Longitudinal blood biomarker trajectories in preclinical Alzheimer's disease
Bibliographic record
Abstract
Abstract INTRODUCTION Plasma biomarkers are altered years prior to Alzheimer's disease (AD) clinical onset. METHODS We measured longitudinal changes in plasma amyloid‐beta (Aβ) 42/40 ratio, pTau181, pTau231, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) in a cohort of older adults at risk of AD ( n = 373 total, n = 229 with Aβ and tau positron emission tomography [PET] scans) considering genetic and demographic factors as possible modifiers of these markers’ progression. RESULTS Aβ 42/40 ratio concentrations decreased, while NfL and GFAP values increased over the 4‐year follow‐up. Apolipoprotein E ( APOE ) ε4 carriers showed faster increase in plasma pTau181 than non‐carriers. Older individuals showed a faster increase in plasma NfL, and females showed a faster increase in plasma GFAP values. In the PET subsample, individuals both Aβ‐PET and tau‐PET positive showed faster plasma pTau181 and GFAP increase compared to PET‐negative individuals. DISCUSSION Plasma markers can track biological change over time, with plasma pTau181 and GFAP markers showing longitudinal change in individuals with preclinical AD. Highlights Longitudinal increase of plasma pTau181 and glial fibrillary acidic protein (GFAP) can be measured in the preclinical phase of AD. Apolipoprotein E ε4 carriers experience faster increase in plasma pTau181 over time than non‐carriers. Female sex showed accelerated increase in plasma GFAP over time compared to males. Aβ 42/40 and pTau231 values are already abnormal at baseline in individuals with both amyloid and tau PET burden.
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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 | 0.002 |
| 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".