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Record W7116853074 · doi:10.1002/alz70862_109930

Comparison of Longitudinal Plasma Assays in Relation to Longitudinal Amyloid Pathology in Alzheimer’s Disease

2025· article· en· W7116853074 on OpenAlexaff
Yara Yakoub, Ting Qiu, Sylvia Villeneuve, Alexa Pichet Binette

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalMcGill University Health CentreMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsDiseaseAmyloid (mycology)Measure (data warehouse)Serum amyloid P componentDegenerative disease

Abstract

fetched live from OpenAlex

Abstract Background Blood‐based biomarkers of AD, especially p ‐tau217, show high concordance with Aβ‐PET load and status. While many studies relied on cross‐sectional data, assessing dynamic changes in these assays is important for future clinical use and trial outcomes. We evaluated the longitudinal trajectories of multiple plasma biomarkers and their associations with subsequent changes in Aβ‐PET status. Method We used data from the ADNI FNIH consortium consisting of 386 participants (72.7 ± 7.1 years old, 52.3% cognitively unimpaired, 49.7% women) with on average three plasma samples and three Aβ‐PET scans collected over 11 years, in which multiple assays were tested (here focussing on 5 p ‐tau217 and 4 Aβ 42/40 assays). We first assessed changes over time for each assay in Aβ‐PET positive ( n = 140, 36.3%) and negative participants. We then focused on progression from Aβ‐negativity to Aβ‐positivity over follow‐up, using cox proportional‐hazard models to evaluate if baseline levels and longitudinal (rate of change) of plasma biomarkers were related to subsequent Aβ‐positivity (total of 49 progressors). Result Across both Aβ‐negative and positive groups, all p ‐tau217 assays, but not the Aβ 42/40 assays, showed increase over time (Figure 1A‐B). In the Aβ‐negative subgroup, higher plasma p ‐tau217 rate of change was associated with increased risk of progression to Aβ‐PET positivity (Figure 2A‐B), with highest hazard rations (HR) seen with the C 2 N assays (HR p ‐tau217 = 2.77 and HR p ‐tau217 % = 2.50). Hazard ratios were higher with p ‐tau217 rates of change compared to baseline values (Figure 2A). With the Aβ 42/40 assays, higher Aβ 42/40 pathology at baseline was associated with progression to Aβ‐PET positivity (HR from 1.44 ‐1.65), but no associations or unexpected associations were seen with the rate of change (Figure 2A). Individual slopes between the different groups (Aβ‐negative, progressors to Aβ‐positive and Aβ‐positive) for all assays are displayed in Figure 3 to aid in assay comparisons. Conclusion All plasma p ‐tau217 assays levels changed over time, which was not the case for the Aβ 42/40 assays. Among Aβ‐negative individuals, while baseline plasma p ‐tau217 and Aβ were associated with progression to Aβ‐positivity over 10 years, across all assays, the rate of change in p ‐tau217 was the measure most strongly predictive of future Aβ‐PET positivity.

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)0.006
metaresearch head score (Gemma)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.058
GPT teacher head0.371
Teacher spread0.313 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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