Circulating extracellular vesicles as an emerging tool for the early detection of Alzheimer’s disease or other dementia
Bibliographic record
Abstract
Abstract Background Alzheimer disease (AD) and vascular dementia (VaD) are the most common form of dementia worldwide. Actually, the search for early peripheral biomarkers for AD and other dementia represent a great challenge. Exosomes or extracellular vesicles (EVs) are key players in cerebral function. We have demonstrated that the levels of some pathogenic proteins related to AD (amyloid, Tau..) were higher in circulating EVs (pEVs) than in plasma with a differential regulation in controls, mild cognitive impairment (MCI) and AD patients. However, it is not clear whether these changes are specific to AD or other dementia subtypes. Our prospective study aims to identify, in pEVs and in plasma, early specific biomarkers in pre‐clinical cognitively impaired non demented patients (CIND) that develop different types of dementia. Method pEVs were isolated from plasma of healthy volunteers, AD patients and CIND patients stratified according to the type of dementia these patients develop five years after as follows: CIND, CIND‐AD, CIND‐VaD and CIND‐mixed dementia. EVs were characterized by NTA analysis, transmission electron microscopy and western‐blot measurement of vesicular proteins such as TSG101 and CD63. The selected brain‐specific proteins were assessed by Luminex assay. Result Our results showed that pEVs display a better discriminating capacity than plasma for most of the studied proteins. DJ‐1 and NSE concentration in EVs were increased in CIND‐VaD patients but not in CIND‐AD suggesting their utility as early markers for VaD. The levels of ANGPTL‐4, lipocalin and α‐synuclein in pEVs were able to discriminate specifically the apparent‐stages of AD. The α‐synuclein/DJ‐1 ratio measured in pEVs was reduced in all groups and could not provide a fair classification of dementia subtypes. In addition, the levels of APP, MMP‐9, progranulin S100B and pentraxin‐2 were unchanged in pEVs and plasma. Conclusion The present work show that several proteins contained in pEVs are differently expressed depending on the type of dementia and prompt the use of pEVs in early dementia diagnosis. This work was supported by the Chaire Louise & André on Alzheimer’s disease, Foundation Armand‐Frappier (CR) and CIHR grant (TF).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".