Trajectories of plasma biomarkers, amyloid-beta burden and cognitive decline in Alzheimer’s disease: A Longitudinal ADNI Study
Bibliographic record
Abstract
As novel amyloid-β targeted therapies emerge, plasma biomarkers have promising potential to serve as screening tools and as surrogate measures for treatment outcomes. Understanding longitudinal trajectories of these biomarkers and how their changes relate to changes in AD pathology and cognition is needed to help track treatment response and guide patient care. We analyzed data from 394 individuals in the ADNI-FNIH dataset who had plasma biomarkers available across 14 assays, Aβ-PET scans and cognitive assessments over a 10-year period. Plasma p-tau217, regardless of the assay used, had the greatest rate of change over time. This increase was related to concurrent increase in Aβ-PET burden only in individuals with low levels of Aβ. The rate of p-tau217 change, rather than its baseline level, was the strongest predictor of future Aβ-PET positivity. On the other hand, in individuals with elevated levels of Aβ, higher rate of change in p-tau217 was associated with faster cognitive decline. These findings highlight a "dual" role of plasma p-tau217 rate of change, being either predictive of accumulating Aβ pathology at early stages and of cognitive decline at later stages of the AD continuum.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".