Combined impact of plasma phospho-tau 217, GFAP and NfL on longitudinal domain-specific cognitive decline
Bibliographic record
Abstract
Abstract Clinical trials are increasingly focused on pre-manifest and early Alzheimer’s disease. Accurately predicting clinical progression is important to avoid unnecessary treatment and improve trial efficiency. Plasma p-tau217, an indicator of tau pathology with strong associations to amyloid-beta pathology, NfL, a marker of axonal damage and neurodegeneration and GFAP, a marker of inflammation, are promising diagnostic and prognostic tools. Their combined use could offer more accurate prognostic insights than either biomarker alone. We examined the trajectories of domain-specific cognitive functions by stratifying participants based on plasma p-tau217, NfL and GFAP levels (high/low). Participants were from the Massachusetts Alzheimer’s Disease Research Center cohort (n = 523). Cognitive functions were assessed using the National Alzheimer’s Coordinating Center Uniform Data Set v1-3: global cognition (CDR sum of box; MMSE, MoCA converted in v3), memory (Logical Memory), executive functions (Trail Making Test B), language (Boston Naming), and language-based executive function (Category Fluency Animals). We used linear mixed-effects models with 8 groups combining 3 plasma biomarkers to predict cognitive trajectories over 7 years, controlling for age, sex, and education. High p-tau217 alone was significantly associated with declines in Logical Memory (coefficient=-0.12; p = 0.03) and Boston Naming (coefficient=-0.16; p < 0.01), but not associated with decline in CDR sum of box and MMSE unless combined with a high burden of NfL and/or GFAP (CDR group*time coefficients=0.17-0.34, p < 0.01; MMSE group*time coefficients=-0.39 to -0.69, p < 0.01). Neither high GFAP alone nor high NfL alone was associated with significant cognitive declines. The combined use of plasma biomarkers provides a promising approach for predicting domain-specific cognitive decline.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.002 | 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".