Cerebral atherosclerosis contributes to Alzheimer’s dementia independently of its hallmark amyloid and tau pathologies
Bibliographic record
Abstract
Abstract Cerebral atherosclerosis is a leading cause of stroke and an important contributor to dementia. However, little is known about its molecular effects on the human brain and how these alterations may contribute to dementia. Here, we investigated these questions using large-scale quantification of over 8300 proteins from 438 post-mortem brains from a discovery and replication cohort. A proteome-wide association study and protein network analysis of cerebral atherosclerosis found 114 proteins and 5 protein co-expression modules associated with cerebral atherosclerosis. Enrichment analysis of these proteins and modules revealed that cerebral atherosclerosis was associated with reductions in synaptic signaling and RNA splicing and increases in oligodendrocyte development and myelination. A subset of these proteins (n=23) and protein modules (n=2) were also associated with Alzheimer’s disease (AD) dementia, implicating a shared mechanism with AD through decreased synaptic signaling and regulation and increased myelination. Notably, neurofilament light (NEFL) and medium (NEFM) chain proteins were among these 23 proteins, and our data suggest they contribute to AD dementia through cerebral atherosclerosis. Together, our findings offer insights into effects of cerebral atherosclerosis on the human brain proteome, and how cerebral atherosclerosis contributes to dementia risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".