P‐tau235: a novel biomarker for staging preclinical Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterised by a long preclinical phase. Although phosphorylated tau (p-tau) species such as p-tau217 and p-tau231 provide accurate detection of early pathological changes, other biomarkers capable of staging disease progression during preclinical AD are still needed. Combining exploratory and targeted mass spectrometry methods in neuropathologically confirmed brain tissue, we observed that p-tau235 is a prominent feature of AD pathology. In addition, p-tau235 seemed to be preceded by p-tau231, in what appeared to be a sequential phosphorylation event. To exploit its biomarker potential in cerebrospinal fluid (CSF), we developed and validated a new p-tau235 Simoa assay. Using three clinical cohorts, we demonstrated that (i) CSF p-235 increases early in AD continuum, and (ii) changes in CSF p-tau235 and p-tau231 levels during preclinical AD are consistent with the sequential phosphorylation evidence in AD brain. In conclusion, CSF p-tau235 appears to be not only a highly specific biomarker of AD but also a promising staging biomarker for the preclinical phase. Thus, it could prove useful tracking disease progression and help enriching clinical trial recruitment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".