Evaluation of Plasma Phosphorylated Tau217 for Differentiating Alzheimer's Disease
Bibliographic record
Abstract
BACKGROUND: Blood-based biomarkers provide an accessible and cost-effective tool for diagnosing Alzheimer's disease (AD). Among these biomarkers, phosphorylated tau 217 (p-tau217) shows promise for AD diagnosis, differential diagnosis, and facilitating access to emerging disease-modifying therapies. This study evaluates the ALZpath plasma p-tau217 assay as a Laboratory Developed Test for distinguishing AD pathology from other concurrent pathologies associated with AD. METHOD: We examined EDTA plasma samples of autopsy confirmed (n = 110) of patients assessed at the UBC Hospital Clinic for Alzheimer's disease. The cohort was characterized as AD only (n = 30), AD with CAA (n = 7), AD with DLB (n = 13), AD with Vascular (n = 7), AD with mix pathologies (n = 13), TDP-43 only (n = 18), Synuclein only(n = 4), Tau only (n = 18). The samples were analyzed by the ALZpath p-tau217 v2 assay on a single Quanterix HD-X SIMOA Analyzer platform. RESULTS: While the plasma p-tau217 concentrations in cases with AD (1.6 ± 0.8 ng/L) were higher than those in AD with DLB cases (1.1 ± 0.7 ng/L) and cases with AD plus vascular pathology (1.0 ± 0.6 ng/L), and lower than in cases with AD plus CAA (1.7 ± 0.6 ng/L), these differences were not statistically significant. However, the concentrations were significantly higher than those in cases with AD with mixed pathologies (0.7 ± 0.4 ng/L), TDP-43 only (0.3 ± 0.2 ng/L), synuclein only (0.4 ± 0.1 ng/L), and tau only (0.3 ± 0.2 ng/L) (p < 0.001). CONCLUSION: In our current study, plasma p-tau217 levels did not differentiate AD with DLB, AD with vascular pathology, or AD with CAA cases from the AD group, though they could differentiate AD from cases with mixed pathologies, TDP-43 only, synuclein only, and tau-only pathology. This suggests co-existing AD pathology, making their p-tau measurements similar to the AD group. Further studies with autopsy-defined samples will be needed to clarify the role of plasma p-tau217 in these groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".