Temporal Modeling of Amyloid and Tau Trajectories in Alzheimer’s Disease using PET and Plasma Biomarkers
Bibliographic record
Abstract
ABSTRACT Objective To compare PET and plasma-based temporal modeling of amyloid and tau biomarkers in Alzheimer’s disease Methods Longitudinal amyloid PET, 18 F-flortaucipir tau-PET, and Fujirebio Lumipulse plasma p-tau 217 from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and University of Pennsylvania Alzheimer’s Disease Research Center (Penn ADRC) were used to generate biomarker trajectory models using Sampled Iterative Local Approximation (SILA). SILA models using plasma p-tau 217 were compared to amyloid and tau PET-based models to estimate tau onset age (ETOA) and estimate amyloid onset age (EAOA), and factors influencing ETOA and time from ETOA to dementia were evaluated for PET and plasma-based models. Results Plasma-based models generated similar results to PET for EAOA and ETOA, with stronger model agreement for ETOA than EAOA. Accuracy of estimated onset age compared to actual onset age was high within modality with slightly greater error when comparing across modalities (i.e. plasma to PET). For both plasma and PET models, earlier ETOA was associated with younger EAOA, female sex, and ≥1 ApoE ε4 allele. Earlier dementia onset after ETOA was associated with later ETOA for both plasma and PET models, while male sex was associated with shorter tau to dementia gap in plasma models. Interpretation Temporal modeling of plasma biomarkers provides comparable information to PET-based models, particularly for tau onset age. Plasma-based temporal modeling can serve as a widely accessible tool for clinical assessment of biological disease duration that places the patient on the disease timeline, which may allow for improved discussion of prognosis and treatment decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".