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Record W4406200839 · doi:10.1002/alz.093070

Longitudinal change of cerebral amyloid and tau and its association with plasma biomarkers in preclinical Alzheimer's disease

2024· article· en· W4406200839 on OpenAlexaff
Alfonso Fajardo, Yara Yakoub, Jordana Remz, Jean‐Paul Soucy, Nicholas J. Ashton, Henrik Zetterberg, Kaj Blennow, John C.S. Breitner, Judes Poirier, Sylvia Villeneuve

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsAlzheimer Society of CanadaMcGill Genome CentreMontreal Neurological Institute and HospitalDouglas CollegeMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsDiseaseAmyloid (mycology)Association (psychology)NeuroscienceAlzheimer's diseaseMedicineAmyloid βPsychologyPathologyPsychotherapist

Abstract

fetched live from OpenAlex

Abstract Background For medical purposes, amyloid‐beta (Aβ) and tau biomarkers are typically dichotomized into positive (+) and negative (‐) status to define individuals with Alzheimer’s disease (AD) pathology. Nevertheless, such AD proteinopathies start accumulating years before reaching clinically‐defined abnormality thresholds. We examined longitudinal change in PET Aβ and tau in cognitively unimpaired (CU) individuals; then we explored their baseline plasma levels and demographic characteristics. Finally, we assessed the association between plasma/demographics and annual rate of change (aRC) of AD pathology. Method We included 110 CU participants from the PREVENT‐AD cohort who underwent longitudinal Aβ and tau PET scans (mean time between scans = 4.33 years, 0.44 SD). We created four accumulator groups: 1) a non‐accumulators group, 2) an Aβ‐only group which included individuals classified as Aβ+ at baseline (SUVr threshold of 1.25, 18 CL) plus those with an Aβ aRC > 2.19 CL, 3) a tau‐only group which included individuals classified as tau+ at baseline (2 SDs above the mean of young participants, SUVr=1.23) plus those with a temporal meta‐ROI aRC > 0.022 (a GMM‐derived cut‐point), and 4) an Aβ & tau accumulator group. We then compared demographic characteristics and baseline plasma biomarker levels of Aβ42/40, GFAP, NfL, pTau181 and pTau217 across the four groups. Finally, we fitted linear regression models to predict Aβ/tau aRC as continuous variables from demographics or baseline plasma, including age and sex as covariates. Results Aβ accumulator groups included a higher proportion of APOE4 carriers (Table 1). Compared to non‐accumulators or tau‐only accumulators, Aβ‐only accumulators and Aβ & tau accumulators showed higher baseline plasma levels of NfL and GFAP. When aRC was treated as a continuous value, APOE4 status and age were positively associated with higher rate of Aβ accumulation. Finally, higher baseline GFAP levels were associated with faster accumulation of both Aβ and tau in the brain Conclusions APOE4 status, increased age and plasma GFAP levels were closely related to faster Aβ aRC. Higher plasma GFAP levels were further associated with faster tau aRC. Therefore, GFAP might be an indicator of AD pathophysiological processes.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.338
Teacher spread0.276 · 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
Published2024
Admission routes1
Has abstractyes

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