Microglia: A pharmacological target for the treatment of age-related cognitive decline and Alzheimer’s disease
Bibliographic record
Abstract
As individuals age, microglia, the resident immune cells of the central nervous system (CNS), become less effective at preserving brain circuits. Increases in microglial inflammatory activity are thought to contribute to age-related declines in cognitive functions and to transitions toward mild cognitive impairment (MCI) and Alzheimer's disease (AD). As microglia possess receptors for communicating with the CNS environment, pharmacological therapies targeting these pathways hold potential for promoting homeostatic microglial functions within the aging CNS. Preclinical and early phase clinical trials investigating the therapeutic effects of pharmacological agents acting on microglia, including reactive oxygen species, TREM2, fractalkine signaling, the complement cascade, and the NLRP3 inflammasome, are currently underway; however, important questions remain unanswered. Current challenges include target selectivity, as many of the signaling pathways are expressed in other cell types. Furthermore, microglia are a heterogenous cell population with transcriptomic, proteomic, and microscopy studies revealing distinct microglial states, whose activities and abundance shift across the lifespan. For example, homeostatic microglia can transform into pathological states characterized by markers of oxidative stress. Selective pharmacological targeting aimed at limiting transitions to pathological states or promoting homeostatic or protective states, could help to avoid potentially harmful off-target effects on beneficial states or other cell types. In this mini-review we cover current microglial pathways of interest for the prevention and treatment of age-related cognitive decline and CNS disorders of aging focusing on MCI and AD. We also discuss the heterogeneity of microglia described in these conditions and how pharmacological agents could target specific microglial states.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".