Neuropathological correlates and genetic architecture of microglial activation in elderly human brain
Bibliographic record
Abstract
Abstract Microglia, the resident immune cells of the brain, have important roles in brain health. However, little is known about the regulation and consequences of microglial activation in the aging human brain. We assessed the effect of microglial activation in the aging human brain by calculating the proportion of activated microglia (PAM), based on morphologically defined stages of activation in four regions sampled postmortem from up to 225 elderly individuals. We found that cortical and not subcortical PAM measures were strongly associated with β-amyloid, tau-related neuropathology, and rates of cognitive decline. Effect sizes for PAM measures are substantial, comparable to that of APOE ɛ4, the strongest genetic risk factor for Alzheimer’s disease. Mediation modeling suggests that PAM accelerates accumulation of tau pathology leading to cognitive decline, supporting an upstream role for microglial activation in Alzheimer’s disease. Genome-wide analyses identified a common variant (rs2997325) influencing cortical PAM that also affected in vivo microglial activation measured by positron emission tomography using [ 11 C]-PBR28 in an independent cohort. Finally, we identify overlaps of PAM’s genetic architecture with those of Alzheimer’s disease, educational attainment, and several other traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".