Timing of Changes in Alzheimer's Disease Plasma Biomarkers as Assessed by Amyloid and Tau <scp>PET</scp> Clocks
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the timing of change of Alzheimer's disease (AD) plasma biomarkers (Aβ42/Aβ40, p-tau217, p-tau181, GFAP, and NfL) from six different assay platforms, alongside established AD biomarkers, using amyloid and tau positron emission tomography (PET)-based AD progression timelines. METHODS: Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including 784 individuals with longitudinal amyloid PET and 359 with longitudinal tau PET, were analyzed to estimate the age at amyloid and tau PET positivity, respectively. Longitudinal plasma biomarker measurements were available from 190 individuals with an estimated amyloid PET positivity age and from 70 individuals with an estimated tau PET positivity age. In a subset of 17 clinical progressors, age at tau PET positivity strongly predicted symptom onset, allowing for estimation of symptom onset age. Biomarker trajectories based on time from amyloid or tau PET positivity or symptom onset were modelled using Generalized Additive Mixed models. Time intervals of significant biomarker change and the earliest timepoints at which biomarkers exceeded predefined abnormality thresholds were identified. RESULTS: All plasma biomarkers except NfL became abnormal prior to established thresholds for amyloid and tau PET positivity. Plasma Aβ42/Aβ40 became abnormal very early in both amyloid PET and tau PET timelines, while plasma GFAP became abnormal early in the tau PET timeline. Plasma Aβ42/Aβ40 levels plateaued, whereas plasma p-tau217, p-tau181, GFAP, and NfL levels increased throughout the modeled disease progression. Some variations in the timing of these changes were observed across different biomarker assays. INTERPRETATION: These findings suggest that the plasma Aβ42/Aβ40 may be useful in identifying individuals with very low levels of amyloid pathology, whereas p-tau, GFAP, and NfL may be useful in staging disease progression. ANN NEUROL 2025;98:508-523.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".