<i>APOE</i> ɛ4 carriership determines a faster plasma p‐tau217 progression in Aβ‐positive individuals
Bibliographic record
Abstract
INTRODUCTION: It is unclear whether the different Alzheimer's disease (AD) progression trajectories of apolipoprotein E (APOE) ɛ4 carriers is reflected by blood phosphorylated tau (p-tau) analytes. METHODS: We assessed longitudinal trajectories in plasma p-tau181, 217, and 231, in amyloid beta-positive (A+) and negative (A-) APOE ɛ4 carriers (E+) or non-carriers (E-). We included 2039 participants from the observational Translational Biomarkers in Aging and Dementia (TRIAD) and Alzheimer's Disease Neuroimaging Initiative cohorts, categorized into 840 A-E-, 251 A-E+, 386 A+E4-, and 616 A+E4+. Longitudinal data were available for 1045 participants. RESULTS: (β = 0.67, p = 0.002), and in ADNI p-tau217 (β = 0.90, p = 0.002) increased faster in A+E4+. This was not the case in E- or A- individuals or for p-tau181 and p-tau231. DISCUSSION: These findings suggest p-tau217 as a marker of faster progression in APOE ɛ4 carriers, highlighting its potential in disease stratification. HIGHLIGHTS: Blood phosphorylated tau (p-tau)217 increases faster in apolipoprotein E (APOE) ɛ4 carriers with amyloid pathology. p-tau181 and p-tau231 do not increase faster in APOE ɛ4 carriers. APOE ɛ4 carriership does not change p-tau in individuals without amyloid pathology.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".