APOE interacts with tau PET to influence memory independently of amyloid PET
Bibliographic record
Abstract
Abstract Objective Apolipoprotein E (APOE) interacts with AD pathology to promote disease progression. Studies of APOE risk primarily focus on amyloid, however, and little research has assessed its interaction with tau pathology independent of amyloid. The current study investigated the moderating effect of APOE genotype on independent associations of amyloid and tau PET with cognition. Methods Participants included 297 older adults without dementia from the Alzheimer’s Disease Neuroimaging Initiative. Regression equations modeled associations between cognitive domains and (1) cortical Aβ PET levels adjusting for tau PET and (2) medial temporal lobe (MTL) tau PET levels adjusting for Aβ PET, including interactions with APOE ε4 carrier status. Results Adjusting for tau PET, Aβ was not associated with cognition and did not interact with ε4 status. In contrast, adjusting for Aβ PET, MTL tau PET was significantly associated with all cognitive domains. Further, there was a moderating effect of ε4 status on MTL tau and memory with the strongest negative associations in ε4 carriers and at high levels of tau. This interaction persisted even among only Aβ negative individuals. Interpretation APOE ε4 genotype strengthens the negative association between MTL tau and memory independently of Aβ, although the converse is not observed, and this association may be particularly strong at high levels of tau. These findings suggest that APOE may interact with tau independently of Aβ and that elevated MTL tau confers negative cognitive consequences in Aβ negative ε4 carriers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".