination marker (GM130). ALIX and CD63 are detected in the EV fraction, while GM130 is not detected, indicating the successful exclusion of cellular debris. (C) negative stain EM image showing the typical cup-shaped EV. (D) Workflow for plasma EV isolation and qPCR analysis. A total of 500 µL of plasma is used to isolate EVs, followed by RNA extraction, reverse transcription into cDNA, and qPCR analysis using SYBR green reagents to evaluate the expression of the transposable element gene alu.
Figure 2.
Figure 2.
Spearman correlation analysis between plasma extracellular vesicle (EV) alu expression (measured in arbitrary units, AU) and plasma biomarkers related to brain aging, including (A) age (year), (B) neurofilament light chain (NfL; pg/ml), (C) amyloid-beta 42 (Aβ42; pg/ml), (D) phosphorylated Tau181 (p-Tau181; pg/ml), (E) amyloid-beta 40 (Aβ40; pg/ml), and (F) the ratio of Aβ42 to Aβ40. Spearman correlation coefficient (r) and p-values are indicated for each panel.
Figure 3.
Figure 3.
Association between euro-cognitive evaluations and age. One-way ANOVA is used to analyze the differences between groups for normally distributed data, and non-normally distributed data are analyzed by non-parametric tests. Significant difference from control is determined based on p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), and p < 0.0001 (****), respectively; ns, not significant.
Figure 4.
Figure 4.
Spearman correlation between EV Alu RNA levels and euro-cognitive evaluations, including (A) MMSE, (B) MCA, (C) Hipocampal volume (mm3). Spearman correlation coefficient (r) and p-values are indicated for each panel.
Figure 5.
Figure 5.
ROC curves discriminating different age groups. (A) Young vs. middle-aged. (B) Middle-aged vs. old. (C) Young vs. old.
See this image and copyright information in PMC

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