Detecting amyloid and tau pathology in Parkinson’s disease, 4R-tauopathies and control subjects with plasma pTau217
Bibliographic record
Abstract
Introduction Plasma phospho-tau 217 (pTau217) is a biomarker for Alzheimer’s disease (AD) pathology, reflecting amyloid (Aβ) and tau burden, but its role in Parkinson disease (PD) and 4-repeat(4R)-tauopathies remains incompletely understood. We measured plasma pTau217 across the cognitive spectrum of Lewy body diseases (PD, Dementia with Lewy bodies [DLB]) and in 4R-tauopathies, comparing these groups to cognitively unimpaired (CU) and mild cognitive impairment (MCI) individuals. Methods Participants included 18 cognitively normal PD (PD-NC), 32 PD with MCI, and 7 PD with dementia (PDD), alongside 4 DLB patients, grouped as PDD/DLB. The 4R-tauopathy group included 28 Progressive Supranuclear Palsy (PSP) and 4 corticobasal syndrome (CBS) patients, compared to 51 CU and 26 MCI individuals. Ptau217 was measured using the fully automated Lumipulse platform, with values adjusted for creatinine levels. Further, the presence of AD-pathology was defined using a validated cut-off based on Aβ-PET. Results PTau217 levels were significantly lower in PD-NC and CU individuals compared to those with greater cognitive impairment (PD-MCI, PDD/DLB, and PSP/CBS), and MCI individuals. AD co-pathology was identified in 28% of PDD/DLB and PSP/CBS patients, 16% of PD-MCI, and none of PD-NC. MCI showed the highest pTau217 positivity (35%), while 8% of CU individuals were positive despite normal cognition. In PD, pTau217 negatively correlated with cognitive performance, as assessed by Montreal Cognitive Assessment (MoCA: rs = −0.38, p = 0.004) and Mini-Mental State Examination (MMSE: rs = −0.37, p = 0.006). Discussion Plasma pTau217 levels serve as a scalable, non-invasive marker of AD-pathology across Lewy body diseases, PSP/CBS, and MCI/CU populations. AD co-pathology independently contributes to cognitive deficits in PD, but not in PSP/CBS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".