pTau heterogeneity as a measure for disease severity in incipient Alzheimer's disease
Bibliographic record
Abstract
Abstract Background The presence of p‐tau in biofluids has previously been proposed to be a response to neurofibrillary tangle pathology, one of the hallmarks of Alzheimer’s disease (AD). However, the increase of p‐tau in cerebrospinal fluid (CSF) precedes detectable neurofibrillary tangle pathology, as indexed by tau Positron Emission Tomography (PET), by up to a decade, suggesting that soluble tau could be an indication of early tau pathology. With this study, we investigated the heterogeneity of p‐tau species in CSF to assess the disease status of participants of the Translational Biomarkers of Aging and Dementia (TRIAD) cohort. Methods Support vector machines were used to determine cutoff values of p‐tau181, p‐tau217, p‐tau231 and p‐tau235 in CSF, identifying a group of participants that were amyloid positive (58 from a total of 165 participants). Amyloid positivity was determined by using an [18F]AZD4694 SUVR threshold value of 1.55 in the neocortex. Using these cutoff values, signatures were calculated on an individual level to identify the number of phosphorylated sites. Based on the number of phosphorylated sites, [18F]MK6240 SUVR maps and [18F]AZD4694 SUVR maps were calculated. Results When combining different CSF p‐tau species, the largest contribution in identifying amyloid positivity came from p‐tau217, followed by p‐tau231, p‐tau181 and p‐tau235. Achieving the cutoff for multiple p‐tau species was associated with more tau pathology particularly in the later Braak stages (fig 1) and increased amyloid‐β plaque accumulation (fig 2). Conclusion Our findings suggest that heterogeneity in p‐tau species carries predictive power in the identification of disease severity in incipient Alzheimer’s Disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".