Glia-Driven Neuroinflammation and Systemic Inflammation in Alzheimer’s Disease
Bibliographic record
Abstract
The neuroinflammatory hypothesis of Alzheimer's disease (AD) was proposed more than 30 years ago. The involvement of the two main types of glial cells microglia and astrocytes, in neuroinflammation, was suggested early on. In this review, we highlight that the exact contributions of reactive glia to AD pathogenesis remain difficult to define, likely due to the heterogeneity of glia populations and alterations in their activation states through the stages of AD progression. In the case of microglia, it is becoming apparent that both beneficially and adversely activated cell populations can be identified at various stages of AD, which could be selectively targeted to either limit their damaging actions or enhance beneficial functions. In the case of astrocytes, less information is available about potential subpopulations of reactive cells; it also remains elusive whether astrocytes contribute to the neuropathology of AD by mainly gaining neurotoxic functions or losing their ability to support neurons due to astrocyte damage. We identify L-type calcium channel blocker, nimodipine, as a candidate drug for AD, which potentially targets both astrocytes and microglia. It has already shown consistent beneficial effects in basic experimental and clinical studies. We also highlight the recent evidence linking peripheral inflammation and neuroinflammation. Several chronic systemic inflammatory diseases, such as obesity, type 2 diabetes mellitus, and periodontitis, can cause immune priming or adverse activation of glia, thus exacerbating neuroinflammation and increasing risk or facilitating the progression of AD. Therefore, reducing peripheral inflammation is a potentially effective strategy for lowering AD prevalence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".