What question did this study set out to answer?

To identify blood-based extracellular vesicle biomarkers for screening cerebral small vessel disease and predicting cognitive impairment progression.

May 16, 2026Open Access

Plasma extracellular vesicle proteins biomarker for cerebral small vessel disease related cognitive impairment

DYDan YangNanjing Brain Hospital PLPinyi LiuNanjing Brain Hospital MHMengxi HanNanjing Brain Hospital

Puntos clave

To identify blood-based extracellular vesicle biomarkers for screening cerebral small vessel disease and predicting cognitive impairment progression.
Multi-center cross-sectional and longitudinal study involving patients with cerebral small vessel disease and healthy controls.
Profiling of plasma extracellular vesicle proteomics was performed using LC-MS/MS.
Machine learning was utilized to optimize diagnostic combinations for identifying cerebral small vessel disease.
Three plasma extracellular vesicle proteins showed robust expression patterns across disease stages, with P < 0.001.
High diagnostic accuracy for cerebrovascular disease was observed (AUC = 0.849-0.897), stratifying high-risk and low-risk populations (P = 0.005).
These biomarkers correlated with specific cognitive functions and CSVD-related structural injuries.

Resumen

BACKGROUND: Cerebral small vessel disease (CSVD) is a major contributor to vascular dementia. Given the absence of effective treatments, the development of blood-based biomarkers for early diagnosis and prediction is paramount to facilitate targeted clinical interventions. We aimed to identified blood-based extracellular vesicle (EV) biomarkers for screening CSVD with normal cognition (CSVD-NC) or cognitive impairment (CSVD-CI), and predicting disease progression. METHODS: We conducted a multi-center cross-sectional and longitudinal study involving patients with CSVD-NC and CSVD-CI, healthy controls (HCs), and patients with CI due to Alzheimer's disease (AD-CI). Plasma EV proteomics were profiled using LC-MS/MS in a discovery set. Candidate EV biomarkers identified in this phase were subsequently validated in a larger independent set using parallel reaction monitoring. Machine learning was applied to develop and optimize diagnostic combinations for identifying CSVD and predicting disease progression. RESULTS: Plasma EV proteins exhibiting progressive changes during CSVD progression were predominantly enriched in immune-inflammatory and ribosomal dysfunction. Three plasma EV proteins (ATP6V1F, HNRNPU, and RPL38) demonstrated robust, cross-dataset consistency from HC to CSVD-NC to CSVD-CI continuum, and displayed distinct expression patterns between CSVD-CI and AD-CI (P < 0.001). These EV biomarkers demonstrated high diagnostic accuracy for CSVD with or without CI (AUC = 0.849-0.897), and effectively stratified high-risk versus low-risk populations for CSVD cognitive progression (P = 0.005). Moreover, they were associated with CSVD-related structural injury and specific cognitive functions: EV ATP6V1F with hippocampal atrophy, language, and memory; EV RPL38 with WMH burden and white matter integrity; and EV HNRNPU with visuospatial function and executive function. CONCLUSION: The identified plasma EV biomarkers reliably reflect CSVD-related vascular injury and cognitive decline, and hold promise as minimally invasive tools for early screening and risk stratification of CSVD-related CI, especially in community settings.

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