The relationship of soluble tau species with Alzheimer's disease amyloid plaque removal and tau pathology
Bibliographic record
Abstract
BACKGROUND: Tau-derived cerebrospinal fluid (CSF) biomarkers correlate with amyloid-beta (Aβ) plaques or tau tangles in Alzheimer's disease (AD). This study assessed the effects of long-term anti-Aβ antibodies on amyloid plaques, tau tangles, and CSF tau species to determine the relationships between them. METHODS: A post-hoc analysis of the DIAN-TU-001 trial (NCT01760005) examined 142 participants at risk for dominantly inherited AD randomized to solanezumab (n = 50), gantenerumab (n = 52), or placebo (n = 40). High-resolution mass spectrometry quantified CSF tau species over four years. RESULTS: Phosphorylated tau (p-tau) species (153, 181, 217, 231) increased early in preclinical AD but were reduced with gantenerumab-mediated Aβ plaque reduction. Nearly a decade later, MTBR-tau243 and p-tau205 increased, showing no association with Aβ reduction, aligning with tau tangle pathology progression. DISCUSSION: Initially changing soluble p-tau species track Aβ plaque reduction, while ptau205 and MTBR-243 reflect tau tangle pathology, informing different pathways of therapeutic strategies. HIGHLIGHTS: p-tau217 and p-tau231 correlate with Aβ-PET and respond to Aβ-plaque lowering therapies. Aβ immunotherapy trials support a direct link between p-tau changes and Aβ plaques Gantenerumab reduces Aβ plaques but does not affect tau NFT-related biomarkers. Blood-based p-tau217 assays may provide a non-invasive tool to monitor Aβ therapies. MTBR-tau243 strongly correlates with tau PET and tracks NFT pathology progression. Further studies are needed to validate tau biomarkers for tracking NFT-targeting therapies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".