Astrocyte and Glial interaction in developing novel therapeutic strategies
Bibliographic record
Abstract
BACKGROUND: Neuroinflammation is hallmark of Alzheimer's disease (AD) that drives the accumulation of amyloid-β (Aβ) and neurofibrillary tangles (NFTs). However, the role of neuroinflammation for progression in preclinical AD has not been defined. METHOD: We used Nucleic acid Linked Immuno-Sandwich Assay (NULISA) for targeted proteomics in 32 cognitively unimpaired individuals younger than 30 years (CUY), 154 cognitively unimpaired older than 30 years (CU), 39 people with mild cognitive impairment (pwMCI), 50 pwAD and 107 people with other neurological diseases (OND). Longitudinal data was available for 146 individuals with a mean follow-up of 26 months. We used unsupervised analyses to identify biological themes and performed cross-sectional association and mediation analyses with Aβ-PET, tau-PET, structural MRI, different blood phospho-tau (p-tau) analytes as well as longitudinal analyses. RESULTS: We defined gene ontology themes and pathways that were differently regulated across the AD continuum. We detected that CSF signatures for glia activation, immune signaling, and calcium signaling gradually increased during aging and across the AD continuum. Notably, glia activation and calcium signaling signatures were already impaired in CU A+ in comparison to CU A-. Further longitudinal analyses in CU revealed an increase of neuroinflammation, glia activation, activation of cell death pathways and deficits in mitochondrial transport in A+ but not in A- participants underlining the specificity for AD. This increase was significantly correlated with p-tau217 progression in CU A+. Finally, mediation analyses in A-T- and A+T- participants revealed that activation of cell death pathways and glia activation mediated the effect of early AD progression measured by p-tau217 on neuroinflammation. Additionally, we found a significant association between neuroinflammation and activation of cell death pathways and glia activation. However, only the effect on cell death pathway activation but not glia activation was mediated by altered synaptic signaling and disturbed mitochondrial axonal transport. CONCLUSION: We show that neuroinflammation and cell death pathways are important drivers of disease progression in preclinical and early AD. We propose that Aβ initiates a vicious cycle of neuroinflammation, glia activation, neuronal dysfunction and activation of cell death pathways that underlines the need for immunomodulatory interventions already in the early AD disease phase.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".