Plasma p-tau217 predicting brain-wide tau accumulation in preclinical AD
Bibliographic record
Abstract
BACKGROUND: Recently developed blood test of Alzheimer's disease (AD) has been recognized as a promising alternative to CSF and PET, as it is noninvasive, cost-effective, and more accessible. Particularly, plasma p-tau217 shows high sensitivity in detecting β-amyloid (Aβ) and tau positivity in early AD. However, the potential value of p-tau217 in revealing Aβ and tau distribution and predicting future development has not been studied. OBJECTIVES: We investigated the dose-response associations between p-tau217 and regional Aβ and tau measured by PET, as well as the longitudinal prediction of p-tau217 for prospective Aβ and tau accumulation measured by longitudinal PET. DESIGN: Cross-sectional and longitudinal analyses. SETTING: We used data in Anti-Amyloid Treatment in Asymptomatic Alzheimer's disease (A4) study (N = 333) for primary analyses and Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 410) for validation. PARTICIPANTS: Cognitively unimpaired older adults (N = 333) from A4 study and cognitively unimpaired older adults (N = 222), mild cognitive impairment (N = 114), and dementia (N = 74) from ADNI. MEASUREMENTS: Flortaucipir PET measured regional Aβ and tau. RESULTS: Plasma p-tau217 was associated with concurrent Aβ in most cortical regions and tau in temporo-parietal cortices. Longitudinally, p-tau217 predicted brain-wide tau accumulation in widespread cortical regions in preclinical AD, but not Aβ change anywhere. CONCLUSIONS: Plasma p-tau217 shows dose-response, brain-wide relationships with concurrent Aβ and future tau development in preclinical AD, suggesting its potential in disease trajectory monitoring and large-scale screening for individuals approaching certain biological stages of AD in clinical trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".