The exploration of using plasma biomarkers of p-tau217 and p-tau181 for screening Alzheimer’s disease in very elderly people
Bibliographic record
Abstract
Introduction Blood-based biomarkers for Alzheimer’s disease (AD), such as phosphorylated tau (p-tau181, p-tau217) and amyloid beta (Aβ), have the potential to serve as screening tools for probable AD in the elderly population. Methods AD screening [Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)] was conducted among very elderly individuals residing in a nursing community and a geriatric hospital. Based on cognitive evaluation, participants were categorized into two groups: cognitively normal (n = 62) and probable AD (n = 78). Plasma concentrations of Aβ42, Aβ40, p-tau181, p-tau217, and glial fibrillary acidic protein (GFAP) were measured using the single molecule array (Simoa) platform. Group comparisons of plasma biomarker levels were performed, and receiver operating characteristic (ROC) curve analyses were conducted for each biomarker relative to AD diagnosis. Results Significant differences were observed in plasma p-tau181, p-tau217, and GFAP levels between the cognitively normal and probable AD groups (p < 0.01). In contrast, Aβ42, Aβ40, and the Aβ42/Aβ40 ratio showed no significant differences (p > 0.01). The area under the ROC curve (AUC) was 0.886 for p-tau181, 0.655 for p-tau217, and 0.869 for GFAP. Discussion Plasma biomarkers p-tau181, p-tau217, and GFAP demonstrate clinical utility in distinguishing AD from normal cognition, suggesting that blood-based testing may serve as a feasible screening tool for early identification of AD in very elderly populations.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".