An aging, pathology burden, and glial senescence build-up hypothesis for late onset Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer's disease (AD) predominantly occurs as a late onset (LOAD) form involving neurodegeneration and cognitive decline with progressive memory loss. Risk factors that include aging promote accumulation of AD pathologies, such as amyloid-beta and tau aggregates, as well as inflammation and oxidative stress. Homeostatic glial states regulate and suppress pathology buildup; inflammatory states exacerbate pathology by releasing pro-inflammatory cytokines. Multiple stresses likely induce glial senescence, which could decrease supportive functions and reinforce inflammation. In this perspective, we hypothesize that aging first drives AD pathology burden, whereafter AD pathology putatively induces glial senescence in LOAD. We hypothesize that increasing glial senescence, particularly local senescent microglia accumulation, sustains and drives perpetuating buildup and spread of AD pathologies, glial aging, and further senescence. We predict that increasing glial senescence, particularly local senescent microglia accumulation, also transitions individuals from healthy cognition into mild cognitive impairment and LOAD diagnosis. These pathophysiological underpinnings may centrally contribute to LOAD onset, but require further mechanistic investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".