P4‐234: APOEε4 POTENTIATES THE RELATIONSHIP BETWEEN AMYLOID‐β AND TAU PATHOLOGIES IN A DOSE‐DEPENDENT MANNER
Bibliographic record
Abstract
APOEε4 is an important risk factor for Alzheimer's disease and is involved in the accumulation of cerebral amyloid-b. However, it is unclear whether APOEε4 is related to other Alzheimer's disease pathologies such as tau. We assessed 207 individuals scanned with [18F]AV1451 and [18F]florbetapir, 49 individuals scanned with [18F]MK6240 and [18F]AZD4694, as well as 487 individuals who underwent lumbar puncture for CSF phosphorylated tau and [18F]florbetapir PET. All individuals were genotyped for APOE4. The interaction models were built to test whether main and interactive effects between APOEε4 and amyloid-β PET SUVR are associated with Tau-PET uptake. APOEε4 allele status was treated as a categorical variable in a dose dependent manner, with ε3ε3<ε3ε4<ε4ε4. Because patients with AD were more likely to be APOEε4 carriers, we adjusted the model for age and clinical diagnosis. Voxel-wise analyses revealed a dose-dependent interaction between APOE ε4 and amyloid SUVR on [18F]AV1451 uptake across the cerebral cortex (figure 1). A similar spatial relationship was recapitulated in individuals scanned with [18F]MK6240 (figure 2). In subjects with CSF measures of phosphorylated tau, the synergistic effect between APOEε4 and neocortical [18F]florbetapir SUVR was related to increased CSF p-tau. A dose dependent effect of APOEε4 was also observed, with heterozygotes (b3=49.4, se=12.4, p<0.0001) and homozygotes (b3= 63.4, se=26.89, p=0.01) having different slopes (figure 3).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".