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Record W4417173712 · doi:10.1212/wnl.0000000000214441

Evaluating Plasma p-tau217 as an Endpoint for Alzheimer Disease Clinical Trials

2025· article· en· W4417173712 on OpenAlexafffund
Pâmela C.L. Ferreira, Bruna Bellaver, Guilherme Povala, Guilherme Bauer‐Negrini, Cristiano Schaffer Aguzzoli, João Pedro Ferrari‐Souza, Douglas Teixeira Leffa, Carolina Soares, Firoza Z Lussier, Marina Scop Medeiros, Cynthia Felix, Emma Patrice Ruppert, Francieli Rohden, Wyllians Vendramini Borelli, Helmet T. Karim, Rebecca L. Koscik, Bradley T. Christian, Rachael Wilson, Chang Hyung Hong, Hyun Woong Roh, Riddhi Patira, Dana Tudorascu, Eduardo R. Zimmer, Tobey J. Betthauser, Thomas K. Karikari, Beth E. Snitz, Sterling C. Johnson, Sang Joon Son, Tharick A. Pascoal

Bibliographic record

VenueNeurology · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
FundersNational Institute on AgingCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNational Research Foundation of KoreaEli Lilly and CompanyBristol-Myers SquibbKorea Disease Control and Prevention AgencyPfizerNovartis Pharmaceuticals CorporationU.S. Department of DefenseMeso Scale DiagnosticsMinistry of Science and ICT, South KoreaBioClinicaNational Research FoundationBiogenKorea Health Industry Development Institute
KeywordsClinical trialClinical endpointAlzheimer's diseaseDiseaseEndpoint DeterminationDegenerative disease

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Plasma phosphorylated tau 217 (p-tau217) levels have been shown to parallel neurofibrillary tangle and cognitive decline over time. Although recent clinical trials have demonstrated exploratory drug effects on plasma p-tau217, little is known about plasma p-tau217 effect size and performance as an endpoint in Alzheimer disease (AD) clinical trials. Therefore, the objective of this study was to assess the longitudinal performance and potential utility of plasma p-tau217 as a primary endpoint in early-stage AD clinical trials. METHOD: This retrospective study included participants from 4 cohorts: ADNI (a multisite observational study), BICWALZS (a South Korean memory clinic-based cohort), MYHAT-NI (a Southwestern Pennsylvania population-based cohort), and WRAP (older adults at risk for AD). Eligible participants had plasma p-tau217 measurements at least at 2 timepoints, along with baseline Aβ PET imaging and clinical assessments. Linear mixed-effects models were used to assess associations between plasma p-tau217 trajectories and clinical or biomarker outcomes. Effect size was defined as the mean annual rate of change in p-tau217 divided by its standard deviation. We calculated the sample size required for a hypothetical clinical trial designed to detect a 25% drug effect with 80% power at a 0.05 test level. RESULTS: A total of 716 individuals were included in the analysis: 413 cognitively unimpaired (CU) participants (58.6% female; mean age = 70.6 years, SD = 7.9) and 303 cognitively impaired (CI) participants (54.7% female; mean age = 73.2 years, SD = 7.4). In Aβ-positive individuals, the annual rate of change in plasma p-tau217 was similar between CU (0.07 pg/mL/y, SD = 0.11) and CI (0.08 pg/mL/y, SD = 0.13) groups. Effect size was 0.64 and 0.62 in CU and CI Aβ-positive individuals, respectively. The minimum sample size required per study group to detect a 25% drug effect was 610 for the CU Aβ-positive and 664 for the CI Aβ-positive group. Notably, selecting individuals with intermediate Aβ levels (Centiloid 20-40) yielded higher effect sizes (CU: 0.85; CI: 0.72), which reduced the required sample sizes per study group to 342 for CU and 492 for CI. DISCUSSION: Our findings support that changes in plasma p-tau217 represent a robust endpoint for clinical trials targeting CU or CI individuals with Aβ pathology.

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 imitation

Not 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.

metaresearch head score (Codex)0.155
metaresearch head score (Gemma)0.129
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.822

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1550.129
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.338
GPT teacher head0.568
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes2
Has abstractyes

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