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Record W7119778413 · doi:10.1002/alz70856_105183

Investigating Salivary Extracellular Vesicles as Biomarkers for Alzheimer's Disease: ExosomeAD Study Design and Baseline Characteristics

2025· article· en· W7119778413 on OpenAlexaboutno aff
Victoria Sanborn, Jonathan D. Drake, Hannah Alaimo, E.M. Teixeira, Jenna R Pracht, Charles Denby, M. Pereira, Sicheng Wen, Peter J. Quesenberry, Jill A. Kreiling, Lori A. Daiello

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
Fundersnot available
KeywordsBiomarkerExtracellular vesiclesExosomeExtracellular vesicleRNASalivaBaseline (sea)

Abstract

fetched live from OpenAlex

Abstract Background Brain‐derived salivary extracellular vesicles (EVs) contain mRNA, miRNA, and protein species which have the potential to be used for molecular characterization of brain health. Prior analysis of salivary EV mRNA identified Alzheimer's disease (AD)‐ and inflammation‐related biomarkers that may be diagnostically useful in this regard, however EV analysis is still in its infancy. The primary objective of this study is to identify a novel biomarker signature for AD using salivary EVs. Method ExosomeAD is a 60‐month longitudinal cohort study enrolling older adults with normal cognition (CN; n = 150) and mild cognitive impairment (MCI; n = 50) at the Rhode Island Hospital Alzheimer's Disease and Memory Disorders Center. Baseline evaluation includes neuropsychological testing, self‐report inventories (mood, subjective cognitive impairment, daily functioning), vital signs, and collection of saliva and blood samples. Salivary EVs are being analyzed for mRNA, miRNA, and protein composition and compared with plasma biomarkers of AD risk assessed by PrecivityAD (C2N Diagnostics) testing (plasma Aß42‐40 ratio, APOE proteotype, and the Amyloid Probability Score (APS)). Participants complete up to 4 annual follow up visits (cognitive testing, surveys, and saliva/blood sample collection). Result Currently, 183 participants (CN=163; MCI=20) have been enrolled. Participants in both groups to‐date are predominately female (CN, n = 115 (71%); MCI, n = 13 (65%). On average, participants with MCI (M age =77.7, standard deviation (SD)=5.6) are older than the CN group (M age =72.1 (5.1)). The group mean Montreal Cognitive Assessment total score in the current sample is lower among those with MCI (M=20.1 (4.1)) vs CN (M=27 (2.2)). MCI participants are more likely to be APOE4 carriers (71.4% vs CN, 31.1%), and have higher median APS (MCI APS =81.5; CN APS =17). More than half of participants endorsed family history positive for dementia (CN=60%; MCI=65%). Conclusion Salivary EVs contain important information about brain health and may be a useful biomarker for AD. However, AD‐specific signatures in EVs have not yet been characterized. If successful, the Exosome Study will be first to demonstrate that salivary EV RNA and protein can be used to detect AD and facilitate the development of an inexpensive and noninvasive screening method for use in specialty and primary care settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.287
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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