Plasma and CSF concentrations of N‐terminal tau fragments associate with in vivo neurofibrillary tangle burden
Bibliographic record
Abstract
INTRODUCTION: Fluid biomarkers capable of specifically tracking tau tangle pathology in vivo are greatly needed. METHODS: We measured cerebrospinal fluid (CSF) and plasma concentrations of N-terminal tau fragments (NTA-tau), using a novel immunoassay (NTA) in the TRIAD cohort, consisting of 272 individuals assessed with amyloid beta (Aβ) positron emission tomography (PET), tau PET, magnetic resonance imaging (MRI) and cognitive assessments. RESULTS: CSF and plasma NTA-tau concentrations were specifically increased in cognitively impaired Aβ-positive groups. CSF and plasma NTA-tau concentrations displayed stronger correlations with tau PET than with Aβ PET and MRI, both in global uptake and at the voxel level. Regression models demonstrated that both CSF and plasma NTA-tau are preferentially associated with tau pathology. Moreover, plasma NTA-tau was associated with longitudinal tau PET accumulation across the aging and Alzheimer's disease (AD) spectrum. DISCUSSION: NTA-tau is a biomarker closely associated with in vivo tau deposition in the AD continuum and has potential as a tau tangle biomarker in clinical settings and trials. HIGHLIGHTS: An assay for detecting N-terminal tau fragments (NTA-tau) in plasma and CSF was evaluated. NTA-tau is more closely associated with tau PET than amyloid PET or neurodegeneration. NTA-tau can successfully track in vivo tau deposition across the AD continuum. Plasma NTA-tau increased over time only in cognitively impaired amyloid-β positive individuals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".