Alzheimer's & dementia (Amsterdam, Netherlands) · 2024
Assessing Alzheimer's disease via plasma extracellular vesicle-derived mRNA.
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Pham Le Hoang Phu, Chang Ching-Fang, Tuchez Katherine, Liu Fei, Chen Yuchao
Аннотация
INTRODUCTION: Alzheimer's disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern. Recently it has been revealed that extracellular vesicles (EVs) play a critical role in AD pathogenesis and progression. Their stability and presence in various biofluids, such as blood, offer a minimally invasive window for monitoring AD-related changes.
METHODS: We analyzed plasma EV-derived messenger RNA (mRNA) from 82 human subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls. With next-generation sequencing, we profiled differentially expressed genes (DEGs), identifying those associated with AD.
RESULTS: Based on DEGs identified in both the MCI and AD groups, a diagnostic model was established based on machine learning, demonstrating an average diagnostic accuracy of over 98% and showed a strong correlation with different AD stages.
DISCUSSION: mRNA derived from plasma EVs shows significant promise as a non-invasive biomarker for the early detection and continuous monitoring of AD.
HIGHLIGHTS: The study conducted next-generation sequencing (NGS) of mRNA derived from human plasma extracellular vesicles (EVs) to assess Alzheimer's disease (AD).Profiling of plasma EV-derived mRNA shows a significantly enriched AD pathway, indicating its potential for AD-related studies.The AD-prediction model achieved a receiver-operating characteristic area under the curve (ROC-AUC) of more than 0.98, with strong correlation to the established Clinical Dementia Rating (CDR).
METHODS: We analyzed plasma EV-derived messenger RNA (mRNA) from 82 human subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls. With next-generation sequencing, we profiled differentially expressed genes (DEGs), identifying those associated with AD.
RESULTS: Based on DEGs identified in both the MCI and AD groups, a diagnostic model was established based on machine learning, demonstrating an average diagnostic accuracy of over 98% and showed a strong correlation with different AD stages.
DISCUSSION: mRNA derived from plasma EVs shows significant promise as a non-invasive biomarker for the early detection and continuous monitoring of AD.
HIGHLIGHTS: The study conducted next-generation sequencing (NGS) of mRNA derived from human plasma extracellular vesicles (EVs) to assess Alzheimer's disease (AD).Profiling of plasma EV-derived mRNA shows a significantly enriched AD pathway, indicating its potential for AD-related studies.The AD-prediction model achieved a receiver-operating characteristic area under the curve (ROC-AUC) of more than 0.98, with strong correlation to the established Clinical Dementia Rating (CDR).