APOEε4 potentiates the relationship between amyloid-β and tau pathologies
Bibliographic record
Abstract
Abstract APOEε4 is the most well-established genetic risk factor for sporadic Alzheimer’s disease and is associated with cerebral amyloid-β. However, the association between APOEε4 and tau pathology, the other major proteinopathy of Alzheimer’s disease, has been controversial. Here, we sought to determine whether the relationship between APOEε4 and tau pathology is determined by local interactions with amyloid-β. We examined three independent samples of cognitively unimpaired, mild cognitive impairment and Alzheimer’s disease subjects: (1) 211 participants who underwent tau-PET with [ 18 F]MK6240 and amyloid-PET with [ 18 F]AZD4694, (2) 264 individuals who underwent tau-PET with [ 18 F]Flortaucipir and amyloid-PET with [ 18 F]Florbetapir and (3) 487 individuals who underwent lumbar puncture and amyloid-PET with [ 18 F]Florbetapir. Using a novel analytical framework, we applied voxel-wise regression models to assess the interactive effect of APOEε4 and amyloid-β on tau load, independently of age and clinical diagnosis. We found that the interaction effect between APOEε4 and amyloid-β, rather than the sum of their independent effects, was related to increased tau load in Alzheimer’s disease-vulnerable regions. The interaction between one APOEε4 allele and amyloid-β was related to increased tau load, while the interaction between amyloid-β and two APOEε4 alleles was related to a more widespread pattern of tau aggregation. Our results contribute to an emerging framework in which the elevated risk of developing dementia conferred by APOEε4 genotype involves mechanisms associated with both amyloid-β and tau aggregation. These results may have implications for future disease-modifying therapeutic trials targeting amyloid or tau pathologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".