Apolipoprotein E4‐driven effects on inflammatory and neurotrophic factors in peripheral extracellular vesicles from cognitively impaired not demented participants converted to Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background In brain, extracellular vesicles (EVs) play an essential role in neuron‐glia interface and ensure the crosstalk between the brain and the periphery. Only a few studies have demonstrated the apolipoprotein E4 variant (APOE ε4)‐driven dysfunction of EVs pathway and the risk to develop Alzheimer’s disease (AD). To better understand the role of APOE ε4 in pre‐clinical AD, we determined levels of pathogenic, neurotrophic and inflammatory proteins in peripheral EVs (pEVs) and in plasma from cognitively impaired‐not demented (CIND) participants stratified upon the absence (APOE ε4‐) or the presence (APOE ε4+) of the ε4 allele of APOE. Method Levels of 15 neurodegenerative, neurotrophic and neuroinflammatory proteins were quantified in pEVs by the multiplex Luminex assay and compared to their plasma levels from cognitively normal and CIND participants Result For the first time, several neurotrophic and inflammatory markers including LCN‐2, S100B, ANGPTL‐4, NPTX‐2 and α‐synuclein were evidenced in pEVs. Some proteins such as α‐Syn, NPTX‐2 and S100B were enriched in pEVs as compared to plasma. APOE ε4+ was associated with differential regulation of 7 markers and compromised the release of pEVs formed by an endosomal route. The pentraxin‐2/α‐synuclein ratio measured in pEVs was able to predict AD 5 years before the onset among APOE ε4+ CIND individuals. Discussion: Our findings suggest an alteration of the endosomal pathway in APOE ε4+ carriers and that pEVs pentraxin‐2/α‐synuclein ratio could serve as a useful early biomarker for AD susceptibility. Conclusion The findings reported herein provide a comprehensive insight and enhance our knowledge on the emerging role of ApoE4 in abnormal pEVs cargo proteins processing and the identification of blood‐based biomarkers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".