Plasma p-tau217 and tau-PET predict future cognitive decline among cognitively unimpaired individuals: implications for clinical trials
Bibliographic record
Abstract
Abstract Plasma p-tau217 and tau positron emission tomography (PET) are strong prognostic biomarkers in Alzheimer’s disease (AD), but their relative performance in predicting future cognitive decline among cognitively unimpaired (CU) individuals is unclear. In a head-to-head comparison study including nine cohorts and 1,474 individuals, we show that plasma p-tau217 and medial temporal lobe tau-PET signal display similar associations with cognitive decline on a global cognitive composite test ( R 2 PET = 0.34 versus R 2 plasma = 0.33, P difference = 0.653) and with progression to mild cognitive impairment (hazard ratio (HR) PET = 1.61 (1.48–1.76) versus HR plasma = 1.57 (1.43–1.72), P difference = 0.322). Combined plasma and PET models were superior to the single-biomarker models ( R 2 = 0.35, P < 0.01). Sequential selection using plasma phosphorylated tau at threonine 217 (p-tau217) and then tau-PET reduced the number of participants required for a clinical trial by 94%, compared to a 76% reduction when using plasma p-tau217 alone. Thus, plasma p-tau217 and tau-PET showed similar performance for predicting future cognitive decline in CU individuals, and their sequential use enhances screening efficiency for preclinical AD 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 | 0.043 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".