Exosomal microRNA biomarkers in Alzheimer's disease: Systematic review and meta-analysis

Recent studies have suggested an association between exosomal microRNAs (miRNAs) and Alzheimer's disease (AD). However, their effectiveness as clinical biomarkers has yet to be definitively established. This study aims to assess the diagnostic value of exosomal miRNAs in AD through a meta-analysis.

As of June 10, 2025, a comprehensive literature search was conducted across eight databases using various search strategies to identify studies that met the inclusion criteria. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was employed to evaluate the quality of the included studies. Threshold heterogeneity was examined using Meta-DiSc 1.4 software. Stata/MP 18.0 software was used to synthesize effect sizes, including sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Subgroup analysis, meta-regression, and sensitivity analysis were conducted to explore potential sources of heterogeneity. Deek's funnel plot was used to assess publication bias.

A total of 14 articles (comprising 23 studies) were included, involving 910 patients and 696 controls. The pooled SEN, SPE, PLR, NLR, DOR, and AUC were 0.83 [95 % CI (0.77-0.88)], 0.84 [95 % CI (0.76-0.90)], 5.24 [95 % CI (3.32-8.26)], 0.20 [95 % CI (0.15-0.29)], 25.70 [95 % CI (12.79-51.62)] and 0.90 [95 % CI (0.87-0.92)], respectively. The Spearman correlation coefficient was 0.070 (P = 0.752 > 0.05), indicating an absence of threshold effect heterogeneity. The asymmetry test of Deek's funnel plot revealed no evidence of publication bias (P = 0.06 > 0.05). Subgroup analysis demonstrated enhanced diagnostic performance for AD using exosomal miRNAs in blood samples with a sample size of ≥ 50 and a specific cut-off value.

The findings of this study suggest that the expression levels of exosomal miRNAs are correlated with AD and may serve as potential diagnostic biomarkers for the disease.