{"title":"Assessing Alzheimer's disease via plasma extracellular vesicle-derived mRNA.","authors":"Le Hoang Phu Pham, Ching-Fang Chang, Katherine Tuchez, Fei Liu, Yuchao Chen","doi":"10.1002/dad2.70006","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>mRNA derived from plasma EVs shows significant promise as a non-invasive biomarker for the early detection and continuous monitoring of AD.</p><p><strong>Highlights: </strong>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).</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"16 3","pages":"e70006"},"PeriodicalIF":4.0000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11399882/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/dad2.70006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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).
期刊介绍：
Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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