Plasma p‐tau231 and p‐tau217 inform on tau tangles aggregation in cognitively impaired individuals
Bibliographic record
Abstract
Abstract INTRODUCTION Phosphorylated tau (p‐tau) biomarkers have been recently proposed to represent brain amyloid‐β (Aβ) pathology. Here, we evaluated the plasma biomarkers' contribution beyond the information provided by demographics (age and sex) to identify Aβ and tau pathologies in individuals segregated as cognitively unimpaired (CU) and impaired (CI). METHODS We assessed 138 CU and 87 CI with available plasma p‐tau231, 217 + , and 181, Aβ42/40, GFAP and Aβ‐ and tau‐PET. RESULTS In CU, only plasma p‐tau231 and p‐tau217 + significantly improved the performance of the demographics in detecting Aβ‐PET positivity, while no plasma biomarker provided additional information to identify tau‐PET positivity. In CI, p‐tau217 + and GFAP significantly contributed to demographics to identify both Aβ‐PET and tau‐PET positivity, while p‐tau231 only provided additional information to identify tau‐PET positivity. DISCUSSION Our results support plasma p‐tau231 and p‐tau217 + as state markers of early Aβ deposition, but in later disease stages they inform on tau tangle accumulation. Highlights It is still unclear how much plasma biomarkers contribute to identification of AD pathology across the AD spectrum beyond the information already provided by demographics (age + sex). Plasma p‐tau231 and p‐tau217 + contribute to demographic information to identify brain Aβ pathology in preclinical AD. In CI individuals, plasma p‐tau231 contributes to age and sex to inform on the accumulation of tau tangles, while p‐tau217 + and GFAP inform on both Aβ deposition and tau pathology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".