Plasma tau biomarkers for biological staging of Alzheimer’s disease
Bibliographic record
Abstract
A blood biomarker-based staging system for Alzheimer's disease (AD) could improve the diagnosis, prognosis and identification of individuals most likely to benefit from specific therapies. Here, using targeted mass spectrometry, we measured six phosphorylated and six nonphosphorylated tau peptides in plasma from two independent cohorts: BioFINDER-2 and TRIAD (n = 689). We also analyzed the ratios of phosphorylated to nonphosphorylated peptides. Our results revealed that specific tau species became abnormal at different points along the disease continuum. Based on these findings, we developed a data-driven, blood-based staging model that demonstrated strong consistency across cohorts (>85% agreement in ≥90% initializations) and reflected changes in other AD biomarkers. These plasma-based stages were associated with clinical diagnoses, positron emission tomography-based stages and distinct patterns of longitudinal disease progression, including Aβ- and tau-positron emission tomography uptake, atrophy and cognitive decline. This study highlights the potential of tau blood-based biomarkers for biological staging in AD, offering a scalable tool for tracking disease progression and guiding clinical decisions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".