ALZpath pTau217: Alzheimer’s disease specificity in the context of multiple comorbidities and predictive capabilities for amyloid burden in combination with other blood‐based biomarkers
Bibliographic record
Abstract
Abstract Background Tau phosphorylated at position 217 (pTau217) is considered to have the highest accuracy in identifying Alzheimer’s disease (AD) pathology using blood. We describe a multi‐cohort evaluation of the Simoa ALZpath pTau217 assay for the prediction of amyloid status in combination with additional blood‐based AD biomarkers (GFAP, pTau181, etc.), as well as comparisons between histopathological and PET based amyloid measurements. We describe the contribution of a spectrum of neurodegenerative diseases and demographic features to circulating plasma pTau217 levels. Method The Simoa ALZpath pTau217 assay is an ultra‐sensitive blood‐based assay developed on the semi‐automated single‐molecule array Simoa platform. We evaluate Simoa ALZpath pTau217 in the Banner Brain and Body Donation Program (characterized by several clinically meaningful comorbidities and mid‐to‐late stage AD; pTau217 subset: mean Braak Score = 4.17, ADNC High Plaques = 28%, ADNC Intermediate Plaques = 33%), the Wisconsin Registry for Alzheimer’s Prevention (a longitudinal cohort focused on healthy cognition through MCI), and a cognitively normal amyloid negative subset of the Australian Imaging, Biomarker and Lifestyle (AIBL) study. Result We discuss ALZpath pTau217 predictive capabilities in the context of PET imaging and post‐mortem histopathology for total and region‐specific pathological burden and the relative contribution of other blood‐based biomarkers and demographic features. We also report AD and MCI specificity in driving plasma levels of ALZpath pTau217 in the context of multiple comorbidities including CAA, DLB, and VAD. Conclusion Findings support ALZpath pTau217 as a high performing biomarker of CNS amyloid and tau burden that is driven specifically by AD and MCI in the context of multiple comorbidities. The ALZpath pTau217 performance capabilities can support timely AD diagnosis and intervention.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".